Drug may still be viable as CMV prophylaxis

Article Type
Changed
Tue, 02/23/2016 - 06:00
Display Headline
Drug may still be viable as CMV prophylaxis

HSCT preparation

Photo by Chad McNeeley

HONOLULU—Despite disappointing results in a phase 3 trial, investigators believe the oral nucleotide analog brincidofovir may still be viable as cytomegalovirus (CMV) prophylaxis in patients undergoing hematopoietic stem cell transplant (HSCT).

As reported last December, brincidofovir did not meet the primary endpoint of the phase 3 SUPPRESS trial, which was to prevent clinically significant CMV infection at week 24 after HSCT.

However, trial investigators said the drug did prevent CMV through week 14, which was the end of the treatment period.

The team believes they have an explanation for these findings, which were presented at the 2016 BMT Tandem Meetings (abstract 5). The trial was supported by Chimerix, the company developing brincidofovir.

The SUPPRESS trial included 452 subjects at high risk for CMV who were randomized to receive brincidofovir or placebo twice weekly for up to 14 weeks following allogeneic HSCT. They were then followed for 10 weeks after treatment.

Baseline characteristics were similar between the treatment arms, although there were more males in the placebo arm than the brincidofovir arm—66% and 54%, respectively. The median age was 56 in the brincidofovir arm and 54 in the placebo arm (overall range, 18-77).

Key results

The primary endpoint was assessed at week 24. At that time, the proportion of patients with clinically significant CMV infection was similar in the brincidofovir and placebo arms—51% and 52%, respectively.

However, the investigators did note that brincidofovir exhibited an antiviral effect during the trial. At the end of the on-treatment period at week 14, patients who received brincidofovir had fewer clinically significant CMV infections than patients in the placebo group—24% and 38%, respectively (P=0.002).

The investigators said the failure to meet the primary endpoint at week 24 appears to be associated with CMV events in the post-treatment period among subjects on the brincidofovir arm, driven by higher use of corticosteroids and other immunosuppressive therapies for the treatment of presumptive graft-versus-host disease (GVHD).

Diarrhea can be a symptom of GVHD in the gut and is also a known side effect of brincidofovir that can be managed by a temporary dose interruption, as described in the safety monitoring and management plan (SMMP) developed during the phase 2 trial of the drug (then known as CMX001).

In the SUPPRESS trial, diarrhea in brincidofovir-treated patients was more frequent and often presumed to be gut GVHD. So patients were treated with corticosteroids rather than undergoing temporary treatment interruption according to the SMMP. Among patients who were managed according to the SMMP, the investigators observed significantly fewer CMV infections (P=0.03) and lower mortality (P<0.001).

There was an 8-fold increase in the use of corticosteroids through week 14 in the brincidofovir arm compared to the placebo arm. The median cumulative dose of prednisone-equivalent corticosteroids was 26 mg/kg and 3 mg/kg, respectively.

The use of corticosteroids and other immunosuppressive therapies for the treatment of GVHD is known to increase the risk of infections, including CMV infections that occur when patients discontinue antiviral therapy.

Among patients who either underwent T-cell depletion or received alemtuzumab/ATG to decrease the risk of GVHD, those who were randomized to receive brincidofovir showed a lower incidence of CMV when compared to placebo, at a rate consistent with what was observed in the phase 2 study.

Additional endpoints

Brincidofovir did not prevent infection with non-CMV DNA viruses, such as BK virus.

And there was no significant difference between the treatment arms with regard to all-cause mortality. The rate was 15.5% in the brincidofovir arm and 10.1% in the placebo arm (P=0.12).

 

 

The investigators said the numerical differences in mortality appear to be driven by higher use of corticosteroids and other immunosuppressive therapies in the subjects who received brincidofovir.

The rate of treatment-emergent adverse events (AEs) was 100% in the brincidofovir arm and 98% in the placebo arm. The rate of grade 3 or higher AEs was 67% and 38%, respectively. The rate of serious AEs was 57% and 38%, respectively.

The rate of AEs leading to treatment discontinuation was 26% and 7%, respectively. And the rate of AEs leading to treatment change or interruption was 45% and 15%, respectively.

The most common AEs in the brincidofovir arm were diarrhea (61%), acute GVHD (57%), abdominal pain (34%), nausea (31%), vomiting (24%), peripheral edema (17%), hyperglycemia (16%), hypokalemia (16%), hypomagnesemia (13%), and ALT elevation (11%). There was no evidence of bone marrow toxicity, kidney toxicity, or viral resistance to brincidofovir.

Brincidofovir development

Chimerix said it will discuss the SUPPRESS data in full with the US Food and Drug Administration and other regulators, including the benefit-to-risk profile in specific subpopulations, as well as the current adenovirus and smallpox data, to determine next steps for the brincidofovir clinical programs.

The development of an intravenous (IV) formulation of brincidofovir is progressing toward clinical testing and has the potential to avoid the gastrointestinal side effects of orally administered brincidofovir.

Preclinical studies of IV brincidofovir have shown a lower risk of gastrointestinal effects based on maintained body weight during dosing and no evidence of injury in preliminary review of the gastrointestinal tract.

If human studies continue to support these findings, IV dosing during the first few weeks after transplant when patients are recovering from conditioning chemotherapy could be explored, with oral brincidofovir therapy available as patients are discharged home.

As there is no preventive therapy approved for CMV in HSCT recipients, Chimerix said it is committed to moving brincidofovir forward in this indication. Plans for brincidofovir in HSCT recipients will be the subject of further discussions with regulators.

Publications
Topics

HSCT preparation

Photo by Chad McNeeley

HONOLULU—Despite disappointing results in a phase 3 trial, investigators believe the oral nucleotide analog brincidofovir may still be viable as cytomegalovirus (CMV) prophylaxis in patients undergoing hematopoietic stem cell transplant (HSCT).

As reported last December, brincidofovir did not meet the primary endpoint of the phase 3 SUPPRESS trial, which was to prevent clinically significant CMV infection at week 24 after HSCT.

However, trial investigators said the drug did prevent CMV through week 14, which was the end of the treatment period.

The team believes they have an explanation for these findings, which were presented at the 2016 BMT Tandem Meetings (abstract 5). The trial was supported by Chimerix, the company developing brincidofovir.

The SUPPRESS trial included 452 subjects at high risk for CMV who were randomized to receive brincidofovir or placebo twice weekly for up to 14 weeks following allogeneic HSCT. They were then followed for 10 weeks after treatment.

Baseline characteristics were similar between the treatment arms, although there were more males in the placebo arm than the brincidofovir arm—66% and 54%, respectively. The median age was 56 in the brincidofovir arm and 54 in the placebo arm (overall range, 18-77).

Key results

The primary endpoint was assessed at week 24. At that time, the proportion of patients with clinically significant CMV infection was similar in the brincidofovir and placebo arms—51% and 52%, respectively.

However, the investigators did note that brincidofovir exhibited an antiviral effect during the trial. At the end of the on-treatment period at week 14, patients who received brincidofovir had fewer clinically significant CMV infections than patients in the placebo group—24% and 38%, respectively (P=0.002).

The investigators said the failure to meet the primary endpoint at week 24 appears to be associated with CMV events in the post-treatment period among subjects on the brincidofovir arm, driven by higher use of corticosteroids and other immunosuppressive therapies for the treatment of presumptive graft-versus-host disease (GVHD).

Diarrhea can be a symptom of GVHD in the gut and is also a known side effect of brincidofovir that can be managed by a temporary dose interruption, as described in the safety monitoring and management plan (SMMP) developed during the phase 2 trial of the drug (then known as CMX001).

In the SUPPRESS trial, diarrhea in brincidofovir-treated patients was more frequent and often presumed to be gut GVHD. So patients were treated with corticosteroids rather than undergoing temporary treatment interruption according to the SMMP. Among patients who were managed according to the SMMP, the investigators observed significantly fewer CMV infections (P=0.03) and lower mortality (P<0.001).

There was an 8-fold increase in the use of corticosteroids through week 14 in the brincidofovir arm compared to the placebo arm. The median cumulative dose of prednisone-equivalent corticosteroids was 26 mg/kg and 3 mg/kg, respectively.

The use of corticosteroids and other immunosuppressive therapies for the treatment of GVHD is known to increase the risk of infections, including CMV infections that occur when patients discontinue antiviral therapy.

Among patients who either underwent T-cell depletion or received alemtuzumab/ATG to decrease the risk of GVHD, those who were randomized to receive brincidofovir showed a lower incidence of CMV when compared to placebo, at a rate consistent with what was observed in the phase 2 study.

Additional endpoints

Brincidofovir did not prevent infection with non-CMV DNA viruses, such as BK virus.

And there was no significant difference between the treatment arms with regard to all-cause mortality. The rate was 15.5% in the brincidofovir arm and 10.1% in the placebo arm (P=0.12).

 

 

The investigators said the numerical differences in mortality appear to be driven by higher use of corticosteroids and other immunosuppressive therapies in the subjects who received brincidofovir.

The rate of treatment-emergent adverse events (AEs) was 100% in the brincidofovir arm and 98% in the placebo arm. The rate of grade 3 or higher AEs was 67% and 38%, respectively. The rate of serious AEs was 57% and 38%, respectively.

The rate of AEs leading to treatment discontinuation was 26% and 7%, respectively. And the rate of AEs leading to treatment change or interruption was 45% and 15%, respectively.

The most common AEs in the brincidofovir arm were diarrhea (61%), acute GVHD (57%), abdominal pain (34%), nausea (31%), vomiting (24%), peripheral edema (17%), hyperglycemia (16%), hypokalemia (16%), hypomagnesemia (13%), and ALT elevation (11%). There was no evidence of bone marrow toxicity, kidney toxicity, or viral resistance to brincidofovir.

Brincidofovir development

Chimerix said it will discuss the SUPPRESS data in full with the US Food and Drug Administration and other regulators, including the benefit-to-risk profile in specific subpopulations, as well as the current adenovirus and smallpox data, to determine next steps for the brincidofovir clinical programs.

The development of an intravenous (IV) formulation of brincidofovir is progressing toward clinical testing and has the potential to avoid the gastrointestinal side effects of orally administered brincidofovir.

Preclinical studies of IV brincidofovir have shown a lower risk of gastrointestinal effects based on maintained body weight during dosing and no evidence of injury in preliminary review of the gastrointestinal tract.

If human studies continue to support these findings, IV dosing during the first few weeks after transplant when patients are recovering from conditioning chemotherapy could be explored, with oral brincidofovir therapy available as patients are discharged home.

As there is no preventive therapy approved for CMV in HSCT recipients, Chimerix said it is committed to moving brincidofovir forward in this indication. Plans for brincidofovir in HSCT recipients will be the subject of further discussions with regulators.

HSCT preparation

Photo by Chad McNeeley

HONOLULU—Despite disappointing results in a phase 3 trial, investigators believe the oral nucleotide analog brincidofovir may still be viable as cytomegalovirus (CMV) prophylaxis in patients undergoing hematopoietic stem cell transplant (HSCT).

As reported last December, brincidofovir did not meet the primary endpoint of the phase 3 SUPPRESS trial, which was to prevent clinically significant CMV infection at week 24 after HSCT.

However, trial investigators said the drug did prevent CMV through week 14, which was the end of the treatment period.

The team believes they have an explanation for these findings, which were presented at the 2016 BMT Tandem Meetings (abstract 5). The trial was supported by Chimerix, the company developing brincidofovir.

The SUPPRESS trial included 452 subjects at high risk for CMV who were randomized to receive brincidofovir or placebo twice weekly for up to 14 weeks following allogeneic HSCT. They were then followed for 10 weeks after treatment.

Baseline characteristics were similar between the treatment arms, although there were more males in the placebo arm than the brincidofovir arm—66% and 54%, respectively. The median age was 56 in the brincidofovir arm and 54 in the placebo arm (overall range, 18-77).

Key results

The primary endpoint was assessed at week 24. At that time, the proportion of patients with clinically significant CMV infection was similar in the brincidofovir and placebo arms—51% and 52%, respectively.

However, the investigators did note that brincidofovir exhibited an antiviral effect during the trial. At the end of the on-treatment period at week 14, patients who received brincidofovir had fewer clinically significant CMV infections than patients in the placebo group—24% and 38%, respectively (P=0.002).

The investigators said the failure to meet the primary endpoint at week 24 appears to be associated with CMV events in the post-treatment period among subjects on the brincidofovir arm, driven by higher use of corticosteroids and other immunosuppressive therapies for the treatment of presumptive graft-versus-host disease (GVHD).

Diarrhea can be a symptom of GVHD in the gut and is also a known side effect of brincidofovir that can be managed by a temporary dose interruption, as described in the safety monitoring and management plan (SMMP) developed during the phase 2 trial of the drug (then known as CMX001).

In the SUPPRESS trial, diarrhea in brincidofovir-treated patients was more frequent and often presumed to be gut GVHD. So patients were treated with corticosteroids rather than undergoing temporary treatment interruption according to the SMMP. Among patients who were managed according to the SMMP, the investigators observed significantly fewer CMV infections (P=0.03) and lower mortality (P<0.001).

There was an 8-fold increase in the use of corticosteroids through week 14 in the brincidofovir arm compared to the placebo arm. The median cumulative dose of prednisone-equivalent corticosteroids was 26 mg/kg and 3 mg/kg, respectively.

The use of corticosteroids and other immunosuppressive therapies for the treatment of GVHD is known to increase the risk of infections, including CMV infections that occur when patients discontinue antiviral therapy.

Among patients who either underwent T-cell depletion or received alemtuzumab/ATG to decrease the risk of GVHD, those who were randomized to receive brincidofovir showed a lower incidence of CMV when compared to placebo, at a rate consistent with what was observed in the phase 2 study.

Additional endpoints

Brincidofovir did not prevent infection with non-CMV DNA viruses, such as BK virus.

And there was no significant difference between the treatment arms with regard to all-cause mortality. The rate was 15.5% in the brincidofovir arm and 10.1% in the placebo arm (P=0.12).

 

 

The investigators said the numerical differences in mortality appear to be driven by higher use of corticosteroids and other immunosuppressive therapies in the subjects who received brincidofovir.

The rate of treatment-emergent adverse events (AEs) was 100% in the brincidofovir arm and 98% in the placebo arm. The rate of grade 3 or higher AEs was 67% and 38%, respectively. The rate of serious AEs was 57% and 38%, respectively.

The rate of AEs leading to treatment discontinuation was 26% and 7%, respectively. And the rate of AEs leading to treatment change or interruption was 45% and 15%, respectively.

The most common AEs in the brincidofovir arm were diarrhea (61%), acute GVHD (57%), abdominal pain (34%), nausea (31%), vomiting (24%), peripheral edema (17%), hyperglycemia (16%), hypokalemia (16%), hypomagnesemia (13%), and ALT elevation (11%). There was no evidence of bone marrow toxicity, kidney toxicity, or viral resistance to brincidofovir.

Brincidofovir development

Chimerix said it will discuss the SUPPRESS data in full with the US Food and Drug Administration and other regulators, including the benefit-to-risk profile in specific subpopulations, as well as the current adenovirus and smallpox data, to determine next steps for the brincidofovir clinical programs.

The development of an intravenous (IV) formulation of brincidofovir is progressing toward clinical testing and has the potential to avoid the gastrointestinal side effects of orally administered brincidofovir.

Preclinical studies of IV brincidofovir have shown a lower risk of gastrointestinal effects based on maintained body weight during dosing and no evidence of injury in preliminary review of the gastrointestinal tract.

If human studies continue to support these findings, IV dosing during the first few weeks after transplant when patients are recovering from conditioning chemotherapy could be explored, with oral brincidofovir therapy available as patients are discharged home.

As there is no preventive therapy approved for CMV in HSCT recipients, Chimerix said it is committed to moving brincidofovir forward in this indication. Plans for brincidofovir in HSCT recipients will be the subject of further discussions with regulators.

Publications
Publications
Topics
Article Type
Display Headline
Drug may still be viable as CMV prophylaxis
Display Headline
Drug may still be viable as CMV prophylaxis
Disallow All Ads
Content Gating
No Gating (article Unlocked/Free)
Alternative CME
Disqus Comments
Default
Use ProPublica

PCP Visits to Hospitalized Patients

Article Type
Changed
Thu, 06/08/2017 - 12:16
Display Headline
Association between in‐hospital supportive visits by primary care physicians and patient outcomes: A population‐based cohort study

Transitions in care are vulnerable periods. As patients are transferred between settings of care (such as from hospital back to the community), communication between healthcare providers is vital for care continuity.[1] A significant number of preventable adverse events may be related to ineffective communication between care providers.[1, 2, 3] The advent of specialized care, such as the introduction of hospitalists in acute care settings, has created an environment in which a patient's most responsible physician can often change multiple times as they move through the healthcare system.[4] Although there are many benefits to this type of concentrated care, the increase in care transitions may result in breakdowns in communication that may then be linked to risks in patient safety and suboptimal patient outcomes.[5, 6, 7, 8]

Improved continuity of care has been demonstrated to enhance patient safety during care transitions.[7] Efforts to develop continuity of care interventions are largely focused on care‐provider continuity, improved facilitation of communication, care planning, and increasing involvement of primary care physicians during follow‐up to hospitalizations and specialist visits.[9, 10] Such continuity of care efforts may provide a moderate benefit, but there remains room for improvement.[10, 11]

One dimension of continuity of care that has received limited attention is the potential impact of primary care physicians hospital visits to their hospitalized patients in a supportive‐care role.[12] In these situations, the primary care physician is neither the most responsible physician nor are they involved directly in their patient's hospital care. However, visiting their patient implies that they are aware of the hospitalization, thereby facilitating the potential for communication between care providers. Primary care physicians can also provide valuable contextual and relevant information as well as be involved in the discharge process. To identify the extent to which primary care physicians visit hospitalized patients and to measure the potential impact of primary care physician supportive visits on future outcomes, we used population‐level data to determine the frequency of supportive‐care visits by primary care physicians to hospitalized patients and to identify the association between these visits, patient outcomes, and health services utilization.

METHODS

Overview

We applied a retrospective cohort design utilizing linked population‐based administrative databases in the province of Ontario, Canada to examine outcome differences between patients who received a supportive‐care in‐hospital visit by their primary care physician compared to those who did not.

Databases

We assembled the cohort from linked and encrypted population‐based healthcare administrative databases. Data were derived from information on patients and physicians from the Ontario Health Insurance Plan, the Canadian Census, the Canadian Institute of Health Information Hospital Discharge Abstract Database, Registered Persons Databases, National Ambulatory Care Reporting System, Corporate Provider Database, Client Agency Program Enrolment, and Home Care Database. These databases have been validated and widely used in numerous studies.[13, 14, 15] All adults aged18 years who were discharged from the hospital in Ontario, Canada between January 1, 2008 and December 31, 2009 were included. Patients transferred to nursing homes or other acute care facilities following discharge, including rehabilitation centers, were excluded because they may have different readmission patterns. Among remaining hospitalized patients, only those with an identifiable primary care physician in the community were included. The patientprimary care physician pairings were identified using validated algorithms based on historical physician billing information.[16] This approach, adapted from previous studies, maximized the comparability among the study groups.[17, 18] In addition to having an historical relationship with the patients, primary care physicians had to have a history of conducting in‐hospital supportive visits (i.e., visits to at least 2 hospital patients within the previous year) for the patientprimary care physician pair to be included. This criterion was included to increase the likelihood that we were capturing a usual physician practice behavior and not a single circumstantial visit by a primary care physician. The history of supportive visits was also identified with physician billing data using a specific fee code.

Exposure

The exposure of interest was an in‐hospital visit in a supportive‐care role by the primary care physician during a patient's hospitalization and was obtained from physician fee codes. The fee paid for a visit during the study period was less than $20 CND.

Outcome Measures

Two different composite outcome measures were examined. The primary outcome was a composite of an emergent hospital readmission, death, or emergency department visit (without hospital admission). A composite measure was utilized to account for all outcomes simultaneously and thus be representative of the overall patient experience.[19] This approach has been applied in several studies examining continuity of care.[19, 20, 21] The secondary outcome examined processes of care. It was a composite evaluating ambulatory health services use postdischarge, specifically the number of primary care physician office visits and formal (ie, paid for by the universal provincial health plan) home‐care services. Home‐care services included both visits for nursing care as well as formal social support such as personal care. All outcome measures were assessed at 30 and 90 days following hospital discharge to assess for short and medium range outcomes.[22]

Patient Characteristics

Patient demographics including age, sex, low income (defined as individual income below $16,018 [CND] or couples income below $24,175 [CND]), living in a rural region, and the number of previous visits with primary care physicians were described from the available data. Readmission risk from the initial hospitalization was calculated based on the LACE score.[23] The LACE score is a validated measure of 30‐day readmission risk based on healthcare administrative data that account for (L) length of stay, (A) acute admission, (C) comorbid disease burden, and number of (E) emergency department visits in previous 6 months.[23] The LACE score ranges from 0 to 19, which correspond to a probability of readmissions of 2% to 43.7%, respectively. We considered individuals to have a high risk of readmission with a LACE score 10, which corresponds to a probability of readmission of 12.2%.[23]

Statistical Analyses

Descriptive statistics were used to compare patient characteristics among those with a primary care physician supportive‐care visit to those without. Logistic regression modeling was conducted to examine the impact of primary care physician visits on outcomes. The results reported here reflect the selection of adjusting for the confounders of age, sex, a history of primary care physician visits, low income, rurality, and the LACE score.

Ethics

The project analysis was conducted at the Institute for Clinical Evaluative Sciences (ICES) in Toronto, Ontario and was approved by the Sunnybrook Health Sciences Centre Research Ethics Board.

RESULTS

Overview

There were 11,316 primary care physicians identified as practicing in Ontario during the study period, of which 3236 had a history of conducting regular in‐hospital visits to 2 or more patients. The final patient cohort consisted of 164,059 hospitalized patients; 19,614 patients received a visit from their primary care physician, whereas 144,445 did not (Figure 1).

Figure 1
Patient cohort development. *Patients were excluded if they were <18 years of age, died before or during index hospitalization, were nonmedical patients (eg, psychiatric or obstetrics), were discharged to an acute care facility (eg, transfer between hospitals), or were missing data or data were not otherwise available. Abbreviations: PCP, primary care physician.

The hospitalized patients who received a visit from their primary care physician were significantly different than the patients who did not receive an in‐hospital visit (Table 1). Notably, patients who received a visit by their primary care physician had longer lengths of hospital stay (9.7 days vs 6.8 days, P<0.001). As well, a greater proportion had a high 30‐day readmission risk (LACE score10: 39.4% vs 29.9%, P<0.001) (Table 1).[21]

Patient Characteristics for the Cohort
VariableaWith PCP Visit (N=19,614)Without PCP Visit (N=144,445)
  • NOTE: Abbreviations: ED, emergency department; PCP, primary care physician; SD, standard deviation.

  • All results are statistically significantly different (P<0.0001).

  • Low income is defined as individual income below $16,018 (CND) or couples income below $24,175 (CND).

  • LACE score is a validated measure predicting readmission risk and accounts for length of stay, acute admission, comorbid disease burden, and number of ED visits in previous 6 months. The probability of readmissions range from 2% for a score of 0 to 43.7% for a LACE score of 19; LACE score of 10 corresponds to a probability of readmission of 12.2%.[20]

Age, meanSD68.3716.8565.7318.54
Sex, no. of males9,393 (47.9%)67,030 (46.4%)
Low income3,937 (20.1%)30,157 (20.9%)
Individuals living in rural regions, no.1,951 (9.9%)25,731 (17.8%)
PCP visits in previous 6 months, meanSD4.764.474.174.28
Length of stay, d, meanSD9.7217.406.7913.17
Acute emergent visits, no.19,138 (97.6%)136,374 (94.4%)
Charlson score, meanSD1.061.600.921.49
ED visits in previous 6 months, meanSD0.951.481.091.98
LACE score, meanSDc9.022.888.103.02
High risk for readmission (LACE score10), no. (%)c7,721 (39.4%)43,126 (29.9%)

Patients who received an in‐hospital visit by their primary care physician were significantly different from those who did not (Table 2). They were older (68.4 years vs 65.7 years), and had a higher risk of readmission (LACE score of 9 vs 8). As well, proportionally fewer patients who received a visit were from rural regions than in the comparator group (9.9% of patients visited were from rural regions vs 17.8% of patients who did not receive a visit) (Table 2).

Results for Primary Outcome of Emergency Department Visit, Hospital Readmission, or Death at 30 and 90 Days PostHospital Discharge and Secondary Outcome of PCP Office Visits and Home‐Care Services
VariablePatients Who Received an In‐hospital Visit (N=19,614)Patients Who Did Not Receive an In‐hospital Visit (N=144,445)P Value
  • NOTE: Abbreviations: ED, emergency department; PCP, primary care physician; SD, standard deviation.

  • Composite endpoint=readmission, ED visit, or death.

  • Composite endpoint=community PCP visit or home‐care service.

Primary outcome of emergency department visit, hospital readmission, or death
30 days postdischarge, no. (%)  
Readmission1,742 (8.9%)11,212 (7.8%)<0.001
ED visit2,039 (10.4%)16,823 (11.6%)<0.001
Death727 (3.7%)4,688 (3.2%)<0.001
Composite endpointa4,227 (21.6%)30,848 (21.4%)0.533
90 days postdischarge  
Readmission2,791 (14.2%)18,257 (12.6%)<0.001
ED visit3,652 (18.6%)29,590 (20.5%)<0.001
Death1,507 (7.7%)9,821 (6.8%)<0.001
Composite endpointa7,125 (36.3%)52,245 (36.2%)0.668
Secondary outcome of PCP office visits and home‐care services
30 days postdischarge  
Community PCP visits, meanSD3.85.13.14.6<0.001
PCP visit, no. (%)15,732 (80.2%)108,266 (75%)<0.001
Home‐care services, no. (%)6,197 (31.6%)38,745 (26.8%)<0.001
Composite endpoint, no. (%)b16,851 (85.9%)117, 290 (81.2%)<0.001
90 days postdischarge  
Community PCP visits, meanSD8.210.16.99.3<0.001
PCP visit, no. (%)18,112 (92.3%)128, 806 (89.2%)<0.001
Home‐care services, no. (%)7,256 (37.0%)45,675 (31.6%)<0.001
Composite endpoint, no. (%)b18, 504 (94.3%)132, 448 (91.7%)<0.001

Individual Outcomes

Patients who received an in‐hospital visit by their primary care physician were also more likely to be readmitted within 30 days of discharge (8.9% vs 7.8%, P<0.001) and within 90 days of discharge (14.2% vs 12.6%, P<0.001). Additionally, patients who were visited by their primary care physician while hospitalized were more likely to die within 30 days postdischarge than those who did not receive an in‐hospital visit (3.7% vs 3.2%, P<0.001) and similarly by 90 days postdischarge (7.7% vs 6.8%, P<0.001) (Table 2).

Patients who received an in‐hospital visit were less likely to visit the emergency department at 30 days (10.4% vs 11.6%, P<0.001) and at 90 days (18.6% vs 20.5%, P<0.001) compared to patients who did not receive an in‐hospital visit (Table 2).

The patients who received in‐hospital visits by their primary care physician had a greater average number of primary care physician visits in the community at 30 days (3.8 vs 3.1, P<0.001) and 90 days (8.2 vs 6.9, P<0.001) (Table 2). Additionally, a higher proportion of patients who received an in‐hospital visit accessed home‐care services at 30 days postdischarge (31.6% vs 26.8%, P<0.001) and 90 days postdischarge (37.0% vs 31.6%, P<0.001) (Table 2).

Primary Outcome

There was no difference in proportion of patients who experienced the composite endpoint at 30 days (4227 [21.6%] vs 30,848 [21.4%], P>0.5) or 90 days (7125 [36.3%] vs 52,245 [36.2%], P>0.6) for patients who received an in‐hospital visit by their primary care physician compared to those who did not. The unadjusted model found no statistically significant difference between the 2 groups upon a primary care physician visit (odds ratio [OR]: 1.01; 95% confidence interval [CI]: 0.98‐1.04). However, once adjusting for differences in the groups for patient factors such as age, sex, location and health status, patients who received an in‐hospital visit by their primary care physician had lower adjusted risk for the composite outcome at 30 days postdischarge (adjusted OR [aOR]: 0.92; 95% CI: 0.89‐0.96) and 90 days postdischarge (aOR: 0.90; 95% CI: 0.87‐0.92) (Table 3). Estimates for each individual component of the composite outcome revealed significantly lower risk for ED visit and death but similar risk for readmission at both 30 days and 90 days after hospital discharge for patients who received and in‐hospital visit from their primary care physician and those who did not (Table 3).

Logistic Regression Modeling at 30 and 90 Days PostHospital Discharge Associated With the Impact of In‐hospital Primary Care Physician Visit
VariableUnadjusted Odds Ratio (95% CI)Adjusted Odds Ratio (95% CI)a
  • NOTE: Abbreviations: CI, confidence interval; ED, emergency department; PCP, primary care physician.

  • Adjusted for age, sex, being of low income, being from a rural region, and LACE score. LACE score is a validated measure predicting readmission risk and accounts for length of stay, acute admission, comorbid disease burden, and number of ED visits in the previous 6 months.[20]

  • Composite endpoint=readmission, ED visit, or death.

  • Composite endpoint=community PCP visit or home‐care service.

Primary outcome of emergency department visit, hospital readmission, or death
30 days postdischarge 
Readmission1.16 (1.10‐1.22)1.03 (0.97‐1.08)
ED visit0.88 (0.84‐0.92)0.88 (0.84‐0.92)
Death1.15 (1.06‐1.24)0.88 (0.81‐0.96)
Composite endpointb1.01 (0.98‐1.05)0.92 (0.89‐0.96)
90 days postdischarge 
Readmission1.15 (1.10‐1.20)1.00 (0.96‐1.04)
ED visit0.89 (0.86‐0.92)0.89 (0.86‐0.93)
Death1.14 (1.08‐1.21)0.87 (0.82‐0.93)
Composite endpointb1.01 (0.98‐1.04)0.90 (0.87‐0.92)
Secondary outcome of PCP office visits and home‐care services
30 days postdischarge 
Community PCP visits1.35 (1.31‐1.41)1.21 (1.16‐1.25)
Home‐care services1.26 (1.22‐1.30)1.05 (1.01‐1.09)
Composite endpointc1.41 (1.34‐1.47)1.16 (1.11‐1.21)
90 days postdischarge
Community PCP visits1.46 (1.39‐1.55)1.25 (1.18‐1.33)
Home‐care services1.27 (1.23‐1.31)1.05 (1.01‐1.08)
Composite endpointc1.51 (1.42‐1.61)1.19 (1.12‐1.27)

Secondary Outcome

Patients who received an in‐hospital visit by their primary care physician were more likely to experience the composite outcome of home‐care services and community primary care physician visits at 30 postdischarge (16,851 [85.9%] vs 117,290 [81.2%], P<0.001) and 90 days postdischarge (18,504 [94.3%] vs 132,448 [91.7%], P<0.001) compared to patients who did not receive an in‐hospital visit (Table 3). Once accounting for patient variables such as age, sex, location, and health status, patients who received an in‐hospital visit by their primary care physician had a higher adjusted risk for the composite outcome at 30 days postdischarge (aOR: 1.16; 95% CI: 1.11‐1.21) and 90 days postdischarge (aOR: 1.19; 95% CI: 1.12‐1.27) (Table 3).

DISCUSSION

Our population‐based study of primary care physicians is among the first to examine outcomes of patients whose primary care physicians have a history of providing supportive visits to hospitalized patients. After controlling for risk differences in patients at hospital discharge, we found that a primary care physician visit to a patient in the hospital was associated with a lower adjusted risk for the composite outcome of death, emergent hospital readmission, or emergency department visit at 30 and 90 days postdischarge compared to hospitalized patients who did not receive a visit by their primary care physician. We found this to be driven by patients having a lower risk of emergency department visits and death, whereas there was a similar risk of hospital readmission. We also found that visited patients were more likely to access home‐care services and have more primary care physician visits in the community following discharge.

The unadjusted model differs substantially from the adjusted model. On the surface this is an apparent paradox where the unadjusted results suggest an association with potential harm or no difference with a supportive visit. Conversely, the adjusted model suggests a reduction in harms. The differences between the unadjusted and adjusted model is driven by changes in the point estimates for readmission and death rates at both 30 and 90 day postdischarge. Prior to adjustment, it appears as if a primary care physician visit is associated with a significant increase of death; however, upon adjustment, it is associated with a significant reduction in death. Interestingly, this is a different effect than that observed with the secondary analysis, where the adjusted analyses demonstrate a more modest (but still positive) effect of supportive‐care visits. This observed change is likely due to differences in the patient groups. We can speculate that this may be an observed phenomenon of primary care physicians opting to visit their sicker patients, as perhaps it should be; however, further research is required to fully understand the real drivers of a supportive visit.

Our results are consistent with an earlier study that identified that a minority number of primary care physicians visit their hospitalized patients.[24] As well, findings from a randomized controlled trial of 364 patients over 60 years old identified a limited impact of primary care physician visits on patient outcomes but noted enhanced access to community health services.[12] Our work highlights the potential impact of primary care physician visits, which could, in theory, be leveraged and be an important role that primary care physicians can play in planning postdischarge care and improving the quality of care following hospitalization.

Our research study did not examine the impact of in‐hospital primary care physician visits on patient satisfaction directly. However, it has been demonstrated that patients have a strong desire for their primary care physician to be involved in their hospital care and their preference is for direct contact, with face‐to‐face visits compared to telephone or other communication.[25] This choice is important because dissatisfaction with services is associated with a loss of patient confidence in care quality and decreased adherence.[26] Also, primary care physicians acknowledge that information exchange is lacking when their patients are discharged, and that improving this aspect of a patient's care transition is important.[20] Research into discharge summaries as a tool to fill the communication gap has noted some success, yet there remains uncertainty regarding the type of information that should be included in a discharge summary, the time frame in which primary care physicians actually receive the summaries, and the accuracy of the information provided.[20, 27]

Our use of population‐based administrative data sources make the findings of our research generalizable to other similarly designed healthcare systems where a primary care physician may visit their hospitalized patients in a supportive‐care role. We were interested in a complex patientphysician interaction with a number of potential confounding factors, and our use of a composite measure represents the broad outcomes from this contact. Our cohort methodology was designed to isolate the exposure of interest while maximizing uniformity between the 2 study groups on other characteristics. Additionally a number of potential confounding factors were considered in an effort to isolate the effect of the primary care physician in‐hospital visit such as age, comorbid disease, and risk of hospital readmission.[12] The findings of our work support that of earlier research, but on a broader and more generalizable scale.[12]

There were notable differences between the intervention and control patient populations in the proportion of patients from rural regions who receive a supportive visit. This may be due to systemic differences between rural and nonrural regions with regard to access to care and ease of visit by primary care physicians. Alternatively, observed differences may be due to limitations of our study design in that some rural environments rely on primary care physicians to be involved in hospital care for the region. As such, they may actually be visiting their patients in a manner that was not captured as a supportive‐care visit. This is an important area that should be pursued in the future.

We acknowledge there are limits to our research findings. First, the nature of administrative data introduces challenges to causal inferences. As such, we are careful to describe associations and not draw causative links as there may be additional variables influencing outcomes including the patientphysician relationship, the location of the hospital relative to the physician practice and/or home, the time of the primary care physician visit, primary care physician hospital privileges for supportive‐care visits, and the number of other patients the primary care physician had in the same hospital at the same time. A second limitation is the use of the selected outcomes, which may not be direct measures of care quality.[28] However, the selected outcomes have been shown to be good quality measures in other work relevant to health policy.[8, 20, 21, 29] Third, the use of a composite outcome may over‐ or underestimate an exposure's impact.[19] Our composite outcome might have been dominated by some of its components. These observations may reflect the reality of primary care physicians visiting their sicker patients, or may be an attribute of the relatively short length of follow‐up of the study design. Fourth, we cannot determine whether there were additional interventions in place that assisted the continuity of care for primary care physician visits.[20, 27] However, this research included a broad range of hospitals throughout a large province where there were no system‐level quality interventions applied during this time. Fifth, our readmission rate may appear lower than other studies. However, our analysis is population based and not limited in focus to seniors.[30] As well, our posthospitalization death rates are similar to others, and the readmission rates are comparable to other Canadian studies.[31] Sixth, patients at higher risk for adverse outcomes may be identified as requiring more communication with their primary care physicians and we may not have fully captured this risk in our adjustment models, thereby underestimating the effect of exposure.[27] Further, primary care physicians may be involved in major medical decisions such as transitions to palliative care. A supportive‐care visit that facilitated these transitions and its ensuing outcomes may not have been included in our analysis. Seventh, our inherent assumption is that more care, such as posthospital primary care visits and home visits, denotes better care. This may not always be the case.[32] Eighth, physicians may find it difficult to visit their patient in the hospital, even when asked.[12] Finally, our findings are contingent on a system that supports primary care physicians being aware of their patients who become hospitalized. This is not only incumbent on any individual (eg, hospitalist) but a system where all providers work cohesively and seamlessly. On balance, however, these limitations do not overshadow our study's findings and conclusions.

Visits by primary care physicians to hospitalized patients are a longstanding tradition. The practice likely varies according to regional, patient, and individual physician characteristics.[16, 17, 18, 25] However, reimbursement codes for these services are present in a number of international healthcare systems' physician fee schedules with fairly modest remuneration amounts. The fairly nominal fee of less than $20 CND for a supportive‐care visit is similar to other systems and does not constitute a strong financial incentive to encourage this practice. The fee likely compensates the primary care physician for some of their time but comes with an opportunity cost to other aspects of their practice. Thus, results may differ in other environments or if the fee were higher, thereby incenting more primary care physicians to conduct visits. Indeed, the entire program for supportive hospital visits cost approximately $2.5 million CND per year for the 13 million people in the province of Ontario. Future work in this area could address the overall value and cost‐effectiveness of any potential fee changes. Still, it highlights the generalizability of our findings to other health systems and the ease in assessing the effect of the practice.

Overall, our findings underscore the importance and relevance for the practice of supportive‐care visits in its association with patient outcomes and health services utilization, which may prove to be an important key factor to improve quality healthcare. Our results suggest that an in‐hospital visit by a primary care physician may improve patient outcomes and increase subsequent support in the community. An in‐hospital supportive visit may be an additional method by which primary care physicians, and healthcare systems as a whole, strive to achieve the best care for patients.

Acknowledgements

Michael Manno, an analyst with the Institute of Clinical Evaluative Sciences (ICES) at the time of this study, assisted with the analyses.

Disclosures: This study was supported by the Institute for Clinical Evaluative Sciences (ICES), which is funded by an annual grant from the Ontario Ministry of Health and Long‐Term Care (MOHLTC). The opinions, results, and conclusions reported in this article are those of the authors and are independent from the funding sources. No endorsement by the ICES or the Ontario MOHLTC is intended or should be inferred. No researcher or persons involved in this study had any declared or otherwise known conflicts of interest. Stacey Brener received funding from a Canadian Institutes of Health Research (CIHR) Master's award in the area of primary care; the Ontario Graduate Student in Science and Technology award, an award from the CIHR Women's College Hospital Interdisciplinary Capacity Enhancement Team, and team grant OTG‐88591 from the CIHR. Susan Bronskill is supported by a CIHR New Investigator Award in the Area of Aging. Chaim Bell is supported by a CIHR/Canadian Patient Safety Institute Chair in Patient Safety and Continuity of Care. These funding agencies had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. The corresponding author had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors report no conflicts of interest.

Files
References
  1. Walraven C, Mamdani M, Fang J, Austin PC. Continuity of care and patient outcomes after hospital discharge. J Gen Intern Med. 2004;19(6):624631.
  2. Brennan TA, Leape LL, Laird NM, et al. Incidence of adverse events and negligence in hospitalized patients: results of the Harvard Medical Practice Study I. 1991. Qual Saf Health Care. 2004;13(2):145151; discussion 51–52.
  3. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161167.
  4. The College of Family Physicians of Canada. Family physicians caring for hospital inpatients. Available at: http://www.cfpc.ca/uploadedFiles/Resources/Resource_Items/FPs20Inpt20Hosp20Care_En.pdf. Published October 2003. Accessed August 15, 2015.
  5. Halm EA, Lee C, Chassin MR. Is volume related to outcome in health care?. A systematic review and methodologic critique of the literature. Ann Intern Med. 2002;137(6):511520.
  6. Lindenauer PK, Rothberg MB, Pekow PS, Kenwood C, Benjamin EM, Auerbach AD. Outcomes of care by hospitalists, general internists, and family physicians. N Engl J Med. 2007;357(25):25892600.
  7. Stiell AP, Forster AJ, Stiell IG, Walraven C. Maintaining continuity of care: a look at the quality of communication between Ontario emergency departments and community physicians. CJEM. 2005;7(3):155161.
  8. Walraven C, Taljaard M, Etchells E, et al. The independent association of provider and information continuity on outcomes after hospital discharge: implications for hospitalists. J Hosp Med. 2010;5(7):398405.
  9. Liss DT, Chubak J, Anderson ML, Saunders KW, Tuzzio L, Reid RJ. Patient‐reported care coordination: associations with primary care continuity and specialty care use. Ann Fam Med. 2011;9(4):323329.
  10. Marchinko S, Clarke D. The Wellness Planner: empowerment, quality of life, and continuity of care in mental illness. Arch Psychiatr Nurs. 2011;25(4):284293.
  11. Tremblay D, Roberge D, Cazale L, et al. Evaluation of the impact of interdisciplinarity in cancer care. BMC Health Serv Res. 2011;11:144.
  12. McInnes E, Mira M, Atkin N, Kennedy P, Cullen J. Can GP input into discharge planning result in better outcomes for the frail aged: results from a randomized controlled trial. Fam Pract. 1999;16(3):289293.
  13. Bell CM, Brener SS, Gunraj N, et al. Association of ICU or hospital admission with unintentional discontinuation of medications for chronic diseases. JAMA. 2011;306(8):840847.
  14. Bell CM, Bajcar J, Bierman AS, Li P, Mamdani MM, Urbach DR. Potentially unintended discontinuation of long‐term medication use after elective surgical procedures. Arch Intern Med. 2006;166(22):25252531.
  15. Juurlink D, Preyra C, Croxford R, Chong A, Austin P, Tu J, Laupacis A. Canadian Institute for Health Information Discharge Abstract Database: A Validation Study. Toronto: Institute for Clinical Evaluative Sciences; 2006. Available at: http://www.ices.on.ca/Publications/Atlases‐and‐Reports/2006/Canadian‐Institute‐for‐Health‐Information. Accessed August 15, 2015.
  16. Chang CH, Stukel TA, Flood AB, Goodman DC. Primary care physician workforce and Medicare beneficiaries' health outcomes. JAMA. 2011;305(20):20962104.
  17. Bynum JP, Bernal‐Delgado E, Gottlieb D, Fisher E. Assigning ambulatory patients and their physicians to hospitals: a method for obtaining population‐based provider performance measurements. Health Serv Res. 2007;42(1 pt 1):4562.
  18. Shah BR, Hux JE, Laupacis A, Zinman B, Cauch‐Dudek K, Booth GL. Administrative data algorithms can describe ambulatory physician utilization. Health Serv Res. 2007;42(4):17831796.
  19. Montori VM, Permanyer‐Miralda G, Ferreira‐Gonzalez I, et al. Validity of composite end points in clinical trials. BMJ. 2005;330(7491):594596.
  20. Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831841.
  21. Lyons JS, O'Mahoney MT, Miller SI, Neme J, Kabat J, Miller F. Predicting readmission to the psychiatric hospital in a managed care environment: implications for quality indicators. Am J Psychiatry. 1997;154(3):337340.
  22. Rumball‐Smith J, Hider P. The validity of readmission rate as a marker of the quality of hospital care, and a recommendation for its definition. N Z Med J. 2009;122(1289):6370.
  23. Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551557.
  24. Walraven C, Taljaard M, Bell CM, et al. Information exchange among physicians caring for the same patient in the community. CMAJ. 2008;179(10):10131018.
  25. Chan B. Supply of physicians' services in Ontario. Hosp Q. 1999;3(2):17.
  26. Bond M, Bowling A, Abery A, McClay M, Dickinson E. Evaluation of outreach clinics held by specialists in general practice in England. J Epidemiol Community Health. 2000;54(2):149156.
  27. Bell CM, Schnipper JL, Auerbach AD, et al. Association of communication between hospital‐based physicians and primary care providers with patient outcomes. J Gen Intern Med. 2009;24(3):381386.
  28. Krumholz HM, Lin Z, Keenan PS, et al. Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia. JAMA. 2013;309(6):587593.
  29. Ashton CM, Wray NP. A conceptual framework for the study of early readmission as an indicator of quality of care. Soc Sci Med. 1996;43(11):15331541.
  30. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360(14):14181428.
  31. Dhalla IA, O'Brien T, Morra D, et al. Effect of a postdischarge virtual ward on readmission or death for high‐risk patients: a randomized clinical trial. JAMA. 2014;312(13):13051312.
  32. Grady D, Redberg RF. Less is more: how less health care can result in better health. Arch Intern Med. 2010;170(9):749750.
Article PDF
Issue
Journal of Hospital Medicine - 11(6)
Page Number
418-424
Sections
Files
Files
Article PDF
Article PDF

Transitions in care are vulnerable periods. As patients are transferred between settings of care (such as from hospital back to the community), communication between healthcare providers is vital for care continuity.[1] A significant number of preventable adverse events may be related to ineffective communication between care providers.[1, 2, 3] The advent of specialized care, such as the introduction of hospitalists in acute care settings, has created an environment in which a patient's most responsible physician can often change multiple times as they move through the healthcare system.[4] Although there are many benefits to this type of concentrated care, the increase in care transitions may result in breakdowns in communication that may then be linked to risks in patient safety and suboptimal patient outcomes.[5, 6, 7, 8]

Improved continuity of care has been demonstrated to enhance patient safety during care transitions.[7] Efforts to develop continuity of care interventions are largely focused on care‐provider continuity, improved facilitation of communication, care planning, and increasing involvement of primary care physicians during follow‐up to hospitalizations and specialist visits.[9, 10] Such continuity of care efforts may provide a moderate benefit, but there remains room for improvement.[10, 11]

One dimension of continuity of care that has received limited attention is the potential impact of primary care physicians hospital visits to their hospitalized patients in a supportive‐care role.[12] In these situations, the primary care physician is neither the most responsible physician nor are they involved directly in their patient's hospital care. However, visiting their patient implies that they are aware of the hospitalization, thereby facilitating the potential for communication between care providers. Primary care physicians can also provide valuable contextual and relevant information as well as be involved in the discharge process. To identify the extent to which primary care physicians visit hospitalized patients and to measure the potential impact of primary care physician supportive visits on future outcomes, we used population‐level data to determine the frequency of supportive‐care visits by primary care physicians to hospitalized patients and to identify the association between these visits, patient outcomes, and health services utilization.

METHODS

Overview

We applied a retrospective cohort design utilizing linked population‐based administrative databases in the province of Ontario, Canada to examine outcome differences between patients who received a supportive‐care in‐hospital visit by their primary care physician compared to those who did not.

Databases

We assembled the cohort from linked and encrypted population‐based healthcare administrative databases. Data were derived from information on patients and physicians from the Ontario Health Insurance Plan, the Canadian Census, the Canadian Institute of Health Information Hospital Discharge Abstract Database, Registered Persons Databases, National Ambulatory Care Reporting System, Corporate Provider Database, Client Agency Program Enrolment, and Home Care Database. These databases have been validated and widely used in numerous studies.[13, 14, 15] All adults aged18 years who were discharged from the hospital in Ontario, Canada between January 1, 2008 and December 31, 2009 were included. Patients transferred to nursing homes or other acute care facilities following discharge, including rehabilitation centers, were excluded because they may have different readmission patterns. Among remaining hospitalized patients, only those with an identifiable primary care physician in the community were included. The patientprimary care physician pairings were identified using validated algorithms based on historical physician billing information.[16] This approach, adapted from previous studies, maximized the comparability among the study groups.[17, 18] In addition to having an historical relationship with the patients, primary care physicians had to have a history of conducting in‐hospital supportive visits (i.e., visits to at least 2 hospital patients within the previous year) for the patientprimary care physician pair to be included. This criterion was included to increase the likelihood that we were capturing a usual physician practice behavior and not a single circumstantial visit by a primary care physician. The history of supportive visits was also identified with physician billing data using a specific fee code.

Exposure

The exposure of interest was an in‐hospital visit in a supportive‐care role by the primary care physician during a patient's hospitalization and was obtained from physician fee codes. The fee paid for a visit during the study period was less than $20 CND.

Outcome Measures

Two different composite outcome measures were examined. The primary outcome was a composite of an emergent hospital readmission, death, or emergency department visit (without hospital admission). A composite measure was utilized to account for all outcomes simultaneously and thus be representative of the overall patient experience.[19] This approach has been applied in several studies examining continuity of care.[19, 20, 21] The secondary outcome examined processes of care. It was a composite evaluating ambulatory health services use postdischarge, specifically the number of primary care physician office visits and formal (ie, paid for by the universal provincial health plan) home‐care services. Home‐care services included both visits for nursing care as well as formal social support such as personal care. All outcome measures were assessed at 30 and 90 days following hospital discharge to assess for short and medium range outcomes.[22]

Patient Characteristics

Patient demographics including age, sex, low income (defined as individual income below $16,018 [CND] or couples income below $24,175 [CND]), living in a rural region, and the number of previous visits with primary care physicians were described from the available data. Readmission risk from the initial hospitalization was calculated based on the LACE score.[23] The LACE score is a validated measure of 30‐day readmission risk based on healthcare administrative data that account for (L) length of stay, (A) acute admission, (C) comorbid disease burden, and number of (E) emergency department visits in previous 6 months.[23] The LACE score ranges from 0 to 19, which correspond to a probability of readmissions of 2% to 43.7%, respectively. We considered individuals to have a high risk of readmission with a LACE score 10, which corresponds to a probability of readmission of 12.2%.[23]

Statistical Analyses

Descriptive statistics were used to compare patient characteristics among those with a primary care physician supportive‐care visit to those without. Logistic regression modeling was conducted to examine the impact of primary care physician visits on outcomes. The results reported here reflect the selection of adjusting for the confounders of age, sex, a history of primary care physician visits, low income, rurality, and the LACE score.

Ethics

The project analysis was conducted at the Institute for Clinical Evaluative Sciences (ICES) in Toronto, Ontario and was approved by the Sunnybrook Health Sciences Centre Research Ethics Board.

RESULTS

Overview

There were 11,316 primary care physicians identified as practicing in Ontario during the study period, of which 3236 had a history of conducting regular in‐hospital visits to 2 or more patients. The final patient cohort consisted of 164,059 hospitalized patients; 19,614 patients received a visit from their primary care physician, whereas 144,445 did not (Figure 1).

Figure 1
Patient cohort development. *Patients were excluded if they were <18 years of age, died before or during index hospitalization, were nonmedical patients (eg, psychiatric or obstetrics), were discharged to an acute care facility (eg, transfer between hospitals), or were missing data or data were not otherwise available. Abbreviations: PCP, primary care physician.

The hospitalized patients who received a visit from their primary care physician were significantly different than the patients who did not receive an in‐hospital visit (Table 1). Notably, patients who received a visit by their primary care physician had longer lengths of hospital stay (9.7 days vs 6.8 days, P<0.001). As well, a greater proportion had a high 30‐day readmission risk (LACE score10: 39.4% vs 29.9%, P<0.001) (Table 1).[21]

Patient Characteristics for the Cohort
VariableaWith PCP Visit (N=19,614)Without PCP Visit (N=144,445)
  • NOTE: Abbreviations: ED, emergency department; PCP, primary care physician; SD, standard deviation.

  • All results are statistically significantly different (P<0.0001).

  • Low income is defined as individual income below $16,018 (CND) or couples income below $24,175 (CND).

  • LACE score is a validated measure predicting readmission risk and accounts for length of stay, acute admission, comorbid disease burden, and number of ED visits in previous 6 months. The probability of readmissions range from 2% for a score of 0 to 43.7% for a LACE score of 19; LACE score of 10 corresponds to a probability of readmission of 12.2%.[20]

Age, meanSD68.3716.8565.7318.54
Sex, no. of males9,393 (47.9%)67,030 (46.4%)
Low income3,937 (20.1%)30,157 (20.9%)
Individuals living in rural regions, no.1,951 (9.9%)25,731 (17.8%)
PCP visits in previous 6 months, meanSD4.764.474.174.28
Length of stay, d, meanSD9.7217.406.7913.17
Acute emergent visits, no.19,138 (97.6%)136,374 (94.4%)
Charlson score, meanSD1.061.600.921.49
ED visits in previous 6 months, meanSD0.951.481.091.98
LACE score, meanSDc9.022.888.103.02
High risk for readmission (LACE score10), no. (%)c7,721 (39.4%)43,126 (29.9%)

Patients who received an in‐hospital visit by their primary care physician were significantly different from those who did not (Table 2). They were older (68.4 years vs 65.7 years), and had a higher risk of readmission (LACE score of 9 vs 8). As well, proportionally fewer patients who received a visit were from rural regions than in the comparator group (9.9% of patients visited were from rural regions vs 17.8% of patients who did not receive a visit) (Table 2).

Results for Primary Outcome of Emergency Department Visit, Hospital Readmission, or Death at 30 and 90 Days PostHospital Discharge and Secondary Outcome of PCP Office Visits and Home‐Care Services
VariablePatients Who Received an In‐hospital Visit (N=19,614)Patients Who Did Not Receive an In‐hospital Visit (N=144,445)P Value
  • NOTE: Abbreviations: ED, emergency department; PCP, primary care physician; SD, standard deviation.

  • Composite endpoint=readmission, ED visit, or death.

  • Composite endpoint=community PCP visit or home‐care service.

Primary outcome of emergency department visit, hospital readmission, or death
30 days postdischarge, no. (%)  
Readmission1,742 (8.9%)11,212 (7.8%)<0.001
ED visit2,039 (10.4%)16,823 (11.6%)<0.001
Death727 (3.7%)4,688 (3.2%)<0.001
Composite endpointa4,227 (21.6%)30,848 (21.4%)0.533
90 days postdischarge  
Readmission2,791 (14.2%)18,257 (12.6%)<0.001
ED visit3,652 (18.6%)29,590 (20.5%)<0.001
Death1,507 (7.7%)9,821 (6.8%)<0.001
Composite endpointa7,125 (36.3%)52,245 (36.2%)0.668
Secondary outcome of PCP office visits and home‐care services
30 days postdischarge  
Community PCP visits, meanSD3.85.13.14.6<0.001
PCP visit, no. (%)15,732 (80.2%)108,266 (75%)<0.001
Home‐care services, no. (%)6,197 (31.6%)38,745 (26.8%)<0.001
Composite endpoint, no. (%)b16,851 (85.9%)117, 290 (81.2%)<0.001
90 days postdischarge  
Community PCP visits, meanSD8.210.16.99.3<0.001
PCP visit, no. (%)18,112 (92.3%)128, 806 (89.2%)<0.001
Home‐care services, no. (%)7,256 (37.0%)45,675 (31.6%)<0.001
Composite endpoint, no. (%)b18, 504 (94.3%)132, 448 (91.7%)<0.001

Individual Outcomes

Patients who received an in‐hospital visit by their primary care physician were also more likely to be readmitted within 30 days of discharge (8.9% vs 7.8%, P<0.001) and within 90 days of discharge (14.2% vs 12.6%, P<0.001). Additionally, patients who were visited by their primary care physician while hospitalized were more likely to die within 30 days postdischarge than those who did not receive an in‐hospital visit (3.7% vs 3.2%, P<0.001) and similarly by 90 days postdischarge (7.7% vs 6.8%, P<0.001) (Table 2).

Patients who received an in‐hospital visit were less likely to visit the emergency department at 30 days (10.4% vs 11.6%, P<0.001) and at 90 days (18.6% vs 20.5%, P<0.001) compared to patients who did not receive an in‐hospital visit (Table 2).

The patients who received in‐hospital visits by their primary care physician had a greater average number of primary care physician visits in the community at 30 days (3.8 vs 3.1, P<0.001) and 90 days (8.2 vs 6.9, P<0.001) (Table 2). Additionally, a higher proportion of patients who received an in‐hospital visit accessed home‐care services at 30 days postdischarge (31.6% vs 26.8%, P<0.001) and 90 days postdischarge (37.0% vs 31.6%, P<0.001) (Table 2).

Primary Outcome

There was no difference in proportion of patients who experienced the composite endpoint at 30 days (4227 [21.6%] vs 30,848 [21.4%], P>0.5) or 90 days (7125 [36.3%] vs 52,245 [36.2%], P>0.6) for patients who received an in‐hospital visit by their primary care physician compared to those who did not. The unadjusted model found no statistically significant difference between the 2 groups upon a primary care physician visit (odds ratio [OR]: 1.01; 95% confidence interval [CI]: 0.98‐1.04). However, once adjusting for differences in the groups for patient factors such as age, sex, location and health status, patients who received an in‐hospital visit by their primary care physician had lower adjusted risk for the composite outcome at 30 days postdischarge (adjusted OR [aOR]: 0.92; 95% CI: 0.89‐0.96) and 90 days postdischarge (aOR: 0.90; 95% CI: 0.87‐0.92) (Table 3). Estimates for each individual component of the composite outcome revealed significantly lower risk for ED visit and death but similar risk for readmission at both 30 days and 90 days after hospital discharge for patients who received and in‐hospital visit from their primary care physician and those who did not (Table 3).

Logistic Regression Modeling at 30 and 90 Days PostHospital Discharge Associated With the Impact of In‐hospital Primary Care Physician Visit
VariableUnadjusted Odds Ratio (95% CI)Adjusted Odds Ratio (95% CI)a
  • NOTE: Abbreviations: CI, confidence interval; ED, emergency department; PCP, primary care physician.

  • Adjusted for age, sex, being of low income, being from a rural region, and LACE score. LACE score is a validated measure predicting readmission risk and accounts for length of stay, acute admission, comorbid disease burden, and number of ED visits in the previous 6 months.[20]

  • Composite endpoint=readmission, ED visit, or death.

  • Composite endpoint=community PCP visit or home‐care service.

Primary outcome of emergency department visit, hospital readmission, or death
30 days postdischarge 
Readmission1.16 (1.10‐1.22)1.03 (0.97‐1.08)
ED visit0.88 (0.84‐0.92)0.88 (0.84‐0.92)
Death1.15 (1.06‐1.24)0.88 (0.81‐0.96)
Composite endpointb1.01 (0.98‐1.05)0.92 (0.89‐0.96)
90 days postdischarge 
Readmission1.15 (1.10‐1.20)1.00 (0.96‐1.04)
ED visit0.89 (0.86‐0.92)0.89 (0.86‐0.93)
Death1.14 (1.08‐1.21)0.87 (0.82‐0.93)
Composite endpointb1.01 (0.98‐1.04)0.90 (0.87‐0.92)
Secondary outcome of PCP office visits and home‐care services
30 days postdischarge 
Community PCP visits1.35 (1.31‐1.41)1.21 (1.16‐1.25)
Home‐care services1.26 (1.22‐1.30)1.05 (1.01‐1.09)
Composite endpointc1.41 (1.34‐1.47)1.16 (1.11‐1.21)
90 days postdischarge
Community PCP visits1.46 (1.39‐1.55)1.25 (1.18‐1.33)
Home‐care services1.27 (1.23‐1.31)1.05 (1.01‐1.08)
Composite endpointc1.51 (1.42‐1.61)1.19 (1.12‐1.27)

Secondary Outcome

Patients who received an in‐hospital visit by their primary care physician were more likely to experience the composite outcome of home‐care services and community primary care physician visits at 30 postdischarge (16,851 [85.9%] vs 117,290 [81.2%], P<0.001) and 90 days postdischarge (18,504 [94.3%] vs 132,448 [91.7%], P<0.001) compared to patients who did not receive an in‐hospital visit (Table 3). Once accounting for patient variables such as age, sex, location, and health status, patients who received an in‐hospital visit by their primary care physician had a higher adjusted risk for the composite outcome at 30 days postdischarge (aOR: 1.16; 95% CI: 1.11‐1.21) and 90 days postdischarge (aOR: 1.19; 95% CI: 1.12‐1.27) (Table 3).

DISCUSSION

Our population‐based study of primary care physicians is among the first to examine outcomes of patients whose primary care physicians have a history of providing supportive visits to hospitalized patients. After controlling for risk differences in patients at hospital discharge, we found that a primary care physician visit to a patient in the hospital was associated with a lower adjusted risk for the composite outcome of death, emergent hospital readmission, or emergency department visit at 30 and 90 days postdischarge compared to hospitalized patients who did not receive a visit by their primary care physician. We found this to be driven by patients having a lower risk of emergency department visits and death, whereas there was a similar risk of hospital readmission. We also found that visited patients were more likely to access home‐care services and have more primary care physician visits in the community following discharge.

The unadjusted model differs substantially from the adjusted model. On the surface this is an apparent paradox where the unadjusted results suggest an association with potential harm or no difference with a supportive visit. Conversely, the adjusted model suggests a reduction in harms. The differences between the unadjusted and adjusted model is driven by changes in the point estimates for readmission and death rates at both 30 and 90 day postdischarge. Prior to adjustment, it appears as if a primary care physician visit is associated with a significant increase of death; however, upon adjustment, it is associated with a significant reduction in death. Interestingly, this is a different effect than that observed with the secondary analysis, where the adjusted analyses demonstrate a more modest (but still positive) effect of supportive‐care visits. This observed change is likely due to differences in the patient groups. We can speculate that this may be an observed phenomenon of primary care physicians opting to visit their sicker patients, as perhaps it should be; however, further research is required to fully understand the real drivers of a supportive visit.

Our results are consistent with an earlier study that identified that a minority number of primary care physicians visit their hospitalized patients.[24] As well, findings from a randomized controlled trial of 364 patients over 60 years old identified a limited impact of primary care physician visits on patient outcomes but noted enhanced access to community health services.[12] Our work highlights the potential impact of primary care physician visits, which could, in theory, be leveraged and be an important role that primary care physicians can play in planning postdischarge care and improving the quality of care following hospitalization.

Our research study did not examine the impact of in‐hospital primary care physician visits on patient satisfaction directly. However, it has been demonstrated that patients have a strong desire for their primary care physician to be involved in their hospital care and their preference is for direct contact, with face‐to‐face visits compared to telephone or other communication.[25] This choice is important because dissatisfaction with services is associated with a loss of patient confidence in care quality and decreased adherence.[26] Also, primary care physicians acknowledge that information exchange is lacking when their patients are discharged, and that improving this aspect of a patient's care transition is important.[20] Research into discharge summaries as a tool to fill the communication gap has noted some success, yet there remains uncertainty regarding the type of information that should be included in a discharge summary, the time frame in which primary care physicians actually receive the summaries, and the accuracy of the information provided.[20, 27]

Our use of population‐based administrative data sources make the findings of our research generalizable to other similarly designed healthcare systems where a primary care physician may visit their hospitalized patients in a supportive‐care role. We were interested in a complex patientphysician interaction with a number of potential confounding factors, and our use of a composite measure represents the broad outcomes from this contact. Our cohort methodology was designed to isolate the exposure of interest while maximizing uniformity between the 2 study groups on other characteristics. Additionally a number of potential confounding factors were considered in an effort to isolate the effect of the primary care physician in‐hospital visit such as age, comorbid disease, and risk of hospital readmission.[12] The findings of our work support that of earlier research, but on a broader and more generalizable scale.[12]

There were notable differences between the intervention and control patient populations in the proportion of patients from rural regions who receive a supportive visit. This may be due to systemic differences between rural and nonrural regions with regard to access to care and ease of visit by primary care physicians. Alternatively, observed differences may be due to limitations of our study design in that some rural environments rely on primary care physicians to be involved in hospital care for the region. As such, they may actually be visiting their patients in a manner that was not captured as a supportive‐care visit. This is an important area that should be pursued in the future.

We acknowledge there are limits to our research findings. First, the nature of administrative data introduces challenges to causal inferences. As such, we are careful to describe associations and not draw causative links as there may be additional variables influencing outcomes including the patientphysician relationship, the location of the hospital relative to the physician practice and/or home, the time of the primary care physician visit, primary care physician hospital privileges for supportive‐care visits, and the number of other patients the primary care physician had in the same hospital at the same time. A second limitation is the use of the selected outcomes, which may not be direct measures of care quality.[28] However, the selected outcomes have been shown to be good quality measures in other work relevant to health policy.[8, 20, 21, 29] Third, the use of a composite outcome may over‐ or underestimate an exposure's impact.[19] Our composite outcome might have been dominated by some of its components. These observations may reflect the reality of primary care physicians visiting their sicker patients, or may be an attribute of the relatively short length of follow‐up of the study design. Fourth, we cannot determine whether there were additional interventions in place that assisted the continuity of care for primary care physician visits.[20, 27] However, this research included a broad range of hospitals throughout a large province where there were no system‐level quality interventions applied during this time. Fifth, our readmission rate may appear lower than other studies. However, our analysis is population based and not limited in focus to seniors.[30] As well, our posthospitalization death rates are similar to others, and the readmission rates are comparable to other Canadian studies.[31] Sixth, patients at higher risk for adverse outcomes may be identified as requiring more communication with their primary care physicians and we may not have fully captured this risk in our adjustment models, thereby underestimating the effect of exposure.[27] Further, primary care physicians may be involved in major medical decisions such as transitions to palliative care. A supportive‐care visit that facilitated these transitions and its ensuing outcomes may not have been included in our analysis. Seventh, our inherent assumption is that more care, such as posthospital primary care visits and home visits, denotes better care. This may not always be the case.[32] Eighth, physicians may find it difficult to visit their patient in the hospital, even when asked.[12] Finally, our findings are contingent on a system that supports primary care physicians being aware of their patients who become hospitalized. This is not only incumbent on any individual (eg, hospitalist) but a system where all providers work cohesively and seamlessly. On balance, however, these limitations do not overshadow our study's findings and conclusions.

Visits by primary care physicians to hospitalized patients are a longstanding tradition. The practice likely varies according to regional, patient, and individual physician characteristics.[16, 17, 18, 25] However, reimbursement codes for these services are present in a number of international healthcare systems' physician fee schedules with fairly modest remuneration amounts. The fairly nominal fee of less than $20 CND for a supportive‐care visit is similar to other systems and does not constitute a strong financial incentive to encourage this practice. The fee likely compensates the primary care physician for some of their time but comes with an opportunity cost to other aspects of their practice. Thus, results may differ in other environments or if the fee were higher, thereby incenting more primary care physicians to conduct visits. Indeed, the entire program for supportive hospital visits cost approximately $2.5 million CND per year for the 13 million people in the province of Ontario. Future work in this area could address the overall value and cost‐effectiveness of any potential fee changes. Still, it highlights the generalizability of our findings to other health systems and the ease in assessing the effect of the practice.

Overall, our findings underscore the importance and relevance for the practice of supportive‐care visits in its association with patient outcomes and health services utilization, which may prove to be an important key factor to improve quality healthcare. Our results suggest that an in‐hospital visit by a primary care physician may improve patient outcomes and increase subsequent support in the community. An in‐hospital supportive visit may be an additional method by which primary care physicians, and healthcare systems as a whole, strive to achieve the best care for patients.

Acknowledgements

Michael Manno, an analyst with the Institute of Clinical Evaluative Sciences (ICES) at the time of this study, assisted with the analyses.

Disclosures: This study was supported by the Institute for Clinical Evaluative Sciences (ICES), which is funded by an annual grant from the Ontario Ministry of Health and Long‐Term Care (MOHLTC). The opinions, results, and conclusions reported in this article are those of the authors and are independent from the funding sources. No endorsement by the ICES or the Ontario MOHLTC is intended or should be inferred. No researcher or persons involved in this study had any declared or otherwise known conflicts of interest. Stacey Brener received funding from a Canadian Institutes of Health Research (CIHR) Master's award in the area of primary care; the Ontario Graduate Student in Science and Technology award, an award from the CIHR Women's College Hospital Interdisciplinary Capacity Enhancement Team, and team grant OTG‐88591 from the CIHR. Susan Bronskill is supported by a CIHR New Investigator Award in the Area of Aging. Chaim Bell is supported by a CIHR/Canadian Patient Safety Institute Chair in Patient Safety and Continuity of Care. These funding agencies had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. The corresponding author had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors report no conflicts of interest.

Transitions in care are vulnerable periods. As patients are transferred between settings of care (such as from hospital back to the community), communication between healthcare providers is vital for care continuity.[1] A significant number of preventable adverse events may be related to ineffective communication between care providers.[1, 2, 3] The advent of specialized care, such as the introduction of hospitalists in acute care settings, has created an environment in which a patient's most responsible physician can often change multiple times as they move through the healthcare system.[4] Although there are many benefits to this type of concentrated care, the increase in care transitions may result in breakdowns in communication that may then be linked to risks in patient safety and suboptimal patient outcomes.[5, 6, 7, 8]

Improved continuity of care has been demonstrated to enhance patient safety during care transitions.[7] Efforts to develop continuity of care interventions are largely focused on care‐provider continuity, improved facilitation of communication, care planning, and increasing involvement of primary care physicians during follow‐up to hospitalizations and specialist visits.[9, 10] Such continuity of care efforts may provide a moderate benefit, but there remains room for improvement.[10, 11]

One dimension of continuity of care that has received limited attention is the potential impact of primary care physicians hospital visits to their hospitalized patients in a supportive‐care role.[12] In these situations, the primary care physician is neither the most responsible physician nor are they involved directly in their patient's hospital care. However, visiting their patient implies that they are aware of the hospitalization, thereby facilitating the potential for communication between care providers. Primary care physicians can also provide valuable contextual and relevant information as well as be involved in the discharge process. To identify the extent to which primary care physicians visit hospitalized patients and to measure the potential impact of primary care physician supportive visits on future outcomes, we used population‐level data to determine the frequency of supportive‐care visits by primary care physicians to hospitalized patients and to identify the association between these visits, patient outcomes, and health services utilization.

METHODS

Overview

We applied a retrospective cohort design utilizing linked population‐based administrative databases in the province of Ontario, Canada to examine outcome differences between patients who received a supportive‐care in‐hospital visit by their primary care physician compared to those who did not.

Databases

We assembled the cohort from linked and encrypted population‐based healthcare administrative databases. Data were derived from information on patients and physicians from the Ontario Health Insurance Plan, the Canadian Census, the Canadian Institute of Health Information Hospital Discharge Abstract Database, Registered Persons Databases, National Ambulatory Care Reporting System, Corporate Provider Database, Client Agency Program Enrolment, and Home Care Database. These databases have been validated and widely used in numerous studies.[13, 14, 15] All adults aged18 years who were discharged from the hospital in Ontario, Canada between January 1, 2008 and December 31, 2009 were included. Patients transferred to nursing homes or other acute care facilities following discharge, including rehabilitation centers, were excluded because they may have different readmission patterns. Among remaining hospitalized patients, only those with an identifiable primary care physician in the community were included. The patientprimary care physician pairings were identified using validated algorithms based on historical physician billing information.[16] This approach, adapted from previous studies, maximized the comparability among the study groups.[17, 18] In addition to having an historical relationship with the patients, primary care physicians had to have a history of conducting in‐hospital supportive visits (i.e., visits to at least 2 hospital patients within the previous year) for the patientprimary care physician pair to be included. This criterion was included to increase the likelihood that we were capturing a usual physician practice behavior and not a single circumstantial visit by a primary care physician. The history of supportive visits was also identified with physician billing data using a specific fee code.

Exposure

The exposure of interest was an in‐hospital visit in a supportive‐care role by the primary care physician during a patient's hospitalization and was obtained from physician fee codes. The fee paid for a visit during the study period was less than $20 CND.

Outcome Measures

Two different composite outcome measures were examined. The primary outcome was a composite of an emergent hospital readmission, death, or emergency department visit (without hospital admission). A composite measure was utilized to account for all outcomes simultaneously and thus be representative of the overall patient experience.[19] This approach has been applied in several studies examining continuity of care.[19, 20, 21] The secondary outcome examined processes of care. It was a composite evaluating ambulatory health services use postdischarge, specifically the number of primary care physician office visits and formal (ie, paid for by the universal provincial health plan) home‐care services. Home‐care services included both visits for nursing care as well as formal social support such as personal care. All outcome measures were assessed at 30 and 90 days following hospital discharge to assess for short and medium range outcomes.[22]

Patient Characteristics

Patient demographics including age, sex, low income (defined as individual income below $16,018 [CND] or couples income below $24,175 [CND]), living in a rural region, and the number of previous visits with primary care physicians were described from the available data. Readmission risk from the initial hospitalization was calculated based on the LACE score.[23] The LACE score is a validated measure of 30‐day readmission risk based on healthcare administrative data that account for (L) length of stay, (A) acute admission, (C) comorbid disease burden, and number of (E) emergency department visits in previous 6 months.[23] The LACE score ranges from 0 to 19, which correspond to a probability of readmissions of 2% to 43.7%, respectively. We considered individuals to have a high risk of readmission with a LACE score 10, which corresponds to a probability of readmission of 12.2%.[23]

Statistical Analyses

Descriptive statistics were used to compare patient characteristics among those with a primary care physician supportive‐care visit to those without. Logistic regression modeling was conducted to examine the impact of primary care physician visits on outcomes. The results reported here reflect the selection of adjusting for the confounders of age, sex, a history of primary care physician visits, low income, rurality, and the LACE score.

Ethics

The project analysis was conducted at the Institute for Clinical Evaluative Sciences (ICES) in Toronto, Ontario and was approved by the Sunnybrook Health Sciences Centre Research Ethics Board.

RESULTS

Overview

There were 11,316 primary care physicians identified as practicing in Ontario during the study period, of which 3236 had a history of conducting regular in‐hospital visits to 2 or more patients. The final patient cohort consisted of 164,059 hospitalized patients; 19,614 patients received a visit from their primary care physician, whereas 144,445 did not (Figure 1).

Figure 1
Patient cohort development. *Patients were excluded if they were <18 years of age, died before or during index hospitalization, were nonmedical patients (eg, psychiatric or obstetrics), were discharged to an acute care facility (eg, transfer between hospitals), or were missing data or data were not otherwise available. Abbreviations: PCP, primary care physician.

The hospitalized patients who received a visit from their primary care physician were significantly different than the patients who did not receive an in‐hospital visit (Table 1). Notably, patients who received a visit by their primary care physician had longer lengths of hospital stay (9.7 days vs 6.8 days, P<0.001). As well, a greater proportion had a high 30‐day readmission risk (LACE score10: 39.4% vs 29.9%, P<0.001) (Table 1).[21]

Patient Characteristics for the Cohort
VariableaWith PCP Visit (N=19,614)Without PCP Visit (N=144,445)
  • NOTE: Abbreviations: ED, emergency department; PCP, primary care physician; SD, standard deviation.

  • All results are statistically significantly different (P<0.0001).

  • Low income is defined as individual income below $16,018 (CND) or couples income below $24,175 (CND).

  • LACE score is a validated measure predicting readmission risk and accounts for length of stay, acute admission, comorbid disease burden, and number of ED visits in previous 6 months. The probability of readmissions range from 2% for a score of 0 to 43.7% for a LACE score of 19; LACE score of 10 corresponds to a probability of readmission of 12.2%.[20]

Age, meanSD68.3716.8565.7318.54
Sex, no. of males9,393 (47.9%)67,030 (46.4%)
Low income3,937 (20.1%)30,157 (20.9%)
Individuals living in rural regions, no.1,951 (9.9%)25,731 (17.8%)
PCP visits in previous 6 months, meanSD4.764.474.174.28
Length of stay, d, meanSD9.7217.406.7913.17
Acute emergent visits, no.19,138 (97.6%)136,374 (94.4%)
Charlson score, meanSD1.061.600.921.49
ED visits in previous 6 months, meanSD0.951.481.091.98
LACE score, meanSDc9.022.888.103.02
High risk for readmission (LACE score10), no. (%)c7,721 (39.4%)43,126 (29.9%)

Patients who received an in‐hospital visit by their primary care physician were significantly different from those who did not (Table 2). They were older (68.4 years vs 65.7 years), and had a higher risk of readmission (LACE score of 9 vs 8). As well, proportionally fewer patients who received a visit were from rural regions than in the comparator group (9.9% of patients visited were from rural regions vs 17.8% of patients who did not receive a visit) (Table 2).

Results for Primary Outcome of Emergency Department Visit, Hospital Readmission, or Death at 30 and 90 Days PostHospital Discharge and Secondary Outcome of PCP Office Visits and Home‐Care Services
VariablePatients Who Received an In‐hospital Visit (N=19,614)Patients Who Did Not Receive an In‐hospital Visit (N=144,445)P Value
  • NOTE: Abbreviations: ED, emergency department; PCP, primary care physician; SD, standard deviation.

  • Composite endpoint=readmission, ED visit, or death.

  • Composite endpoint=community PCP visit or home‐care service.

Primary outcome of emergency department visit, hospital readmission, or death
30 days postdischarge, no. (%)  
Readmission1,742 (8.9%)11,212 (7.8%)<0.001
ED visit2,039 (10.4%)16,823 (11.6%)<0.001
Death727 (3.7%)4,688 (3.2%)<0.001
Composite endpointa4,227 (21.6%)30,848 (21.4%)0.533
90 days postdischarge  
Readmission2,791 (14.2%)18,257 (12.6%)<0.001
ED visit3,652 (18.6%)29,590 (20.5%)<0.001
Death1,507 (7.7%)9,821 (6.8%)<0.001
Composite endpointa7,125 (36.3%)52,245 (36.2%)0.668
Secondary outcome of PCP office visits and home‐care services
30 days postdischarge  
Community PCP visits, meanSD3.85.13.14.6<0.001
PCP visit, no. (%)15,732 (80.2%)108,266 (75%)<0.001
Home‐care services, no. (%)6,197 (31.6%)38,745 (26.8%)<0.001
Composite endpoint, no. (%)b16,851 (85.9%)117, 290 (81.2%)<0.001
90 days postdischarge  
Community PCP visits, meanSD8.210.16.99.3<0.001
PCP visit, no. (%)18,112 (92.3%)128, 806 (89.2%)<0.001
Home‐care services, no. (%)7,256 (37.0%)45,675 (31.6%)<0.001
Composite endpoint, no. (%)b18, 504 (94.3%)132, 448 (91.7%)<0.001

Individual Outcomes

Patients who received an in‐hospital visit by their primary care physician were also more likely to be readmitted within 30 days of discharge (8.9% vs 7.8%, P<0.001) and within 90 days of discharge (14.2% vs 12.6%, P<0.001). Additionally, patients who were visited by their primary care physician while hospitalized were more likely to die within 30 days postdischarge than those who did not receive an in‐hospital visit (3.7% vs 3.2%, P<0.001) and similarly by 90 days postdischarge (7.7% vs 6.8%, P<0.001) (Table 2).

Patients who received an in‐hospital visit were less likely to visit the emergency department at 30 days (10.4% vs 11.6%, P<0.001) and at 90 days (18.6% vs 20.5%, P<0.001) compared to patients who did not receive an in‐hospital visit (Table 2).

The patients who received in‐hospital visits by their primary care physician had a greater average number of primary care physician visits in the community at 30 days (3.8 vs 3.1, P<0.001) and 90 days (8.2 vs 6.9, P<0.001) (Table 2). Additionally, a higher proportion of patients who received an in‐hospital visit accessed home‐care services at 30 days postdischarge (31.6% vs 26.8%, P<0.001) and 90 days postdischarge (37.0% vs 31.6%, P<0.001) (Table 2).

Primary Outcome

There was no difference in proportion of patients who experienced the composite endpoint at 30 days (4227 [21.6%] vs 30,848 [21.4%], P>0.5) or 90 days (7125 [36.3%] vs 52,245 [36.2%], P>0.6) for patients who received an in‐hospital visit by their primary care physician compared to those who did not. The unadjusted model found no statistically significant difference between the 2 groups upon a primary care physician visit (odds ratio [OR]: 1.01; 95% confidence interval [CI]: 0.98‐1.04). However, once adjusting for differences in the groups for patient factors such as age, sex, location and health status, patients who received an in‐hospital visit by their primary care physician had lower adjusted risk for the composite outcome at 30 days postdischarge (adjusted OR [aOR]: 0.92; 95% CI: 0.89‐0.96) and 90 days postdischarge (aOR: 0.90; 95% CI: 0.87‐0.92) (Table 3). Estimates for each individual component of the composite outcome revealed significantly lower risk for ED visit and death but similar risk for readmission at both 30 days and 90 days after hospital discharge for patients who received and in‐hospital visit from their primary care physician and those who did not (Table 3).

Logistic Regression Modeling at 30 and 90 Days PostHospital Discharge Associated With the Impact of In‐hospital Primary Care Physician Visit
VariableUnadjusted Odds Ratio (95% CI)Adjusted Odds Ratio (95% CI)a
  • NOTE: Abbreviations: CI, confidence interval; ED, emergency department; PCP, primary care physician.

  • Adjusted for age, sex, being of low income, being from a rural region, and LACE score. LACE score is a validated measure predicting readmission risk and accounts for length of stay, acute admission, comorbid disease burden, and number of ED visits in the previous 6 months.[20]

  • Composite endpoint=readmission, ED visit, or death.

  • Composite endpoint=community PCP visit or home‐care service.

Primary outcome of emergency department visit, hospital readmission, or death
30 days postdischarge 
Readmission1.16 (1.10‐1.22)1.03 (0.97‐1.08)
ED visit0.88 (0.84‐0.92)0.88 (0.84‐0.92)
Death1.15 (1.06‐1.24)0.88 (0.81‐0.96)
Composite endpointb1.01 (0.98‐1.05)0.92 (0.89‐0.96)
90 days postdischarge 
Readmission1.15 (1.10‐1.20)1.00 (0.96‐1.04)
ED visit0.89 (0.86‐0.92)0.89 (0.86‐0.93)
Death1.14 (1.08‐1.21)0.87 (0.82‐0.93)
Composite endpointb1.01 (0.98‐1.04)0.90 (0.87‐0.92)
Secondary outcome of PCP office visits and home‐care services
30 days postdischarge 
Community PCP visits1.35 (1.31‐1.41)1.21 (1.16‐1.25)
Home‐care services1.26 (1.22‐1.30)1.05 (1.01‐1.09)
Composite endpointc1.41 (1.34‐1.47)1.16 (1.11‐1.21)
90 days postdischarge
Community PCP visits1.46 (1.39‐1.55)1.25 (1.18‐1.33)
Home‐care services1.27 (1.23‐1.31)1.05 (1.01‐1.08)
Composite endpointc1.51 (1.42‐1.61)1.19 (1.12‐1.27)

Secondary Outcome

Patients who received an in‐hospital visit by their primary care physician were more likely to experience the composite outcome of home‐care services and community primary care physician visits at 30 postdischarge (16,851 [85.9%] vs 117,290 [81.2%], P<0.001) and 90 days postdischarge (18,504 [94.3%] vs 132,448 [91.7%], P<0.001) compared to patients who did not receive an in‐hospital visit (Table 3). Once accounting for patient variables such as age, sex, location, and health status, patients who received an in‐hospital visit by their primary care physician had a higher adjusted risk for the composite outcome at 30 days postdischarge (aOR: 1.16; 95% CI: 1.11‐1.21) and 90 days postdischarge (aOR: 1.19; 95% CI: 1.12‐1.27) (Table 3).

DISCUSSION

Our population‐based study of primary care physicians is among the first to examine outcomes of patients whose primary care physicians have a history of providing supportive visits to hospitalized patients. After controlling for risk differences in patients at hospital discharge, we found that a primary care physician visit to a patient in the hospital was associated with a lower adjusted risk for the composite outcome of death, emergent hospital readmission, or emergency department visit at 30 and 90 days postdischarge compared to hospitalized patients who did not receive a visit by their primary care physician. We found this to be driven by patients having a lower risk of emergency department visits and death, whereas there was a similar risk of hospital readmission. We also found that visited patients were more likely to access home‐care services and have more primary care physician visits in the community following discharge.

The unadjusted model differs substantially from the adjusted model. On the surface this is an apparent paradox where the unadjusted results suggest an association with potential harm or no difference with a supportive visit. Conversely, the adjusted model suggests a reduction in harms. The differences between the unadjusted and adjusted model is driven by changes in the point estimates for readmission and death rates at both 30 and 90 day postdischarge. Prior to adjustment, it appears as if a primary care physician visit is associated with a significant increase of death; however, upon adjustment, it is associated with a significant reduction in death. Interestingly, this is a different effect than that observed with the secondary analysis, where the adjusted analyses demonstrate a more modest (but still positive) effect of supportive‐care visits. This observed change is likely due to differences in the patient groups. We can speculate that this may be an observed phenomenon of primary care physicians opting to visit their sicker patients, as perhaps it should be; however, further research is required to fully understand the real drivers of a supportive visit.

Our results are consistent with an earlier study that identified that a minority number of primary care physicians visit their hospitalized patients.[24] As well, findings from a randomized controlled trial of 364 patients over 60 years old identified a limited impact of primary care physician visits on patient outcomes but noted enhanced access to community health services.[12] Our work highlights the potential impact of primary care physician visits, which could, in theory, be leveraged and be an important role that primary care physicians can play in planning postdischarge care and improving the quality of care following hospitalization.

Our research study did not examine the impact of in‐hospital primary care physician visits on patient satisfaction directly. However, it has been demonstrated that patients have a strong desire for their primary care physician to be involved in their hospital care and their preference is for direct contact, with face‐to‐face visits compared to telephone or other communication.[25] This choice is important because dissatisfaction with services is associated with a loss of patient confidence in care quality and decreased adherence.[26] Also, primary care physicians acknowledge that information exchange is lacking when their patients are discharged, and that improving this aspect of a patient's care transition is important.[20] Research into discharge summaries as a tool to fill the communication gap has noted some success, yet there remains uncertainty regarding the type of information that should be included in a discharge summary, the time frame in which primary care physicians actually receive the summaries, and the accuracy of the information provided.[20, 27]

Our use of population‐based administrative data sources make the findings of our research generalizable to other similarly designed healthcare systems where a primary care physician may visit their hospitalized patients in a supportive‐care role. We were interested in a complex patientphysician interaction with a number of potential confounding factors, and our use of a composite measure represents the broad outcomes from this contact. Our cohort methodology was designed to isolate the exposure of interest while maximizing uniformity between the 2 study groups on other characteristics. Additionally a number of potential confounding factors were considered in an effort to isolate the effect of the primary care physician in‐hospital visit such as age, comorbid disease, and risk of hospital readmission.[12] The findings of our work support that of earlier research, but on a broader and more generalizable scale.[12]

There were notable differences between the intervention and control patient populations in the proportion of patients from rural regions who receive a supportive visit. This may be due to systemic differences between rural and nonrural regions with regard to access to care and ease of visit by primary care physicians. Alternatively, observed differences may be due to limitations of our study design in that some rural environments rely on primary care physicians to be involved in hospital care for the region. As such, they may actually be visiting their patients in a manner that was not captured as a supportive‐care visit. This is an important area that should be pursued in the future.

We acknowledge there are limits to our research findings. First, the nature of administrative data introduces challenges to causal inferences. As such, we are careful to describe associations and not draw causative links as there may be additional variables influencing outcomes including the patientphysician relationship, the location of the hospital relative to the physician practice and/or home, the time of the primary care physician visit, primary care physician hospital privileges for supportive‐care visits, and the number of other patients the primary care physician had in the same hospital at the same time. A second limitation is the use of the selected outcomes, which may not be direct measures of care quality.[28] However, the selected outcomes have been shown to be good quality measures in other work relevant to health policy.[8, 20, 21, 29] Third, the use of a composite outcome may over‐ or underestimate an exposure's impact.[19] Our composite outcome might have been dominated by some of its components. These observations may reflect the reality of primary care physicians visiting their sicker patients, or may be an attribute of the relatively short length of follow‐up of the study design. Fourth, we cannot determine whether there were additional interventions in place that assisted the continuity of care for primary care physician visits.[20, 27] However, this research included a broad range of hospitals throughout a large province where there were no system‐level quality interventions applied during this time. Fifth, our readmission rate may appear lower than other studies. However, our analysis is population based and not limited in focus to seniors.[30] As well, our posthospitalization death rates are similar to others, and the readmission rates are comparable to other Canadian studies.[31] Sixth, patients at higher risk for adverse outcomes may be identified as requiring more communication with their primary care physicians and we may not have fully captured this risk in our adjustment models, thereby underestimating the effect of exposure.[27] Further, primary care physicians may be involved in major medical decisions such as transitions to palliative care. A supportive‐care visit that facilitated these transitions and its ensuing outcomes may not have been included in our analysis. Seventh, our inherent assumption is that more care, such as posthospital primary care visits and home visits, denotes better care. This may not always be the case.[32] Eighth, physicians may find it difficult to visit their patient in the hospital, even when asked.[12] Finally, our findings are contingent on a system that supports primary care physicians being aware of their patients who become hospitalized. This is not only incumbent on any individual (eg, hospitalist) but a system where all providers work cohesively and seamlessly. On balance, however, these limitations do not overshadow our study's findings and conclusions.

Visits by primary care physicians to hospitalized patients are a longstanding tradition. The practice likely varies according to regional, patient, and individual physician characteristics.[16, 17, 18, 25] However, reimbursement codes for these services are present in a number of international healthcare systems' physician fee schedules with fairly modest remuneration amounts. The fairly nominal fee of less than $20 CND for a supportive‐care visit is similar to other systems and does not constitute a strong financial incentive to encourage this practice. The fee likely compensates the primary care physician for some of their time but comes with an opportunity cost to other aspects of their practice. Thus, results may differ in other environments or if the fee were higher, thereby incenting more primary care physicians to conduct visits. Indeed, the entire program for supportive hospital visits cost approximately $2.5 million CND per year for the 13 million people in the province of Ontario. Future work in this area could address the overall value and cost‐effectiveness of any potential fee changes. Still, it highlights the generalizability of our findings to other health systems and the ease in assessing the effect of the practice.

Overall, our findings underscore the importance and relevance for the practice of supportive‐care visits in its association with patient outcomes and health services utilization, which may prove to be an important key factor to improve quality healthcare. Our results suggest that an in‐hospital visit by a primary care physician may improve patient outcomes and increase subsequent support in the community. An in‐hospital supportive visit may be an additional method by which primary care physicians, and healthcare systems as a whole, strive to achieve the best care for patients.

Acknowledgements

Michael Manno, an analyst with the Institute of Clinical Evaluative Sciences (ICES) at the time of this study, assisted with the analyses.

Disclosures: This study was supported by the Institute for Clinical Evaluative Sciences (ICES), which is funded by an annual grant from the Ontario Ministry of Health and Long‐Term Care (MOHLTC). The opinions, results, and conclusions reported in this article are those of the authors and are independent from the funding sources. No endorsement by the ICES or the Ontario MOHLTC is intended or should be inferred. No researcher or persons involved in this study had any declared or otherwise known conflicts of interest. Stacey Brener received funding from a Canadian Institutes of Health Research (CIHR) Master's award in the area of primary care; the Ontario Graduate Student in Science and Technology award, an award from the CIHR Women's College Hospital Interdisciplinary Capacity Enhancement Team, and team grant OTG‐88591 from the CIHR. Susan Bronskill is supported by a CIHR New Investigator Award in the Area of Aging. Chaim Bell is supported by a CIHR/Canadian Patient Safety Institute Chair in Patient Safety and Continuity of Care. These funding agencies had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript. The corresponding author had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors report no conflicts of interest.

References
  1. Walraven C, Mamdani M, Fang J, Austin PC. Continuity of care and patient outcomes after hospital discharge. J Gen Intern Med. 2004;19(6):624631.
  2. Brennan TA, Leape LL, Laird NM, et al. Incidence of adverse events and negligence in hospitalized patients: results of the Harvard Medical Practice Study I. 1991. Qual Saf Health Care. 2004;13(2):145151; discussion 51–52.
  3. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161167.
  4. The College of Family Physicians of Canada. Family physicians caring for hospital inpatients. Available at: http://www.cfpc.ca/uploadedFiles/Resources/Resource_Items/FPs20Inpt20Hosp20Care_En.pdf. Published October 2003. Accessed August 15, 2015.
  5. Halm EA, Lee C, Chassin MR. Is volume related to outcome in health care?. A systematic review and methodologic critique of the literature. Ann Intern Med. 2002;137(6):511520.
  6. Lindenauer PK, Rothberg MB, Pekow PS, Kenwood C, Benjamin EM, Auerbach AD. Outcomes of care by hospitalists, general internists, and family physicians. N Engl J Med. 2007;357(25):25892600.
  7. Stiell AP, Forster AJ, Stiell IG, Walraven C. Maintaining continuity of care: a look at the quality of communication between Ontario emergency departments and community physicians. CJEM. 2005;7(3):155161.
  8. Walraven C, Taljaard M, Etchells E, et al. The independent association of provider and information continuity on outcomes after hospital discharge: implications for hospitalists. J Hosp Med. 2010;5(7):398405.
  9. Liss DT, Chubak J, Anderson ML, Saunders KW, Tuzzio L, Reid RJ. Patient‐reported care coordination: associations with primary care continuity and specialty care use. Ann Fam Med. 2011;9(4):323329.
  10. Marchinko S, Clarke D. The Wellness Planner: empowerment, quality of life, and continuity of care in mental illness. Arch Psychiatr Nurs. 2011;25(4):284293.
  11. Tremblay D, Roberge D, Cazale L, et al. Evaluation of the impact of interdisciplinarity in cancer care. BMC Health Serv Res. 2011;11:144.
  12. McInnes E, Mira M, Atkin N, Kennedy P, Cullen J. Can GP input into discharge planning result in better outcomes for the frail aged: results from a randomized controlled trial. Fam Pract. 1999;16(3):289293.
  13. Bell CM, Brener SS, Gunraj N, et al. Association of ICU or hospital admission with unintentional discontinuation of medications for chronic diseases. JAMA. 2011;306(8):840847.
  14. Bell CM, Bajcar J, Bierman AS, Li P, Mamdani MM, Urbach DR. Potentially unintended discontinuation of long‐term medication use after elective surgical procedures. Arch Intern Med. 2006;166(22):25252531.
  15. Juurlink D, Preyra C, Croxford R, Chong A, Austin P, Tu J, Laupacis A. Canadian Institute for Health Information Discharge Abstract Database: A Validation Study. Toronto: Institute for Clinical Evaluative Sciences; 2006. Available at: http://www.ices.on.ca/Publications/Atlases‐and‐Reports/2006/Canadian‐Institute‐for‐Health‐Information. Accessed August 15, 2015.
  16. Chang CH, Stukel TA, Flood AB, Goodman DC. Primary care physician workforce and Medicare beneficiaries' health outcomes. JAMA. 2011;305(20):20962104.
  17. Bynum JP, Bernal‐Delgado E, Gottlieb D, Fisher E. Assigning ambulatory patients and their physicians to hospitals: a method for obtaining population‐based provider performance measurements. Health Serv Res. 2007;42(1 pt 1):4562.
  18. Shah BR, Hux JE, Laupacis A, Zinman B, Cauch‐Dudek K, Booth GL. Administrative data algorithms can describe ambulatory physician utilization. Health Serv Res. 2007;42(4):17831796.
  19. Montori VM, Permanyer‐Miralda G, Ferreira‐Gonzalez I, et al. Validity of composite end points in clinical trials. BMJ. 2005;330(7491):594596.
  20. Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831841.
  21. Lyons JS, O'Mahoney MT, Miller SI, Neme J, Kabat J, Miller F. Predicting readmission to the psychiatric hospital in a managed care environment: implications for quality indicators. Am J Psychiatry. 1997;154(3):337340.
  22. Rumball‐Smith J, Hider P. The validity of readmission rate as a marker of the quality of hospital care, and a recommendation for its definition. N Z Med J. 2009;122(1289):6370.
  23. Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551557.
  24. Walraven C, Taljaard M, Bell CM, et al. Information exchange among physicians caring for the same patient in the community. CMAJ. 2008;179(10):10131018.
  25. Chan B. Supply of physicians' services in Ontario. Hosp Q. 1999;3(2):17.
  26. Bond M, Bowling A, Abery A, McClay M, Dickinson E. Evaluation of outreach clinics held by specialists in general practice in England. J Epidemiol Community Health. 2000;54(2):149156.
  27. Bell CM, Schnipper JL, Auerbach AD, et al. Association of communication between hospital‐based physicians and primary care providers with patient outcomes. J Gen Intern Med. 2009;24(3):381386.
  28. Krumholz HM, Lin Z, Keenan PS, et al. Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia. JAMA. 2013;309(6):587593.
  29. Ashton CM, Wray NP. A conceptual framework for the study of early readmission as an indicator of quality of care. Soc Sci Med. 1996;43(11):15331541.
  30. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360(14):14181428.
  31. Dhalla IA, O'Brien T, Morra D, et al. Effect of a postdischarge virtual ward on readmission or death for high‐risk patients: a randomized clinical trial. JAMA. 2014;312(13):13051312.
  32. Grady D, Redberg RF. Less is more: how less health care can result in better health. Arch Intern Med. 2010;170(9):749750.
References
  1. Walraven C, Mamdani M, Fang J, Austin PC. Continuity of care and patient outcomes after hospital discharge. J Gen Intern Med. 2004;19(6):624631.
  2. Brennan TA, Leape LL, Laird NM, et al. Incidence of adverse events and negligence in hospitalized patients: results of the Harvard Medical Practice Study I. 1991. Qual Saf Health Care. 2004;13(2):145151; discussion 51–52.
  3. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW. The incidence and severity of adverse events affecting patients after discharge from the hospital. Ann Intern Med. 2003;138(3):161167.
  4. The College of Family Physicians of Canada. Family physicians caring for hospital inpatients. Available at: http://www.cfpc.ca/uploadedFiles/Resources/Resource_Items/FPs20Inpt20Hosp20Care_En.pdf. Published October 2003. Accessed August 15, 2015.
  5. Halm EA, Lee C, Chassin MR. Is volume related to outcome in health care?. A systematic review and methodologic critique of the literature. Ann Intern Med. 2002;137(6):511520.
  6. Lindenauer PK, Rothberg MB, Pekow PS, Kenwood C, Benjamin EM, Auerbach AD. Outcomes of care by hospitalists, general internists, and family physicians. N Engl J Med. 2007;357(25):25892600.
  7. Stiell AP, Forster AJ, Stiell IG, Walraven C. Maintaining continuity of care: a look at the quality of communication between Ontario emergency departments and community physicians. CJEM. 2005;7(3):155161.
  8. Walraven C, Taljaard M, Etchells E, et al. The independent association of provider and information continuity on outcomes after hospital discharge: implications for hospitalists. J Hosp Med. 2010;5(7):398405.
  9. Liss DT, Chubak J, Anderson ML, Saunders KW, Tuzzio L, Reid RJ. Patient‐reported care coordination: associations with primary care continuity and specialty care use. Ann Fam Med. 2011;9(4):323329.
  10. Marchinko S, Clarke D. The Wellness Planner: empowerment, quality of life, and continuity of care in mental illness. Arch Psychiatr Nurs. 2011;25(4):284293.
  11. Tremblay D, Roberge D, Cazale L, et al. Evaluation of the impact of interdisciplinarity in cancer care. BMC Health Serv Res. 2011;11:144.
  12. McInnes E, Mira M, Atkin N, Kennedy P, Cullen J. Can GP input into discharge planning result in better outcomes for the frail aged: results from a randomized controlled trial. Fam Pract. 1999;16(3):289293.
  13. Bell CM, Brener SS, Gunraj N, et al. Association of ICU or hospital admission with unintentional discontinuation of medications for chronic diseases. JAMA. 2011;306(8):840847.
  14. Bell CM, Bajcar J, Bierman AS, Li P, Mamdani MM, Urbach DR. Potentially unintended discontinuation of long‐term medication use after elective surgical procedures. Arch Intern Med. 2006;166(22):25252531.
  15. Juurlink D, Preyra C, Croxford R, Chong A, Austin P, Tu J, Laupacis A. Canadian Institute for Health Information Discharge Abstract Database: A Validation Study. Toronto: Institute for Clinical Evaluative Sciences; 2006. Available at: http://www.ices.on.ca/Publications/Atlases‐and‐Reports/2006/Canadian‐Institute‐for‐Health‐Information. Accessed August 15, 2015.
  16. Chang CH, Stukel TA, Flood AB, Goodman DC. Primary care physician workforce and Medicare beneficiaries' health outcomes. JAMA. 2011;305(20):20962104.
  17. Bynum JP, Bernal‐Delgado E, Gottlieb D, Fisher E. Assigning ambulatory patients and their physicians to hospitals: a method for obtaining population‐based provider performance measurements. Health Serv Res. 2007;42(1 pt 1):4562.
  18. Shah BR, Hux JE, Laupacis A, Zinman B, Cauch‐Dudek K, Booth GL. Administrative data algorithms can describe ambulatory physician utilization. Health Serv Res. 2007;42(4):17831796.
  19. Montori VM, Permanyer‐Miralda G, Ferreira‐Gonzalez I, et al. Validity of composite end points in clinical trials. BMJ. 2005;330(7491):594596.
  20. Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831841.
  21. Lyons JS, O'Mahoney MT, Miller SI, Neme J, Kabat J, Miller F. Predicting readmission to the psychiatric hospital in a managed care environment: implications for quality indicators. Am J Psychiatry. 1997;154(3):337340.
  22. Rumball‐Smith J, Hider P. The validity of readmission rate as a marker of the quality of hospital care, and a recommendation for its definition. N Z Med J. 2009;122(1289):6370.
  23. Walraven C, Dhalla IA, Bell C, et al. Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community. CMAJ. 2010;182(6):551557.
  24. Walraven C, Taljaard M, Bell CM, et al. Information exchange among physicians caring for the same patient in the community. CMAJ. 2008;179(10):10131018.
  25. Chan B. Supply of physicians' services in Ontario. Hosp Q. 1999;3(2):17.
  26. Bond M, Bowling A, Abery A, McClay M, Dickinson E. Evaluation of outreach clinics held by specialists in general practice in England. J Epidemiol Community Health. 2000;54(2):149156.
  27. Bell CM, Schnipper JL, Auerbach AD, et al. Association of communication between hospital‐based physicians and primary care providers with patient outcomes. J Gen Intern Med. 2009;24(3):381386.
  28. Krumholz HM, Lin Z, Keenan PS, et al. Relationship between hospital readmission and mortality rates for patients hospitalized with acute myocardial infarction, heart failure, or pneumonia. JAMA. 2013;309(6):587593.
  29. Ashton CM, Wray NP. A conceptual framework for the study of early readmission as an indicator of quality of care. Soc Sci Med. 1996;43(11):15331541.
  30. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee‐for‐service program. N Engl J Med. 2009;360(14):14181428.
  31. Dhalla IA, O'Brien T, Morra D, et al. Effect of a postdischarge virtual ward on readmission or death for high‐risk patients: a randomized clinical trial. JAMA. 2014;312(13):13051312.
  32. Grady D, Redberg RF. Less is more: how less health care can result in better health. Arch Intern Med. 2010;170(9):749750.
Issue
Journal of Hospital Medicine - 11(6)
Issue
Journal of Hospital Medicine - 11(6)
Page Number
418-424
Page Number
418-424
Article Type
Display Headline
Association between in‐hospital supportive visits by primary care physicians and patient outcomes: A population‐based cohort study
Display Headline
Association between in‐hospital supportive visits by primary care physicians and patient outcomes: A population‐based cohort study
Sections
Article Source

© 2016 Society of Hospital Medicine

Disallow All Ads
Correspondence Location
Address for correspondence and reprint requests: Chaim Bell, MD, Mount Sinai Hospital, 600 University Avenue, Suite 433, Toronto, Ontario M5G 1X5, Canada; Telephone: 416‐586‐4800; Fax: 416‐586‐8864; E‐mail: [email protected]
Content Gating
No Gating (article Unlocked/Free)
Alternative CME
Article PDF Media
Media Files

Timely Discharge Communication

Article Type
Changed
Mon, 01/02/2017 - 19:34
Display Headline
Timely discharge communication: Just the fax?

In July 2003, as a fresh intern, I was introduced to care transitions and our tool for information transfer at hospital dischargethe fax machine. After writing our discharge order and discharge prescriptions, the team would compose the discharge summary and transmit the document via fax. I asked my resident where these faxes were going, because they were all sent to the same number in the hospital. Humorously, he did not know. Summaries were completed within days, or sometimes weeks, of discharge and faxed to a mysterious destination for filing and presumably for dissemination to outside providers. The message was clear to me that discharge summaries were not very useful or important, and they were definitely not seen as a critical part of the care‐transition process.

This attitude toward the discharge summary is not surprising. Historically, when physicians cared for their patients prior to, during, and after hospitalization, the goal of the discharge summary was to document patients' care for hospital records. It was not critical as a communication tool unless a patient was being transferred to another healthcare facility and a new care team. However, that all changed with decreasing hospital length of stay, the contemporaneous rise in postacute care discharges, the rise of the hospitalist care model, and the resulting transition of care from hospitalist to outpatient physician. Clear, rapid completion and communication of discharge summaries became essential for safe transitions in care.

The lack of focus on the discharge summary as a communication tool is reflected in regulations and standards of accreditation bodies. In 1986, the Medicare Condition of Participation required that inpatient records be completed within 30 days of discharge. Despite all of the changes in healthcare, the 30‐day requirement for discharge summary completion has persisted, often as a medical staff requirement. Similarly, The Joint Commission requires that discharge summaries include 6 components (reason for hospitalization, findings, treatment provided, discharge condition, instructions, and physician signature) but does not provide a timeframe. As a result of this lack of emphasis on timely completion of discharge summaries, studies have shown that although summaries usually include core elements, they are not completed in a timely fashion. Consequently, most postdischarge visits occur without the benefit of a discharge summary.[1] The most complex patients, who ideally are seen within a few days of discharge, are the least likely to have received the discharge summary at the first postdischarge visit.

Although it seems intuitively obvious that more timely communication of discharge summaries should lead to better outcomes, especially lower readmission rates, few studies have examined this issue, and the findings have not been consistent.[2, 3, 4, 5] Is it possible that physicians and other members of the healthcare team often communicate with each other through telephone calls and text messaging, especially about the sickest patients? If so, timely discharge summaries could have a small marginal effect on outcomes. Therefore, the study in this issue of the Journal of Hospital Medicine by Hoyer and colleagues is a welcome addition to the literature.[6] They found that discharge summary completion 3 or more days after discharge was associated with an adjusted odds ratio of 1.09, and the odds ratio increased with every additional 3‐day delay in completion.

It is possible that the analysis by Hoyer et al. underestimated the benefit of timely discharge summaries. To achieve full benefit, the discharge summary must be completed, accurately delivered, read by the receiving provider, and used at the first follow‐up visit. Their claims‐based analysis did not contains these latter elements, which would bias their results toward the null hypothesis. Future studies should examine how receipt of a summary, as opposed to transmission, is associated with postdischarge outcomes.

In subgroup analyses, no associations between discharge summary timeliness and readmissions were found for patients cared for on the gynecology‐obstetrics and surgical sciences services. Although caution is always needed when interpreting subgroup analyses, it is possible that the lack of association is attributable to the relatively acute conditions of many patients on these services, the relative provider continuity that persists in surgical disciplines, or whether these disciplines use other means of communication more frequently (eg, postdischarge phone calls among providers), mitigating the impact of the written discharge summary. Additional studies are needed to examine these issues. In addition, studies should examine how community or social factors might attenuate the benefit of timely communication, and explore the effect of discharge summaries on outcomes for patients admitted to an observation level of care, which is increasingly common and for which discharge summaries are less likely to be required.

The findings of the study by Hoyer et al. support proposed federal legislationthe Improving Medicare Post‐Acute Care Transformation Act of 2014. The proposed rule for discharge planning would change the Medicare Conditions of Participation to require transmission of discharge information, including the discharge summary, within 48 hours of discharge (https://federalregister.gov/a/2015‐27840A).[7] If enacted, this rule could substantially improve the timely availability of discharge information following care transition.

Fortunately, the work of preparing and transmitting the discharge summary is already part of the physician workflow, albeit often delayed. This traditional means of communication could even remain unchanged in form, if the order of the workflow could be altered in terms of timeliness, and no additional work would be created. With the hospitalization fresher in memory at the time of discharge, work might even be reduced. This efficiency presents a reasonable and immediately actionable appeal to providers.

The challenge to providers and systems remains to refine the quality and efficiency of communication and to move health communication into the 21st century. Tremendous potential exists for interactive communication among providers at discharge, which will not only build the quality of information delivered, but possibly also the qualitative experience of communication, building relationships in our increasingly complex and fragmented delivery networks. This may be a disappointment to the manufacturers of fax machines, but it will be a welcome improvement for caregivers and patients.

Disclosure

Nothing to report.

References
  1. Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831841.
  2. Balaban RB, Weissman JS, Samuel PA, Woolhandler S. Redefining and redesigning hospital discharge to enhance patient care: a randomized controlled study. J Gen Intern Med. 2008;23(8):12281233.
  3. Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150(3):178187.
  4. Koehler BE, Richter KM, Youngblood L, et al. Reduction of 30‐day postdischarge hospital readmission or emergency department (ED) visit rates in high‐risk elderly medical patients through delivery of a targeted care bundle. J Hosp Med. 2009;4(4):211218.
  5. Salim Al‐Damluji M, Dzara K, Hodshon B, et al. Association of discharge summary quality with readmission risk for patients hospitalized with heart failure exacerbation. Circ Cardiovasc Qual Outcomes. 2015;8(1):109111.
  6. Hoyer EH, Odonkor CA, Bhatia SN, Deutschendorf A, Brotman DJ. Association between days‐to‐complete inpatient discharge summaries with all‐payer hospital readmissions in Maryland. J Hosp Med. 2016;11(00):000000.
  7. Revisions to requirements for discharge planning for hospitals, critical access hospitals, and home health agencies. Fed Regist. 2015;80(212):6812668155.
Article PDF
Issue
Journal of Hospital Medicine - 11(6)
Page Number
455-456
Sections
Article PDF
Article PDF

In July 2003, as a fresh intern, I was introduced to care transitions and our tool for information transfer at hospital dischargethe fax machine. After writing our discharge order and discharge prescriptions, the team would compose the discharge summary and transmit the document via fax. I asked my resident where these faxes were going, because they were all sent to the same number in the hospital. Humorously, he did not know. Summaries were completed within days, or sometimes weeks, of discharge and faxed to a mysterious destination for filing and presumably for dissemination to outside providers. The message was clear to me that discharge summaries were not very useful or important, and they were definitely not seen as a critical part of the care‐transition process.

This attitude toward the discharge summary is not surprising. Historically, when physicians cared for their patients prior to, during, and after hospitalization, the goal of the discharge summary was to document patients' care for hospital records. It was not critical as a communication tool unless a patient was being transferred to another healthcare facility and a new care team. However, that all changed with decreasing hospital length of stay, the contemporaneous rise in postacute care discharges, the rise of the hospitalist care model, and the resulting transition of care from hospitalist to outpatient physician. Clear, rapid completion and communication of discharge summaries became essential for safe transitions in care.

The lack of focus on the discharge summary as a communication tool is reflected in regulations and standards of accreditation bodies. In 1986, the Medicare Condition of Participation required that inpatient records be completed within 30 days of discharge. Despite all of the changes in healthcare, the 30‐day requirement for discharge summary completion has persisted, often as a medical staff requirement. Similarly, The Joint Commission requires that discharge summaries include 6 components (reason for hospitalization, findings, treatment provided, discharge condition, instructions, and physician signature) but does not provide a timeframe. As a result of this lack of emphasis on timely completion of discharge summaries, studies have shown that although summaries usually include core elements, they are not completed in a timely fashion. Consequently, most postdischarge visits occur without the benefit of a discharge summary.[1] The most complex patients, who ideally are seen within a few days of discharge, are the least likely to have received the discharge summary at the first postdischarge visit.

Although it seems intuitively obvious that more timely communication of discharge summaries should lead to better outcomes, especially lower readmission rates, few studies have examined this issue, and the findings have not been consistent.[2, 3, 4, 5] Is it possible that physicians and other members of the healthcare team often communicate with each other through telephone calls and text messaging, especially about the sickest patients? If so, timely discharge summaries could have a small marginal effect on outcomes. Therefore, the study in this issue of the Journal of Hospital Medicine by Hoyer and colleagues is a welcome addition to the literature.[6] They found that discharge summary completion 3 or more days after discharge was associated with an adjusted odds ratio of 1.09, and the odds ratio increased with every additional 3‐day delay in completion.

It is possible that the analysis by Hoyer et al. underestimated the benefit of timely discharge summaries. To achieve full benefit, the discharge summary must be completed, accurately delivered, read by the receiving provider, and used at the first follow‐up visit. Their claims‐based analysis did not contains these latter elements, which would bias their results toward the null hypothesis. Future studies should examine how receipt of a summary, as opposed to transmission, is associated with postdischarge outcomes.

In subgroup analyses, no associations between discharge summary timeliness and readmissions were found for patients cared for on the gynecology‐obstetrics and surgical sciences services. Although caution is always needed when interpreting subgroup analyses, it is possible that the lack of association is attributable to the relatively acute conditions of many patients on these services, the relative provider continuity that persists in surgical disciplines, or whether these disciplines use other means of communication more frequently (eg, postdischarge phone calls among providers), mitigating the impact of the written discharge summary. Additional studies are needed to examine these issues. In addition, studies should examine how community or social factors might attenuate the benefit of timely communication, and explore the effect of discharge summaries on outcomes for patients admitted to an observation level of care, which is increasingly common and for which discharge summaries are less likely to be required.

The findings of the study by Hoyer et al. support proposed federal legislationthe Improving Medicare Post‐Acute Care Transformation Act of 2014. The proposed rule for discharge planning would change the Medicare Conditions of Participation to require transmission of discharge information, including the discharge summary, within 48 hours of discharge (https://federalregister.gov/a/2015‐27840A).[7] If enacted, this rule could substantially improve the timely availability of discharge information following care transition.

Fortunately, the work of preparing and transmitting the discharge summary is already part of the physician workflow, albeit often delayed. This traditional means of communication could even remain unchanged in form, if the order of the workflow could be altered in terms of timeliness, and no additional work would be created. With the hospitalization fresher in memory at the time of discharge, work might even be reduced. This efficiency presents a reasonable and immediately actionable appeal to providers.

The challenge to providers and systems remains to refine the quality and efficiency of communication and to move health communication into the 21st century. Tremendous potential exists for interactive communication among providers at discharge, which will not only build the quality of information delivered, but possibly also the qualitative experience of communication, building relationships in our increasingly complex and fragmented delivery networks. This may be a disappointment to the manufacturers of fax machines, but it will be a welcome improvement for caregivers and patients.

Disclosure

Nothing to report.

In July 2003, as a fresh intern, I was introduced to care transitions and our tool for information transfer at hospital dischargethe fax machine. After writing our discharge order and discharge prescriptions, the team would compose the discharge summary and transmit the document via fax. I asked my resident where these faxes were going, because they were all sent to the same number in the hospital. Humorously, he did not know. Summaries were completed within days, or sometimes weeks, of discharge and faxed to a mysterious destination for filing and presumably for dissemination to outside providers. The message was clear to me that discharge summaries were not very useful or important, and they were definitely not seen as a critical part of the care‐transition process.

This attitude toward the discharge summary is not surprising. Historically, when physicians cared for their patients prior to, during, and after hospitalization, the goal of the discharge summary was to document patients' care for hospital records. It was not critical as a communication tool unless a patient was being transferred to another healthcare facility and a new care team. However, that all changed with decreasing hospital length of stay, the contemporaneous rise in postacute care discharges, the rise of the hospitalist care model, and the resulting transition of care from hospitalist to outpatient physician. Clear, rapid completion and communication of discharge summaries became essential for safe transitions in care.

The lack of focus on the discharge summary as a communication tool is reflected in regulations and standards of accreditation bodies. In 1986, the Medicare Condition of Participation required that inpatient records be completed within 30 days of discharge. Despite all of the changes in healthcare, the 30‐day requirement for discharge summary completion has persisted, often as a medical staff requirement. Similarly, The Joint Commission requires that discharge summaries include 6 components (reason for hospitalization, findings, treatment provided, discharge condition, instructions, and physician signature) but does not provide a timeframe. As a result of this lack of emphasis on timely completion of discharge summaries, studies have shown that although summaries usually include core elements, they are not completed in a timely fashion. Consequently, most postdischarge visits occur without the benefit of a discharge summary.[1] The most complex patients, who ideally are seen within a few days of discharge, are the least likely to have received the discharge summary at the first postdischarge visit.

Although it seems intuitively obvious that more timely communication of discharge summaries should lead to better outcomes, especially lower readmission rates, few studies have examined this issue, and the findings have not been consistent.[2, 3, 4, 5] Is it possible that physicians and other members of the healthcare team often communicate with each other through telephone calls and text messaging, especially about the sickest patients? If so, timely discharge summaries could have a small marginal effect on outcomes. Therefore, the study in this issue of the Journal of Hospital Medicine by Hoyer and colleagues is a welcome addition to the literature.[6] They found that discharge summary completion 3 or more days after discharge was associated with an adjusted odds ratio of 1.09, and the odds ratio increased with every additional 3‐day delay in completion.

It is possible that the analysis by Hoyer et al. underestimated the benefit of timely discharge summaries. To achieve full benefit, the discharge summary must be completed, accurately delivered, read by the receiving provider, and used at the first follow‐up visit. Their claims‐based analysis did not contains these latter elements, which would bias their results toward the null hypothesis. Future studies should examine how receipt of a summary, as opposed to transmission, is associated with postdischarge outcomes.

In subgroup analyses, no associations between discharge summary timeliness and readmissions were found for patients cared for on the gynecology‐obstetrics and surgical sciences services. Although caution is always needed when interpreting subgroup analyses, it is possible that the lack of association is attributable to the relatively acute conditions of many patients on these services, the relative provider continuity that persists in surgical disciplines, or whether these disciplines use other means of communication more frequently (eg, postdischarge phone calls among providers), mitigating the impact of the written discharge summary. Additional studies are needed to examine these issues. In addition, studies should examine how community or social factors might attenuate the benefit of timely communication, and explore the effect of discharge summaries on outcomes for patients admitted to an observation level of care, which is increasingly common and for which discharge summaries are less likely to be required.

The findings of the study by Hoyer et al. support proposed federal legislationthe Improving Medicare Post‐Acute Care Transformation Act of 2014. The proposed rule for discharge planning would change the Medicare Conditions of Participation to require transmission of discharge information, including the discharge summary, within 48 hours of discharge (https://federalregister.gov/a/2015‐27840A).[7] If enacted, this rule could substantially improve the timely availability of discharge information following care transition.

Fortunately, the work of preparing and transmitting the discharge summary is already part of the physician workflow, albeit often delayed. This traditional means of communication could even remain unchanged in form, if the order of the workflow could be altered in terms of timeliness, and no additional work would be created. With the hospitalization fresher in memory at the time of discharge, work might even be reduced. This efficiency presents a reasonable and immediately actionable appeal to providers.

The challenge to providers and systems remains to refine the quality and efficiency of communication and to move health communication into the 21st century. Tremendous potential exists for interactive communication among providers at discharge, which will not only build the quality of information delivered, but possibly also the qualitative experience of communication, building relationships in our increasingly complex and fragmented delivery networks. This may be a disappointment to the manufacturers of fax machines, but it will be a welcome improvement for caregivers and patients.

Disclosure

Nothing to report.

References
  1. Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831841.
  2. Balaban RB, Weissman JS, Samuel PA, Woolhandler S. Redefining and redesigning hospital discharge to enhance patient care: a randomized controlled study. J Gen Intern Med. 2008;23(8):12281233.
  3. Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150(3):178187.
  4. Koehler BE, Richter KM, Youngblood L, et al. Reduction of 30‐day postdischarge hospital readmission or emergency department (ED) visit rates in high‐risk elderly medical patients through delivery of a targeted care bundle. J Hosp Med. 2009;4(4):211218.
  5. Salim Al‐Damluji M, Dzara K, Hodshon B, et al. Association of discharge summary quality with readmission risk for patients hospitalized with heart failure exacerbation. Circ Cardiovasc Qual Outcomes. 2015;8(1):109111.
  6. Hoyer EH, Odonkor CA, Bhatia SN, Deutschendorf A, Brotman DJ. Association between days‐to‐complete inpatient discharge summaries with all‐payer hospital readmissions in Maryland. J Hosp Med. 2016;11(00):000000.
  7. Revisions to requirements for discharge planning for hospitals, critical access hospitals, and home health agencies. Fed Regist. 2015;80(212):6812668155.
References
  1. Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831841.
  2. Balaban RB, Weissman JS, Samuel PA, Woolhandler S. Redefining and redesigning hospital discharge to enhance patient care: a randomized controlled study. J Gen Intern Med. 2008;23(8):12281233.
  3. Jack BW, Chetty VK, Anthony D, et al. A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med. 2009;150(3):178187.
  4. Koehler BE, Richter KM, Youngblood L, et al. Reduction of 30‐day postdischarge hospital readmission or emergency department (ED) visit rates in high‐risk elderly medical patients through delivery of a targeted care bundle. J Hosp Med. 2009;4(4):211218.
  5. Salim Al‐Damluji M, Dzara K, Hodshon B, et al. Association of discharge summary quality with readmission risk for patients hospitalized with heart failure exacerbation. Circ Cardiovasc Qual Outcomes. 2015;8(1):109111.
  6. Hoyer EH, Odonkor CA, Bhatia SN, Deutschendorf A, Brotman DJ. Association between days‐to‐complete inpatient discharge summaries with all‐payer hospital readmissions in Maryland. J Hosp Med. 2016;11(00):000000.
  7. Revisions to requirements for discharge planning for hospitals, critical access hospitals, and home health agencies. Fed Regist. 2015;80(212):6812668155.
Issue
Journal of Hospital Medicine - 11(6)
Issue
Journal of Hospital Medicine - 11(6)
Page Number
455-456
Page Number
455-456
Article Type
Display Headline
Timely discharge communication: Just the fax?
Display Headline
Timely discharge communication: Just the fax?
Sections
Article Source
© 2016 Society of Hospital Medicine
Disallow All Ads
Correspondence Location
Address for correspondence and reprint requests: Luke O. Hansen, MD, Division of Hospital Medicine, Feinberg School of Medicine, Northwestern University, 211 E. Ontario Street, Suite 700, Chicago, IL 60611; Telephone: 312‐926‐0066; Fax: 312‐926‐4588; E‐mail: [email protected]
Content Gating
Gated (full article locked unless allowed per User)
Gating Strategy
First Peek Free
Article PDF Media

Discharge Summaries and Readmissions

Article Type
Changed
Wed, 07/19/2017 - 14:02
Display Headline
Association between days to complete inpatient discharge summaries with all‐payer hospital readmissions in Maryland

Across the continuum of care, the discharge summary is a critical tool for communication among care providers.[1] In the United States, the Joint Commission policies mandate that all hospital providers complete a discharge summary for patients with specific components to foster effective communication with future providers.[2] Because outpatient providers and emergency physicians rely on clinical information in the discharge summary to ensure appropriate postdischarge continuity of care, timely documentation is potentially an essential aspect of readmission reduction initiatives.[3, 4, 5] Prior reports indicate that poor discharge documentation of follow‐up plan‐of‐care increases the risk of hospitalization, whereas structured instructions, patient education, and direct communications with primary care physicians (PCPs) reduce repeat hospital visits.[6, 7, 8, 9] However, the current literature is limited in its narrow focus on the contents of discharge summaries, considered only same‐hospital readmissions, or considered readmissions within 3 months of discharge.[10, 11, 12, 13] Moreover, some prior research has suggested no association between discharge summary timeliness with readmission,[12, 13, 14] whereas another study did find a relationship,[15] hence the need to study this further is important. Filling this gap in knowledge could provide an avenue to track and improve quality of patient care, as delays in discharge summaries have been linked with pot‐discharge adverse outcomes and patient safety concerns.[15, 16, 17, 18] Because readmissions often occur soon after discharge, having timely discharge summaries may be particularly important to outcomes.[19, 20]

This research began under the framework of evaluating a bundle of care coordination strategies that were implemented at the Johns Hopkins Health System. These strategies were informed by the early Centers for Medicare and Medicaid Services (CMS) demonstration projects and other best practices that have been documented in the literature to improve utilization and improve communication during transitions of care.[21, 22, 23, 24, 25] Later they were augmented through a contract with the Center of Medicare and Medicaid Innovation to improve access to healthcare services and improve patient outcomes through improved care coordination processes. One of the domains our institution has increased efforts to improve is in provider handoffs. Toward that goal, we have worked to disentangle the effects of different factors of provider‐to‐provider communication that may influence readmissions.[26] For example, effective written provider handoffs in the form of accurate and timely discharge summaries was considered a key care coordination component of this program, but there was institutional resistance to endorsing an expectation that discharge summary turnaround should be shortened. To build a case for this concept, we sought to test the hypothesis that, at our hospital, longer time to complete hospital discharge summaries was associated with increased readmission rates. Unique to this analysis is that, in the state of Maryland, there is statewide reporting of readmissions, so we were able to account for intra‐ and interhospital readmissions for an all‐payer population. The authors anticipated that findings from this study would help inform discharge quality‐improvement initiatives and reemphasize the importance of timely discharge documentation across all disciplines as part of quality patient care.

METHODS

Study Population and Setting

We conducted a single‐center, retrospective cohort study of 87,994 consecutive patients discharged from Johns Hopkins Hospital, which is a 1000‐bed, tertiary academic medical center in Baltimore, Maryland between January 1, 2013 and December 31, 2014. One thousand ninety‐three (1.2%) of the records on days to complete the discharge summary were missing and were excluded from the analysis.

Data Source and Covariates

Data were derived from several sources. The Johns Hopkins Hospital data mart financial database, used for mandatory reporting to the State of Maryland, provided the following patient data: age, gender, race/ethnicity, payer (Medicare, Medicaid, and other) as a proxy for socioeconomic status,[27] hospital service prior to discharge (gynecologyobstetrics, medicine, neurosciences, oncology, pediatrics, and surgical sciences), hospital length of stay (LOS) prior to discharge, Agency for Healthcare Research and Quality (AHRQ) Comorbidity Index (which is an update to the original Elixhauser methodology[28]), and all‐payerrefined diagnosis‐related group (APRDRG) and severity of illness (SOI) combinations (a tool to group patients into clinically comparable disease and SOI categories expected to use similar resources and experience similar outcomes). The Health Services Cost Review Commission (HSCRC) in Maryland provided the observed readmission rate in Maryland for each APRDRG‐SOI combination and served as an expected readmission rate. This risk stratification methodology is similar to the approach used in previous studies.[26, 29] Discharge summary turnaround time was obtained from institutional administrative databases used to track compliance with discharge summary completion. Discharge location (home, facility, home with homecare or hospice, or other) was obtained from Curaspan databases (Curaspan Health Group, Inc., Newton, MA).

Primary Outcome: 30‐Day Readmission

The primary outcome was unplanned rehospitalizations to an acute care hospital in Maryland within 30 days of discharge from Johns Hopkins Hospital. This was as defined by the Maryland HSCRC using an algorithm to exclude readmissions that were likely to be scheduled, as defined by the index admission diagnosis and readmission diagnosis; this algorithm is updated based on the CMS all‐cause readmission algorithm.[30, 31]

Primary Exposure: Days to Complete the Discharge Summary

Discharge summary completion time was defined as the date when the discharge attending physician electronically signs the discharge summary. At our institution, an auto‐fax system sends documents (eg, discharge summaries, clinic notes) to linked providers (eg, primary care providers) shortly after midnight from the day the document is signed by an attending physician. During the period of the project, the policy for discharge summaries at the Johns Hopkins Hospital went from requiring them to be completed within 30 days to 14 days, and we were hoping to use our analyses to inform decision makers why this was important. To emphasize the need for timely completion of discharge summaries, we dichotomized the number of days to complete the discharge summary into >3 versus 3 days (20th percentile cutoff) and modeled it as a continuous variable (per 3‐day increase in days to complete the discharge summary).

Statistical Analysis

To evaluate differences in patient characteristics by readmission status, analysis of variance and 2 tests were used for continuous and dichotomous variables, respectively. Logistic regression was used to evaluate the association between days to complete the discharge summary >3 days and readmission status, adjusting for potentially confounding variables. Before inclusion in the logistic regression model, we confirmed a lack of multicollinearity in the multivariable regression model using variance inflation factors. We evaluated residual versus predicted value plots and residual versus fitted value plots with a locally weighted scatterplot smoothing line. In a sensitivity analysis we evaluated the association between readmission status and different cutoffs (>8 days, 50th percentile; and >14 days, 70% percentile). In a separate analysis, we used interaction terms to test whether the association between the association between days to complete the discharge summary >3 days and hospital readmission varied by the covariates in the analysis (age, sex, race, payer, hospital service, discharge location, LOS, APRDRG‐SOI expected readmission rate, and AHRQ Comorbidity Index). We observed a significant interaction between 30‐day readmission and days to complete the discharge summary >3 days by hospital service. Hence, we separately calculated the adjusted mean readmission rates separately for each hospital service using the least squared means method for the multivariable logistic regression analysis and adjusting for the previously mentioned covariates. In a separate analysis, we used linear regression to evaluate the association between LOS and days to complete the discharge summary, adjusting for potentially confounding variables. Statistical significance was defined as a 2‐sided P < 0.05. Data were analyzed with R (version 2.15.0; R Foundation for Statistical Computing, Vienna, Austria; http://www.r‐project.org). The Johns Hopkins Institutional Review Board approved the study.

RESULTS

Readmitted Patients

In the study period, 14,248 out of 87,994 (16.2%) consecutive eligible patients were readmitted to a hospital in Maryland from patients discharged from Johns Hopkins Hospital between January 1, 2013 and December 31, 2014. A total of 11,027 (77.4%) of the readmissions were back to Johns Hopkins Hospital. Table 1 compares characteristics of readmitted versus nonreadmitted patients, with the following variables being significantly different between these patient groups: age, gender, healthcare payer, hospital service, discharge location, length of stay expected readmission rate, AHRQ Comorbidity Index, and days to complete inpatient discharge summary.

Characteristics of All Patients*
CharacteristicsAll Patients, N = 87,994Not Readmitted, N = 73,746Readmitted, N = 14,248P Value
  • NOTE: Abbreviations: AHRQ, Agency for Healthcare Research and Quality; APRDRG, All‐PayerRefined Diagnosis‐Related Group; SNF, skilled nursing facility; SOI, severity of illness. *Binary and categorical data are presented as n (%), and continuous variables are represented as mean (standard deviation). Proportions may not add to 100% due to rounding. Three days represents the 20th percentile cutoff for the days to complete a discharge summary.

Age, y42.1 (25.1)41.3 (25.4)46.4 (23.1)<0.001
Male43,210 (49.1%)35,851 (48.6%)7,359 (51.6%)<0.001
Race   <0.001
Caucasian45,705 (51.9%)3,8661 (52.4%)7,044 (49.4%) 
African American32,777 (37.2%)2,6841 (36.4%)5,936 (41.7%) 
Other9,512 (10.8%)8,244 (11.2%)1,268 (8.9%) 
Payer   <0.001
Medicare22,345 (25.4%)17,614 (23.9%)4,731 (33.2%) 
Medicaid24,080 (27.4%)20,100 (27.3%)3,980 (27.9%) 
Other41,569 (47.2%)36,032 (48.9%)5,537 (38.9%) 
Hospital service   <0.001
Gynecologyobstetrics9,299 (10.6%)8,829 (12.0%)470 (3.3%) 
Medicine26,036 (29.6%)20,069 (27.2%)5,967 (41.9%) 
Neurosciences8,269 (9.4%)7,331 (9.9%)938 (6.6%) 
Oncology5,222 (5.9%)3,898 (5.3%)1,324 (9.3%) 
Pediatrics17,029 (19.4%)14,684 (19.9%)2,345 (16.5%) 
Surgical sciences22,139 (25.2%)18,935 (25.7%)3,204 (22.5%) 
Discharge location   <0.001
Home65,478 (74.4%)56,359 (76.4%)9,119 (64.0%) 
Home with homecare or hospice9,524 (10.8%)7,440 (10.1%)2,084 (14.6%) 
Facility (SNF, rehabilitation facility)5,398 (6.1%)4,131 (5.6%)1,267 (8.9%) 
Other7,594 (8.6%)5,816 (7.9%)1,778 (12.5%) 
Length of stay, d5.5 (8.6)5.1 (7.8)7.5 (11.6)<0.001
APRDRG‐SOI Expected Readmission Rate, %14.4 (9.5)13.3 (9.2)20.1 (9.0)<0.001
AHRQ Comorbidity Index (1 point)2.5 (1.4)2.4 (1.4)3.0 (1.8)<0.001
Discharge summary completed >3 days66,242 (75.3%)55,329 (75.0%)10,913 (76.6%)<0.001

Association Between Days to Complete the Discharge Summary and Readmission

After hospital discharge, median (IQR) number of days to complete discharge summaries was 8 (416) days. After hospital discharge, median (IQR) number of days to complete discharge summaries and the number of days from discharge to readmission was 8 (416) and 11 (519) days, respectively (P < 0.001). Six thousand one hundred one patients (42.8%) were readmitted before their discharge summary was completed. The median (IQR) days to complete discharge summaries by hospital service in order from shortest to longest was: oncology, 6 (212) days; surgical sciences, 6 (312) days; pediatrics, 7 (315) days; gynecologyobstetrics, 8 (415) days; medicine, 9 (420) days; neurosciences, 12 (621) days.

When we divided the number of days to complete the discharge summary into deciles (02, 2.13, 3.14, 4.16, 6.18, 8.210, 10.114, 14.119, 19.130, >30), a longer number of days to complete discharge summaries had higher unadjusted and adjusted readmission rates (Figure 1). In unadjusted analysis, Table 2 shows that older age, male sex, African American race, oncological versus medicine hospital service, discharge location, longer LOS, higher APRDRG‐SOI expected readmission rate, and higher AHRQ Comorbidity Index were associated with readmission. Days to complete the discharge summary >3 days versus 3 days was associated with a higher readmission rate, with an unadjusted odds ratio (OR) and 95% confidence interval (CI) of 1.09 (95% CI: 1.04 to 1.13, P < 0.001).

Association Between Patient Characteristics, Discharge Summary Completion >3 Days, and 30‐Day Readmission Status
CharacteristicBivariable Analysis*Multivariable Analysis*
OR (95% CI)P ValueOR (95% CI)P Value
  • NOTE: Abbreviations: AHRQ, Agency for Healthcare Research and Quality; APRDRG, All‐PayerRefined Diagnosis‐Related Group; CI, confidence interval; OR, odds ratio; SNF, skilled nursing facility; SOI, severity of illness. *Calculated using logistic regression analysis.

Age, 10 y1.09 (1.08 to 1.09)<0.0010.97 (0.95 to 0.98)<0.001
Male1.13 (1.09 to 1.17)<0.0011.01 (0.97 to 1.05)0.76
Race    
CaucasianReferent Referent 
African American1.21 (1.17 to 1.26)<0.0011.01 (0.96 to 1.05)0.74
Other0.84 (0.79 to 0.90)<0.0010.92 (0.86 to 0.98)0.01
Payer    
MedicareReferent Referent 
Medicaid0.74 (0.70 to 0.77)<0.0011.03 (0.97 to 1.09)0.42
Other0.57 (0.55 to 0.60)<0.0010.86 (0.82 to 0.91)<0.001
Hospital service    
MedicineReferent Referent 
Gynecologyobstetrics0.18 (0.16 to 0.20)<0.0010.50 (0.45 to 0.56)<0.001
Neurosciences0.43 (0.40 to 0.46)<0.0010.76 (0.70 to 0.82)<0.001
Oncology1.14 (1.07 to 1.22)<0.0011.18 (1.10 to 1.28)<0.001
Pediatrics0.54 (0.51 to 0.57)<0.0010.77 (0.71 to 0.83)<0.001
Surgical sciences0.57 (0.54 to 0.60)<0.0010.92 (0.87 to 0.97)0.002
Discharge location    
Home  Referent 
Facility (SNF, rehabilitation facility)1.90 (1.77 to 2.03)<0.0011.11 (1.02 to 1.19)0.009
Home with homecare or hospice1.73 (1.64 to 1.83)<0.0011.26 (1.19 to 1.34)<0.001
Other1.89 (1.78 to 2.00)<0.0011.25 (1.18 to 1.34)<0.001
Length of stay, d1.03 (1.02 to 1.03)<0.0011.00 (1.00 to 1.01)<0.001
APRDRG‐SOI expected readmission rate, %1.08 (1.07 to 1.08)<0.0011.06 (1.06 to 1.06)<0.001
AHRQ Comorbidity Index (1 point)1.27 (1.26 to 1.28)<0.0011.11 (1.09 to 1.12)<0.001
Discharge summary completed >3 days1.09 (1.04 to 1.14)<0.0011.09 (1.05 to 1.14)<0.001
Figure 1
The association between days to complete the hospital discharge summary and 30‐day readmissions in Maryland: percentage of patients readmitted to any acute care hospital in Maryland by days to complete discharge summary deciles (0‐2, 2.1–3, 3.1–4, 4.1–6, 6.1–8, 8.2–10, 10.1–14, 14.1–19, 19.1–30, >30). Plots show the mean (dots) and 95% confidence bands with a locally weighted scatterplot smoothing line (dashed line). (A) Plots the unadjusted association between days to complete discharge summary and 30‐day readmissions. (B) Plots the adjusted association between days to complete discharge summary and 30‐day readmissions. Adjusted mean readmission rates were calculated using the least squared means method for the multivariable logistic regression analysis, and were adjusted for age, sex, race, payer, hospital service, discharge location, LOS, APRDRG‐SOI expected readmission rate, and AHRQ Comorbidity Index. Abbreviations: AHRQ, Agency for Healthcare Research and Quality; APRDRG, All‐Payer–Refined Diagnosis‐Related Group; DC, discharge; LOS, length of stay; SOI, severity of illness.

Multivariable and Secondary Analyses

In adjusted analysis (Table 2), patients discharged from an oncologic service relative to a medicine hospital service (OR: 1.19, 95% CI: 1.10 to 1.28, P < 0.001), patients discharged to a facility, home with homecare or hospice, or other location compared to home (facility OR: 1.11, 95% CI: 1.02 to 1.19, P = 0.009; home with homecare or hospice OR: 1.26, 95% CI: 1.19 to 1.34, P < 0.001; other OR: 1.25, 95% CI: 1.18 to 1.34, P < 0.001), patients with longer LOS (OR: 1.11 per day, 95% CI: 1.10 to 1.12, P < 0.001), patients with a higher expected readmission rates (OR: 1.01 per percent, 95% CI: 1.00 to 1.01, P < 0.001), and patients with a higher AHRQ comorbidity index (OR: 1.06 per 1 point, 95% CI: 1.06 to 1.06, P < 0.001) had higher 30‐day readmission rates. Overall, days to complete the discharge summary >3 days versus 3 days was associated with a higher readmission rate (OR: 1.09, 95% CI: 1.05 to 1.14, P < 0.001).

In a sensitivity analysis, discharge summary completion >8 days (median) versus 8 days was associated with higher unadjusted readmission rate (OR: 1.11, 95% CI: 1.07 to 1.15, P < 0.001) and a higher adjusted readmission rate (OR: 1.06, 95% CI: 1.02 to 1.10, P < 0.001). Discharge summary completion >14 days (70th percentile) versus 14 days was also associated with higher unadjusted readmission rate (OR: 1.15, 95% CI: 1.08 to 1.21, P < 0.001) and a higher adjusted readmission rate (OR: 1.09, 95% CI: 1.02 to 1.16, P = 0.008). The association between days to complete the discharge summary >3 days and readmissions was found to vary significantly by hospital service (P = 0.03). For comparing days to complete the discharge summary >3 versus 3 days, Table 3 shows that neurosciences, pediatrics, oncology, and medicine hospital services were associated with significantly increased adjusted mean readmission rates. Additionally, when days to complete the discharge summary was modeled as a continuous variable, we found that for every 3 days the odds of readmission increased by 1% (OR: 1.01, 95% CI: 1.00 to 1.01, P < 0.001).

Association Between Patient Discharge Summary Completion >3 Days and 30‐Day Readmission Status by Hospital Service
Days to Complete Discharge Summary by Hospital ServiceAdjusted Mean Readmission Rate (95% CI)*P Value
  • NOTE: Abbreviations: AHRQ, Agency for Healthcare Research and Quality; APRDRG, All‐PayerRefined Diagnosis‐Related Group; CI, confidence interval; SOI, severity of illness. *Adjusted mean readmission rates were calculated separately for each hospital service using the least squared means method for the multivariable logistic regression analysis and were adjusted for age, sex, race, payer, hospital service, discharge location, length of stay, APRDRG‐SOI expected readmission rate, discharged location, and AHRQ Comorbidity Index.

Gynecologyobstetrics 0.30
03 days, n = 1,7925.4 (4.1 to 6.7) 
>3 days, n = 7,5076.0 (4.9 to 7.0) 
Medicine 0.04
03 days, n = 6,13721.1 (20.0 to 22.3) 
>3 days, n = 19,89922.4 (21.6 to 23.2) 
Neurosciences 0.02
03 days, n = 1,11610.1 (8.2 to 12.1) 
>3 days, n = 7,15312.5 (11.6 to 13.5) 
Oncology 0.01
03 days, n = 1,88525.0 (22.6 to 27.4) 
>3 days, n = 3,33728.2 (26.6 to 30.2) 
Pediatrics 0.001
03 days, n = 4,5619.5 (6.9 to 12.2) 
>3 days, n = 12,46811.4 (8.9 to 13.9) 
Surgical sciences 0.89
03 days, n = 6,26115.2 (14.2 to 16.1) 
>3 days, n = 15,87815.1 (14.4 to 15.8) 

In an unadjusted analysis, we found that the relationship between LOS and days to complete the discharge summary was not significant ( coefficient and 95% CI:, 0.01, 0.02 to 0.00, P = 0.20). However, we found a small but significant relationship in our multivariable analysis, such that each hospitalization day was associated with a 0.01 (95% CI: 0.00 to 0.02, P = 0.03) increase in days to complete the discharge summary.

DISCUSSION

In this single‐center retrospective analysis, the number of days to complete the discharge summary was significantly associated with readmissions after hospitalization. This association was independent of age, gender, comorbidity index, payer, discharge location, length of hospital stay, expected readmission rate based on diagnosis and severity of illness, and all hospital services. The odds of readmission for patients with delayed discharge summaries was small but significant. This is important in the current landscape of readmissions, particularly for institutions who are challenged to reduce readmission rates, and a small relative difference in readmissions may be the difference between getting penalized or not. In the context of prior studies, the results highlight the role of timely discharge summary as an under‐recognized metric, which may be a valid litmus test for care coordination. The findings also emphasize the potential of early summaries to expedite communication and to help facilitate quality of patient care. Hence, the study results extend the literature examining the relationship of delay in discharge summary with unfavorable patient outcomes.[15, 32]

In contrast to prior reports with limited focus on same‐hospital readmissions,[18, 33, 34, 35] readmissions beyond 30 days,[12] or focused on a specific patient population,[13, 36] this study evaluates both intra‐ and interhospital 30‐day readmissions in Maryland in an all‐payer, multi‐institution, diverse patient population. Additionally, prior research is conflicting with respect to whether timely discharges summaries are significantly associated with increased hospital readmissions.[12, 13, 14, 15] Although it is not surprising that inadequate care during hospitalization could result in readmissions, the role of discharge summaries remain underappreciated. Having a timely discharge summary may not always prevent readmissions, but our study showed that 43% of readmission occurred before the discharge summary completion. Not having a completed discharge summary at the time of readmission may have been a driver for the positive association between timely completion and 30‐day readmission we observed. This study highlights that delay in the discharge summary could be a marker of poor transitions of care, because suboptimal dissemination of critical information to care providers may result in discontinuity of patient care posthospitalization.

A plausible mechanism of the association between discharge summary delays and readmissions could be the provision of collateral information, which may potentially alter the threshold for readmissions. For example, in the emergency room/emergency department (ER/ED) setting, discharge summaries may help with preventable readmissions. For patients who present repeatedly with the same complaint, timely summaries to ER/ED providers may help reframe the patient complaints, such as patient has concern X, which was previously identified to be related to diagnosis Y. As others have shown, the content of discharge summaries, format, and accessibility (electronic vs paper chart), as well as timely distribution of summaries, are key factors that impact quality outcomes.[2, 12, 15, 37, 38] By detailing prior hospital information (ie, discharge medications, prior presentations, tests completed), summaries could help prevent errors in medication dosing, reduce unnecessary testing, and help facilitate admission triage. Summaries may have information regarding a new diagnosis such as the results of an endoscopic evaluation that revealed the source of occult gastrointestinal bleeding, which could help contextualize a complaint of repeat melena and redirect goals of care. Discussions of goals of care in the discharge summary may guide primary providers in continued care management plans.

Our study findings underscore a positive correlation between late discharge summaries and readmissions. However, the extent that this is a causal relationship is unclear; the association of delay in days to complete the discharge summary with readmission may be an epiphenomenon related to processes related to quality of clinical care. For example, delays in discharge summary completion could be a marker of other system issues, such as a stressed work environment. It is possible that providers who fail to complete timely discharge summaries may also fail to do other important functions related to transitions of care and care coordination. However, even if this is so, timely discharge summaries could become a focal point for discussion for optimization of care transitions. A discharge summary could be delayed because the patient has already been readmitted before the summary was distributed, thus making that original summary less relevant. Delays could also be a reflection of the data complexity for patients with longer hospital stays. This is supported by the small but significant relationship between LOS and days to complete the discharge summary in this study. Lastly, delays in discharge summary completion may also be a proxy of provider communication and can reflect the culture of communication at the institution.

Although unplanned hospital readmission is an important outcome, many readmissions may be related to other factors such as disease progression, rather than late summaries or the lack of postdischarge communication. For instance, prior reports did not find any association between the PCP seeing the discharge summaries or direct communications with the PCP and 30‐day clinical outcomes for readmission and death.[26, 39] However, these studies were limited in their use of self‐reported handoffs, did not measure quality of information transfer, and failed to capture a broader audience beyond the PCP, such as ED physicians or specialists.

Our results suggest that the relationship between days to complete discharge summaries and 30‐day readmissions may vary depending on whether the hospitalization is primarily surgical/procedural versus medical treatment. A recent study found that most readmissions after surgery were associated with new complications related to the procedure and not exacerbation of prior index hospitalization complications.[40] Hence, treatment for common causes of hospital readmissions after surgical or gynecological procedures, such as wound infections, acute anemia, ileus, or dehydration, may not necessarily require a completed discharge summary for appropriate management. However, we caution extending this finding to clinical practice before further studies are conducted on specific procedures and in different clinical settings.

Results from this study also support institutional policies that specify the need for practitioners to complete discharge summaries contemporaneously, such as at the time of discharge or within a couple of days. Unlike other forms of communication that are optional, discharge summaries are required, so we recommend that practitioners be held accountable for short turnaround times. For example, providers could be graded and rated on timely completions of discharge summaries, among other performance variables. Anecdotally at our institutions, we have heard from practitioners that it takes less time to complete them when you do them on the day of discharge, because the hospitalization course is fresher in their mind and they have to wade through less information in the medical record to complete an accurate discharge summary. To this point, a barrier to on‐time completion is that providers may have misconceptions about what is really vital information to convey to the next provider. In agreement with past research and in the era of the electronic medical record system, we recommend that the discharge summary should be a quick synthesis of key findings that incorporates only the important elements, such as why the patient was hospitalized, what were key findings and key responses to therapy, what is pending at the time of discharge, what medications the patient is currently taking, and what are the follow‐up plans, rather than a lengthy expose of all the findings.[13, 36, 41, 42]

Lastly, our study results should be taken in the context of its limitations. As a single‐center study, findings may lack generalizability. In particular, the results may not generalize to hospitals that lack access to statewide reporting. We were also not able to assess readmission for patients who may have been readmitted to a hospital outside of Maryland. Although we adjusted for pertinent variables such as age, gender, healthcare payer, hospital service, comorbidity index, discharge location, LOS, and expected readmission rates, there may be other relevant confounders that we failed to capture or measure optimally. Median days to complete the discharge summary in this study was 8 days, which is longer than practices at other institutions, and may also limit this study's generalizability.[15, 36, 42] However, prior research supports our findings,[15] and a systematic review found that only 29% and 52% of discharge summaries were completed by 2 weeks and 4 weeks, respectively.[9] Finally, as noted above and perhaps most important, it is possible that discharge summary turnaround time does not in itself causally impact readmissions, but rather reflects an underlying commitment of the inpatient team to effectively coordinate care following hospital discharge.

CONCLUSION

In sum, this study delineates an underappreciated but important relationship of timely discharge summary completion and readmission outcomes. The discharge summary may be a relevant metric reflecting quality of patient care. Healthcare providers may begin to target timely discharge summaries as a potential focal point of quality‐improvement projects with the goal to facilitate better patient outcomes.

Disclosures

The authors certify that no party having a direct interest in the results of the research supporting this article has or will confer a benefit on us or on any organization with which we are associated, and, if applicable, the authors certify that all financial and material support for this research (eg, Centers for Medicare and Medicaid Services, National Institutes of Health, or National Health Service grants) and work are clearly identified. This study was supported by funding opportunity, number CMS‐1C1‐12‐0001, from the Centers for Medicare and Medicaid Services and Center for Medicare and Medicaid Innovation. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Department of Health and Human Services or any of its agencies.

Files
References
  1. Moy NY, Lee SJ, Chan T, et al. Development and sustainability of an inpatient‐to‐outpatient discharge handoff tool: a quality improvement project. Jt Comm J Qual Patient Saf. 2014;40(5):219227.
  2. Henriksen K, Battles JB, Keyes MA, Grady ML, Kind AJ, Smith MA. Documentation of mandated discharge summary components in transitions from acute to subacute care. In: Henriksen K, Battles JB, Keyes MA, et al., eds. Advances in Patient Safety: New Directions and Alternative Approaches. Vol. 2. Culture and Redesign. Rockville, MD: Agency for Healthcare Research and Quality; 2008.
  3. Chugh A, Williams MV, Grigsby J, Coleman EA. Better transitions: improving comprehension of discharge instructions. Front Health Serv Manage. 2009;25(3):1132.
  4. Ben‐Morderchai B, Herman A, Kerzman H, Irony A. Structured discharge education improves early outcome in orthopedic patients. Int J Orthop Trauma Nurs. 2010;14(2):6674.
  5. Hansen LO, Strater A, Smith L, et al. Hospital discharge documentation and risk of rehospitalisation. BMJ Qual Saf. 2011;20(9):773778.
  6. Greenwald JL, Denham CR, Jack BW. The hospital discharge: a review of a high risk care transition with highlights of a reengineered discharge process. J Patient Saf. 2007;3(2):97106.
  7. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520528.
  8. Grafft CA, McDonald FS, Ruud KL, Liesinger JT, Johnson MG, Naessens JM. Effect of hospital follow‐up appointment on clinical event outcomes and mortality. Arch Intern Med. 2010;170(11):955960.
  9. Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831841.
  10. Kind AJ, Thorpe CT, Sattin JA, Walz SE, Smith MA. Provider characteristics, clinical‐work processes and their relationship to discharge summary quality for sub‐acute care patients. J Gen Intern Med. 2012;27(1):7884.
  11. Bradley EH, Curry L, Horwitz LI, et al. Contemporary evidence about hospital strategies for reducing 30‐day readmissions: a national study. J Am Coll Cardiol. 2012;60(7):607614.
  12. Walraven C, Seth R, Austin PC, Laupacis A. Effect of discharge summary availability during post‐discharge visits on hospital readmission. J Gen Intern Med. 2002;17(3):186192.
  13. Salim Al‐Damluji M, Dzara K, Hodshon B, et al. Association of discharge summary quality with readmission risk for patients hospitalized with heart failure exacerbation. Circ Cardiovasc Qual Outcomes. 2015;8(1):109111.
  14. Walraven C, Taljaard M, Etchells E, et al. The independent association of provider and information continuity on outcomes after hospital discharge: implications for hospitalists. J Hosp Med. 2010;5(7):398405.
  15. Li JYZ, Yong TY, Hakendorf P, Ben‐Tovim D, Thompson CH. Timeliness in discharge summary dissemination is associated with patients' clinical outcomes. J Eval Clin Pract. 2013;19(1):7679.
  16. Gandara E, Moniz T, Ungar J, et al. Communication and information deficits in patients discharged to rehabilitation facilities: an evaluation of five acute care hospitals. J Hosp Med. 2009;4(8):E28E33.
  17. Hunter T, Nelson JR, Birmingham J. Preventing readmissions through comprehensive discharge planning. Prof Case Manag. 2013;18(2):5663; quiz 64–65.
  18. Dhalla IA, O'Brien T, Morra D, et al. Effect of a postdischarge virtual ward on readmission or death for high‐risk patients: a randomized clinical trial. JAMA. 2014;312(13):13051312..
  19. Reed RL, Pearlman RA, Buchner DM. Risk factors for early unplanned hospital readmission in the elderly. J Gen Intern Med. 1991;6(3):223228.
  20. Graham KL, Wilker EH, Howell MD, Davis RB, Marcantonio ER. Differences between early and late readmissions among patients: a cohort study. Ann Intern Med. 2015;162(11):741749.
  21. Gage B, Smith L, Morley M, et al. Post‐acute care payment reform demonstration report to congress supplement‐interim report. Centers for Medicare 14(3):114; quiz 88–89.
  22. Naylor MD, Brooten D, Campbell R, et al. Comprehensive discharge planning and home follow‐up of hospitalized elders: a randomized clinical trial. JAMA. 1999;281(7):613620.
  23. Coleman EA, Min SJ, Chomiak A, Kramer AM. Posthospital care transitions: patterns, complications, and risk identification. Health Serv Res. 2004;39(5):14491465.
  24. Snow V, Beck D, Budnitz T, et al. Transitions of care consensus policy statement: American College of Physicians, Society of General Internal Medicine, Society of Hospital Medicine, American Geriatrics Society, American College of Emergency Physicians, and Society for Academic Emergency Medicine. J Hosp Med. 2009;4(6):364370.
  25. Oduyebo I, Lehmann CU, Pollack CE, et al. Association of self‐reported hospital discharge handoffs with 30‐day readmissions. JAMA Intern Med. 2013;173(8):624629.
  26. Adler NE, Newman K. Socioeconomic disparities in health: pathways and policies. Health Aff (Millwood). 2002;21(2):6076.
  27. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):827.
  28. Hoyer EH, Needham DM, Miller J, Deutschendorf A, Friedman M, Brotman DJ. Functional status impairment is associated with unplanned readmissions. Arch Phys Med Rehabil. 2013;94(10):19511958.
  29. Centers for Medicare 35(10):10441059.
  30. Coleman EA, Chugh A, Williams MV, et al. Understanding and execution of discharge instructions. Am J Med Qual. 2013;28(5):383391.
  31. Odonkor CA, Hurst PV, Kondo N, Makary MA, Pronovost PJ. Beyond the hospital gates: elucidating the interactive association of social support, depressive symptoms, and physical function with 30‐day readmissions. Am J Phys Med Rehabil. 2015;94(7):555567.
  32. Finn KM, Heffner R, Chang Y, et al. Improving the discharge process by embedding a discharge facilitator in a resident team. J Hosp Med. 2011;6(9):494500.
  33. Al‐Damluji MS, Dzara K, Hodshon B, et al. Hospital variation in quality of discharge summaries for patients hospitalized with heart failure exacerbation. Circ Cardiovasc Qual Outcomes. 2015;8(1):7786
  34. Mourad M, Cucina R, Ramanathan R, Vidyarthi AR. Addressing the business of discharge: building a case for an electronic discharge summary. J Hosp Med. 2011;6(1):3742.
  35. Regalbuto R, Maurer MS, Chapel D, Mendez J, Shaffer JA. Joint commission requirements for discharge instructions in patients with heart failure: is understanding important for preventing readmissions? J Card Fail. 2014;20(9):641649.
  36. Bell CM, Schnipper JL, Auerbach AD, et al. Association of communication between hospital‐based physicians and primary care providers with patient outcomes. J Gen Intern Med. 2009;24(3):381386.
  37. Merkow RP, Ju MH, Chung JW, et al. Underlying reasons associated with hospital readmission following surgery in the united states. JAMA. 2015;313(5):483495.
  38. Rao P, Andrei A, Fried A, Gonzalez D, Shine D. Assessing quality and efficiency of discharge summaries. Am J Med Qual. 2005;20(6):337343.
  39. Horwitz LI, Jenq GY, Brewster UC, et al. Comprehensive quality of discharge summaries at an academic medical center. J Hosp Med. 2013;8(8):436443.
Article PDF
Issue
Journal of Hospital Medicine - 11(6)
Page Number
393-400
Sections
Files
Files
Article PDF
Article PDF

Across the continuum of care, the discharge summary is a critical tool for communication among care providers.[1] In the United States, the Joint Commission policies mandate that all hospital providers complete a discharge summary for patients with specific components to foster effective communication with future providers.[2] Because outpatient providers and emergency physicians rely on clinical information in the discharge summary to ensure appropriate postdischarge continuity of care, timely documentation is potentially an essential aspect of readmission reduction initiatives.[3, 4, 5] Prior reports indicate that poor discharge documentation of follow‐up plan‐of‐care increases the risk of hospitalization, whereas structured instructions, patient education, and direct communications with primary care physicians (PCPs) reduce repeat hospital visits.[6, 7, 8, 9] However, the current literature is limited in its narrow focus on the contents of discharge summaries, considered only same‐hospital readmissions, or considered readmissions within 3 months of discharge.[10, 11, 12, 13] Moreover, some prior research has suggested no association between discharge summary timeliness with readmission,[12, 13, 14] whereas another study did find a relationship,[15] hence the need to study this further is important. Filling this gap in knowledge could provide an avenue to track and improve quality of patient care, as delays in discharge summaries have been linked with pot‐discharge adverse outcomes and patient safety concerns.[15, 16, 17, 18] Because readmissions often occur soon after discharge, having timely discharge summaries may be particularly important to outcomes.[19, 20]

This research began under the framework of evaluating a bundle of care coordination strategies that were implemented at the Johns Hopkins Health System. These strategies were informed by the early Centers for Medicare and Medicaid Services (CMS) demonstration projects and other best practices that have been documented in the literature to improve utilization and improve communication during transitions of care.[21, 22, 23, 24, 25] Later they were augmented through a contract with the Center of Medicare and Medicaid Innovation to improve access to healthcare services and improve patient outcomes through improved care coordination processes. One of the domains our institution has increased efforts to improve is in provider handoffs. Toward that goal, we have worked to disentangle the effects of different factors of provider‐to‐provider communication that may influence readmissions.[26] For example, effective written provider handoffs in the form of accurate and timely discharge summaries was considered a key care coordination component of this program, but there was institutional resistance to endorsing an expectation that discharge summary turnaround should be shortened. To build a case for this concept, we sought to test the hypothesis that, at our hospital, longer time to complete hospital discharge summaries was associated with increased readmission rates. Unique to this analysis is that, in the state of Maryland, there is statewide reporting of readmissions, so we were able to account for intra‐ and interhospital readmissions for an all‐payer population. The authors anticipated that findings from this study would help inform discharge quality‐improvement initiatives and reemphasize the importance of timely discharge documentation across all disciplines as part of quality patient care.

METHODS

Study Population and Setting

We conducted a single‐center, retrospective cohort study of 87,994 consecutive patients discharged from Johns Hopkins Hospital, which is a 1000‐bed, tertiary academic medical center in Baltimore, Maryland between January 1, 2013 and December 31, 2014. One thousand ninety‐three (1.2%) of the records on days to complete the discharge summary were missing and were excluded from the analysis.

Data Source and Covariates

Data were derived from several sources. The Johns Hopkins Hospital data mart financial database, used for mandatory reporting to the State of Maryland, provided the following patient data: age, gender, race/ethnicity, payer (Medicare, Medicaid, and other) as a proxy for socioeconomic status,[27] hospital service prior to discharge (gynecologyobstetrics, medicine, neurosciences, oncology, pediatrics, and surgical sciences), hospital length of stay (LOS) prior to discharge, Agency for Healthcare Research and Quality (AHRQ) Comorbidity Index (which is an update to the original Elixhauser methodology[28]), and all‐payerrefined diagnosis‐related group (APRDRG) and severity of illness (SOI) combinations (a tool to group patients into clinically comparable disease and SOI categories expected to use similar resources and experience similar outcomes). The Health Services Cost Review Commission (HSCRC) in Maryland provided the observed readmission rate in Maryland for each APRDRG‐SOI combination and served as an expected readmission rate. This risk stratification methodology is similar to the approach used in previous studies.[26, 29] Discharge summary turnaround time was obtained from institutional administrative databases used to track compliance with discharge summary completion. Discharge location (home, facility, home with homecare or hospice, or other) was obtained from Curaspan databases (Curaspan Health Group, Inc., Newton, MA).

Primary Outcome: 30‐Day Readmission

The primary outcome was unplanned rehospitalizations to an acute care hospital in Maryland within 30 days of discharge from Johns Hopkins Hospital. This was as defined by the Maryland HSCRC using an algorithm to exclude readmissions that were likely to be scheduled, as defined by the index admission diagnosis and readmission diagnosis; this algorithm is updated based on the CMS all‐cause readmission algorithm.[30, 31]

Primary Exposure: Days to Complete the Discharge Summary

Discharge summary completion time was defined as the date when the discharge attending physician electronically signs the discharge summary. At our institution, an auto‐fax system sends documents (eg, discharge summaries, clinic notes) to linked providers (eg, primary care providers) shortly after midnight from the day the document is signed by an attending physician. During the period of the project, the policy for discharge summaries at the Johns Hopkins Hospital went from requiring them to be completed within 30 days to 14 days, and we were hoping to use our analyses to inform decision makers why this was important. To emphasize the need for timely completion of discharge summaries, we dichotomized the number of days to complete the discharge summary into >3 versus 3 days (20th percentile cutoff) and modeled it as a continuous variable (per 3‐day increase in days to complete the discharge summary).

Statistical Analysis

To evaluate differences in patient characteristics by readmission status, analysis of variance and 2 tests were used for continuous and dichotomous variables, respectively. Logistic regression was used to evaluate the association between days to complete the discharge summary >3 days and readmission status, adjusting for potentially confounding variables. Before inclusion in the logistic regression model, we confirmed a lack of multicollinearity in the multivariable regression model using variance inflation factors. We evaluated residual versus predicted value plots and residual versus fitted value plots with a locally weighted scatterplot smoothing line. In a sensitivity analysis we evaluated the association between readmission status and different cutoffs (>8 days, 50th percentile; and >14 days, 70% percentile). In a separate analysis, we used interaction terms to test whether the association between the association between days to complete the discharge summary >3 days and hospital readmission varied by the covariates in the analysis (age, sex, race, payer, hospital service, discharge location, LOS, APRDRG‐SOI expected readmission rate, and AHRQ Comorbidity Index). We observed a significant interaction between 30‐day readmission and days to complete the discharge summary >3 days by hospital service. Hence, we separately calculated the adjusted mean readmission rates separately for each hospital service using the least squared means method for the multivariable logistic regression analysis and adjusting for the previously mentioned covariates. In a separate analysis, we used linear regression to evaluate the association between LOS and days to complete the discharge summary, adjusting for potentially confounding variables. Statistical significance was defined as a 2‐sided P < 0.05. Data were analyzed with R (version 2.15.0; R Foundation for Statistical Computing, Vienna, Austria; http://www.r‐project.org). The Johns Hopkins Institutional Review Board approved the study.

RESULTS

Readmitted Patients

In the study period, 14,248 out of 87,994 (16.2%) consecutive eligible patients were readmitted to a hospital in Maryland from patients discharged from Johns Hopkins Hospital between January 1, 2013 and December 31, 2014. A total of 11,027 (77.4%) of the readmissions were back to Johns Hopkins Hospital. Table 1 compares characteristics of readmitted versus nonreadmitted patients, with the following variables being significantly different between these patient groups: age, gender, healthcare payer, hospital service, discharge location, length of stay expected readmission rate, AHRQ Comorbidity Index, and days to complete inpatient discharge summary.

Characteristics of All Patients*
CharacteristicsAll Patients, N = 87,994Not Readmitted, N = 73,746Readmitted, N = 14,248P Value
  • NOTE: Abbreviations: AHRQ, Agency for Healthcare Research and Quality; APRDRG, All‐PayerRefined Diagnosis‐Related Group; SNF, skilled nursing facility; SOI, severity of illness. *Binary and categorical data are presented as n (%), and continuous variables are represented as mean (standard deviation). Proportions may not add to 100% due to rounding. Three days represents the 20th percentile cutoff for the days to complete a discharge summary.

Age, y42.1 (25.1)41.3 (25.4)46.4 (23.1)<0.001
Male43,210 (49.1%)35,851 (48.6%)7,359 (51.6%)<0.001
Race   <0.001
Caucasian45,705 (51.9%)3,8661 (52.4%)7,044 (49.4%) 
African American32,777 (37.2%)2,6841 (36.4%)5,936 (41.7%) 
Other9,512 (10.8%)8,244 (11.2%)1,268 (8.9%) 
Payer   <0.001
Medicare22,345 (25.4%)17,614 (23.9%)4,731 (33.2%) 
Medicaid24,080 (27.4%)20,100 (27.3%)3,980 (27.9%) 
Other41,569 (47.2%)36,032 (48.9%)5,537 (38.9%) 
Hospital service   <0.001
Gynecologyobstetrics9,299 (10.6%)8,829 (12.0%)470 (3.3%) 
Medicine26,036 (29.6%)20,069 (27.2%)5,967 (41.9%) 
Neurosciences8,269 (9.4%)7,331 (9.9%)938 (6.6%) 
Oncology5,222 (5.9%)3,898 (5.3%)1,324 (9.3%) 
Pediatrics17,029 (19.4%)14,684 (19.9%)2,345 (16.5%) 
Surgical sciences22,139 (25.2%)18,935 (25.7%)3,204 (22.5%) 
Discharge location   <0.001
Home65,478 (74.4%)56,359 (76.4%)9,119 (64.0%) 
Home with homecare or hospice9,524 (10.8%)7,440 (10.1%)2,084 (14.6%) 
Facility (SNF, rehabilitation facility)5,398 (6.1%)4,131 (5.6%)1,267 (8.9%) 
Other7,594 (8.6%)5,816 (7.9%)1,778 (12.5%) 
Length of stay, d5.5 (8.6)5.1 (7.8)7.5 (11.6)<0.001
APRDRG‐SOI Expected Readmission Rate, %14.4 (9.5)13.3 (9.2)20.1 (9.0)<0.001
AHRQ Comorbidity Index (1 point)2.5 (1.4)2.4 (1.4)3.0 (1.8)<0.001
Discharge summary completed >3 days66,242 (75.3%)55,329 (75.0%)10,913 (76.6%)<0.001

Association Between Days to Complete the Discharge Summary and Readmission

After hospital discharge, median (IQR) number of days to complete discharge summaries was 8 (416) days. After hospital discharge, median (IQR) number of days to complete discharge summaries and the number of days from discharge to readmission was 8 (416) and 11 (519) days, respectively (P < 0.001). Six thousand one hundred one patients (42.8%) were readmitted before their discharge summary was completed. The median (IQR) days to complete discharge summaries by hospital service in order from shortest to longest was: oncology, 6 (212) days; surgical sciences, 6 (312) days; pediatrics, 7 (315) days; gynecologyobstetrics, 8 (415) days; medicine, 9 (420) days; neurosciences, 12 (621) days.

When we divided the number of days to complete the discharge summary into deciles (02, 2.13, 3.14, 4.16, 6.18, 8.210, 10.114, 14.119, 19.130, >30), a longer number of days to complete discharge summaries had higher unadjusted and adjusted readmission rates (Figure 1). In unadjusted analysis, Table 2 shows that older age, male sex, African American race, oncological versus medicine hospital service, discharge location, longer LOS, higher APRDRG‐SOI expected readmission rate, and higher AHRQ Comorbidity Index were associated with readmission. Days to complete the discharge summary >3 days versus 3 days was associated with a higher readmission rate, with an unadjusted odds ratio (OR) and 95% confidence interval (CI) of 1.09 (95% CI: 1.04 to 1.13, P < 0.001).

Association Between Patient Characteristics, Discharge Summary Completion >3 Days, and 30‐Day Readmission Status
CharacteristicBivariable Analysis*Multivariable Analysis*
OR (95% CI)P ValueOR (95% CI)P Value
  • NOTE: Abbreviations: AHRQ, Agency for Healthcare Research and Quality; APRDRG, All‐PayerRefined Diagnosis‐Related Group; CI, confidence interval; OR, odds ratio; SNF, skilled nursing facility; SOI, severity of illness. *Calculated using logistic regression analysis.

Age, 10 y1.09 (1.08 to 1.09)<0.0010.97 (0.95 to 0.98)<0.001
Male1.13 (1.09 to 1.17)<0.0011.01 (0.97 to 1.05)0.76
Race    
CaucasianReferent Referent 
African American1.21 (1.17 to 1.26)<0.0011.01 (0.96 to 1.05)0.74
Other0.84 (0.79 to 0.90)<0.0010.92 (0.86 to 0.98)0.01
Payer    
MedicareReferent Referent 
Medicaid0.74 (0.70 to 0.77)<0.0011.03 (0.97 to 1.09)0.42
Other0.57 (0.55 to 0.60)<0.0010.86 (0.82 to 0.91)<0.001
Hospital service    
MedicineReferent Referent 
Gynecologyobstetrics0.18 (0.16 to 0.20)<0.0010.50 (0.45 to 0.56)<0.001
Neurosciences0.43 (0.40 to 0.46)<0.0010.76 (0.70 to 0.82)<0.001
Oncology1.14 (1.07 to 1.22)<0.0011.18 (1.10 to 1.28)<0.001
Pediatrics0.54 (0.51 to 0.57)<0.0010.77 (0.71 to 0.83)<0.001
Surgical sciences0.57 (0.54 to 0.60)<0.0010.92 (0.87 to 0.97)0.002
Discharge location    
Home  Referent 
Facility (SNF, rehabilitation facility)1.90 (1.77 to 2.03)<0.0011.11 (1.02 to 1.19)0.009
Home with homecare or hospice1.73 (1.64 to 1.83)<0.0011.26 (1.19 to 1.34)<0.001
Other1.89 (1.78 to 2.00)<0.0011.25 (1.18 to 1.34)<0.001
Length of stay, d1.03 (1.02 to 1.03)<0.0011.00 (1.00 to 1.01)<0.001
APRDRG‐SOI expected readmission rate, %1.08 (1.07 to 1.08)<0.0011.06 (1.06 to 1.06)<0.001
AHRQ Comorbidity Index (1 point)1.27 (1.26 to 1.28)<0.0011.11 (1.09 to 1.12)<0.001
Discharge summary completed >3 days1.09 (1.04 to 1.14)<0.0011.09 (1.05 to 1.14)<0.001
Figure 1
The association between days to complete the hospital discharge summary and 30‐day readmissions in Maryland: percentage of patients readmitted to any acute care hospital in Maryland by days to complete discharge summary deciles (0‐2, 2.1–3, 3.1–4, 4.1–6, 6.1–8, 8.2–10, 10.1–14, 14.1–19, 19.1–30, >30). Plots show the mean (dots) and 95% confidence bands with a locally weighted scatterplot smoothing line (dashed line). (A) Plots the unadjusted association between days to complete discharge summary and 30‐day readmissions. (B) Plots the adjusted association between days to complete discharge summary and 30‐day readmissions. Adjusted mean readmission rates were calculated using the least squared means method for the multivariable logistic regression analysis, and were adjusted for age, sex, race, payer, hospital service, discharge location, LOS, APRDRG‐SOI expected readmission rate, and AHRQ Comorbidity Index. Abbreviations: AHRQ, Agency for Healthcare Research and Quality; APRDRG, All‐Payer–Refined Diagnosis‐Related Group; DC, discharge; LOS, length of stay; SOI, severity of illness.

Multivariable and Secondary Analyses

In adjusted analysis (Table 2), patients discharged from an oncologic service relative to a medicine hospital service (OR: 1.19, 95% CI: 1.10 to 1.28, P < 0.001), patients discharged to a facility, home with homecare or hospice, or other location compared to home (facility OR: 1.11, 95% CI: 1.02 to 1.19, P = 0.009; home with homecare or hospice OR: 1.26, 95% CI: 1.19 to 1.34, P < 0.001; other OR: 1.25, 95% CI: 1.18 to 1.34, P < 0.001), patients with longer LOS (OR: 1.11 per day, 95% CI: 1.10 to 1.12, P < 0.001), patients with a higher expected readmission rates (OR: 1.01 per percent, 95% CI: 1.00 to 1.01, P < 0.001), and patients with a higher AHRQ comorbidity index (OR: 1.06 per 1 point, 95% CI: 1.06 to 1.06, P < 0.001) had higher 30‐day readmission rates. Overall, days to complete the discharge summary >3 days versus 3 days was associated with a higher readmission rate (OR: 1.09, 95% CI: 1.05 to 1.14, P < 0.001).

In a sensitivity analysis, discharge summary completion >8 days (median) versus 8 days was associated with higher unadjusted readmission rate (OR: 1.11, 95% CI: 1.07 to 1.15, P < 0.001) and a higher adjusted readmission rate (OR: 1.06, 95% CI: 1.02 to 1.10, P < 0.001). Discharge summary completion >14 days (70th percentile) versus 14 days was also associated with higher unadjusted readmission rate (OR: 1.15, 95% CI: 1.08 to 1.21, P < 0.001) and a higher adjusted readmission rate (OR: 1.09, 95% CI: 1.02 to 1.16, P = 0.008). The association between days to complete the discharge summary >3 days and readmissions was found to vary significantly by hospital service (P = 0.03). For comparing days to complete the discharge summary >3 versus 3 days, Table 3 shows that neurosciences, pediatrics, oncology, and medicine hospital services were associated with significantly increased adjusted mean readmission rates. Additionally, when days to complete the discharge summary was modeled as a continuous variable, we found that for every 3 days the odds of readmission increased by 1% (OR: 1.01, 95% CI: 1.00 to 1.01, P < 0.001).

Association Between Patient Discharge Summary Completion >3 Days and 30‐Day Readmission Status by Hospital Service
Days to Complete Discharge Summary by Hospital ServiceAdjusted Mean Readmission Rate (95% CI)*P Value
  • NOTE: Abbreviations: AHRQ, Agency for Healthcare Research and Quality; APRDRG, All‐PayerRefined Diagnosis‐Related Group; CI, confidence interval; SOI, severity of illness. *Adjusted mean readmission rates were calculated separately for each hospital service using the least squared means method for the multivariable logistic regression analysis and were adjusted for age, sex, race, payer, hospital service, discharge location, length of stay, APRDRG‐SOI expected readmission rate, discharged location, and AHRQ Comorbidity Index.

Gynecologyobstetrics 0.30
03 days, n = 1,7925.4 (4.1 to 6.7) 
>3 days, n = 7,5076.0 (4.9 to 7.0) 
Medicine 0.04
03 days, n = 6,13721.1 (20.0 to 22.3) 
>3 days, n = 19,89922.4 (21.6 to 23.2) 
Neurosciences 0.02
03 days, n = 1,11610.1 (8.2 to 12.1) 
>3 days, n = 7,15312.5 (11.6 to 13.5) 
Oncology 0.01
03 days, n = 1,88525.0 (22.6 to 27.4) 
>3 days, n = 3,33728.2 (26.6 to 30.2) 
Pediatrics 0.001
03 days, n = 4,5619.5 (6.9 to 12.2) 
>3 days, n = 12,46811.4 (8.9 to 13.9) 
Surgical sciences 0.89
03 days, n = 6,26115.2 (14.2 to 16.1) 
>3 days, n = 15,87815.1 (14.4 to 15.8) 

In an unadjusted analysis, we found that the relationship between LOS and days to complete the discharge summary was not significant ( coefficient and 95% CI:, 0.01, 0.02 to 0.00, P = 0.20). However, we found a small but significant relationship in our multivariable analysis, such that each hospitalization day was associated with a 0.01 (95% CI: 0.00 to 0.02, P = 0.03) increase in days to complete the discharge summary.

DISCUSSION

In this single‐center retrospective analysis, the number of days to complete the discharge summary was significantly associated with readmissions after hospitalization. This association was independent of age, gender, comorbidity index, payer, discharge location, length of hospital stay, expected readmission rate based on diagnosis and severity of illness, and all hospital services. The odds of readmission for patients with delayed discharge summaries was small but significant. This is important in the current landscape of readmissions, particularly for institutions who are challenged to reduce readmission rates, and a small relative difference in readmissions may be the difference between getting penalized or not. In the context of prior studies, the results highlight the role of timely discharge summary as an under‐recognized metric, which may be a valid litmus test for care coordination. The findings also emphasize the potential of early summaries to expedite communication and to help facilitate quality of patient care. Hence, the study results extend the literature examining the relationship of delay in discharge summary with unfavorable patient outcomes.[15, 32]

In contrast to prior reports with limited focus on same‐hospital readmissions,[18, 33, 34, 35] readmissions beyond 30 days,[12] or focused on a specific patient population,[13, 36] this study evaluates both intra‐ and interhospital 30‐day readmissions in Maryland in an all‐payer, multi‐institution, diverse patient population. Additionally, prior research is conflicting with respect to whether timely discharges summaries are significantly associated with increased hospital readmissions.[12, 13, 14, 15] Although it is not surprising that inadequate care during hospitalization could result in readmissions, the role of discharge summaries remain underappreciated. Having a timely discharge summary may not always prevent readmissions, but our study showed that 43% of readmission occurred before the discharge summary completion. Not having a completed discharge summary at the time of readmission may have been a driver for the positive association between timely completion and 30‐day readmission we observed. This study highlights that delay in the discharge summary could be a marker of poor transitions of care, because suboptimal dissemination of critical information to care providers may result in discontinuity of patient care posthospitalization.

A plausible mechanism of the association between discharge summary delays and readmissions could be the provision of collateral information, which may potentially alter the threshold for readmissions. For example, in the emergency room/emergency department (ER/ED) setting, discharge summaries may help with preventable readmissions. For patients who present repeatedly with the same complaint, timely summaries to ER/ED providers may help reframe the patient complaints, such as patient has concern X, which was previously identified to be related to diagnosis Y. As others have shown, the content of discharge summaries, format, and accessibility (electronic vs paper chart), as well as timely distribution of summaries, are key factors that impact quality outcomes.[2, 12, 15, 37, 38] By detailing prior hospital information (ie, discharge medications, prior presentations, tests completed), summaries could help prevent errors in medication dosing, reduce unnecessary testing, and help facilitate admission triage. Summaries may have information regarding a new diagnosis such as the results of an endoscopic evaluation that revealed the source of occult gastrointestinal bleeding, which could help contextualize a complaint of repeat melena and redirect goals of care. Discussions of goals of care in the discharge summary may guide primary providers in continued care management plans.

Our study findings underscore a positive correlation between late discharge summaries and readmissions. However, the extent that this is a causal relationship is unclear; the association of delay in days to complete the discharge summary with readmission may be an epiphenomenon related to processes related to quality of clinical care. For example, delays in discharge summary completion could be a marker of other system issues, such as a stressed work environment. It is possible that providers who fail to complete timely discharge summaries may also fail to do other important functions related to transitions of care and care coordination. However, even if this is so, timely discharge summaries could become a focal point for discussion for optimization of care transitions. A discharge summary could be delayed because the patient has already been readmitted before the summary was distributed, thus making that original summary less relevant. Delays could also be a reflection of the data complexity for patients with longer hospital stays. This is supported by the small but significant relationship between LOS and days to complete the discharge summary in this study. Lastly, delays in discharge summary completion may also be a proxy of provider communication and can reflect the culture of communication at the institution.

Although unplanned hospital readmission is an important outcome, many readmissions may be related to other factors such as disease progression, rather than late summaries or the lack of postdischarge communication. For instance, prior reports did not find any association between the PCP seeing the discharge summaries or direct communications with the PCP and 30‐day clinical outcomes for readmission and death.[26, 39] However, these studies were limited in their use of self‐reported handoffs, did not measure quality of information transfer, and failed to capture a broader audience beyond the PCP, such as ED physicians or specialists.

Our results suggest that the relationship between days to complete discharge summaries and 30‐day readmissions may vary depending on whether the hospitalization is primarily surgical/procedural versus medical treatment. A recent study found that most readmissions after surgery were associated with new complications related to the procedure and not exacerbation of prior index hospitalization complications.[40] Hence, treatment for common causes of hospital readmissions after surgical or gynecological procedures, such as wound infections, acute anemia, ileus, or dehydration, may not necessarily require a completed discharge summary for appropriate management. However, we caution extending this finding to clinical practice before further studies are conducted on specific procedures and in different clinical settings.

Results from this study also support institutional policies that specify the need for practitioners to complete discharge summaries contemporaneously, such as at the time of discharge or within a couple of days. Unlike other forms of communication that are optional, discharge summaries are required, so we recommend that practitioners be held accountable for short turnaround times. For example, providers could be graded and rated on timely completions of discharge summaries, among other performance variables. Anecdotally at our institutions, we have heard from practitioners that it takes less time to complete them when you do them on the day of discharge, because the hospitalization course is fresher in their mind and they have to wade through less information in the medical record to complete an accurate discharge summary. To this point, a barrier to on‐time completion is that providers may have misconceptions about what is really vital information to convey to the next provider. In agreement with past research and in the era of the electronic medical record system, we recommend that the discharge summary should be a quick synthesis of key findings that incorporates only the important elements, such as why the patient was hospitalized, what were key findings and key responses to therapy, what is pending at the time of discharge, what medications the patient is currently taking, and what are the follow‐up plans, rather than a lengthy expose of all the findings.[13, 36, 41, 42]

Lastly, our study results should be taken in the context of its limitations. As a single‐center study, findings may lack generalizability. In particular, the results may not generalize to hospitals that lack access to statewide reporting. We were also not able to assess readmission for patients who may have been readmitted to a hospital outside of Maryland. Although we adjusted for pertinent variables such as age, gender, healthcare payer, hospital service, comorbidity index, discharge location, LOS, and expected readmission rates, there may be other relevant confounders that we failed to capture or measure optimally. Median days to complete the discharge summary in this study was 8 days, which is longer than practices at other institutions, and may also limit this study's generalizability.[15, 36, 42] However, prior research supports our findings,[15] and a systematic review found that only 29% and 52% of discharge summaries were completed by 2 weeks and 4 weeks, respectively.[9] Finally, as noted above and perhaps most important, it is possible that discharge summary turnaround time does not in itself causally impact readmissions, but rather reflects an underlying commitment of the inpatient team to effectively coordinate care following hospital discharge.

CONCLUSION

In sum, this study delineates an underappreciated but important relationship of timely discharge summary completion and readmission outcomes. The discharge summary may be a relevant metric reflecting quality of patient care. Healthcare providers may begin to target timely discharge summaries as a potential focal point of quality‐improvement projects with the goal to facilitate better patient outcomes.

Disclosures

The authors certify that no party having a direct interest in the results of the research supporting this article has or will confer a benefit on us or on any organization with which we are associated, and, if applicable, the authors certify that all financial and material support for this research (eg, Centers for Medicare and Medicaid Services, National Institutes of Health, or National Health Service grants) and work are clearly identified. This study was supported by funding opportunity, number CMS‐1C1‐12‐0001, from the Centers for Medicare and Medicaid Services and Center for Medicare and Medicaid Innovation. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Department of Health and Human Services or any of its agencies.

Across the continuum of care, the discharge summary is a critical tool for communication among care providers.[1] In the United States, the Joint Commission policies mandate that all hospital providers complete a discharge summary for patients with specific components to foster effective communication with future providers.[2] Because outpatient providers and emergency physicians rely on clinical information in the discharge summary to ensure appropriate postdischarge continuity of care, timely documentation is potentially an essential aspect of readmission reduction initiatives.[3, 4, 5] Prior reports indicate that poor discharge documentation of follow‐up plan‐of‐care increases the risk of hospitalization, whereas structured instructions, patient education, and direct communications with primary care physicians (PCPs) reduce repeat hospital visits.[6, 7, 8, 9] However, the current literature is limited in its narrow focus on the contents of discharge summaries, considered only same‐hospital readmissions, or considered readmissions within 3 months of discharge.[10, 11, 12, 13] Moreover, some prior research has suggested no association between discharge summary timeliness with readmission,[12, 13, 14] whereas another study did find a relationship,[15] hence the need to study this further is important. Filling this gap in knowledge could provide an avenue to track and improve quality of patient care, as delays in discharge summaries have been linked with pot‐discharge adverse outcomes and patient safety concerns.[15, 16, 17, 18] Because readmissions often occur soon after discharge, having timely discharge summaries may be particularly important to outcomes.[19, 20]

This research began under the framework of evaluating a bundle of care coordination strategies that were implemented at the Johns Hopkins Health System. These strategies were informed by the early Centers for Medicare and Medicaid Services (CMS) demonstration projects and other best practices that have been documented in the literature to improve utilization and improve communication during transitions of care.[21, 22, 23, 24, 25] Later they were augmented through a contract with the Center of Medicare and Medicaid Innovation to improve access to healthcare services and improve patient outcomes through improved care coordination processes. One of the domains our institution has increased efforts to improve is in provider handoffs. Toward that goal, we have worked to disentangle the effects of different factors of provider‐to‐provider communication that may influence readmissions.[26] For example, effective written provider handoffs in the form of accurate and timely discharge summaries was considered a key care coordination component of this program, but there was institutional resistance to endorsing an expectation that discharge summary turnaround should be shortened. To build a case for this concept, we sought to test the hypothesis that, at our hospital, longer time to complete hospital discharge summaries was associated with increased readmission rates. Unique to this analysis is that, in the state of Maryland, there is statewide reporting of readmissions, so we were able to account for intra‐ and interhospital readmissions for an all‐payer population. The authors anticipated that findings from this study would help inform discharge quality‐improvement initiatives and reemphasize the importance of timely discharge documentation across all disciplines as part of quality patient care.

METHODS

Study Population and Setting

We conducted a single‐center, retrospective cohort study of 87,994 consecutive patients discharged from Johns Hopkins Hospital, which is a 1000‐bed, tertiary academic medical center in Baltimore, Maryland between January 1, 2013 and December 31, 2014. One thousand ninety‐three (1.2%) of the records on days to complete the discharge summary were missing and were excluded from the analysis.

Data Source and Covariates

Data were derived from several sources. The Johns Hopkins Hospital data mart financial database, used for mandatory reporting to the State of Maryland, provided the following patient data: age, gender, race/ethnicity, payer (Medicare, Medicaid, and other) as a proxy for socioeconomic status,[27] hospital service prior to discharge (gynecologyobstetrics, medicine, neurosciences, oncology, pediatrics, and surgical sciences), hospital length of stay (LOS) prior to discharge, Agency for Healthcare Research and Quality (AHRQ) Comorbidity Index (which is an update to the original Elixhauser methodology[28]), and all‐payerrefined diagnosis‐related group (APRDRG) and severity of illness (SOI) combinations (a tool to group patients into clinically comparable disease and SOI categories expected to use similar resources and experience similar outcomes). The Health Services Cost Review Commission (HSCRC) in Maryland provided the observed readmission rate in Maryland for each APRDRG‐SOI combination and served as an expected readmission rate. This risk stratification methodology is similar to the approach used in previous studies.[26, 29] Discharge summary turnaround time was obtained from institutional administrative databases used to track compliance with discharge summary completion. Discharge location (home, facility, home with homecare or hospice, or other) was obtained from Curaspan databases (Curaspan Health Group, Inc., Newton, MA).

Primary Outcome: 30‐Day Readmission

The primary outcome was unplanned rehospitalizations to an acute care hospital in Maryland within 30 days of discharge from Johns Hopkins Hospital. This was as defined by the Maryland HSCRC using an algorithm to exclude readmissions that were likely to be scheduled, as defined by the index admission diagnosis and readmission diagnosis; this algorithm is updated based on the CMS all‐cause readmission algorithm.[30, 31]

Primary Exposure: Days to Complete the Discharge Summary

Discharge summary completion time was defined as the date when the discharge attending physician electronically signs the discharge summary. At our institution, an auto‐fax system sends documents (eg, discharge summaries, clinic notes) to linked providers (eg, primary care providers) shortly after midnight from the day the document is signed by an attending physician. During the period of the project, the policy for discharge summaries at the Johns Hopkins Hospital went from requiring them to be completed within 30 days to 14 days, and we were hoping to use our analyses to inform decision makers why this was important. To emphasize the need for timely completion of discharge summaries, we dichotomized the number of days to complete the discharge summary into >3 versus 3 days (20th percentile cutoff) and modeled it as a continuous variable (per 3‐day increase in days to complete the discharge summary).

Statistical Analysis

To evaluate differences in patient characteristics by readmission status, analysis of variance and 2 tests were used for continuous and dichotomous variables, respectively. Logistic regression was used to evaluate the association between days to complete the discharge summary >3 days and readmission status, adjusting for potentially confounding variables. Before inclusion in the logistic regression model, we confirmed a lack of multicollinearity in the multivariable regression model using variance inflation factors. We evaluated residual versus predicted value plots and residual versus fitted value plots with a locally weighted scatterplot smoothing line. In a sensitivity analysis we evaluated the association between readmission status and different cutoffs (>8 days, 50th percentile; and >14 days, 70% percentile). In a separate analysis, we used interaction terms to test whether the association between the association between days to complete the discharge summary >3 days and hospital readmission varied by the covariates in the analysis (age, sex, race, payer, hospital service, discharge location, LOS, APRDRG‐SOI expected readmission rate, and AHRQ Comorbidity Index). We observed a significant interaction between 30‐day readmission and days to complete the discharge summary >3 days by hospital service. Hence, we separately calculated the adjusted mean readmission rates separately for each hospital service using the least squared means method for the multivariable logistic regression analysis and adjusting for the previously mentioned covariates. In a separate analysis, we used linear regression to evaluate the association between LOS and days to complete the discharge summary, adjusting for potentially confounding variables. Statistical significance was defined as a 2‐sided P < 0.05. Data were analyzed with R (version 2.15.0; R Foundation for Statistical Computing, Vienna, Austria; http://www.r‐project.org). The Johns Hopkins Institutional Review Board approved the study.

RESULTS

Readmitted Patients

In the study period, 14,248 out of 87,994 (16.2%) consecutive eligible patients were readmitted to a hospital in Maryland from patients discharged from Johns Hopkins Hospital between January 1, 2013 and December 31, 2014. A total of 11,027 (77.4%) of the readmissions were back to Johns Hopkins Hospital. Table 1 compares characteristics of readmitted versus nonreadmitted patients, with the following variables being significantly different between these patient groups: age, gender, healthcare payer, hospital service, discharge location, length of stay expected readmission rate, AHRQ Comorbidity Index, and days to complete inpatient discharge summary.

Characteristics of All Patients*
CharacteristicsAll Patients, N = 87,994Not Readmitted, N = 73,746Readmitted, N = 14,248P Value
  • NOTE: Abbreviations: AHRQ, Agency for Healthcare Research and Quality; APRDRG, All‐PayerRefined Diagnosis‐Related Group; SNF, skilled nursing facility; SOI, severity of illness. *Binary and categorical data are presented as n (%), and continuous variables are represented as mean (standard deviation). Proportions may not add to 100% due to rounding. Three days represents the 20th percentile cutoff for the days to complete a discharge summary.

Age, y42.1 (25.1)41.3 (25.4)46.4 (23.1)<0.001
Male43,210 (49.1%)35,851 (48.6%)7,359 (51.6%)<0.001
Race   <0.001
Caucasian45,705 (51.9%)3,8661 (52.4%)7,044 (49.4%) 
African American32,777 (37.2%)2,6841 (36.4%)5,936 (41.7%) 
Other9,512 (10.8%)8,244 (11.2%)1,268 (8.9%) 
Payer   <0.001
Medicare22,345 (25.4%)17,614 (23.9%)4,731 (33.2%) 
Medicaid24,080 (27.4%)20,100 (27.3%)3,980 (27.9%) 
Other41,569 (47.2%)36,032 (48.9%)5,537 (38.9%) 
Hospital service   <0.001
Gynecologyobstetrics9,299 (10.6%)8,829 (12.0%)470 (3.3%) 
Medicine26,036 (29.6%)20,069 (27.2%)5,967 (41.9%) 
Neurosciences8,269 (9.4%)7,331 (9.9%)938 (6.6%) 
Oncology5,222 (5.9%)3,898 (5.3%)1,324 (9.3%) 
Pediatrics17,029 (19.4%)14,684 (19.9%)2,345 (16.5%) 
Surgical sciences22,139 (25.2%)18,935 (25.7%)3,204 (22.5%) 
Discharge location   <0.001
Home65,478 (74.4%)56,359 (76.4%)9,119 (64.0%) 
Home with homecare or hospice9,524 (10.8%)7,440 (10.1%)2,084 (14.6%) 
Facility (SNF, rehabilitation facility)5,398 (6.1%)4,131 (5.6%)1,267 (8.9%) 
Other7,594 (8.6%)5,816 (7.9%)1,778 (12.5%) 
Length of stay, d5.5 (8.6)5.1 (7.8)7.5 (11.6)<0.001
APRDRG‐SOI Expected Readmission Rate, %14.4 (9.5)13.3 (9.2)20.1 (9.0)<0.001
AHRQ Comorbidity Index (1 point)2.5 (1.4)2.4 (1.4)3.0 (1.8)<0.001
Discharge summary completed >3 days66,242 (75.3%)55,329 (75.0%)10,913 (76.6%)<0.001

Association Between Days to Complete the Discharge Summary and Readmission

After hospital discharge, median (IQR) number of days to complete discharge summaries was 8 (416) days. After hospital discharge, median (IQR) number of days to complete discharge summaries and the number of days from discharge to readmission was 8 (416) and 11 (519) days, respectively (P < 0.001). Six thousand one hundred one patients (42.8%) were readmitted before their discharge summary was completed. The median (IQR) days to complete discharge summaries by hospital service in order from shortest to longest was: oncology, 6 (212) days; surgical sciences, 6 (312) days; pediatrics, 7 (315) days; gynecologyobstetrics, 8 (415) days; medicine, 9 (420) days; neurosciences, 12 (621) days.

When we divided the number of days to complete the discharge summary into deciles (02, 2.13, 3.14, 4.16, 6.18, 8.210, 10.114, 14.119, 19.130, >30), a longer number of days to complete discharge summaries had higher unadjusted and adjusted readmission rates (Figure 1). In unadjusted analysis, Table 2 shows that older age, male sex, African American race, oncological versus medicine hospital service, discharge location, longer LOS, higher APRDRG‐SOI expected readmission rate, and higher AHRQ Comorbidity Index were associated with readmission. Days to complete the discharge summary >3 days versus 3 days was associated with a higher readmission rate, with an unadjusted odds ratio (OR) and 95% confidence interval (CI) of 1.09 (95% CI: 1.04 to 1.13, P < 0.001).

Association Between Patient Characteristics, Discharge Summary Completion >3 Days, and 30‐Day Readmission Status
CharacteristicBivariable Analysis*Multivariable Analysis*
OR (95% CI)P ValueOR (95% CI)P Value
  • NOTE: Abbreviations: AHRQ, Agency for Healthcare Research and Quality; APRDRG, All‐PayerRefined Diagnosis‐Related Group; CI, confidence interval; OR, odds ratio; SNF, skilled nursing facility; SOI, severity of illness. *Calculated using logistic regression analysis.

Age, 10 y1.09 (1.08 to 1.09)<0.0010.97 (0.95 to 0.98)<0.001
Male1.13 (1.09 to 1.17)<0.0011.01 (0.97 to 1.05)0.76
Race    
CaucasianReferent Referent 
African American1.21 (1.17 to 1.26)<0.0011.01 (0.96 to 1.05)0.74
Other0.84 (0.79 to 0.90)<0.0010.92 (0.86 to 0.98)0.01
Payer    
MedicareReferent Referent 
Medicaid0.74 (0.70 to 0.77)<0.0011.03 (0.97 to 1.09)0.42
Other0.57 (0.55 to 0.60)<0.0010.86 (0.82 to 0.91)<0.001
Hospital service    
MedicineReferent Referent 
Gynecologyobstetrics0.18 (0.16 to 0.20)<0.0010.50 (0.45 to 0.56)<0.001
Neurosciences0.43 (0.40 to 0.46)<0.0010.76 (0.70 to 0.82)<0.001
Oncology1.14 (1.07 to 1.22)<0.0011.18 (1.10 to 1.28)<0.001
Pediatrics0.54 (0.51 to 0.57)<0.0010.77 (0.71 to 0.83)<0.001
Surgical sciences0.57 (0.54 to 0.60)<0.0010.92 (0.87 to 0.97)0.002
Discharge location    
Home  Referent 
Facility (SNF, rehabilitation facility)1.90 (1.77 to 2.03)<0.0011.11 (1.02 to 1.19)0.009
Home with homecare or hospice1.73 (1.64 to 1.83)<0.0011.26 (1.19 to 1.34)<0.001
Other1.89 (1.78 to 2.00)<0.0011.25 (1.18 to 1.34)<0.001
Length of stay, d1.03 (1.02 to 1.03)<0.0011.00 (1.00 to 1.01)<0.001
APRDRG‐SOI expected readmission rate, %1.08 (1.07 to 1.08)<0.0011.06 (1.06 to 1.06)<0.001
AHRQ Comorbidity Index (1 point)1.27 (1.26 to 1.28)<0.0011.11 (1.09 to 1.12)<0.001
Discharge summary completed >3 days1.09 (1.04 to 1.14)<0.0011.09 (1.05 to 1.14)<0.001
Figure 1
The association between days to complete the hospital discharge summary and 30‐day readmissions in Maryland: percentage of patients readmitted to any acute care hospital in Maryland by days to complete discharge summary deciles (0‐2, 2.1–3, 3.1–4, 4.1–6, 6.1–8, 8.2–10, 10.1–14, 14.1–19, 19.1–30, >30). Plots show the mean (dots) and 95% confidence bands with a locally weighted scatterplot smoothing line (dashed line). (A) Plots the unadjusted association between days to complete discharge summary and 30‐day readmissions. (B) Plots the adjusted association between days to complete discharge summary and 30‐day readmissions. Adjusted mean readmission rates were calculated using the least squared means method for the multivariable logistic regression analysis, and were adjusted for age, sex, race, payer, hospital service, discharge location, LOS, APRDRG‐SOI expected readmission rate, and AHRQ Comorbidity Index. Abbreviations: AHRQ, Agency for Healthcare Research and Quality; APRDRG, All‐Payer–Refined Diagnosis‐Related Group; DC, discharge; LOS, length of stay; SOI, severity of illness.

Multivariable and Secondary Analyses

In adjusted analysis (Table 2), patients discharged from an oncologic service relative to a medicine hospital service (OR: 1.19, 95% CI: 1.10 to 1.28, P < 0.001), patients discharged to a facility, home with homecare or hospice, or other location compared to home (facility OR: 1.11, 95% CI: 1.02 to 1.19, P = 0.009; home with homecare or hospice OR: 1.26, 95% CI: 1.19 to 1.34, P < 0.001; other OR: 1.25, 95% CI: 1.18 to 1.34, P < 0.001), patients with longer LOS (OR: 1.11 per day, 95% CI: 1.10 to 1.12, P < 0.001), patients with a higher expected readmission rates (OR: 1.01 per percent, 95% CI: 1.00 to 1.01, P < 0.001), and patients with a higher AHRQ comorbidity index (OR: 1.06 per 1 point, 95% CI: 1.06 to 1.06, P < 0.001) had higher 30‐day readmission rates. Overall, days to complete the discharge summary >3 days versus 3 days was associated with a higher readmission rate (OR: 1.09, 95% CI: 1.05 to 1.14, P < 0.001).

In a sensitivity analysis, discharge summary completion >8 days (median) versus 8 days was associated with higher unadjusted readmission rate (OR: 1.11, 95% CI: 1.07 to 1.15, P < 0.001) and a higher adjusted readmission rate (OR: 1.06, 95% CI: 1.02 to 1.10, P < 0.001). Discharge summary completion >14 days (70th percentile) versus 14 days was also associated with higher unadjusted readmission rate (OR: 1.15, 95% CI: 1.08 to 1.21, P < 0.001) and a higher adjusted readmission rate (OR: 1.09, 95% CI: 1.02 to 1.16, P = 0.008). The association between days to complete the discharge summary >3 days and readmissions was found to vary significantly by hospital service (P = 0.03). For comparing days to complete the discharge summary >3 versus 3 days, Table 3 shows that neurosciences, pediatrics, oncology, and medicine hospital services were associated with significantly increased adjusted mean readmission rates. Additionally, when days to complete the discharge summary was modeled as a continuous variable, we found that for every 3 days the odds of readmission increased by 1% (OR: 1.01, 95% CI: 1.00 to 1.01, P < 0.001).

Association Between Patient Discharge Summary Completion >3 Days and 30‐Day Readmission Status by Hospital Service
Days to Complete Discharge Summary by Hospital ServiceAdjusted Mean Readmission Rate (95% CI)*P Value
  • NOTE: Abbreviations: AHRQ, Agency for Healthcare Research and Quality; APRDRG, All‐PayerRefined Diagnosis‐Related Group; CI, confidence interval; SOI, severity of illness. *Adjusted mean readmission rates were calculated separately for each hospital service using the least squared means method for the multivariable logistic regression analysis and were adjusted for age, sex, race, payer, hospital service, discharge location, length of stay, APRDRG‐SOI expected readmission rate, discharged location, and AHRQ Comorbidity Index.

Gynecologyobstetrics 0.30
03 days, n = 1,7925.4 (4.1 to 6.7) 
>3 days, n = 7,5076.0 (4.9 to 7.0) 
Medicine 0.04
03 days, n = 6,13721.1 (20.0 to 22.3) 
>3 days, n = 19,89922.4 (21.6 to 23.2) 
Neurosciences 0.02
03 days, n = 1,11610.1 (8.2 to 12.1) 
>3 days, n = 7,15312.5 (11.6 to 13.5) 
Oncology 0.01
03 days, n = 1,88525.0 (22.6 to 27.4) 
>3 days, n = 3,33728.2 (26.6 to 30.2) 
Pediatrics 0.001
03 days, n = 4,5619.5 (6.9 to 12.2) 
>3 days, n = 12,46811.4 (8.9 to 13.9) 
Surgical sciences 0.89
03 days, n = 6,26115.2 (14.2 to 16.1) 
>3 days, n = 15,87815.1 (14.4 to 15.8) 

In an unadjusted analysis, we found that the relationship between LOS and days to complete the discharge summary was not significant ( coefficient and 95% CI:, 0.01, 0.02 to 0.00, P = 0.20). However, we found a small but significant relationship in our multivariable analysis, such that each hospitalization day was associated with a 0.01 (95% CI: 0.00 to 0.02, P = 0.03) increase in days to complete the discharge summary.

DISCUSSION

In this single‐center retrospective analysis, the number of days to complete the discharge summary was significantly associated with readmissions after hospitalization. This association was independent of age, gender, comorbidity index, payer, discharge location, length of hospital stay, expected readmission rate based on diagnosis and severity of illness, and all hospital services. The odds of readmission for patients with delayed discharge summaries was small but significant. This is important in the current landscape of readmissions, particularly for institutions who are challenged to reduce readmission rates, and a small relative difference in readmissions may be the difference between getting penalized or not. In the context of prior studies, the results highlight the role of timely discharge summary as an under‐recognized metric, which may be a valid litmus test for care coordination. The findings also emphasize the potential of early summaries to expedite communication and to help facilitate quality of patient care. Hence, the study results extend the literature examining the relationship of delay in discharge summary with unfavorable patient outcomes.[15, 32]

In contrast to prior reports with limited focus on same‐hospital readmissions,[18, 33, 34, 35] readmissions beyond 30 days,[12] or focused on a specific patient population,[13, 36] this study evaluates both intra‐ and interhospital 30‐day readmissions in Maryland in an all‐payer, multi‐institution, diverse patient population. Additionally, prior research is conflicting with respect to whether timely discharges summaries are significantly associated with increased hospital readmissions.[12, 13, 14, 15] Although it is not surprising that inadequate care during hospitalization could result in readmissions, the role of discharge summaries remain underappreciated. Having a timely discharge summary may not always prevent readmissions, but our study showed that 43% of readmission occurred before the discharge summary completion. Not having a completed discharge summary at the time of readmission may have been a driver for the positive association between timely completion and 30‐day readmission we observed. This study highlights that delay in the discharge summary could be a marker of poor transitions of care, because suboptimal dissemination of critical information to care providers may result in discontinuity of patient care posthospitalization.

A plausible mechanism of the association between discharge summary delays and readmissions could be the provision of collateral information, which may potentially alter the threshold for readmissions. For example, in the emergency room/emergency department (ER/ED) setting, discharge summaries may help with preventable readmissions. For patients who present repeatedly with the same complaint, timely summaries to ER/ED providers may help reframe the patient complaints, such as patient has concern X, which was previously identified to be related to diagnosis Y. As others have shown, the content of discharge summaries, format, and accessibility (electronic vs paper chart), as well as timely distribution of summaries, are key factors that impact quality outcomes.[2, 12, 15, 37, 38] By detailing prior hospital information (ie, discharge medications, prior presentations, tests completed), summaries could help prevent errors in medication dosing, reduce unnecessary testing, and help facilitate admission triage. Summaries may have information regarding a new diagnosis such as the results of an endoscopic evaluation that revealed the source of occult gastrointestinal bleeding, which could help contextualize a complaint of repeat melena and redirect goals of care. Discussions of goals of care in the discharge summary may guide primary providers in continued care management plans.

Our study findings underscore a positive correlation between late discharge summaries and readmissions. However, the extent that this is a causal relationship is unclear; the association of delay in days to complete the discharge summary with readmission may be an epiphenomenon related to processes related to quality of clinical care. For example, delays in discharge summary completion could be a marker of other system issues, such as a stressed work environment. It is possible that providers who fail to complete timely discharge summaries may also fail to do other important functions related to transitions of care and care coordination. However, even if this is so, timely discharge summaries could become a focal point for discussion for optimization of care transitions. A discharge summary could be delayed because the patient has already been readmitted before the summary was distributed, thus making that original summary less relevant. Delays could also be a reflection of the data complexity for patients with longer hospital stays. This is supported by the small but significant relationship between LOS and days to complete the discharge summary in this study. Lastly, delays in discharge summary completion may also be a proxy of provider communication and can reflect the culture of communication at the institution.

Although unplanned hospital readmission is an important outcome, many readmissions may be related to other factors such as disease progression, rather than late summaries or the lack of postdischarge communication. For instance, prior reports did not find any association between the PCP seeing the discharge summaries or direct communications with the PCP and 30‐day clinical outcomes for readmission and death.[26, 39] However, these studies were limited in their use of self‐reported handoffs, did not measure quality of information transfer, and failed to capture a broader audience beyond the PCP, such as ED physicians or specialists.

Our results suggest that the relationship between days to complete discharge summaries and 30‐day readmissions may vary depending on whether the hospitalization is primarily surgical/procedural versus medical treatment. A recent study found that most readmissions after surgery were associated with new complications related to the procedure and not exacerbation of prior index hospitalization complications.[40] Hence, treatment for common causes of hospital readmissions after surgical or gynecological procedures, such as wound infections, acute anemia, ileus, or dehydration, may not necessarily require a completed discharge summary for appropriate management. However, we caution extending this finding to clinical practice before further studies are conducted on specific procedures and in different clinical settings.

Results from this study also support institutional policies that specify the need for practitioners to complete discharge summaries contemporaneously, such as at the time of discharge or within a couple of days. Unlike other forms of communication that are optional, discharge summaries are required, so we recommend that practitioners be held accountable for short turnaround times. For example, providers could be graded and rated on timely completions of discharge summaries, among other performance variables. Anecdotally at our institutions, we have heard from practitioners that it takes less time to complete them when you do them on the day of discharge, because the hospitalization course is fresher in their mind and they have to wade through less information in the medical record to complete an accurate discharge summary. To this point, a barrier to on‐time completion is that providers may have misconceptions about what is really vital information to convey to the next provider. In agreement with past research and in the era of the electronic medical record system, we recommend that the discharge summary should be a quick synthesis of key findings that incorporates only the important elements, such as why the patient was hospitalized, what were key findings and key responses to therapy, what is pending at the time of discharge, what medications the patient is currently taking, and what are the follow‐up plans, rather than a lengthy expose of all the findings.[13, 36, 41, 42]

Lastly, our study results should be taken in the context of its limitations. As a single‐center study, findings may lack generalizability. In particular, the results may not generalize to hospitals that lack access to statewide reporting. We were also not able to assess readmission for patients who may have been readmitted to a hospital outside of Maryland. Although we adjusted for pertinent variables such as age, gender, healthcare payer, hospital service, comorbidity index, discharge location, LOS, and expected readmission rates, there may be other relevant confounders that we failed to capture or measure optimally. Median days to complete the discharge summary in this study was 8 days, which is longer than practices at other institutions, and may also limit this study's generalizability.[15, 36, 42] However, prior research supports our findings,[15] and a systematic review found that only 29% and 52% of discharge summaries were completed by 2 weeks and 4 weeks, respectively.[9] Finally, as noted above and perhaps most important, it is possible that discharge summary turnaround time does not in itself causally impact readmissions, but rather reflects an underlying commitment of the inpatient team to effectively coordinate care following hospital discharge.

CONCLUSION

In sum, this study delineates an underappreciated but important relationship of timely discharge summary completion and readmission outcomes. The discharge summary may be a relevant metric reflecting quality of patient care. Healthcare providers may begin to target timely discharge summaries as a potential focal point of quality‐improvement projects with the goal to facilitate better patient outcomes.

Disclosures

The authors certify that no party having a direct interest in the results of the research supporting this article has or will confer a benefit on us or on any organization with which we are associated, and, if applicable, the authors certify that all financial and material support for this research (eg, Centers for Medicare and Medicaid Services, National Institutes of Health, or National Health Service grants) and work are clearly identified. This study was supported by funding opportunity, number CMS‐1C1‐12‐0001, from the Centers for Medicare and Medicaid Services and Center for Medicare and Medicaid Innovation. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the Department of Health and Human Services or any of its agencies.

References
  1. Moy NY, Lee SJ, Chan T, et al. Development and sustainability of an inpatient‐to‐outpatient discharge handoff tool: a quality improvement project. Jt Comm J Qual Patient Saf. 2014;40(5):219227.
  2. Henriksen K, Battles JB, Keyes MA, Grady ML, Kind AJ, Smith MA. Documentation of mandated discharge summary components in transitions from acute to subacute care. In: Henriksen K, Battles JB, Keyes MA, et al., eds. Advances in Patient Safety: New Directions and Alternative Approaches. Vol. 2. Culture and Redesign. Rockville, MD: Agency for Healthcare Research and Quality; 2008.
  3. Chugh A, Williams MV, Grigsby J, Coleman EA. Better transitions: improving comprehension of discharge instructions. Front Health Serv Manage. 2009;25(3):1132.
  4. Ben‐Morderchai B, Herman A, Kerzman H, Irony A. Structured discharge education improves early outcome in orthopedic patients. Int J Orthop Trauma Nurs. 2010;14(2):6674.
  5. Hansen LO, Strater A, Smith L, et al. Hospital discharge documentation and risk of rehospitalisation. BMJ Qual Saf. 2011;20(9):773778.
  6. Greenwald JL, Denham CR, Jack BW. The hospital discharge: a review of a high risk care transition with highlights of a reengineered discharge process. J Patient Saf. 2007;3(2):97106.
  7. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520528.
  8. Grafft CA, McDonald FS, Ruud KL, Liesinger JT, Johnson MG, Naessens JM. Effect of hospital follow‐up appointment on clinical event outcomes and mortality. Arch Intern Med. 2010;170(11):955960.
  9. Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831841.
  10. Kind AJ, Thorpe CT, Sattin JA, Walz SE, Smith MA. Provider characteristics, clinical‐work processes and their relationship to discharge summary quality for sub‐acute care patients. J Gen Intern Med. 2012;27(1):7884.
  11. Bradley EH, Curry L, Horwitz LI, et al. Contemporary evidence about hospital strategies for reducing 30‐day readmissions: a national study. J Am Coll Cardiol. 2012;60(7):607614.
  12. Walraven C, Seth R, Austin PC, Laupacis A. Effect of discharge summary availability during post‐discharge visits on hospital readmission. J Gen Intern Med. 2002;17(3):186192.
  13. Salim Al‐Damluji M, Dzara K, Hodshon B, et al. Association of discharge summary quality with readmission risk for patients hospitalized with heart failure exacerbation. Circ Cardiovasc Qual Outcomes. 2015;8(1):109111.
  14. Walraven C, Taljaard M, Etchells E, et al. The independent association of provider and information continuity on outcomes after hospital discharge: implications for hospitalists. J Hosp Med. 2010;5(7):398405.
  15. Li JYZ, Yong TY, Hakendorf P, Ben‐Tovim D, Thompson CH. Timeliness in discharge summary dissemination is associated with patients' clinical outcomes. J Eval Clin Pract. 2013;19(1):7679.
  16. Gandara E, Moniz T, Ungar J, et al. Communication and information deficits in patients discharged to rehabilitation facilities: an evaluation of five acute care hospitals. J Hosp Med. 2009;4(8):E28E33.
  17. Hunter T, Nelson JR, Birmingham J. Preventing readmissions through comprehensive discharge planning. Prof Case Manag. 2013;18(2):5663; quiz 64–65.
  18. Dhalla IA, O'Brien T, Morra D, et al. Effect of a postdischarge virtual ward on readmission or death for high‐risk patients: a randomized clinical trial. JAMA. 2014;312(13):13051312..
  19. Reed RL, Pearlman RA, Buchner DM. Risk factors for early unplanned hospital readmission in the elderly. J Gen Intern Med. 1991;6(3):223228.
  20. Graham KL, Wilker EH, Howell MD, Davis RB, Marcantonio ER. Differences between early and late readmissions among patients: a cohort study. Ann Intern Med. 2015;162(11):741749.
  21. Gage B, Smith L, Morley M, et al. Post‐acute care payment reform demonstration report to congress supplement‐interim report. Centers for Medicare 14(3):114; quiz 88–89.
  22. Naylor MD, Brooten D, Campbell R, et al. Comprehensive discharge planning and home follow‐up of hospitalized elders: a randomized clinical trial. JAMA. 1999;281(7):613620.
  23. Coleman EA, Min SJ, Chomiak A, Kramer AM. Posthospital care transitions: patterns, complications, and risk identification. Health Serv Res. 2004;39(5):14491465.
  24. Snow V, Beck D, Budnitz T, et al. Transitions of care consensus policy statement: American College of Physicians, Society of General Internal Medicine, Society of Hospital Medicine, American Geriatrics Society, American College of Emergency Physicians, and Society for Academic Emergency Medicine. J Hosp Med. 2009;4(6):364370.
  25. Oduyebo I, Lehmann CU, Pollack CE, et al. Association of self‐reported hospital discharge handoffs with 30‐day readmissions. JAMA Intern Med. 2013;173(8):624629.
  26. Adler NE, Newman K. Socioeconomic disparities in health: pathways and policies. Health Aff (Millwood). 2002;21(2):6076.
  27. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):827.
  28. Hoyer EH, Needham DM, Miller J, Deutschendorf A, Friedman M, Brotman DJ. Functional status impairment is associated with unplanned readmissions. Arch Phys Med Rehabil. 2013;94(10):19511958.
  29. Centers for Medicare 35(10):10441059.
  30. Coleman EA, Chugh A, Williams MV, et al. Understanding and execution of discharge instructions. Am J Med Qual. 2013;28(5):383391.
  31. Odonkor CA, Hurst PV, Kondo N, Makary MA, Pronovost PJ. Beyond the hospital gates: elucidating the interactive association of social support, depressive symptoms, and physical function with 30‐day readmissions. Am J Phys Med Rehabil. 2015;94(7):555567.
  32. Finn KM, Heffner R, Chang Y, et al. Improving the discharge process by embedding a discharge facilitator in a resident team. J Hosp Med. 2011;6(9):494500.
  33. Al‐Damluji MS, Dzara K, Hodshon B, et al. Hospital variation in quality of discharge summaries for patients hospitalized with heart failure exacerbation. Circ Cardiovasc Qual Outcomes. 2015;8(1):7786
  34. Mourad M, Cucina R, Ramanathan R, Vidyarthi AR. Addressing the business of discharge: building a case for an electronic discharge summary. J Hosp Med. 2011;6(1):3742.
  35. Regalbuto R, Maurer MS, Chapel D, Mendez J, Shaffer JA. Joint commission requirements for discharge instructions in patients with heart failure: is understanding important for preventing readmissions? J Card Fail. 2014;20(9):641649.
  36. Bell CM, Schnipper JL, Auerbach AD, et al. Association of communication between hospital‐based physicians and primary care providers with patient outcomes. J Gen Intern Med. 2009;24(3):381386.
  37. Merkow RP, Ju MH, Chung JW, et al. Underlying reasons associated with hospital readmission following surgery in the united states. JAMA. 2015;313(5):483495.
  38. Rao P, Andrei A, Fried A, Gonzalez D, Shine D. Assessing quality and efficiency of discharge summaries. Am J Med Qual. 2005;20(6):337343.
  39. Horwitz LI, Jenq GY, Brewster UC, et al. Comprehensive quality of discharge summaries at an academic medical center. J Hosp Med. 2013;8(8):436443.
References
  1. Moy NY, Lee SJ, Chan T, et al. Development and sustainability of an inpatient‐to‐outpatient discharge handoff tool: a quality improvement project. Jt Comm J Qual Patient Saf. 2014;40(5):219227.
  2. Henriksen K, Battles JB, Keyes MA, Grady ML, Kind AJ, Smith MA. Documentation of mandated discharge summary components in transitions from acute to subacute care. In: Henriksen K, Battles JB, Keyes MA, et al., eds. Advances in Patient Safety: New Directions and Alternative Approaches. Vol. 2. Culture and Redesign. Rockville, MD: Agency for Healthcare Research and Quality; 2008.
  3. Chugh A, Williams MV, Grigsby J, Coleman EA. Better transitions: improving comprehension of discharge instructions. Front Health Serv Manage. 2009;25(3):1132.
  4. Ben‐Morderchai B, Herman A, Kerzman H, Irony A. Structured discharge education improves early outcome in orthopedic patients. Int J Orthop Trauma Nurs. 2010;14(2):6674.
  5. Hansen LO, Strater A, Smith L, et al. Hospital discharge documentation and risk of rehospitalisation. BMJ Qual Saf. 2011;20(9):773778.
  6. Greenwald JL, Denham CR, Jack BW. The hospital discharge: a review of a high risk care transition with highlights of a reengineered discharge process. J Patient Saf. 2007;3(2):97106.
  7. Hansen LO, Young RS, Hinami K, Leung A, Williams MV. Interventions to reduce 30‐day rehospitalization: a systematic review. Ann Intern Med. 2011;155(8):520528.
  8. Grafft CA, McDonald FS, Ruud KL, Liesinger JT, Johnson MG, Naessens JM. Effect of hospital follow‐up appointment on clinical event outcomes and mortality. Arch Intern Med. 2010;170(11):955960.
  9. Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital‐based and primary care physicians: implications for patient safety and continuity of care. JAMA. 2007;297(8):831841.
  10. Kind AJ, Thorpe CT, Sattin JA, Walz SE, Smith MA. Provider characteristics, clinical‐work processes and their relationship to discharge summary quality for sub‐acute care patients. J Gen Intern Med. 2012;27(1):7884.
  11. Bradley EH, Curry L, Horwitz LI, et al. Contemporary evidence about hospital strategies for reducing 30‐day readmissions: a national study. J Am Coll Cardiol. 2012;60(7):607614.
  12. Walraven C, Seth R, Austin PC, Laupacis A. Effect of discharge summary availability during post‐discharge visits on hospital readmission. J Gen Intern Med. 2002;17(3):186192.
  13. Salim Al‐Damluji M, Dzara K, Hodshon B, et al. Association of discharge summary quality with readmission risk for patients hospitalized with heart failure exacerbation. Circ Cardiovasc Qual Outcomes. 2015;8(1):109111.
  14. Walraven C, Taljaard M, Etchells E, et al. The independent association of provider and information continuity on outcomes after hospital discharge: implications for hospitalists. J Hosp Med. 2010;5(7):398405.
  15. Li JYZ, Yong TY, Hakendorf P, Ben‐Tovim D, Thompson CH. Timeliness in discharge summary dissemination is associated with patients' clinical outcomes. J Eval Clin Pract. 2013;19(1):7679.
  16. Gandara E, Moniz T, Ungar J, et al. Communication and information deficits in patients discharged to rehabilitation facilities: an evaluation of five acute care hospitals. J Hosp Med. 2009;4(8):E28E33.
  17. Hunter T, Nelson JR, Birmingham J. Preventing readmissions through comprehensive discharge planning. Prof Case Manag. 2013;18(2):5663; quiz 64–65.
  18. Dhalla IA, O'Brien T, Morra D, et al. Effect of a postdischarge virtual ward on readmission or death for high‐risk patients: a randomized clinical trial. JAMA. 2014;312(13):13051312..
  19. Reed RL, Pearlman RA, Buchner DM. Risk factors for early unplanned hospital readmission in the elderly. J Gen Intern Med. 1991;6(3):223228.
  20. Graham KL, Wilker EH, Howell MD, Davis RB, Marcantonio ER. Differences between early and late readmissions among patients: a cohort study. Ann Intern Med. 2015;162(11):741749.
  21. Gage B, Smith L, Morley M, et al. Post‐acute care payment reform demonstration report to congress supplement‐interim report. Centers for Medicare 14(3):114; quiz 88–89.
  22. Naylor MD, Brooten D, Campbell R, et al. Comprehensive discharge planning and home follow‐up of hospitalized elders: a randomized clinical trial. JAMA. 1999;281(7):613620.
  23. Coleman EA, Min SJ, Chomiak A, Kramer AM. Posthospital care transitions: patterns, complications, and risk identification. Health Serv Res. 2004;39(5):14491465.
  24. Snow V, Beck D, Budnitz T, et al. Transitions of care consensus policy statement: American College of Physicians, Society of General Internal Medicine, Society of Hospital Medicine, American Geriatrics Society, American College of Emergency Physicians, and Society for Academic Emergency Medicine. J Hosp Med. 2009;4(6):364370.
  25. Oduyebo I, Lehmann CU, Pollack CE, et al. Association of self‐reported hospital discharge handoffs with 30‐day readmissions. JAMA Intern Med. 2013;173(8):624629.
  26. Adler NE, Newman K. Socioeconomic disparities in health: pathways and policies. Health Aff (Millwood). 2002;21(2):6076.
  27. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care. 1998;36(1):827.
  28. Hoyer EH, Needham DM, Miller J, Deutschendorf A, Friedman M, Brotman DJ. Functional status impairment is associated with unplanned readmissions. Arch Phys Med Rehabil. 2013;94(10):19511958.
  29. Centers for Medicare 35(10):10441059.
  30. Coleman EA, Chugh A, Williams MV, et al. Understanding and execution of discharge instructions. Am J Med Qual. 2013;28(5):383391.
  31. Odonkor CA, Hurst PV, Kondo N, Makary MA, Pronovost PJ. Beyond the hospital gates: elucidating the interactive association of social support, depressive symptoms, and physical function with 30‐day readmissions. Am J Phys Med Rehabil. 2015;94(7):555567.
  32. Finn KM, Heffner R, Chang Y, et al. Improving the discharge process by embedding a discharge facilitator in a resident team. J Hosp Med. 2011;6(9):494500.
  33. Al‐Damluji MS, Dzara K, Hodshon B, et al. Hospital variation in quality of discharge summaries for patients hospitalized with heart failure exacerbation. Circ Cardiovasc Qual Outcomes. 2015;8(1):7786
  34. Mourad M, Cucina R, Ramanathan R, Vidyarthi AR. Addressing the business of discharge: building a case for an electronic discharge summary. J Hosp Med. 2011;6(1):3742.
  35. Regalbuto R, Maurer MS, Chapel D, Mendez J, Shaffer JA. Joint commission requirements for discharge instructions in patients with heart failure: is understanding important for preventing readmissions? J Card Fail. 2014;20(9):641649.
  36. Bell CM, Schnipper JL, Auerbach AD, et al. Association of communication between hospital‐based physicians and primary care providers with patient outcomes. J Gen Intern Med. 2009;24(3):381386.
  37. Merkow RP, Ju MH, Chung JW, et al. Underlying reasons associated with hospital readmission following surgery in the united states. JAMA. 2015;313(5):483495.
  38. Rao P, Andrei A, Fried A, Gonzalez D, Shine D. Assessing quality and efficiency of discharge summaries. Am J Med Qual. 2005;20(6):337343.
  39. Horwitz LI, Jenq GY, Brewster UC, et al. Comprehensive quality of discharge summaries at an academic medical center. J Hosp Med. 2013;8(8):436443.
Issue
Journal of Hospital Medicine - 11(6)
Issue
Journal of Hospital Medicine - 11(6)
Page Number
393-400
Page Number
393-400
Article Type
Display Headline
Association between days to complete inpatient discharge summaries with all‐payer hospital readmissions in Maryland
Display Headline
Association between days to complete inpatient discharge summaries with all‐payer hospital readmissions in Maryland
Sections
Article Source

© 2016 Society of Hospital Medicine

Disallow All Ads
Correspondence Location
Address for correspondence and reprint requests: Erik H. Hoyer, MD, 600 N Wolfe Street, Phipps 174, Baltimore, MD 21287; Telephone: 410‐502‐2438; Fax: 410‐502‐2419; E‐mail: [email protected]
Content Gating
No Gating (article Unlocked/Free)
Alternative CME
Article PDF Media
Media Files

Prednisolone, indomethacin similarly effective for acute gout

Article Type
Changed
Fri, 01/18/2019 - 15:43
Display Headline
Prednisolone, indomethacin similarly effective for acute gout

Oral prednisolone is as effective as indomethacin for relieving pain in acute gout and should be considered a first-line treatment option, according to a report published online Feb. 22 in Annals of Internal Medicine.

Colchicine and nonsteroidal anti-inflammatory drugs have been considered the first-line treatment for acute gout for many years. “However, their use is limited in elderly adults and in patients with comorbid conditions (such as renal insufficiency or gastrointestinal disease) because of their potential adverse effects and drug interactions,” said Dr. Timothy Hudson Rainer of the emergency medicine academic unit, Cardiff (Wales) University, and his associates.

©ThamKC/Thinkstock

They performed a double-blind, randomized trial comparing oral indomethacin against oral prednisolone in 416 patients who presented during a 2-year period to the emergency departments of four Hong Kong hospitals, where acute gout typically is treated in the ED. Those who were randomized to indomethacin initially received 50 mg (two 25-mg tablets) of the drug three times a day and six tablets of oral placebo prednisolone once a day for 2 days, followed by 25 mg of indomethacin three times a day and six tablets of placebo prednisolone once a day for 3 days. Prednisolone-treated patients initially received 30 mg (three 10-mg tablets) of the drug once a day and two tablets of placebo indomethacin three times a day for 2 days, followed by 30 mg (three 10-mg tablets) of prednisolone once a day and one tablet of placebo indomethacin three times a day for 3 days. All the patients received 1 g oral paracetamol to be taken every 6 hours as needed. The mean patient age was 65 years, and most (74%) of the study participants had a history of recurrent gout. The patients were followed for 2 weeks.

Scores on several measures of joint pain, redness, and tenderness were equivalent between the two treatment groups throughout the study period. During a 2-hour period at the emergency department, 100-mm visual analog scale (VAS) pain scores at rest declined by 6.54 mm/hour with indomethacin and by 5.05 mm/hour with prednisolone, and with activity, the declines were 11.69 mm/hour and 11.38 mm/hour, respectively. VAS scores declined during days 1-14 of treatment by similar mean amounts both at rest (1.80 mm/day for indomethacin and 1.68 mm/day for prednisolone) and with activity (2.96 mm/day vs. 3.19 mm/day, respectively). All VAS pain score improvements except for the one at rest in the ED exceeded 13 mm, meeting the definition for clinically meaningful improvement. The number of patients who showed clinically meaningful declines in pain scores also was equivalent between the two groups in both the intention-to-treat and the per-protocol analyses, the investigators said (Ann Intern Med. 2016 Feb 23. doi: 10.7326/M14-2070).

Both groups also showed similar responses in secondary endpoints of improvement in redness and tenderness of the affected joints, need for additional paracetamol, and patient satisfaction with analgesia.

There were no serious adverse events, but seven patients in the indomethacin group and one in the prednisolone group discontinued treatment because of adverse signs or symptoms. This included abdominal pain, dizziness, and lethargy among patients taking indomethacin and mild hyperkalemia in the patient taking prednisolone. The rate of minor adverse events was significantly higher with indomethacin (19%) than with prednisolone (6%).

“Our study provides robust evidence that oral corticosteroids are as effective at treating pain and as acceptable to patients as NSAIDs,” Dr. Rainer and his associates noted.

This trial was supported by the Hong Kong government’s Health and Health Services Research Grant Committee. Dr. Rainer and his associates reported having no relevant financial disclosures.

References

Author and Disclosure Information

Publications
Topics
Author and Disclosure Information

Author and Disclosure Information

Oral prednisolone is as effective as indomethacin for relieving pain in acute gout and should be considered a first-line treatment option, according to a report published online Feb. 22 in Annals of Internal Medicine.

Colchicine and nonsteroidal anti-inflammatory drugs have been considered the first-line treatment for acute gout for many years. “However, their use is limited in elderly adults and in patients with comorbid conditions (such as renal insufficiency or gastrointestinal disease) because of their potential adverse effects and drug interactions,” said Dr. Timothy Hudson Rainer of the emergency medicine academic unit, Cardiff (Wales) University, and his associates.

©ThamKC/Thinkstock

They performed a double-blind, randomized trial comparing oral indomethacin against oral prednisolone in 416 patients who presented during a 2-year period to the emergency departments of four Hong Kong hospitals, where acute gout typically is treated in the ED. Those who were randomized to indomethacin initially received 50 mg (two 25-mg tablets) of the drug three times a day and six tablets of oral placebo prednisolone once a day for 2 days, followed by 25 mg of indomethacin three times a day and six tablets of placebo prednisolone once a day for 3 days. Prednisolone-treated patients initially received 30 mg (three 10-mg tablets) of the drug once a day and two tablets of placebo indomethacin three times a day for 2 days, followed by 30 mg (three 10-mg tablets) of prednisolone once a day and one tablet of placebo indomethacin three times a day for 3 days. All the patients received 1 g oral paracetamol to be taken every 6 hours as needed. The mean patient age was 65 years, and most (74%) of the study participants had a history of recurrent gout. The patients were followed for 2 weeks.

Scores on several measures of joint pain, redness, and tenderness were equivalent between the two treatment groups throughout the study period. During a 2-hour period at the emergency department, 100-mm visual analog scale (VAS) pain scores at rest declined by 6.54 mm/hour with indomethacin and by 5.05 mm/hour with prednisolone, and with activity, the declines were 11.69 mm/hour and 11.38 mm/hour, respectively. VAS scores declined during days 1-14 of treatment by similar mean amounts both at rest (1.80 mm/day for indomethacin and 1.68 mm/day for prednisolone) and with activity (2.96 mm/day vs. 3.19 mm/day, respectively). All VAS pain score improvements except for the one at rest in the ED exceeded 13 mm, meeting the definition for clinically meaningful improvement. The number of patients who showed clinically meaningful declines in pain scores also was equivalent between the two groups in both the intention-to-treat and the per-protocol analyses, the investigators said (Ann Intern Med. 2016 Feb 23. doi: 10.7326/M14-2070).

Both groups also showed similar responses in secondary endpoints of improvement in redness and tenderness of the affected joints, need for additional paracetamol, and patient satisfaction with analgesia.

There were no serious adverse events, but seven patients in the indomethacin group and one in the prednisolone group discontinued treatment because of adverse signs or symptoms. This included abdominal pain, dizziness, and lethargy among patients taking indomethacin and mild hyperkalemia in the patient taking prednisolone. The rate of minor adverse events was significantly higher with indomethacin (19%) than with prednisolone (6%).

“Our study provides robust evidence that oral corticosteroids are as effective at treating pain and as acceptable to patients as NSAIDs,” Dr. Rainer and his associates noted.

This trial was supported by the Hong Kong government’s Health and Health Services Research Grant Committee. Dr. Rainer and his associates reported having no relevant financial disclosures.

Oral prednisolone is as effective as indomethacin for relieving pain in acute gout and should be considered a first-line treatment option, according to a report published online Feb. 22 in Annals of Internal Medicine.

Colchicine and nonsteroidal anti-inflammatory drugs have been considered the first-line treatment for acute gout for many years. “However, their use is limited in elderly adults and in patients with comorbid conditions (such as renal insufficiency or gastrointestinal disease) because of their potential adverse effects and drug interactions,” said Dr. Timothy Hudson Rainer of the emergency medicine academic unit, Cardiff (Wales) University, and his associates.

©ThamKC/Thinkstock

They performed a double-blind, randomized trial comparing oral indomethacin against oral prednisolone in 416 patients who presented during a 2-year period to the emergency departments of four Hong Kong hospitals, where acute gout typically is treated in the ED. Those who were randomized to indomethacin initially received 50 mg (two 25-mg tablets) of the drug three times a day and six tablets of oral placebo prednisolone once a day for 2 days, followed by 25 mg of indomethacin three times a day and six tablets of placebo prednisolone once a day for 3 days. Prednisolone-treated patients initially received 30 mg (three 10-mg tablets) of the drug once a day and two tablets of placebo indomethacin three times a day for 2 days, followed by 30 mg (three 10-mg tablets) of prednisolone once a day and one tablet of placebo indomethacin three times a day for 3 days. All the patients received 1 g oral paracetamol to be taken every 6 hours as needed. The mean patient age was 65 years, and most (74%) of the study participants had a history of recurrent gout. The patients were followed for 2 weeks.

Scores on several measures of joint pain, redness, and tenderness were equivalent between the two treatment groups throughout the study period. During a 2-hour period at the emergency department, 100-mm visual analog scale (VAS) pain scores at rest declined by 6.54 mm/hour with indomethacin and by 5.05 mm/hour with prednisolone, and with activity, the declines were 11.69 mm/hour and 11.38 mm/hour, respectively. VAS scores declined during days 1-14 of treatment by similar mean amounts both at rest (1.80 mm/day for indomethacin and 1.68 mm/day for prednisolone) and with activity (2.96 mm/day vs. 3.19 mm/day, respectively). All VAS pain score improvements except for the one at rest in the ED exceeded 13 mm, meeting the definition for clinically meaningful improvement. The number of patients who showed clinically meaningful declines in pain scores also was equivalent between the two groups in both the intention-to-treat and the per-protocol analyses, the investigators said (Ann Intern Med. 2016 Feb 23. doi: 10.7326/M14-2070).

Both groups also showed similar responses in secondary endpoints of improvement in redness and tenderness of the affected joints, need for additional paracetamol, and patient satisfaction with analgesia.

There were no serious adverse events, but seven patients in the indomethacin group and one in the prednisolone group discontinued treatment because of adverse signs or symptoms. This included abdominal pain, dizziness, and lethargy among patients taking indomethacin and mild hyperkalemia in the patient taking prednisolone. The rate of minor adverse events was significantly higher with indomethacin (19%) than with prednisolone (6%).

“Our study provides robust evidence that oral corticosteroids are as effective at treating pain and as acceptable to patients as NSAIDs,” Dr. Rainer and his associates noted.

This trial was supported by the Hong Kong government’s Health and Health Services Research Grant Committee. Dr. Rainer and his associates reported having no relevant financial disclosures.

References

References

Publications
Publications
Topics
Article Type
Display Headline
Prednisolone, indomethacin similarly effective for acute gout
Display Headline
Prednisolone, indomethacin similarly effective for acute gout
Article Source

FROM ANNALS OF INTERNAL MEDICINE

PURLs Copyright

Inside the Article

Vitals

Key clinical point: Oral prednisolone is as effective as indomethacin for relieving pain in acute gout and should be considered a first-line treatment option.

Major finding: VAS scores declined during days 1-14 of treatment by similar mean amounts both at rest (1.80 mm/day for indomethacin and 1.68 mm/day for prednisolone) and with activity (2.96 mm/day vs. 3.19 mm/day, respectively).

Data source: A multicenter, double-blind, randomized clinical trial involving 416 patients presenting to an ED for acute gout.

Disclosures: This trial was supported by the Hong Kong government’s Health and Health Services Research Grant Committee. Dr. Rainer and his associates reported having no relevant financial disclosures.

Robotic colectomy takes longer, comparable results

Article Type
Changed
Wed, 01/02/2019 - 09:29
Display Headline
Robotic colectomy takes longer, comparable results

JACKSONVILLE, FLA. – Robotic-assisted colectomy took longer than the laparoscopic operation but didn’t result in better surgical outcomes in a large NSQIP data–based study.

As health care moves away from fee-for-service to a value-based model, the longer operative times and comparative outcomes to laparoscopic colectomy suggest that the use of robotic technologies in straightforward colon resections may not be justified at this time, investigators at Duke University concluded.

Dr. Brian Ezekian

“This is the largest analysis to date of robotic-assisted vs. laparoscopic colectomy,” Dr. Brian Ezekian, general surgery resident at Duke, reported at the Association for Academic Surgery/Society of University Surgeons Academic Surgical Congress. “While the robotic approach is still in its infancy, the technology is associated with increased operative times without improved clinical outcomes, so our study suggests that the routine use of robotic surgery for colectomy may not be financially justifiable at this time.”

The study sampled the American College of Surgeons’ National Surgical Quality Improvement Program (NSQIP) database for patients who had either a robotic or laparoscopic colectomy from 2012 to 2013. Among the 15,976 patients included, 498 of them (3.1%) had robotic colectomy, Dr. Ezekian said.

“The major finding of our study was that robotic-assisted colectomy was associated with roughly 30-minute longer operative times, whereas the short-term clinical outcomes were comparable between the two groups,” Dr. Ezekian said. “This held true for a subset analysis of patients undergoing segmental colectomy only.”

The analysis found no significant difference between the two approaches in rates of wound complications, urinary tract infections, cardiopulmonary or thromboembolic complications, kidney failure or insufficiency, anastomotic leaks, transfusions, unplanned readmissions, or 30-day death.

The key difference was in the operative times associated with each approach. The median time for robotic-assisted colectomy was 196 minutes vs. 166 minutes for the laparoscopic approach. The study found a similar gap for segmental resections only: 190 minutes for the robotic-assisted approach vs. 153 minutes for the laparoscopic approach.

Dr. Ezekian acknowledged that this observation might merely reflect an early experience with this novel technology. “A future direction for this research is to see if operative times for robotic-assisted surgery decrease over time once there are more years in the NSQIP database or in single-institution studies,” he said.

The authors had no financial relationships to disclose.

References

Meeting/Event
Author and Disclosure Information

Publications
Topics
Sections
Author and Disclosure Information

Author and Disclosure Information

Meeting/Event
Meeting/Event

JACKSONVILLE, FLA. – Robotic-assisted colectomy took longer than the laparoscopic operation but didn’t result in better surgical outcomes in a large NSQIP data–based study.

As health care moves away from fee-for-service to a value-based model, the longer operative times and comparative outcomes to laparoscopic colectomy suggest that the use of robotic technologies in straightforward colon resections may not be justified at this time, investigators at Duke University concluded.

Dr. Brian Ezekian

“This is the largest analysis to date of robotic-assisted vs. laparoscopic colectomy,” Dr. Brian Ezekian, general surgery resident at Duke, reported at the Association for Academic Surgery/Society of University Surgeons Academic Surgical Congress. “While the robotic approach is still in its infancy, the technology is associated with increased operative times without improved clinical outcomes, so our study suggests that the routine use of robotic surgery for colectomy may not be financially justifiable at this time.”

The study sampled the American College of Surgeons’ National Surgical Quality Improvement Program (NSQIP) database for patients who had either a robotic or laparoscopic colectomy from 2012 to 2013. Among the 15,976 patients included, 498 of them (3.1%) had robotic colectomy, Dr. Ezekian said.

“The major finding of our study was that robotic-assisted colectomy was associated with roughly 30-minute longer operative times, whereas the short-term clinical outcomes were comparable between the two groups,” Dr. Ezekian said. “This held true for a subset analysis of patients undergoing segmental colectomy only.”

The analysis found no significant difference between the two approaches in rates of wound complications, urinary tract infections, cardiopulmonary or thromboembolic complications, kidney failure or insufficiency, anastomotic leaks, transfusions, unplanned readmissions, or 30-day death.

The key difference was in the operative times associated with each approach. The median time for robotic-assisted colectomy was 196 minutes vs. 166 minutes for the laparoscopic approach. The study found a similar gap for segmental resections only: 190 minutes for the robotic-assisted approach vs. 153 minutes for the laparoscopic approach.

Dr. Ezekian acknowledged that this observation might merely reflect an early experience with this novel technology. “A future direction for this research is to see if operative times for robotic-assisted surgery decrease over time once there are more years in the NSQIP database or in single-institution studies,” he said.

The authors had no financial relationships to disclose.

JACKSONVILLE, FLA. – Robotic-assisted colectomy took longer than the laparoscopic operation but didn’t result in better surgical outcomes in a large NSQIP data–based study.

As health care moves away from fee-for-service to a value-based model, the longer operative times and comparative outcomes to laparoscopic colectomy suggest that the use of robotic technologies in straightforward colon resections may not be justified at this time, investigators at Duke University concluded.

Dr. Brian Ezekian

“This is the largest analysis to date of robotic-assisted vs. laparoscopic colectomy,” Dr. Brian Ezekian, general surgery resident at Duke, reported at the Association for Academic Surgery/Society of University Surgeons Academic Surgical Congress. “While the robotic approach is still in its infancy, the technology is associated with increased operative times without improved clinical outcomes, so our study suggests that the routine use of robotic surgery for colectomy may not be financially justifiable at this time.”

The study sampled the American College of Surgeons’ National Surgical Quality Improvement Program (NSQIP) database for patients who had either a robotic or laparoscopic colectomy from 2012 to 2013. Among the 15,976 patients included, 498 of them (3.1%) had robotic colectomy, Dr. Ezekian said.

“The major finding of our study was that robotic-assisted colectomy was associated with roughly 30-minute longer operative times, whereas the short-term clinical outcomes were comparable between the two groups,” Dr. Ezekian said. “This held true for a subset analysis of patients undergoing segmental colectomy only.”

The analysis found no significant difference between the two approaches in rates of wound complications, urinary tract infections, cardiopulmonary or thromboembolic complications, kidney failure or insufficiency, anastomotic leaks, transfusions, unplanned readmissions, or 30-day death.

The key difference was in the operative times associated with each approach. The median time for robotic-assisted colectomy was 196 minutes vs. 166 minutes for the laparoscopic approach. The study found a similar gap for segmental resections only: 190 minutes for the robotic-assisted approach vs. 153 minutes for the laparoscopic approach.

Dr. Ezekian acknowledged that this observation might merely reflect an early experience with this novel technology. “A future direction for this research is to see if operative times for robotic-assisted surgery decrease over time once there are more years in the NSQIP database or in single-institution studies,” he said.

The authors had no financial relationships to disclose.

References

References

Publications
Publications
Topics
Article Type
Display Headline
Robotic colectomy takes longer, comparable results
Display Headline
Robotic colectomy takes longer, comparable results
Sections
Article Source

AT THE ACADEMIC SURGICAL CONGRESS

PURLs Copyright

Inside the Article

Vitals

Key clinical point: Robotic-assisted colectomy for straightforward resections involves longer operative times than laparoscopic surgery.

Major finding: Robotic-assisted colectomy was associated with roughly 30-minute longer operative times than laparoscopic surgery with comparable short-term clinical outcomes.

Data source: Analysis of 15,976 cases of colectomy in the American College of Surgeons National Surgical Quality Improvement Program performed from 2012 to 2014.

Disclosures: The study authors reported having no financial disclosures.

Optimizing Outcomes of Total Joint Arthroplasty Under the Comprehensive Care for Joint Replacement

Article Type
Changed
Thu, 09/19/2019 - 13:27
Display Headline
Optimizing Outcomes of Total Joint Arthroplasty Under the Comprehensive Care for Joint Replacement

On July 9, 2015, the Centers for Medicare and Medicaid Services announced the Comprehensive Care for Joint Replacement model, which aims to improve coordination of the whole episode of care for total hip and knee replacement.1 At stake is the fact that hip and knee replacements are the most common inpatient procedures among Medicare beneficiaries, costing over $7 billion in 20141 and projected to grow to $50 billion by 2030.2 Under Medicare’s new initiative, hospitals and physicians are held accountable for the quality and cost of care delivered from the time of surgery through 90 days after discharge. For the first time in the history of our profession, large-scale reimbursement is based on outcomes and value rather than fee-for-service. As a result, a hospital can either earn a reward or be held liable for added expenses related to events such as prolonged hospitalization, readmissions, and complications.

How can we optimize outcomes for total joint arthroplasty (TJA) patients in this era of Medicare (r)evolution? A good outcome starts with good patient selection. Numerous studies have been published on patient-related risk factors for postoperative TJA complications including obesity, congestive heart failure, lung disease, and depression.3,4 The risks and benefits of TJA should be carefully weighed in high-risk patients and surgery delayed until appropriate medical optimization has been achieved. Following the famous saying, “Good surgeons know how to operate, better surgeons know when to operate, and the best surgeons know when not to operate,” one cannot overemphasize the need for an objective assessment of the likelihood of patient outcome weighed against patient risk factors.

Moderating patient expectation is another crucial component given the changing demographics of our country. Patients seeking TJA today are younger, more obese, and better educated; live longer; and have higher expectations.5 Unrealistic expectations can have a profound impact on surgical outcomes, leading to frustration, dissatisfaction, and unnecessary resource utilization. For example, despite alleviating pain and restoring function in a severely degenerative joint, TJA does not necessarily translate to weight loss. There is currently conflicting evidence on this topic,6-8 and the expectation of weight loss after TJA cannot be supported. There is also a paucity of data regarding return to athletic activity after TJA and the effect of athletic activity on TJA survivorship.9 Communication and transparency are needed to moderate unrealistic expectations before surgery, outlining clear and achievable goals.

Clinical pathways for TJA have seen tremendous improvements in the past decade with the advent of multimodal analgesia, rapid recovery programs, use of spinal and regional anesthesia, and evidence-based guidelines for prevention of venous thromboembolic disease. Adequate pain control is critical to recovery. In a prospective, randomized controlled trial, Lamplot and colleagues10 showed that the use of multimodal analgesia correlated with improved pain scores, decreased narcotic usage, faster functional recovery, and higher patient satisfaction after total knee arthroplasty (TKA). In another study, Quack and colleagues11 performed a systematic review of the literature on fast-track rehabilitation and found that it reduced both inpatient length of stay and costs after TKA. With respect to anesthetic choice, Pugely and colleagues12 reviewed a national database of 14,052 cases of primary TKA and found that patients with multiple comorbidities were at higher risk of complications after general anesthesia when compared with spinal anesthesia. We should continue to invest in safer and more effective modalities for pain control and functional recovery.

Last but not least, in today’s era of Medicare’s Comprehensive Care for Joint Replacement, the role of low-volume orthopedic surgeons performing TJA deserves special mention. Over the next few years, we could likely see a decline in the role of low-volume surgeons in favor of high-volume surgeons. While most orthopedic surgeons are comfortable doing primary TJA, failed cases and complications are frequently referred to larger centers, which may create frustration among patients owing to fragmentation of care. The economic pressures related to bundled payments could further influence this transition. Given the lack of a widespread, long-standing national joint registry, the incidence of failed TJA performed by low-volume orthopedic surgeons compared with high-volume orthopedic surgeons is unknown. However, multiple studies have shown surgeon volume to be associated with lower rates of complication, mortality, readmission, reoperation, and discharge to postacute facilities.13-16 As hospitals assume further financial risk, considerable data on physician performance will undoubtedly be gathered and leveraged. Time and data will determine the value of this transition of care.

Today, more than ever, we are challenged to provide efficient, high-quality, patient-centered care. As our nation grapples with reforming a broken health care system, initiatives like the Comprehensive Care for Joint Replacement will continue to emerge in the future. Orthopedic surgeons are the gatekeepers of the system and therefore hold significant responsibility to patients and society. Ensuring good outcomes should be a top priority not just from a financial standpoint, but as a moral obligation. We shall continue to be leaders in the face of challenges, using innovation and integrity to produce the best results and advance our profession.

References

1.    Comprehensive Care for Joint Replacement model. Centers for Medicare and Medicaid Services website. https://innovation.cms.gov/initiatives/cjr. Updated December 21, 2015. Accessed December 30, 2015.

2.    Wilson NA, Schneller ES, Montgomery K, Bozic KJ. Hip and knee implants: current trends and policy considerations. Health Aff. 2008;27(6):1587-1598.

3.    Bozic KJ, Lau E, Ong K, et al. Risk factors for early revision after primary total hip arthroplasty in Medicare patients. Clin Orthop Relat Res. 2014;472(2):449-454.

4.    Bozic KJ, Lau E, Ong K, et al. Risk factors for early revision after primary TKA in Medicare patients. Clin Orthop Relat Res. 2014;472(1):232-237.

5.    Mason JB. The new demands by patients in the modern era of total joint arthroplasty: a point of view. Clin Orthop Relat Res. 2008;466(1):146-152.

6.    Riddle DL, Singh JA, Harmsen WS, Schleck CD, Lewallen DG. Clinically important body weight gain following knee arthroplasty: a five-year comparative cohort study. Arthritis Care Res. 2013;65(5):669-677.

7.    Zeni JA Jr, Snyder-Mackler L. Most patients gain weight in the 2 years after total knee arthroplasty: comparison to a healthy control group. Osteoarthritis Cartilage. 2010;18(4):510-514.

8.    Ast MP, Abdel MP, Lee YY, Lyman S, Ruel AV, Westrich GH. Weight changes after total hip or knee arthroplasty: prevalence, predictors, and effects on outcomes. J Bone Joint Surg Am. 2015;97(11):911-919.

9.    Healy WL, Sharma S, Schwartz B, Iorio R. Athletic activity after total joint arthroplasty. J Bone Joint Surg Am. 2008;90(10):2245-2252.

10.  Lamplot JD, Wagner ER, Manning DW. Multimodal pain management in total knee arthroplasty: a prospective randomized controlled trial. J Arthroplasty. 2014;29(2):329-334.

11.  Quack V, Ippendorf AV, Betsch M, et al. Multidisciplinary rehabilitation and fast-track rehabilitation after knee replacement: faster, better, cheaper? A survey and systematic review of literature [in German]. Rehabilitation (Stuttg). 2015;54(4):245-251.

12.  Pugely AJ, Martin CT, Gao Y, Mendoza-Lattes S, Callaghan JJ. Differences in short-term complications between spinal and general anesthesia for primary total knee arthroplasty. J Bone Joint Surg Am. 2013;95(3):193-199.

13.  Katz JN, Losina E, Barrett J, et al. Association between hospital and surgeon procedure volume and outcomes of total hip replacement in the United States medicare population. J Bone Joint Surg Am. 2001;83(11):1622-1629.

14.  Manley M, Ong K, Lau E, Kurtz SM. Effect of volume on total hip arthroplasty revision rates in the United States Medicare population. J Bone Joint Surg Am. 2008;90(11):2446-2451.

15.  Bozic KJ, Maselli J, Pekow PS, Lindenauer PK, Vail TP, Auerbach AD. The influence of procedure volumes and standardization of care on quality and efficiency in total joint replacement surgery. J Bone Joint Surg Am. 2010;92(16):2643-2652.

16.  Lau RL, Perruccio AV, Gandhi R, Mahomed NN. The role of surgeon volume on patient outcome in total knee arthroplasty: a systematic review of the literature. BMC Musculoskelet Disord. 2012;13:250. 

Article PDF
Author and Disclosure Information

Mohamad J. Halawi, MD, Kenneth Greene, MD, and Wael K. Barsoum, MD

Authors’ Disclosure Statement: The authors report no actual or potential conflict of interest in relation to this article.

Issue
The American Journal of Orthopedics - 45(3)
Publications
Topics
Page Number
E112-E113
Legacy Keywords
total joint arthroplasty, TJA, arthroplasty, joint, joint replacement, replacement, from the resident advisory board, online exclusive, RAB, resident, surgery, risk factors, outcomes, hospital, medicare, practice management, halawi, greene, barsoum
Sections
Author and Disclosure Information

Mohamad J. Halawi, MD, Kenneth Greene, MD, and Wael K. Barsoum, MD

Authors’ Disclosure Statement: The authors report no actual or potential conflict of interest in relation to this article.

Author and Disclosure Information

Mohamad J. Halawi, MD, Kenneth Greene, MD, and Wael K. Barsoum, MD

Authors’ Disclosure Statement: The authors report no actual or potential conflict of interest in relation to this article.

Article PDF
Article PDF

On July 9, 2015, the Centers for Medicare and Medicaid Services announced the Comprehensive Care for Joint Replacement model, which aims to improve coordination of the whole episode of care for total hip and knee replacement.1 At stake is the fact that hip and knee replacements are the most common inpatient procedures among Medicare beneficiaries, costing over $7 billion in 20141 and projected to grow to $50 billion by 2030.2 Under Medicare’s new initiative, hospitals and physicians are held accountable for the quality and cost of care delivered from the time of surgery through 90 days after discharge. For the first time in the history of our profession, large-scale reimbursement is based on outcomes and value rather than fee-for-service. As a result, a hospital can either earn a reward or be held liable for added expenses related to events such as prolonged hospitalization, readmissions, and complications.

How can we optimize outcomes for total joint arthroplasty (TJA) patients in this era of Medicare (r)evolution? A good outcome starts with good patient selection. Numerous studies have been published on patient-related risk factors for postoperative TJA complications including obesity, congestive heart failure, lung disease, and depression.3,4 The risks and benefits of TJA should be carefully weighed in high-risk patients and surgery delayed until appropriate medical optimization has been achieved. Following the famous saying, “Good surgeons know how to operate, better surgeons know when to operate, and the best surgeons know when not to operate,” one cannot overemphasize the need for an objective assessment of the likelihood of patient outcome weighed against patient risk factors.

Moderating patient expectation is another crucial component given the changing demographics of our country. Patients seeking TJA today are younger, more obese, and better educated; live longer; and have higher expectations.5 Unrealistic expectations can have a profound impact on surgical outcomes, leading to frustration, dissatisfaction, and unnecessary resource utilization. For example, despite alleviating pain and restoring function in a severely degenerative joint, TJA does not necessarily translate to weight loss. There is currently conflicting evidence on this topic,6-8 and the expectation of weight loss after TJA cannot be supported. There is also a paucity of data regarding return to athletic activity after TJA and the effect of athletic activity on TJA survivorship.9 Communication and transparency are needed to moderate unrealistic expectations before surgery, outlining clear and achievable goals.

Clinical pathways for TJA have seen tremendous improvements in the past decade with the advent of multimodal analgesia, rapid recovery programs, use of spinal and regional anesthesia, and evidence-based guidelines for prevention of venous thromboembolic disease. Adequate pain control is critical to recovery. In a prospective, randomized controlled trial, Lamplot and colleagues10 showed that the use of multimodal analgesia correlated with improved pain scores, decreased narcotic usage, faster functional recovery, and higher patient satisfaction after total knee arthroplasty (TKA). In another study, Quack and colleagues11 performed a systematic review of the literature on fast-track rehabilitation and found that it reduced both inpatient length of stay and costs after TKA. With respect to anesthetic choice, Pugely and colleagues12 reviewed a national database of 14,052 cases of primary TKA and found that patients with multiple comorbidities were at higher risk of complications after general anesthesia when compared with spinal anesthesia. We should continue to invest in safer and more effective modalities for pain control and functional recovery.

Last but not least, in today’s era of Medicare’s Comprehensive Care for Joint Replacement, the role of low-volume orthopedic surgeons performing TJA deserves special mention. Over the next few years, we could likely see a decline in the role of low-volume surgeons in favor of high-volume surgeons. While most orthopedic surgeons are comfortable doing primary TJA, failed cases and complications are frequently referred to larger centers, which may create frustration among patients owing to fragmentation of care. The economic pressures related to bundled payments could further influence this transition. Given the lack of a widespread, long-standing national joint registry, the incidence of failed TJA performed by low-volume orthopedic surgeons compared with high-volume orthopedic surgeons is unknown. However, multiple studies have shown surgeon volume to be associated with lower rates of complication, mortality, readmission, reoperation, and discharge to postacute facilities.13-16 As hospitals assume further financial risk, considerable data on physician performance will undoubtedly be gathered and leveraged. Time and data will determine the value of this transition of care.

Today, more than ever, we are challenged to provide efficient, high-quality, patient-centered care. As our nation grapples with reforming a broken health care system, initiatives like the Comprehensive Care for Joint Replacement will continue to emerge in the future. Orthopedic surgeons are the gatekeepers of the system and therefore hold significant responsibility to patients and society. Ensuring good outcomes should be a top priority not just from a financial standpoint, but as a moral obligation. We shall continue to be leaders in the face of challenges, using innovation and integrity to produce the best results and advance our profession.

On July 9, 2015, the Centers for Medicare and Medicaid Services announced the Comprehensive Care for Joint Replacement model, which aims to improve coordination of the whole episode of care for total hip and knee replacement.1 At stake is the fact that hip and knee replacements are the most common inpatient procedures among Medicare beneficiaries, costing over $7 billion in 20141 and projected to grow to $50 billion by 2030.2 Under Medicare’s new initiative, hospitals and physicians are held accountable for the quality and cost of care delivered from the time of surgery through 90 days after discharge. For the first time in the history of our profession, large-scale reimbursement is based on outcomes and value rather than fee-for-service. As a result, a hospital can either earn a reward or be held liable for added expenses related to events such as prolonged hospitalization, readmissions, and complications.

How can we optimize outcomes for total joint arthroplasty (TJA) patients in this era of Medicare (r)evolution? A good outcome starts with good patient selection. Numerous studies have been published on patient-related risk factors for postoperative TJA complications including obesity, congestive heart failure, lung disease, and depression.3,4 The risks and benefits of TJA should be carefully weighed in high-risk patients and surgery delayed until appropriate medical optimization has been achieved. Following the famous saying, “Good surgeons know how to operate, better surgeons know when to operate, and the best surgeons know when not to operate,” one cannot overemphasize the need for an objective assessment of the likelihood of patient outcome weighed against patient risk factors.

Moderating patient expectation is another crucial component given the changing demographics of our country. Patients seeking TJA today are younger, more obese, and better educated; live longer; and have higher expectations.5 Unrealistic expectations can have a profound impact on surgical outcomes, leading to frustration, dissatisfaction, and unnecessary resource utilization. For example, despite alleviating pain and restoring function in a severely degenerative joint, TJA does not necessarily translate to weight loss. There is currently conflicting evidence on this topic,6-8 and the expectation of weight loss after TJA cannot be supported. There is also a paucity of data regarding return to athletic activity after TJA and the effect of athletic activity on TJA survivorship.9 Communication and transparency are needed to moderate unrealistic expectations before surgery, outlining clear and achievable goals.

Clinical pathways for TJA have seen tremendous improvements in the past decade with the advent of multimodal analgesia, rapid recovery programs, use of spinal and regional anesthesia, and evidence-based guidelines for prevention of venous thromboembolic disease. Adequate pain control is critical to recovery. In a prospective, randomized controlled trial, Lamplot and colleagues10 showed that the use of multimodal analgesia correlated with improved pain scores, decreased narcotic usage, faster functional recovery, and higher patient satisfaction after total knee arthroplasty (TKA). In another study, Quack and colleagues11 performed a systematic review of the literature on fast-track rehabilitation and found that it reduced both inpatient length of stay and costs after TKA. With respect to anesthetic choice, Pugely and colleagues12 reviewed a national database of 14,052 cases of primary TKA and found that patients with multiple comorbidities were at higher risk of complications after general anesthesia when compared with spinal anesthesia. We should continue to invest in safer and more effective modalities for pain control and functional recovery.

Last but not least, in today’s era of Medicare’s Comprehensive Care for Joint Replacement, the role of low-volume orthopedic surgeons performing TJA deserves special mention. Over the next few years, we could likely see a decline in the role of low-volume surgeons in favor of high-volume surgeons. While most orthopedic surgeons are comfortable doing primary TJA, failed cases and complications are frequently referred to larger centers, which may create frustration among patients owing to fragmentation of care. The economic pressures related to bundled payments could further influence this transition. Given the lack of a widespread, long-standing national joint registry, the incidence of failed TJA performed by low-volume orthopedic surgeons compared with high-volume orthopedic surgeons is unknown. However, multiple studies have shown surgeon volume to be associated with lower rates of complication, mortality, readmission, reoperation, and discharge to postacute facilities.13-16 As hospitals assume further financial risk, considerable data on physician performance will undoubtedly be gathered and leveraged. Time and data will determine the value of this transition of care.

Today, more than ever, we are challenged to provide efficient, high-quality, patient-centered care. As our nation grapples with reforming a broken health care system, initiatives like the Comprehensive Care for Joint Replacement will continue to emerge in the future. Orthopedic surgeons are the gatekeepers of the system and therefore hold significant responsibility to patients and society. Ensuring good outcomes should be a top priority not just from a financial standpoint, but as a moral obligation. We shall continue to be leaders in the face of challenges, using innovation and integrity to produce the best results and advance our profession.

References

1.    Comprehensive Care for Joint Replacement model. Centers for Medicare and Medicaid Services website. https://innovation.cms.gov/initiatives/cjr. Updated December 21, 2015. Accessed December 30, 2015.

2.    Wilson NA, Schneller ES, Montgomery K, Bozic KJ. Hip and knee implants: current trends and policy considerations. Health Aff. 2008;27(6):1587-1598.

3.    Bozic KJ, Lau E, Ong K, et al. Risk factors for early revision after primary total hip arthroplasty in Medicare patients. Clin Orthop Relat Res. 2014;472(2):449-454.

4.    Bozic KJ, Lau E, Ong K, et al. Risk factors for early revision after primary TKA in Medicare patients. Clin Orthop Relat Res. 2014;472(1):232-237.

5.    Mason JB. The new demands by patients in the modern era of total joint arthroplasty: a point of view. Clin Orthop Relat Res. 2008;466(1):146-152.

6.    Riddle DL, Singh JA, Harmsen WS, Schleck CD, Lewallen DG. Clinically important body weight gain following knee arthroplasty: a five-year comparative cohort study. Arthritis Care Res. 2013;65(5):669-677.

7.    Zeni JA Jr, Snyder-Mackler L. Most patients gain weight in the 2 years after total knee arthroplasty: comparison to a healthy control group. Osteoarthritis Cartilage. 2010;18(4):510-514.

8.    Ast MP, Abdel MP, Lee YY, Lyman S, Ruel AV, Westrich GH. Weight changes after total hip or knee arthroplasty: prevalence, predictors, and effects on outcomes. J Bone Joint Surg Am. 2015;97(11):911-919.

9.    Healy WL, Sharma S, Schwartz B, Iorio R. Athletic activity after total joint arthroplasty. J Bone Joint Surg Am. 2008;90(10):2245-2252.

10.  Lamplot JD, Wagner ER, Manning DW. Multimodal pain management in total knee arthroplasty: a prospective randomized controlled trial. J Arthroplasty. 2014;29(2):329-334.

11.  Quack V, Ippendorf AV, Betsch M, et al. Multidisciplinary rehabilitation and fast-track rehabilitation after knee replacement: faster, better, cheaper? A survey and systematic review of literature [in German]. Rehabilitation (Stuttg). 2015;54(4):245-251.

12.  Pugely AJ, Martin CT, Gao Y, Mendoza-Lattes S, Callaghan JJ. Differences in short-term complications between spinal and general anesthesia for primary total knee arthroplasty. J Bone Joint Surg Am. 2013;95(3):193-199.

13.  Katz JN, Losina E, Barrett J, et al. Association between hospital and surgeon procedure volume and outcomes of total hip replacement in the United States medicare population. J Bone Joint Surg Am. 2001;83(11):1622-1629.

14.  Manley M, Ong K, Lau E, Kurtz SM. Effect of volume on total hip arthroplasty revision rates in the United States Medicare population. J Bone Joint Surg Am. 2008;90(11):2446-2451.

15.  Bozic KJ, Maselli J, Pekow PS, Lindenauer PK, Vail TP, Auerbach AD. The influence of procedure volumes and standardization of care on quality and efficiency in total joint replacement surgery. J Bone Joint Surg Am. 2010;92(16):2643-2652.

16.  Lau RL, Perruccio AV, Gandhi R, Mahomed NN. The role of surgeon volume on patient outcome in total knee arthroplasty: a systematic review of the literature. BMC Musculoskelet Disord. 2012;13:250. 

References

1.    Comprehensive Care for Joint Replacement model. Centers for Medicare and Medicaid Services website. https://innovation.cms.gov/initiatives/cjr. Updated December 21, 2015. Accessed December 30, 2015.

2.    Wilson NA, Schneller ES, Montgomery K, Bozic KJ. Hip and knee implants: current trends and policy considerations. Health Aff. 2008;27(6):1587-1598.

3.    Bozic KJ, Lau E, Ong K, et al. Risk factors for early revision after primary total hip arthroplasty in Medicare patients. Clin Orthop Relat Res. 2014;472(2):449-454.

4.    Bozic KJ, Lau E, Ong K, et al. Risk factors for early revision after primary TKA in Medicare patients. Clin Orthop Relat Res. 2014;472(1):232-237.

5.    Mason JB. The new demands by patients in the modern era of total joint arthroplasty: a point of view. Clin Orthop Relat Res. 2008;466(1):146-152.

6.    Riddle DL, Singh JA, Harmsen WS, Schleck CD, Lewallen DG. Clinically important body weight gain following knee arthroplasty: a five-year comparative cohort study. Arthritis Care Res. 2013;65(5):669-677.

7.    Zeni JA Jr, Snyder-Mackler L. Most patients gain weight in the 2 years after total knee arthroplasty: comparison to a healthy control group. Osteoarthritis Cartilage. 2010;18(4):510-514.

8.    Ast MP, Abdel MP, Lee YY, Lyman S, Ruel AV, Westrich GH. Weight changes after total hip or knee arthroplasty: prevalence, predictors, and effects on outcomes. J Bone Joint Surg Am. 2015;97(11):911-919.

9.    Healy WL, Sharma S, Schwartz B, Iorio R. Athletic activity after total joint arthroplasty. J Bone Joint Surg Am. 2008;90(10):2245-2252.

10.  Lamplot JD, Wagner ER, Manning DW. Multimodal pain management in total knee arthroplasty: a prospective randomized controlled trial. J Arthroplasty. 2014;29(2):329-334.

11.  Quack V, Ippendorf AV, Betsch M, et al. Multidisciplinary rehabilitation and fast-track rehabilitation after knee replacement: faster, better, cheaper? A survey and systematic review of literature [in German]. Rehabilitation (Stuttg). 2015;54(4):245-251.

12.  Pugely AJ, Martin CT, Gao Y, Mendoza-Lattes S, Callaghan JJ. Differences in short-term complications between spinal and general anesthesia for primary total knee arthroplasty. J Bone Joint Surg Am. 2013;95(3):193-199.

13.  Katz JN, Losina E, Barrett J, et al. Association between hospital and surgeon procedure volume and outcomes of total hip replacement in the United States medicare population. J Bone Joint Surg Am. 2001;83(11):1622-1629.

14.  Manley M, Ong K, Lau E, Kurtz SM. Effect of volume on total hip arthroplasty revision rates in the United States Medicare population. J Bone Joint Surg Am. 2008;90(11):2446-2451.

15.  Bozic KJ, Maselli J, Pekow PS, Lindenauer PK, Vail TP, Auerbach AD. The influence of procedure volumes and standardization of care on quality and efficiency in total joint replacement surgery. J Bone Joint Surg Am. 2010;92(16):2643-2652.

16.  Lau RL, Perruccio AV, Gandhi R, Mahomed NN. The role of surgeon volume on patient outcome in total knee arthroplasty: a systematic review of the literature. BMC Musculoskelet Disord. 2012;13:250. 

Issue
The American Journal of Orthopedics - 45(3)
Issue
The American Journal of Orthopedics - 45(3)
Page Number
E112-E113
Page Number
E112-E113
Publications
Publications
Topics
Article Type
Display Headline
Optimizing Outcomes of Total Joint Arthroplasty Under the Comprehensive Care for Joint Replacement
Display Headline
Optimizing Outcomes of Total Joint Arthroplasty Under the Comprehensive Care for Joint Replacement
Legacy Keywords
total joint arthroplasty, TJA, arthroplasty, joint, joint replacement, replacement, from the resident advisory board, online exclusive, RAB, resident, surgery, risk factors, outcomes, hospital, medicare, practice management, halawi, greene, barsoum
Legacy Keywords
total joint arthroplasty, TJA, arthroplasty, joint, joint replacement, replacement, from the resident advisory board, online exclusive, RAB, resident, surgery, risk factors, outcomes, hospital, medicare, practice management, halawi, greene, barsoum
Sections
Article Source

PURLs Copyright

Inside the Article

Article PDF Media

NSQIP calculator shown inadequate to stratify risk in stage I non–small cell lung cancer.

Risk calculators can be useful, but...
Article Type
Changed
Fri, 01/04/2019 - 13:11
Display Headline
NSQIP calculator shown inadequate to stratify risk in stage I non–small cell lung cancer.

A study performed to validate the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator for use in patients receiving surgery or stereotactic body radiation therapy (SBRT) for stage I non–small cell lung cancer showed the calculator to be inadequate for both classification and risk stratification. The study was reported in the March issue of the Journal of Thoracic and Cardiovascular Surgery (2016;151;697-705).

Dr. Pamela Samson of Washington University in St. Louis and her colleagues performed a retrospective analysis of 485 patients with clinical stage I NSCLC who underwent either surgery (277) or SBRT (195) from 2009 to 2012. Surgery was either wedge resection (19.3%) or lobectomy (74.5%), with smaller percentages receiving segmentectomy (4.0%), pneumonectomy (1.5%), and bilobectomy (0.7%). A large majority of surgical patients (84.1%) underwent a video-assisted thoracoscopic surgery (VATS) approach.

 

Dr. Pamela Samson

The researchers calculated NSQIP complication risk estimates for both surgical and SBRT patients using the NSQIP Surgical Risk Calculator. They compared predicted risk with actual adverse events.

Compared with patients undergoing VATS wedge resection, patients receiving SBRT were older, had larger tumors, lower forced expiratory volume (FEV1) and diffusing capacity of the lungs for carbon monoxide (DLCO), higher American Society of Anesthesiologist scores, higher rates of dyspnea and higher NSQIP serious complication risk estimates, all significant at P less than .05. Similar disparities were seen in comparing patients receiving SBRT vs. VATS lobectomy.

The actual serious complication rate for surgical patients was significantly higher than the NSQIP risk calculator prediction (16.6% vs. 8.8%), as was the rate of pneumonia (6.0% vs. 3.2%), both at P less than .05.

Overall, the NSQIP Surgical Risk Calculator provided a fair level of discrimination between VATS lobectomy and SBRT on receiver operating characteristic (ROC) curve analysis, but it was a poor model for differentiating between VATS wedge resection and SBRT. “Unfortunately, it is this latter population of the highest risk surgical patients (for whom a lobectomy is not a surgical option) where risk models and decision aids are needed most,” Dr. Samson and her colleagues stated.

“Counseling the high-risk but operable patient with clinical stage I NSCLC in regard to lobectomy, sublobar resection, or SBRT is challenging for both the clinician and the patient,” according to the researchers. “We believe that a model tailored to patients with clinical stage I needs to serve as both an estimator of operative risks and a patient decision aid for surgery versus SBRT, especially with projected increases in the number of early-stage lung cancers as a result of increased lung cancer screening efforts,” they added.

“Our analysis suggests that the NSQIP Surgical Risk Calculator likely does not profile the risk of a patient with lung cancer closely enough to dichotomize surgical and inoperable SBRT cases (especially when patients are being considered for a wedge resection) or adequately estimate a surgical patient’s risk of serious complications,” Dr. Samson and her colleagues concluded.

The study was supported by grants from National Institutes of Health. The authors had no relevant financial disclosures.

[email protected]

Body

In their reported study, Dr. Samson and her colleagues found that the NSQIP tool underestimated morbidity. They also found that risk predicted by the NSQIP tool was not necessarily aligned with their institution’s actual treatment selection for stage I NSCLC, which they based upon a number of factors. “This study potentially has important clinical implications,” according to Dr. Xiaofei Wang and Dr. Mark F. Berry in their invited commentary (J Thorac Cardiovasc Surg. 2016 Mar;151:706-7). “This present study shows that even a robust, well-managed tool from the NSQIP does not adequately stratify surgical risk... Their analysis implies that the treatment decision made by the institutional clinicians is optimal.”

“The lackluster performance of the NSQIP score is understandable, because it was not designed to optimally differentiate patients who benefited most from surgery or SBRT. Randomized clinical trials or well-controlled prospective observations are needed to develop and validate specific predictive tools for optimal treatment selection. These models must consider not only treatment morbidity, but also the cost of possible recurrence with each therapy,” Dr. Wang and Dr. Berry stated.

“Perhaps the most important conclusion that can be drawn from this present study is that current risk assessment tools can be helpful, but cannot replace evaluation by clinicians for whom all management options are available when therapy is chosen for a specific patient,” they concluded.

Dr. Wang is from the department of biostatistics and bioinformatics at Duke University, Durham, N.C., and Dr. Berry is from the department of cardiothoracic surgery, Stanford University, Stanford, Calif. They had no relevant financial disclosures.

Publications
Topics
Sections
Body

In their reported study, Dr. Samson and her colleagues found that the NSQIP tool underestimated morbidity. They also found that risk predicted by the NSQIP tool was not necessarily aligned with their institution’s actual treatment selection for stage I NSCLC, which they based upon a number of factors. “This study potentially has important clinical implications,” according to Dr. Xiaofei Wang and Dr. Mark F. Berry in their invited commentary (J Thorac Cardiovasc Surg. 2016 Mar;151:706-7). “This present study shows that even a robust, well-managed tool from the NSQIP does not adequately stratify surgical risk... Their analysis implies that the treatment decision made by the institutional clinicians is optimal.”

“The lackluster performance of the NSQIP score is understandable, because it was not designed to optimally differentiate patients who benefited most from surgery or SBRT. Randomized clinical trials or well-controlled prospective observations are needed to develop and validate specific predictive tools for optimal treatment selection. These models must consider not only treatment morbidity, but also the cost of possible recurrence with each therapy,” Dr. Wang and Dr. Berry stated.

“Perhaps the most important conclusion that can be drawn from this present study is that current risk assessment tools can be helpful, but cannot replace evaluation by clinicians for whom all management options are available when therapy is chosen for a specific patient,” they concluded.

Dr. Wang is from the department of biostatistics and bioinformatics at Duke University, Durham, N.C., and Dr. Berry is from the department of cardiothoracic surgery, Stanford University, Stanford, Calif. They had no relevant financial disclosures.

Body

In their reported study, Dr. Samson and her colleagues found that the NSQIP tool underestimated morbidity. They also found that risk predicted by the NSQIP tool was not necessarily aligned with their institution’s actual treatment selection for stage I NSCLC, which they based upon a number of factors. “This study potentially has important clinical implications,” according to Dr. Xiaofei Wang and Dr. Mark F. Berry in their invited commentary (J Thorac Cardiovasc Surg. 2016 Mar;151:706-7). “This present study shows that even a robust, well-managed tool from the NSQIP does not adequately stratify surgical risk... Their analysis implies that the treatment decision made by the institutional clinicians is optimal.”

“The lackluster performance of the NSQIP score is understandable, because it was not designed to optimally differentiate patients who benefited most from surgery or SBRT. Randomized clinical trials or well-controlled prospective observations are needed to develop and validate specific predictive tools for optimal treatment selection. These models must consider not only treatment morbidity, but also the cost of possible recurrence with each therapy,” Dr. Wang and Dr. Berry stated.

“Perhaps the most important conclusion that can be drawn from this present study is that current risk assessment tools can be helpful, but cannot replace evaluation by clinicians for whom all management options are available when therapy is chosen for a specific patient,” they concluded.

Dr. Wang is from the department of biostatistics and bioinformatics at Duke University, Durham, N.C., and Dr. Berry is from the department of cardiothoracic surgery, Stanford University, Stanford, Calif. They had no relevant financial disclosures.

Title
Risk calculators can be useful, but...
Risk calculators can be useful, but...

A study performed to validate the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator for use in patients receiving surgery or stereotactic body radiation therapy (SBRT) for stage I non–small cell lung cancer showed the calculator to be inadequate for both classification and risk stratification. The study was reported in the March issue of the Journal of Thoracic and Cardiovascular Surgery (2016;151;697-705).

Dr. Pamela Samson of Washington University in St. Louis and her colleagues performed a retrospective analysis of 485 patients with clinical stage I NSCLC who underwent either surgery (277) or SBRT (195) from 2009 to 2012. Surgery was either wedge resection (19.3%) or lobectomy (74.5%), with smaller percentages receiving segmentectomy (4.0%), pneumonectomy (1.5%), and bilobectomy (0.7%). A large majority of surgical patients (84.1%) underwent a video-assisted thoracoscopic surgery (VATS) approach.

 

Dr. Pamela Samson

The researchers calculated NSQIP complication risk estimates for both surgical and SBRT patients using the NSQIP Surgical Risk Calculator. They compared predicted risk with actual adverse events.

Compared with patients undergoing VATS wedge resection, patients receiving SBRT were older, had larger tumors, lower forced expiratory volume (FEV1) and diffusing capacity of the lungs for carbon monoxide (DLCO), higher American Society of Anesthesiologist scores, higher rates of dyspnea and higher NSQIP serious complication risk estimates, all significant at P less than .05. Similar disparities were seen in comparing patients receiving SBRT vs. VATS lobectomy.

The actual serious complication rate for surgical patients was significantly higher than the NSQIP risk calculator prediction (16.6% vs. 8.8%), as was the rate of pneumonia (6.0% vs. 3.2%), both at P less than .05.

Overall, the NSQIP Surgical Risk Calculator provided a fair level of discrimination between VATS lobectomy and SBRT on receiver operating characteristic (ROC) curve analysis, but it was a poor model for differentiating between VATS wedge resection and SBRT. “Unfortunately, it is this latter population of the highest risk surgical patients (for whom a lobectomy is not a surgical option) where risk models and decision aids are needed most,” Dr. Samson and her colleagues stated.

“Counseling the high-risk but operable patient with clinical stage I NSCLC in regard to lobectomy, sublobar resection, or SBRT is challenging for both the clinician and the patient,” according to the researchers. “We believe that a model tailored to patients with clinical stage I needs to serve as both an estimator of operative risks and a patient decision aid for surgery versus SBRT, especially with projected increases in the number of early-stage lung cancers as a result of increased lung cancer screening efforts,” they added.

“Our analysis suggests that the NSQIP Surgical Risk Calculator likely does not profile the risk of a patient with lung cancer closely enough to dichotomize surgical and inoperable SBRT cases (especially when patients are being considered for a wedge resection) or adequately estimate a surgical patient’s risk of serious complications,” Dr. Samson and her colleagues concluded.

The study was supported by grants from National Institutes of Health. The authors had no relevant financial disclosures.

[email protected]

A study performed to validate the National Surgical Quality Improvement Program (NSQIP) Surgical Risk Calculator for use in patients receiving surgery or stereotactic body radiation therapy (SBRT) for stage I non–small cell lung cancer showed the calculator to be inadequate for both classification and risk stratification. The study was reported in the March issue of the Journal of Thoracic and Cardiovascular Surgery (2016;151;697-705).

Dr. Pamela Samson of Washington University in St. Louis and her colleagues performed a retrospective analysis of 485 patients with clinical stage I NSCLC who underwent either surgery (277) or SBRT (195) from 2009 to 2012. Surgery was either wedge resection (19.3%) or lobectomy (74.5%), with smaller percentages receiving segmentectomy (4.0%), pneumonectomy (1.5%), and bilobectomy (0.7%). A large majority of surgical patients (84.1%) underwent a video-assisted thoracoscopic surgery (VATS) approach.

 

Dr. Pamela Samson

The researchers calculated NSQIP complication risk estimates for both surgical and SBRT patients using the NSQIP Surgical Risk Calculator. They compared predicted risk with actual adverse events.

Compared with patients undergoing VATS wedge resection, patients receiving SBRT were older, had larger tumors, lower forced expiratory volume (FEV1) and diffusing capacity of the lungs for carbon monoxide (DLCO), higher American Society of Anesthesiologist scores, higher rates of dyspnea and higher NSQIP serious complication risk estimates, all significant at P less than .05. Similar disparities were seen in comparing patients receiving SBRT vs. VATS lobectomy.

The actual serious complication rate for surgical patients was significantly higher than the NSQIP risk calculator prediction (16.6% vs. 8.8%), as was the rate of pneumonia (6.0% vs. 3.2%), both at P less than .05.

Overall, the NSQIP Surgical Risk Calculator provided a fair level of discrimination between VATS lobectomy and SBRT on receiver operating characteristic (ROC) curve analysis, but it was a poor model for differentiating between VATS wedge resection and SBRT. “Unfortunately, it is this latter population of the highest risk surgical patients (for whom a lobectomy is not a surgical option) where risk models and decision aids are needed most,” Dr. Samson and her colleagues stated.

“Counseling the high-risk but operable patient with clinical stage I NSCLC in regard to lobectomy, sublobar resection, or SBRT is challenging for both the clinician and the patient,” according to the researchers. “We believe that a model tailored to patients with clinical stage I needs to serve as both an estimator of operative risks and a patient decision aid for surgery versus SBRT, especially with projected increases in the number of early-stage lung cancers as a result of increased lung cancer screening efforts,” they added.

“Our analysis suggests that the NSQIP Surgical Risk Calculator likely does not profile the risk of a patient with lung cancer closely enough to dichotomize surgical and inoperable SBRT cases (especially when patients are being considered for a wedge resection) or adequately estimate a surgical patient’s risk of serious complications,” Dr. Samson and her colleagues concluded.

The study was supported by grants from National Institutes of Health. The authors had no relevant financial disclosures.

[email protected]

Publications
Publications
Topics
Article Type
Display Headline
NSQIP calculator shown inadequate to stratify risk in stage I non–small cell lung cancer.
Display Headline
NSQIP calculator shown inadequate to stratify risk in stage I non–small cell lung cancer.
Sections
Article Source

FROM JOURNAL OF THORACIC AND CARDIOVASCULAR SURGERY

Disallow All Ads
Alternative CME
Vitals

Key clinical point: The current NSQIP Surgical Risk Calculator does not adequately estimate risk among patients with clinical stage I non–small cell lung cancer.

Major finding: The NSQIP risk calculator significantly underestimated serious complication risk in operative patients (16.6% actual risk vs. 8.8% predicted) and did not adequately stratify risk between surgical and stereotactic body radiation therapy (SBRT) patients.

Data source: Researchers retrospectively assessed 279 NSCLC stage I lung cancer patients who underwent surgery vs. 206 patients who underwent SBRT from 2009 to 2012.

Disclosures: The study was supported by grants from the National Institutes of Health. The authors had no relevant financial disclosures.

Tibialis Posterior Tendon Entrapment Within Posterior Malleolar Fracture Fragment

Article Type
Changed
Thu, 09/19/2019 - 13:27
Display Headline
Tibialis Posterior Tendon Entrapment Within Posterior Malleolar Fracture Fragment

Irreducible ankle fracture-dislocation secondary to tibialis posterior tendon interposition is a rare but documented complication most commonly associated with Lauge-Hansen classification pronation–external rotation ankle fractures.1-4 Entrapment of the tibialis posterior tendon has been documented in the syndesmosis (tibiotalar joint)1,2,4 and within a medial malleolus fracture.5 To our knowledge, however, there are no case reports of entrapment of the tibialis posterior tendon in a posterior malleolus fracture.

Ankle arthroscopy performed at time of fracture fixation is gaining in popularity because of its enhanced ability to document and treat intra-articular pathology associated with the initial injury.6,7 In addition, percutaneous fixation of a posterior malleolar fragment with arthroscopic assessment of the articular surface reduction may be valuable, as evaluation of tibial plafond fracture reduction by plain radiographs and fluoroscopy has proved to have limitations.8,9

In this article, we present the case of a patient who underwent attempted arthroscopy-assisted reduction of the posterior malleolus with entrapment of the tibialis posterior tendon within the posterior malleolar fracture fragment. The tendon was irreducible with arthroscopic techniques, necessitating posteromedial incision and subsequent open reduction of the incarcerated structure. The patient provided written informed consent for print and electronic publication of this case report.

Case Report

A 67-year-old man slipped and fell on ice while jogging and subsequently presented to the emergency department with a closed bimalleolar ankle fracture-dislocation. Plain radiography (Figure 1) and computed tomography (CT) showed an oblique lateral malleolar fracture and a large posterior malleolar fracture. Further examination of the CT scan revealed entrapment of the tibialis posterior tendon within the posterior malleolar fracture (Figure 2).

Two days after injury, the patient was taken to the operating room for ankle arthroscopy with planned extrication of the entrapped tibialis posterior tendon and possible arthroscopy-assisted percutaneous fixation of the posterior malleolar fracture and open fixation of the distal fibula fracture. Diagnostic arthroscopy revealed a deltoid ligament injury (Figure 3) and a loose piece of articular cartilage (~1 cm in diameter), which was excised. No donor site for this cartilage fragment was identified with further arthroscopic evaluation. During arthroscopic examination, the tibialis posterior tendon was visualized within the joint, incarcerated within the posterior malleolar fracture (Figure 4). Attempts to release the tibialis posterior tendon from the fracture site using arthroscopic instruments and closed reduction techniques were unsuccessful, both with and without noninvasive skeletal traction applied to the ankle.

 

After multiple unsuccessful attempts to extract the tibialis posterior tendon arthroscopically, traction was removed, and a separate incision was made over the posteromedial aspect of the ankle. The tibialis posterior tendon was identified within the fracture site and was removed using an angled clamp (Figure 5). The fracture was reduced and held provisionally with a large tenaculum clamp. Two anterior-to-posterior, partially threaded cannulated screws were placed for fixation after adequate fracture reduction was confirmed on fluoroscopy. As a medial incision was made to extract the tibialis posterior tendon, the joint could not retain arthroscopic fluid, and visualization of the posterior fracture fragment after tendon removal was difficult. Therefore, arthroscopy-assisted reduction could not be completed.

Next, the lateral malleolus was open-reduced, and fixation was achieved using a standard interfragmentary lag screw and a lateral neutralization plate technique (Figure 6). After surgery, the patient was immobilized in a posterior splint with side gussets. Two weeks later, the incisions were healing well, and the tibialis posterior tendon was functioning normally. The sutures were removed, the patient was transitioned to a controlled ankle movement (CAM) boot, and ankle and subtalar range-of-motion exercises were initiated. The patient remained non-weight-bearing for 6 weeks. Radiographs 6 weeks after surgery showed healing fractures with stable hardware (Figure 7). The patient demonstrated 5/5 strength of the tibialis posterior tendon without subluxation or dislocation. There was no tenderness to palpation over the fracture sites or tibialis posterior tendon. The patient began progressive weight-bearing in a CAM boot and physical therapy for range of motion and strengthening.

Discussion

Tibialis posterior tendon injuries—including rupture, dislocation, and entrapment—are well-described complications of ankle injuries.1,2,5,10 Most commonly, the tibialis posterior tendon has been reported to cause a mechanical block to reduction in lateral subtalar dislocations.11-13 In addition, there are case reports of isolated traumatic dislocations of the tibialis posterior tendon without rupture, requiring operative stabilization and retinaculum repair with or without deepening of the posterior groove.14,15

Posterior malleolar ankle fractures remain controversial, with respect to both need for fixation and fixation methods. Although multiple investigators have advocated operative treatment for such fractures that exceed 25% to 33% of the anteroposterior dimension of the tibial plafond, there are no conclusive studies or evidence-based guidelines for treating these fractures.16,17 Anatomical reduction and plating are important to restore articular congruity and increase syndesmotic stability; recent studies have demonstrated that fixation of posterior malleolar fractures provides more syndesmotic stability than trans-syndesmotic screws do.18,19 Indirect reduction of the posterior malleolar fragment after fibula fixation is often accepted as adequate. Whether indirect or direct reduction is attempted, close attention should be given to plain radiographs after attempted reduction, and consideration should be given to possible soft-tissue or bony interposition if malreduction is identified.16,17 Plain radiographs are unreliable in assessing posterior malleolar fragment size as well as amount of comminution and impaction.8,9 Therefore, an arthroscopy-assisted approach coupled with percutaneous fixation may provide more reliable fracture reduction over indirect fracture reduction with fibular fixation, with less dissection than a formal posterolateral approach with posterior plating.

 

 

Not all ankle fractures require CT. However, for posterior malleolus fractures thought to require fixation, preoperative CT may help in determining if percutaneous fixation with or without arthroscopic guidance is a feasible treatment option. Ideally, percutaneous reduction can obviate the need for a larger posterolateral incision and buttress plate and, with arthroscopic assistance, may be superior to indirect reduction with fluoroscopy.

In our patient’s case, arthroscopic assistance facilitated diagnosis of an entrapped structure that would have been difficult to identify, particularly without preoperative CT. It may be difficult to identify imperfect reduction of the posterior malleolus on plain radiographs alone, and arthroscopy-assisted fixation enhances the surgeon’s ability to consider reduction, view incarcerated structures within the joint, and treat articular injuries. We do not routinely use ankle arthroscopy as an adjunct to ankle fracture fixation, but judicious use in select cases can facilitate treatment of intra-articular injuries and facilitate visualization and reduction of posterior malleolar fracture fragments before percutaneous anterior-to-posterior cannulated screw fixation. If an open incision is required, as in the present case, visualization becomes difficult secondary to fluid extravasation. However, we think avoiding the morbidity associated with an open incision is worthwhile for fixation of posterior malleolus fractures.

Conclusion

Close inspection of both preoperative and intraoperative radiographs is required to ensure adequate reduction of a posterior malleolar fragment without soft-tissue or bony interposition in the reduction of ankle fractures. Although not previously reported, posterior tendon entrapment within the posterior malleolus fracture may occur and may require arthroscopic or open techniques to ensure adequate extrication of the tendon to allow for posterior malleolar fracture reduction and fixation. This case report highlights one indication for arthroscopy in the treatment of ankle fractures despite the fact that the tibialis posterior tendon was openly removed. Arthroscopic assistance in acute ankle injuries allows the surgeon to evaluate articular cartilage injuries and ensure there are no interposed structures while checking reduction of the posterior malleolar fracture fragment when present.

References

1.    Ermis MN, Yagmurlu MF, Kilinc AS, Karakas ES. Irreducible fracture dislocation of the ankle caused by tibialis posterior tendon interposition. J Foot Ankle Surg. 2010;49(2):166-171.

2.    Curry EE, O’Brien TS, Johnson JE. Fibular nonunion and equinovarus deformity secondary to posterior tibial tendon incarceration in the syndesmosis: a case report after a bimalleolar fracture-dislocation. Foot Ankle Int. 1999;20(8):527-531.

3.    Coonrad RW, Bugg EI Jr. Trapping of the posterior tibial tendon and interposition of soft tissue in severe fractures about the ankle joint. J Bone Joint Surg Am. 1954;36(4):744-750.

4.    Pankovich AM. Fracture-dislocation of the ankle. Trapping of the postero-medial ankle tendons and neurovascular bundle in the tibiofibular interosseous space: a case report. J Trauma. 1976;16(11):927-929.

5.    Khamaisy S, Leibner ED, Elishoov O. Tibialis posterior entrapment: case report. Foot Ankle Int. 2012;33(5):441-443.

6.    Hsu AR, Gross CE, Lee S, Carreira DS. Extended indications for foot and ankle arthroscopy. J Am Acad Orthop Surg. 2014;22(1):10-19.

7.    Stufkens SA, Knupp M, Horisberger M, Lampert C, Hintermann B. Cartilage lesions and the development of osteoarthritis after internal fixation of ankle fractures: a prospective study. J Bone Joint Surg Am. 2010;92(2):279-286.

8.    Büchler L, Tannast M, Bonel HM, Weber M. Reliability of radiologic assessment of the fracture anatomy at the posterior tibial plafond in malleolar fractures. J Orthop Trauma. 2009;23(3):208-212.

9.    Ferries JS, DeCoster TA, Firoozbakhsh KK, Garcia JF, Miller RA. Plain radiographic interpretation in trimalleolar ankle fractures poorly assesses posterior fragment size. J Orthop Trauma. 1994;8(4):328-331.

10.  Jarvis HC, Cannada LK. Acute tibialis posterior tendon rupture associated with a distal tibial fracture. Orthopedics. 2012;35(4):e595-e597.

11.  Woodruff MJ, Brown JN, Mountney J. A mechanism for entrapment of the tibialis posterior tendon in lateral subtalar dislocation. Injury. 1996;27(3):193-194.

12.  Leitner B. Obstacles to reduction in subtalar dislocations. J Bone Joint Surg Am. 1954;36(2):299-306.

13.  Waldrop J, Ebraheim NA, Shapiro P, Jackson WT. Anatomical considerations of posterior tibialis tendon entrapment in irreducible lateral subtalar dislocation. Foot Ankle. 1992;13(8):458-461.

14.  Goucher NR, Coughlin MJ, Kristensen RM. Dislocation of the posterior tibial tendon: a literature review and presentation of two cases. Iowa Orthop J. 2006;26:122-126.

15.  Olivé Vilás R, Redón Montojo N, Pino Sorroche S. Traumatic dislocation of tibialis posterior tendon: a case report in a tae-kwon-do athlete. Clin J Sport Med. 2009;19(1):68-69.

16.  Gardner MJ, Streubel PN, McCormick JJ, Klein SE, Johnson JE, Ricci WM. Surgeon practices regarding operative treatment of posterior malleolus fractures. Foot Ankle Int. 2011;32(4):385-393.

17.  Irwin TA, Lien J, Kadakia AR. Posterior malleolus fracture. J Am Acad Orthop Surg. 2013;21(1):32-40.

18.    Gardner MJ, Brodsky A, Briggs SM, Nielson JH, Lorich DG. Fixation of posterior malleolar fractures provides greater syndesmotic stability. Clin Orthop Relat Res. 2006;(447):165-171.

19.  Miller AN, Carroll EA, Parker RJ, Helfet DL, Lorich DG. Posterior malleolar stabilization of syndesmotic injuries is equivalent to screw fixation. Clin Orthop Relat Res. 2010;468(4):1129-1135.

Article PDF
Author and Disclosure Information

Amanda Fantry, MD, Craig Lareau, MD, Bryan Vopat, MD, and Brad Blankenhorn, MD

Authors’ Disclosure Statement: The authors report no actual or potential conflict of interest in relation to this article.

Issue
The American Journal of Orthopedics - 45(3)
Publications
Topics
Page Number
E103-E107
Legacy Keywords
tibialis, posterior, tendon, malleolar fracture fragment, fracture management, fracture, trauma, malleolar, arthroscopy, open reduction, ankle, fantry, lareau, vopat, blankenhorn, case report and literature review, online exclusive
Sections
Author and Disclosure Information

Amanda Fantry, MD, Craig Lareau, MD, Bryan Vopat, MD, and Brad Blankenhorn, MD

Authors’ Disclosure Statement: The authors report no actual or potential conflict of interest in relation to this article.

Author and Disclosure Information

Amanda Fantry, MD, Craig Lareau, MD, Bryan Vopat, MD, and Brad Blankenhorn, MD

Authors’ Disclosure Statement: The authors report no actual or potential conflict of interest in relation to this article.

Article PDF
Article PDF

Irreducible ankle fracture-dislocation secondary to tibialis posterior tendon interposition is a rare but documented complication most commonly associated with Lauge-Hansen classification pronation–external rotation ankle fractures.1-4 Entrapment of the tibialis posterior tendon has been documented in the syndesmosis (tibiotalar joint)1,2,4 and within a medial malleolus fracture.5 To our knowledge, however, there are no case reports of entrapment of the tibialis posterior tendon in a posterior malleolus fracture.

Ankle arthroscopy performed at time of fracture fixation is gaining in popularity because of its enhanced ability to document and treat intra-articular pathology associated with the initial injury.6,7 In addition, percutaneous fixation of a posterior malleolar fragment with arthroscopic assessment of the articular surface reduction may be valuable, as evaluation of tibial plafond fracture reduction by plain radiographs and fluoroscopy has proved to have limitations.8,9

In this article, we present the case of a patient who underwent attempted arthroscopy-assisted reduction of the posterior malleolus with entrapment of the tibialis posterior tendon within the posterior malleolar fracture fragment. The tendon was irreducible with arthroscopic techniques, necessitating posteromedial incision and subsequent open reduction of the incarcerated structure. The patient provided written informed consent for print and electronic publication of this case report.

Case Report

A 67-year-old man slipped and fell on ice while jogging and subsequently presented to the emergency department with a closed bimalleolar ankle fracture-dislocation. Plain radiography (Figure 1) and computed tomography (CT) showed an oblique lateral malleolar fracture and a large posterior malleolar fracture. Further examination of the CT scan revealed entrapment of the tibialis posterior tendon within the posterior malleolar fracture (Figure 2).

Two days after injury, the patient was taken to the operating room for ankle arthroscopy with planned extrication of the entrapped tibialis posterior tendon and possible arthroscopy-assisted percutaneous fixation of the posterior malleolar fracture and open fixation of the distal fibula fracture. Diagnostic arthroscopy revealed a deltoid ligament injury (Figure 3) and a loose piece of articular cartilage (~1 cm in diameter), which was excised. No donor site for this cartilage fragment was identified with further arthroscopic evaluation. During arthroscopic examination, the tibialis posterior tendon was visualized within the joint, incarcerated within the posterior malleolar fracture (Figure 4). Attempts to release the tibialis posterior tendon from the fracture site using arthroscopic instruments and closed reduction techniques were unsuccessful, both with and without noninvasive skeletal traction applied to the ankle.

 

After multiple unsuccessful attempts to extract the tibialis posterior tendon arthroscopically, traction was removed, and a separate incision was made over the posteromedial aspect of the ankle. The tibialis posterior tendon was identified within the fracture site and was removed using an angled clamp (Figure 5). The fracture was reduced and held provisionally with a large tenaculum clamp. Two anterior-to-posterior, partially threaded cannulated screws were placed for fixation after adequate fracture reduction was confirmed on fluoroscopy. As a medial incision was made to extract the tibialis posterior tendon, the joint could not retain arthroscopic fluid, and visualization of the posterior fracture fragment after tendon removal was difficult. Therefore, arthroscopy-assisted reduction could not be completed.

Next, the lateral malleolus was open-reduced, and fixation was achieved using a standard interfragmentary lag screw and a lateral neutralization plate technique (Figure 6). After surgery, the patient was immobilized in a posterior splint with side gussets. Two weeks later, the incisions were healing well, and the tibialis posterior tendon was functioning normally. The sutures were removed, the patient was transitioned to a controlled ankle movement (CAM) boot, and ankle and subtalar range-of-motion exercises were initiated. The patient remained non-weight-bearing for 6 weeks. Radiographs 6 weeks after surgery showed healing fractures with stable hardware (Figure 7). The patient demonstrated 5/5 strength of the tibialis posterior tendon without subluxation or dislocation. There was no tenderness to palpation over the fracture sites or tibialis posterior tendon. The patient began progressive weight-bearing in a CAM boot and physical therapy for range of motion and strengthening.

Discussion

Tibialis posterior tendon injuries—including rupture, dislocation, and entrapment—are well-described complications of ankle injuries.1,2,5,10 Most commonly, the tibialis posterior tendon has been reported to cause a mechanical block to reduction in lateral subtalar dislocations.11-13 In addition, there are case reports of isolated traumatic dislocations of the tibialis posterior tendon without rupture, requiring operative stabilization and retinaculum repair with or without deepening of the posterior groove.14,15

Posterior malleolar ankle fractures remain controversial, with respect to both need for fixation and fixation methods. Although multiple investigators have advocated operative treatment for such fractures that exceed 25% to 33% of the anteroposterior dimension of the tibial plafond, there are no conclusive studies or evidence-based guidelines for treating these fractures.16,17 Anatomical reduction and plating are important to restore articular congruity and increase syndesmotic stability; recent studies have demonstrated that fixation of posterior malleolar fractures provides more syndesmotic stability than trans-syndesmotic screws do.18,19 Indirect reduction of the posterior malleolar fragment after fibula fixation is often accepted as adequate. Whether indirect or direct reduction is attempted, close attention should be given to plain radiographs after attempted reduction, and consideration should be given to possible soft-tissue or bony interposition if malreduction is identified.16,17 Plain radiographs are unreliable in assessing posterior malleolar fragment size as well as amount of comminution and impaction.8,9 Therefore, an arthroscopy-assisted approach coupled with percutaneous fixation may provide more reliable fracture reduction over indirect fracture reduction with fibular fixation, with less dissection than a formal posterolateral approach with posterior plating.

 

 

Not all ankle fractures require CT. However, for posterior malleolus fractures thought to require fixation, preoperative CT may help in determining if percutaneous fixation with or without arthroscopic guidance is a feasible treatment option. Ideally, percutaneous reduction can obviate the need for a larger posterolateral incision and buttress plate and, with arthroscopic assistance, may be superior to indirect reduction with fluoroscopy.

In our patient’s case, arthroscopic assistance facilitated diagnosis of an entrapped structure that would have been difficult to identify, particularly without preoperative CT. It may be difficult to identify imperfect reduction of the posterior malleolus on plain radiographs alone, and arthroscopy-assisted fixation enhances the surgeon’s ability to consider reduction, view incarcerated structures within the joint, and treat articular injuries. We do not routinely use ankle arthroscopy as an adjunct to ankle fracture fixation, but judicious use in select cases can facilitate treatment of intra-articular injuries and facilitate visualization and reduction of posterior malleolar fracture fragments before percutaneous anterior-to-posterior cannulated screw fixation. If an open incision is required, as in the present case, visualization becomes difficult secondary to fluid extravasation. However, we think avoiding the morbidity associated with an open incision is worthwhile for fixation of posterior malleolus fractures.

Conclusion

Close inspection of both preoperative and intraoperative radiographs is required to ensure adequate reduction of a posterior malleolar fragment without soft-tissue or bony interposition in the reduction of ankle fractures. Although not previously reported, posterior tendon entrapment within the posterior malleolus fracture may occur and may require arthroscopic or open techniques to ensure adequate extrication of the tendon to allow for posterior malleolar fracture reduction and fixation. This case report highlights one indication for arthroscopy in the treatment of ankle fractures despite the fact that the tibialis posterior tendon was openly removed. Arthroscopic assistance in acute ankle injuries allows the surgeon to evaluate articular cartilage injuries and ensure there are no interposed structures while checking reduction of the posterior malleolar fracture fragment when present.

Irreducible ankle fracture-dislocation secondary to tibialis posterior tendon interposition is a rare but documented complication most commonly associated with Lauge-Hansen classification pronation–external rotation ankle fractures.1-4 Entrapment of the tibialis posterior tendon has been documented in the syndesmosis (tibiotalar joint)1,2,4 and within a medial malleolus fracture.5 To our knowledge, however, there are no case reports of entrapment of the tibialis posterior tendon in a posterior malleolus fracture.

Ankle arthroscopy performed at time of fracture fixation is gaining in popularity because of its enhanced ability to document and treat intra-articular pathology associated with the initial injury.6,7 In addition, percutaneous fixation of a posterior malleolar fragment with arthroscopic assessment of the articular surface reduction may be valuable, as evaluation of tibial plafond fracture reduction by plain radiographs and fluoroscopy has proved to have limitations.8,9

In this article, we present the case of a patient who underwent attempted arthroscopy-assisted reduction of the posterior malleolus with entrapment of the tibialis posterior tendon within the posterior malleolar fracture fragment. The tendon was irreducible with arthroscopic techniques, necessitating posteromedial incision and subsequent open reduction of the incarcerated structure. The patient provided written informed consent for print and electronic publication of this case report.

Case Report

A 67-year-old man slipped and fell on ice while jogging and subsequently presented to the emergency department with a closed bimalleolar ankle fracture-dislocation. Plain radiography (Figure 1) and computed tomography (CT) showed an oblique lateral malleolar fracture and a large posterior malleolar fracture. Further examination of the CT scan revealed entrapment of the tibialis posterior tendon within the posterior malleolar fracture (Figure 2).

Two days after injury, the patient was taken to the operating room for ankle arthroscopy with planned extrication of the entrapped tibialis posterior tendon and possible arthroscopy-assisted percutaneous fixation of the posterior malleolar fracture and open fixation of the distal fibula fracture. Diagnostic arthroscopy revealed a deltoid ligament injury (Figure 3) and a loose piece of articular cartilage (~1 cm in diameter), which was excised. No donor site for this cartilage fragment was identified with further arthroscopic evaluation. During arthroscopic examination, the tibialis posterior tendon was visualized within the joint, incarcerated within the posterior malleolar fracture (Figure 4). Attempts to release the tibialis posterior tendon from the fracture site using arthroscopic instruments and closed reduction techniques were unsuccessful, both with and without noninvasive skeletal traction applied to the ankle.

 

After multiple unsuccessful attempts to extract the tibialis posterior tendon arthroscopically, traction was removed, and a separate incision was made over the posteromedial aspect of the ankle. The tibialis posterior tendon was identified within the fracture site and was removed using an angled clamp (Figure 5). The fracture was reduced and held provisionally with a large tenaculum clamp. Two anterior-to-posterior, partially threaded cannulated screws were placed for fixation after adequate fracture reduction was confirmed on fluoroscopy. As a medial incision was made to extract the tibialis posterior tendon, the joint could not retain arthroscopic fluid, and visualization of the posterior fracture fragment after tendon removal was difficult. Therefore, arthroscopy-assisted reduction could not be completed.

Next, the lateral malleolus was open-reduced, and fixation was achieved using a standard interfragmentary lag screw and a lateral neutralization plate technique (Figure 6). After surgery, the patient was immobilized in a posterior splint with side gussets. Two weeks later, the incisions were healing well, and the tibialis posterior tendon was functioning normally. The sutures were removed, the patient was transitioned to a controlled ankle movement (CAM) boot, and ankle and subtalar range-of-motion exercises were initiated. The patient remained non-weight-bearing for 6 weeks. Radiographs 6 weeks after surgery showed healing fractures with stable hardware (Figure 7). The patient demonstrated 5/5 strength of the tibialis posterior tendon without subluxation or dislocation. There was no tenderness to palpation over the fracture sites or tibialis posterior tendon. The patient began progressive weight-bearing in a CAM boot and physical therapy for range of motion and strengthening.

Discussion

Tibialis posterior tendon injuries—including rupture, dislocation, and entrapment—are well-described complications of ankle injuries.1,2,5,10 Most commonly, the tibialis posterior tendon has been reported to cause a mechanical block to reduction in lateral subtalar dislocations.11-13 In addition, there are case reports of isolated traumatic dislocations of the tibialis posterior tendon without rupture, requiring operative stabilization and retinaculum repair with or without deepening of the posterior groove.14,15

Posterior malleolar ankle fractures remain controversial, with respect to both need for fixation and fixation methods. Although multiple investigators have advocated operative treatment for such fractures that exceed 25% to 33% of the anteroposterior dimension of the tibial plafond, there are no conclusive studies or evidence-based guidelines for treating these fractures.16,17 Anatomical reduction and plating are important to restore articular congruity and increase syndesmotic stability; recent studies have demonstrated that fixation of posterior malleolar fractures provides more syndesmotic stability than trans-syndesmotic screws do.18,19 Indirect reduction of the posterior malleolar fragment after fibula fixation is often accepted as adequate. Whether indirect or direct reduction is attempted, close attention should be given to plain radiographs after attempted reduction, and consideration should be given to possible soft-tissue or bony interposition if malreduction is identified.16,17 Plain radiographs are unreliable in assessing posterior malleolar fragment size as well as amount of comminution and impaction.8,9 Therefore, an arthroscopy-assisted approach coupled with percutaneous fixation may provide more reliable fracture reduction over indirect fracture reduction with fibular fixation, with less dissection than a formal posterolateral approach with posterior plating.

 

 

Not all ankle fractures require CT. However, for posterior malleolus fractures thought to require fixation, preoperative CT may help in determining if percutaneous fixation with or without arthroscopic guidance is a feasible treatment option. Ideally, percutaneous reduction can obviate the need for a larger posterolateral incision and buttress plate and, with arthroscopic assistance, may be superior to indirect reduction with fluoroscopy.

In our patient’s case, arthroscopic assistance facilitated diagnosis of an entrapped structure that would have been difficult to identify, particularly without preoperative CT. It may be difficult to identify imperfect reduction of the posterior malleolus on plain radiographs alone, and arthroscopy-assisted fixation enhances the surgeon’s ability to consider reduction, view incarcerated structures within the joint, and treat articular injuries. We do not routinely use ankle arthroscopy as an adjunct to ankle fracture fixation, but judicious use in select cases can facilitate treatment of intra-articular injuries and facilitate visualization and reduction of posterior malleolar fracture fragments before percutaneous anterior-to-posterior cannulated screw fixation. If an open incision is required, as in the present case, visualization becomes difficult secondary to fluid extravasation. However, we think avoiding the morbidity associated with an open incision is worthwhile for fixation of posterior malleolus fractures.

Conclusion

Close inspection of both preoperative and intraoperative radiographs is required to ensure adequate reduction of a posterior malleolar fragment without soft-tissue or bony interposition in the reduction of ankle fractures. Although not previously reported, posterior tendon entrapment within the posterior malleolus fracture may occur and may require arthroscopic or open techniques to ensure adequate extrication of the tendon to allow for posterior malleolar fracture reduction and fixation. This case report highlights one indication for arthroscopy in the treatment of ankle fractures despite the fact that the tibialis posterior tendon was openly removed. Arthroscopic assistance in acute ankle injuries allows the surgeon to evaluate articular cartilage injuries and ensure there are no interposed structures while checking reduction of the posterior malleolar fracture fragment when present.

References

1.    Ermis MN, Yagmurlu MF, Kilinc AS, Karakas ES. Irreducible fracture dislocation of the ankle caused by tibialis posterior tendon interposition. J Foot Ankle Surg. 2010;49(2):166-171.

2.    Curry EE, O’Brien TS, Johnson JE. Fibular nonunion and equinovarus deformity secondary to posterior tibial tendon incarceration in the syndesmosis: a case report after a bimalleolar fracture-dislocation. Foot Ankle Int. 1999;20(8):527-531.

3.    Coonrad RW, Bugg EI Jr. Trapping of the posterior tibial tendon and interposition of soft tissue in severe fractures about the ankle joint. J Bone Joint Surg Am. 1954;36(4):744-750.

4.    Pankovich AM. Fracture-dislocation of the ankle. Trapping of the postero-medial ankle tendons and neurovascular bundle in the tibiofibular interosseous space: a case report. J Trauma. 1976;16(11):927-929.

5.    Khamaisy S, Leibner ED, Elishoov O. Tibialis posterior entrapment: case report. Foot Ankle Int. 2012;33(5):441-443.

6.    Hsu AR, Gross CE, Lee S, Carreira DS. Extended indications for foot and ankle arthroscopy. J Am Acad Orthop Surg. 2014;22(1):10-19.

7.    Stufkens SA, Knupp M, Horisberger M, Lampert C, Hintermann B. Cartilage lesions and the development of osteoarthritis after internal fixation of ankle fractures: a prospective study. J Bone Joint Surg Am. 2010;92(2):279-286.

8.    Büchler L, Tannast M, Bonel HM, Weber M. Reliability of radiologic assessment of the fracture anatomy at the posterior tibial plafond in malleolar fractures. J Orthop Trauma. 2009;23(3):208-212.

9.    Ferries JS, DeCoster TA, Firoozbakhsh KK, Garcia JF, Miller RA. Plain radiographic interpretation in trimalleolar ankle fractures poorly assesses posterior fragment size. J Orthop Trauma. 1994;8(4):328-331.

10.  Jarvis HC, Cannada LK. Acute tibialis posterior tendon rupture associated with a distal tibial fracture. Orthopedics. 2012;35(4):e595-e597.

11.  Woodruff MJ, Brown JN, Mountney J. A mechanism for entrapment of the tibialis posterior tendon in lateral subtalar dislocation. Injury. 1996;27(3):193-194.

12.  Leitner B. Obstacles to reduction in subtalar dislocations. J Bone Joint Surg Am. 1954;36(2):299-306.

13.  Waldrop J, Ebraheim NA, Shapiro P, Jackson WT. Anatomical considerations of posterior tibialis tendon entrapment in irreducible lateral subtalar dislocation. Foot Ankle. 1992;13(8):458-461.

14.  Goucher NR, Coughlin MJ, Kristensen RM. Dislocation of the posterior tibial tendon: a literature review and presentation of two cases. Iowa Orthop J. 2006;26:122-126.

15.  Olivé Vilás R, Redón Montojo N, Pino Sorroche S. Traumatic dislocation of tibialis posterior tendon: a case report in a tae-kwon-do athlete. Clin J Sport Med. 2009;19(1):68-69.

16.  Gardner MJ, Streubel PN, McCormick JJ, Klein SE, Johnson JE, Ricci WM. Surgeon practices regarding operative treatment of posterior malleolus fractures. Foot Ankle Int. 2011;32(4):385-393.

17.  Irwin TA, Lien J, Kadakia AR. Posterior malleolus fracture. J Am Acad Orthop Surg. 2013;21(1):32-40.

18.    Gardner MJ, Brodsky A, Briggs SM, Nielson JH, Lorich DG. Fixation of posterior malleolar fractures provides greater syndesmotic stability. Clin Orthop Relat Res. 2006;(447):165-171.

19.  Miller AN, Carroll EA, Parker RJ, Helfet DL, Lorich DG. Posterior malleolar stabilization of syndesmotic injuries is equivalent to screw fixation. Clin Orthop Relat Res. 2010;468(4):1129-1135.

References

1.    Ermis MN, Yagmurlu MF, Kilinc AS, Karakas ES. Irreducible fracture dislocation of the ankle caused by tibialis posterior tendon interposition. J Foot Ankle Surg. 2010;49(2):166-171.

2.    Curry EE, O’Brien TS, Johnson JE. Fibular nonunion and equinovarus deformity secondary to posterior tibial tendon incarceration in the syndesmosis: a case report after a bimalleolar fracture-dislocation. Foot Ankle Int. 1999;20(8):527-531.

3.    Coonrad RW, Bugg EI Jr. Trapping of the posterior tibial tendon and interposition of soft tissue in severe fractures about the ankle joint. J Bone Joint Surg Am. 1954;36(4):744-750.

4.    Pankovich AM. Fracture-dislocation of the ankle. Trapping of the postero-medial ankle tendons and neurovascular bundle in the tibiofibular interosseous space: a case report. J Trauma. 1976;16(11):927-929.

5.    Khamaisy S, Leibner ED, Elishoov O. Tibialis posterior entrapment: case report. Foot Ankle Int. 2012;33(5):441-443.

6.    Hsu AR, Gross CE, Lee S, Carreira DS. Extended indications for foot and ankle arthroscopy. J Am Acad Orthop Surg. 2014;22(1):10-19.

7.    Stufkens SA, Knupp M, Horisberger M, Lampert C, Hintermann B. Cartilage lesions and the development of osteoarthritis after internal fixation of ankle fractures: a prospective study. J Bone Joint Surg Am. 2010;92(2):279-286.

8.    Büchler L, Tannast M, Bonel HM, Weber M. Reliability of radiologic assessment of the fracture anatomy at the posterior tibial plafond in malleolar fractures. J Orthop Trauma. 2009;23(3):208-212.

9.    Ferries JS, DeCoster TA, Firoozbakhsh KK, Garcia JF, Miller RA. Plain radiographic interpretation in trimalleolar ankle fractures poorly assesses posterior fragment size. J Orthop Trauma. 1994;8(4):328-331.

10.  Jarvis HC, Cannada LK. Acute tibialis posterior tendon rupture associated with a distal tibial fracture. Orthopedics. 2012;35(4):e595-e597.

11.  Woodruff MJ, Brown JN, Mountney J. A mechanism for entrapment of the tibialis posterior tendon in lateral subtalar dislocation. Injury. 1996;27(3):193-194.

12.  Leitner B. Obstacles to reduction in subtalar dislocations. J Bone Joint Surg Am. 1954;36(2):299-306.

13.  Waldrop J, Ebraheim NA, Shapiro P, Jackson WT. Anatomical considerations of posterior tibialis tendon entrapment in irreducible lateral subtalar dislocation. Foot Ankle. 1992;13(8):458-461.

14.  Goucher NR, Coughlin MJ, Kristensen RM. Dislocation of the posterior tibial tendon: a literature review and presentation of two cases. Iowa Orthop J. 2006;26:122-126.

15.  Olivé Vilás R, Redón Montojo N, Pino Sorroche S. Traumatic dislocation of tibialis posterior tendon: a case report in a tae-kwon-do athlete. Clin J Sport Med. 2009;19(1):68-69.

16.  Gardner MJ, Streubel PN, McCormick JJ, Klein SE, Johnson JE, Ricci WM. Surgeon practices regarding operative treatment of posterior malleolus fractures. Foot Ankle Int. 2011;32(4):385-393.

17.  Irwin TA, Lien J, Kadakia AR. Posterior malleolus fracture. J Am Acad Orthop Surg. 2013;21(1):32-40.

18.    Gardner MJ, Brodsky A, Briggs SM, Nielson JH, Lorich DG. Fixation of posterior malleolar fractures provides greater syndesmotic stability. Clin Orthop Relat Res. 2006;(447):165-171.

19.  Miller AN, Carroll EA, Parker RJ, Helfet DL, Lorich DG. Posterior malleolar stabilization of syndesmotic injuries is equivalent to screw fixation. Clin Orthop Relat Res. 2010;468(4):1129-1135.

Issue
The American Journal of Orthopedics - 45(3)
Issue
The American Journal of Orthopedics - 45(3)
Page Number
E103-E107
Page Number
E103-E107
Publications
Publications
Topics
Article Type
Display Headline
Tibialis Posterior Tendon Entrapment Within Posterior Malleolar Fracture Fragment
Display Headline
Tibialis Posterior Tendon Entrapment Within Posterior Malleolar Fracture Fragment
Legacy Keywords
tibialis, posterior, tendon, malleolar fracture fragment, fracture management, fracture, trauma, malleolar, arthroscopy, open reduction, ankle, fantry, lareau, vopat, blankenhorn, case report and literature review, online exclusive
Legacy Keywords
tibialis, posterior, tendon, malleolar fracture fragment, fracture management, fracture, trauma, malleolar, arthroscopy, open reduction, ankle, fantry, lareau, vopat, blankenhorn, case report and literature review, online exclusive
Sections
Article Source

PURLs Copyright

Inside the Article

Article PDF Media

Are Hook Plates Advantageous Compared to Antiglide Plates for Vertical Shear Malleolar Fractures?

Article Type
Changed
Thu, 09/19/2019 - 13:27
Display Headline
Are Hook Plates Advantageous Compared to Antiglide Plates for Vertical Shear Malleolar Fractures?

Supination-adduction (SAD)-type fractures of the ankle comprise approximately 5% to 20% of ankle fractures.1-3 As the name describes, this fracture is caused by forceful adduction of the supinated foot. There are 2 stages of the fracture pattern: the injury usually occurs first on the lateral side of the ankle with injury to the soft tissues or a low transverse fracture of the distal fibula. With continued force, in the second stage, the talus causes a shearing of the medial malleolus, creating the vertical shear fracture pattern.4-7 The vertical shear medial malleolus fracture pattern is the subject of this investigation.

Several techniques have been traditionally recommended for fixation of SAD-type ankle fracture, including: a 2-screw construct without plate fixation, oriented perpendicular to the fracture; and an AG plate construct with variable positioning and numbers of screws for fixation. There have been, however, only 2 published articles about the biomechanical properties of fixation of vertical shear medial malleolar fractures, which reported conflicting results.8,9 The most recent of these studies argued that one-third tubular plate fixation offers significant mechanical advantage over screw-only fixation, supporting the use of AG plates for fixation of SAD ankle fractures.8

An additional design for fixation of medial malleolus fractures has been introduced, consisting of a hook plate (HP) contoured for the medial malleolus. To our knowledge, no studies have investigated HP’s biomechanical properties. Thus, the objective of this study was to investigate and compare the biomechanical properties of 3 constructs for fixation of SAD-ankle fractures: an antiglide (AG) plate, an AG plate with an additional lag-screw across the fracture, and a precontoured HP.

Materials and Methods

Thirty 4th-generation–composite polyurethane models of the left tibia were obtained (Sawbones, Pacific Research Laboratories, Inc.). Largely, our methods accorded with the precedent set by other studies on these fracture types.8,9

Prior to creation of the fractures, each model was individually evaluated for pretest stiffness by using the slope of the linear portion of the load-displacement curve during offset-axial loading. This demonstrated the baseline elasticity of the models during loading. Assessing pretest stiffness was performed to reduce potential variables in the stiffness of individual models in the analysis of the testing data.

The models were numbered 1 through 30 on the shaft and on the medial malleolus. A custom jig was constructed with a table saw to create identical vertical shear medial malleolar fracture patterns in each model. The jig created the vertical shear SAD fracture described by Lauge-Hansen.7 All models were randomly assigned to 1 of 3 groups; each group consisted of 10 models (Figures 1A, 1B).

The 10 specimens in group 1 were fixed with a 5-hole, 3.5-mm, one-third tubular plate (Smith & Nephew) in a traditional AG fashion. The plates were placed at the same location on all tibiae. The proximal hole and the hole closest to the fracture line were filled with 3.5-mm cortical screws, which were long enough to achieve bicortical fixation. No lag screws were placed in this specimen group. In group 2, specimens were fixed with the same plate used in group 1 (Smith & Nephew). In this modified AG (MAG) construct, specimens were fixed identically to group 1 for plate placement and fixation of the 2 proximal screws. In this group, an additional screw was placed perpendicular to the fracture and parallel to the distal tibial articular surface. In both groups (AG and MAG), the plates were not bent before application.

Group 3 consisted of specimens fixed with a 5-hole, precontoured medial malleolar HP (Arthrex). This HP construct was fixed with two 3.5-mm cortical screws long enough to achieve bicortical fixation. The plate also engaged the bone at the tip of the medial malleolus by using 2 sharp prongs. The screws were placed in the most proximal hole and the hole just proximal to the fracture line. No lag screws were placed in the HP construct.

All models were tested in offset-axial loading to replicate a SAD moment similar to previous studies. To test offset-axial loading, a vice held each model identically with a 17º angle from the longitudinal axis (Figure 2). Loading was performed with a material testing system; a material testing system plunger was directed at the inferior articulating cartilage surface of the medial malleolus. The specimens were loaded at a rate of 1 mm/sec until 2 mm of displacement was reached (Figure 3) or catastrophic failure occurred. The raw data analyzed consisted of the initial stiffness of the construct and the overall load-to-failure. The slope of the linear portion of the load-displacement curve of stiffness determined stiffness of the construct.

 

 

 

One-way analysis of variance with post hoc Tukey HSD data analysis was performed to determine if there were statistical differences among the different fixation constructs during load-to-failure. To prevent skewing of results by different values of model elasticity, pretest stiffness was accounted for by calculating a ratio of construct stiffness as a function of pretest model stiffness. Total force-to-failure was the recorded maximum force (in N) to cause failure. A P value of < .05 was set for significance. All data were analyzed using SPSS software (SPSS Version 15.0; SPSS Inc.).

Results

Analysis of pretest stiffness showed no significant difference among models (P = .490). All models failed by a gap of 2 mm at the distal fracture site except for 3 models in the MAG group. These 3 models failed at a much higher load than the remainder of the models and failed by fracture of the models.

The MAG group demonstrated significantly superior stiffness to the 2 other models tested (Figure 4). On average, this group required 753.5 N of force before failure. This was 530 N higher than the HP (P < .05) and 638 N higher than the AG constructs, respectively (P < .05). The HP and AG groups required forces of 223.2 N and 115.5 N for failure, respectively. These numbers were not significant (P= .063).

The absolute construct stiffness and construct stiffness as a function of pretest stiffness of the MAG group was the highest of all groups, 271.7 N/mm and 57.2%, respectively (Figure 5). These numbers showed significance when compared with the values of the HP group (P < .05 for both) and the AG group (P < .05 for both). The average stiffness of the HP group was 159.7 N/mm, which was 36.8% of pretest stiffness.

The AG group had the lowest construct stiffness and percent of pretest stiffness (128.1 N/mm and 29.6%). The HP and AG groups were not statistically different in these comparisons, P = .350 for construct stiffness and P = .395 for percent of pretest stiffness.

Discussion

These results support the use of a one-third tubular plate and lag-screw construct for fixation of vertical shear medial malleolus fractures. This is clinically important because one-third tubular plates with 3.5-mm screws are readily available and cost significantly less than a precountoured anatomic-specific type of fixation. These results are based on the biomechanical properties of the constructs tested in this study.

The previous 2 studies8,9showed conflicting results about the most biomechanically sound fixation for SAD medial malleolar fractures. The study by Toolan and colleagues9 reported that 2 screws placed perpendicular to the fracture demonstrated the strongest overall construct. This study compared 3 separate types of 2-screw–only fixations and 2 plate-and-screw fixations. One construct was similar to the AG group in our study, and the other construct had a lag screw at the apex of the fracture. This previous study,9 however, did not investigate a similar construct to the MAG group that was tested in our study.

According to Dumigan and associates,8 a construct that consisted of a 4-hole plate with 2 screws proximal to the fracture and 2 lag screws showed the strongest fixation. This study, however, did not include a group like our study’s AG group, which is the traditional AG form of fixation.

In our study, we examined the biomechanic properties of a traditional fixation (AG construct), a commonly used fixation (MAG construct), and a newer construct (HP construct). The HP group is unique to this study and, to our knowledge, there is no literature on its use as fixation for this fracture. We did not include a 2-screw–only group, which is a limitation, because this fixation type is not common for the SAD fracture. This study also did not include an HP construct with an additional lag screw, which is an available option as well.

The current investigation used synthetic bone models constructed for biomechanical testing. The models were thought to provide a consistent model for fixation as opposed to using potentially osteopenic cadaveric bone. Each model was the same size and laterality. The stiffness as determined by pretest stiffness was not significantly different among models. Because all models were similar in composition and size, this allowed for more consistent osteotomies and similarly sized malleolar fragments. Theoretically, this allowed a more uniform comparison of all specimens and constructs.

Using models, however, is a limit of this study. While the models were of similar biomechanical quality, it is possible that a model may not reproduce the biology of a cavaderic specimen or the physiology of a construct in vivo. Of the 2 studies that investigated SAD fractures, the Dumigan study8 used cadaveric specimens. The fact that these models were all mildly osteoporotic and were embalmed specimens were study limits. The Toolan study9 used synthetic models. Although these models were consistent, they were models of bones and not intended for biomechanical studies, thereby increasing the potential for skewed results.

 

 

Our study investigated loading only in the offset-axial direction, a difference when compared to the Dumigan and colleagues8 and Toolan and colleagues9 studies. The offest transverse loading previously investigated would most likely represent an external rotation moment. While fixation in vivo could experience an external rotation moment, the specific fracture pattern of interest fails in offset-axial loading. In the original discription of the SAD fracture, Lauge-Hanson7 stated that the talus causes the vertically oriented medial malleolar fracture in the extreme of ankle supination with an adduction moment. Considering this, we investigated failure with a force in the direction that causes this type of fracture.

There are some additional limitations. This study demonstrated superiority of a one-third tubular plate with 2 screws proximally and 1 lag screw. While this was shown in the laboratory under pure offset-axial loading conditions, this may not reproduce daily forces experienced by the constructs. Additionally, this study examined load-to-failure of the constructs and did not investigate cyclic loading that a construct would experience in vivo. Because the testing is not recognizably consistent with day-to-day stresses of these constructs in vivo, this confounds the clinical application of our study.

The stiffness required for clinical healing is undetermined and, therefore, all 3 types of fixation could be adequate clinically. Patients are typically instructed to adhere to weight-bearing limitations on the affected extremity, and casts or splints are applied postoperatively for extended periods of time. Clinical studies would have significant benefit in the evaluation of fixation of vertical shear medial malleolar fractures.

Conclusion

AG plating technique with lag-screw placement is biomechanically superior to the other 2 constructs investigated. The clinical applications of these results are not known, and clinical trials are suggested to determine the best type of fixation for SAD-type medial malleolar fractures.

References

1.    Hak DJ, Egol KA, Gardner MJ, Haskell A. The “not so simple” ankle fracture: avoiding problems and pitfalls to improve patient outcomes. Instr Course Lect. 2011;60:73-88.

2.    Hamilton WC. Supination-adduction injuries. In: Hamilton WC, ed. Traumatic Disorders of the Ankle. 1st ed. New York, NY: Springer-Verlag; 1984:101-112.

3.    McConnell T, Tornetta P. Marginal plafond impaction in association with supination-adduction ankle fractures: a report of eight cases. J Orthop Trauma. 2001;15(6):447-449.

4.    Arimoto HK, Forrester DM. Classification of ankle fractures: an algorithm. AJR Am J Roentgenol. 1980;135(5):1057-1063.

5.    Carr JB. Malleolar fractures and soft tissue injuries of the ankle. In: Browner BD, Jupiter JB, Levine AM, Trafton PG, Krettek C, eds. Skeletal Trauma: Basic Science, Management and Reconstruction. 4th ed. Philadelphia, PA: Saunders Elsevier; 2009:2515-2584.

6.    Davidovitch RI, Egol KA. Ankle fractures. In: Bucholz RW HJ, Court-Brown CM, Tornetta P III, eds. Rockwood and Green’s Fractures in Adults. 7th ed. Philadelphia, PA: Lippincott, Williams, & Wilkins; 2010:1975-2021.

7.    Lauge-Hansen N. Fractures of the ankle. II. Combined experimental-surgical and experimental-roentgenologic investigations. Arch Surg. 1950;60(5):957-985.

8.    Dumigan RM, Bronson DG, Early JS. Analysis of fixation methods for vertical shear fractures of the medial malleolus. J Orthop Trauma. 2006;20(10):687-691.

9.    Toolan BC, Koval KJ, Kummer FJ, Sanders R, Zuckerman JD. Vertical shear fractures of the medial malleolus: a biomechanical study of five internal fixation techniques. Foot Ankle Int. 1994;15(9):483-489.

Article PDF
Author and Disclosure Information

Daniel A. Jones, MD, Lisa K. Cannada, MD, and J. Gary Bledsoe, PhD

Authors’ Disclosure Statement: This study was supported by grants to the authors’ institution in the form of implants from Smith & Nephew and Arthrex.

Issue
The American Journal of Orthopedics - 45(3)
Publications
Topics
Page Number
E98-E102
Legacy Keywords
hook plates, hook, antiglide, plates, shear, malleolar fractures, fracture, fracture management, trauma, ankle, ankle fractures, jones, cannada, bledsoe, study, online exclusive
Sections
Author and Disclosure Information

Daniel A. Jones, MD, Lisa K. Cannada, MD, and J. Gary Bledsoe, PhD

Authors’ Disclosure Statement: This study was supported by grants to the authors’ institution in the form of implants from Smith & Nephew and Arthrex.

Author and Disclosure Information

Daniel A. Jones, MD, Lisa K. Cannada, MD, and J. Gary Bledsoe, PhD

Authors’ Disclosure Statement: This study was supported by grants to the authors’ institution in the form of implants from Smith & Nephew and Arthrex.

Article PDF
Article PDF

Supination-adduction (SAD)-type fractures of the ankle comprise approximately 5% to 20% of ankle fractures.1-3 As the name describes, this fracture is caused by forceful adduction of the supinated foot. There are 2 stages of the fracture pattern: the injury usually occurs first on the lateral side of the ankle with injury to the soft tissues or a low transverse fracture of the distal fibula. With continued force, in the second stage, the talus causes a shearing of the medial malleolus, creating the vertical shear fracture pattern.4-7 The vertical shear medial malleolus fracture pattern is the subject of this investigation.

Several techniques have been traditionally recommended for fixation of SAD-type ankle fracture, including: a 2-screw construct without plate fixation, oriented perpendicular to the fracture; and an AG plate construct with variable positioning and numbers of screws for fixation. There have been, however, only 2 published articles about the biomechanical properties of fixation of vertical shear medial malleolar fractures, which reported conflicting results.8,9 The most recent of these studies argued that one-third tubular plate fixation offers significant mechanical advantage over screw-only fixation, supporting the use of AG plates for fixation of SAD ankle fractures.8

An additional design for fixation of medial malleolus fractures has been introduced, consisting of a hook plate (HP) contoured for the medial malleolus. To our knowledge, no studies have investigated HP’s biomechanical properties. Thus, the objective of this study was to investigate and compare the biomechanical properties of 3 constructs for fixation of SAD-ankle fractures: an antiglide (AG) plate, an AG plate with an additional lag-screw across the fracture, and a precontoured HP.

Materials and Methods

Thirty 4th-generation–composite polyurethane models of the left tibia were obtained (Sawbones, Pacific Research Laboratories, Inc.). Largely, our methods accorded with the precedent set by other studies on these fracture types.8,9

Prior to creation of the fractures, each model was individually evaluated for pretest stiffness by using the slope of the linear portion of the load-displacement curve during offset-axial loading. This demonstrated the baseline elasticity of the models during loading. Assessing pretest stiffness was performed to reduce potential variables in the stiffness of individual models in the analysis of the testing data.

The models were numbered 1 through 30 on the shaft and on the medial malleolus. A custom jig was constructed with a table saw to create identical vertical shear medial malleolar fracture patterns in each model. The jig created the vertical shear SAD fracture described by Lauge-Hansen.7 All models were randomly assigned to 1 of 3 groups; each group consisted of 10 models (Figures 1A, 1B).

The 10 specimens in group 1 were fixed with a 5-hole, 3.5-mm, one-third tubular plate (Smith & Nephew) in a traditional AG fashion. The plates were placed at the same location on all tibiae. The proximal hole and the hole closest to the fracture line were filled with 3.5-mm cortical screws, which were long enough to achieve bicortical fixation. No lag screws were placed in this specimen group. In group 2, specimens were fixed with the same plate used in group 1 (Smith & Nephew). In this modified AG (MAG) construct, specimens were fixed identically to group 1 for plate placement and fixation of the 2 proximal screws. In this group, an additional screw was placed perpendicular to the fracture and parallel to the distal tibial articular surface. In both groups (AG and MAG), the plates were not bent before application.

Group 3 consisted of specimens fixed with a 5-hole, precontoured medial malleolar HP (Arthrex). This HP construct was fixed with two 3.5-mm cortical screws long enough to achieve bicortical fixation. The plate also engaged the bone at the tip of the medial malleolus by using 2 sharp prongs. The screws were placed in the most proximal hole and the hole just proximal to the fracture line. No lag screws were placed in the HP construct.

All models were tested in offset-axial loading to replicate a SAD moment similar to previous studies. To test offset-axial loading, a vice held each model identically with a 17º angle from the longitudinal axis (Figure 2). Loading was performed with a material testing system; a material testing system plunger was directed at the inferior articulating cartilage surface of the medial malleolus. The specimens were loaded at a rate of 1 mm/sec until 2 mm of displacement was reached (Figure 3) or catastrophic failure occurred. The raw data analyzed consisted of the initial stiffness of the construct and the overall load-to-failure. The slope of the linear portion of the load-displacement curve of stiffness determined stiffness of the construct.

 

 

 

One-way analysis of variance with post hoc Tukey HSD data analysis was performed to determine if there were statistical differences among the different fixation constructs during load-to-failure. To prevent skewing of results by different values of model elasticity, pretest stiffness was accounted for by calculating a ratio of construct stiffness as a function of pretest model stiffness. Total force-to-failure was the recorded maximum force (in N) to cause failure. A P value of < .05 was set for significance. All data were analyzed using SPSS software (SPSS Version 15.0; SPSS Inc.).

Results

Analysis of pretest stiffness showed no significant difference among models (P = .490). All models failed by a gap of 2 mm at the distal fracture site except for 3 models in the MAG group. These 3 models failed at a much higher load than the remainder of the models and failed by fracture of the models.

The MAG group demonstrated significantly superior stiffness to the 2 other models tested (Figure 4). On average, this group required 753.5 N of force before failure. This was 530 N higher than the HP (P < .05) and 638 N higher than the AG constructs, respectively (P < .05). The HP and AG groups required forces of 223.2 N and 115.5 N for failure, respectively. These numbers were not significant (P= .063).

The absolute construct stiffness and construct stiffness as a function of pretest stiffness of the MAG group was the highest of all groups, 271.7 N/mm and 57.2%, respectively (Figure 5). These numbers showed significance when compared with the values of the HP group (P < .05 for both) and the AG group (P < .05 for both). The average stiffness of the HP group was 159.7 N/mm, which was 36.8% of pretest stiffness.

The AG group had the lowest construct stiffness and percent of pretest stiffness (128.1 N/mm and 29.6%). The HP and AG groups were not statistically different in these comparisons, P = .350 for construct stiffness and P = .395 for percent of pretest stiffness.

Discussion

These results support the use of a one-third tubular plate and lag-screw construct for fixation of vertical shear medial malleolus fractures. This is clinically important because one-third tubular plates with 3.5-mm screws are readily available and cost significantly less than a precountoured anatomic-specific type of fixation. These results are based on the biomechanical properties of the constructs tested in this study.

The previous 2 studies8,9showed conflicting results about the most biomechanically sound fixation for SAD medial malleolar fractures. The study by Toolan and colleagues9 reported that 2 screws placed perpendicular to the fracture demonstrated the strongest overall construct. This study compared 3 separate types of 2-screw–only fixations and 2 plate-and-screw fixations. One construct was similar to the AG group in our study, and the other construct had a lag screw at the apex of the fracture. This previous study,9 however, did not investigate a similar construct to the MAG group that was tested in our study.

According to Dumigan and associates,8 a construct that consisted of a 4-hole plate with 2 screws proximal to the fracture and 2 lag screws showed the strongest fixation. This study, however, did not include a group like our study’s AG group, which is the traditional AG form of fixation.

In our study, we examined the biomechanic properties of a traditional fixation (AG construct), a commonly used fixation (MAG construct), and a newer construct (HP construct). The HP group is unique to this study and, to our knowledge, there is no literature on its use as fixation for this fracture. We did not include a 2-screw–only group, which is a limitation, because this fixation type is not common for the SAD fracture. This study also did not include an HP construct with an additional lag screw, which is an available option as well.

The current investigation used synthetic bone models constructed for biomechanical testing. The models were thought to provide a consistent model for fixation as opposed to using potentially osteopenic cadaveric bone. Each model was the same size and laterality. The stiffness as determined by pretest stiffness was not significantly different among models. Because all models were similar in composition and size, this allowed for more consistent osteotomies and similarly sized malleolar fragments. Theoretically, this allowed a more uniform comparison of all specimens and constructs.

Using models, however, is a limit of this study. While the models were of similar biomechanical quality, it is possible that a model may not reproduce the biology of a cavaderic specimen or the physiology of a construct in vivo. Of the 2 studies that investigated SAD fractures, the Dumigan study8 used cadaveric specimens. The fact that these models were all mildly osteoporotic and were embalmed specimens were study limits. The Toolan study9 used synthetic models. Although these models were consistent, they were models of bones and not intended for biomechanical studies, thereby increasing the potential for skewed results.

 

 

Our study investigated loading only in the offset-axial direction, a difference when compared to the Dumigan and colleagues8 and Toolan and colleagues9 studies. The offest transverse loading previously investigated would most likely represent an external rotation moment. While fixation in vivo could experience an external rotation moment, the specific fracture pattern of interest fails in offset-axial loading. In the original discription of the SAD fracture, Lauge-Hanson7 stated that the talus causes the vertically oriented medial malleolar fracture in the extreme of ankle supination with an adduction moment. Considering this, we investigated failure with a force in the direction that causes this type of fracture.

There are some additional limitations. This study demonstrated superiority of a one-third tubular plate with 2 screws proximally and 1 lag screw. While this was shown in the laboratory under pure offset-axial loading conditions, this may not reproduce daily forces experienced by the constructs. Additionally, this study examined load-to-failure of the constructs and did not investigate cyclic loading that a construct would experience in vivo. Because the testing is not recognizably consistent with day-to-day stresses of these constructs in vivo, this confounds the clinical application of our study.

The stiffness required for clinical healing is undetermined and, therefore, all 3 types of fixation could be adequate clinically. Patients are typically instructed to adhere to weight-bearing limitations on the affected extremity, and casts or splints are applied postoperatively for extended periods of time. Clinical studies would have significant benefit in the evaluation of fixation of vertical shear medial malleolar fractures.

Conclusion

AG plating technique with lag-screw placement is biomechanically superior to the other 2 constructs investigated. The clinical applications of these results are not known, and clinical trials are suggested to determine the best type of fixation for SAD-type medial malleolar fractures.

Supination-adduction (SAD)-type fractures of the ankle comprise approximately 5% to 20% of ankle fractures.1-3 As the name describes, this fracture is caused by forceful adduction of the supinated foot. There are 2 stages of the fracture pattern: the injury usually occurs first on the lateral side of the ankle with injury to the soft tissues or a low transverse fracture of the distal fibula. With continued force, in the second stage, the talus causes a shearing of the medial malleolus, creating the vertical shear fracture pattern.4-7 The vertical shear medial malleolus fracture pattern is the subject of this investigation.

Several techniques have been traditionally recommended for fixation of SAD-type ankle fracture, including: a 2-screw construct without plate fixation, oriented perpendicular to the fracture; and an AG plate construct with variable positioning and numbers of screws for fixation. There have been, however, only 2 published articles about the biomechanical properties of fixation of vertical shear medial malleolar fractures, which reported conflicting results.8,9 The most recent of these studies argued that one-third tubular plate fixation offers significant mechanical advantage over screw-only fixation, supporting the use of AG plates for fixation of SAD ankle fractures.8

An additional design for fixation of medial malleolus fractures has been introduced, consisting of a hook plate (HP) contoured for the medial malleolus. To our knowledge, no studies have investigated HP’s biomechanical properties. Thus, the objective of this study was to investigate and compare the biomechanical properties of 3 constructs for fixation of SAD-ankle fractures: an antiglide (AG) plate, an AG plate with an additional lag-screw across the fracture, and a precontoured HP.

Materials and Methods

Thirty 4th-generation–composite polyurethane models of the left tibia were obtained (Sawbones, Pacific Research Laboratories, Inc.). Largely, our methods accorded with the precedent set by other studies on these fracture types.8,9

Prior to creation of the fractures, each model was individually evaluated for pretest stiffness by using the slope of the linear portion of the load-displacement curve during offset-axial loading. This demonstrated the baseline elasticity of the models during loading. Assessing pretest stiffness was performed to reduce potential variables in the stiffness of individual models in the analysis of the testing data.

The models were numbered 1 through 30 on the shaft and on the medial malleolus. A custom jig was constructed with a table saw to create identical vertical shear medial malleolar fracture patterns in each model. The jig created the vertical shear SAD fracture described by Lauge-Hansen.7 All models were randomly assigned to 1 of 3 groups; each group consisted of 10 models (Figures 1A, 1B).

The 10 specimens in group 1 were fixed with a 5-hole, 3.5-mm, one-third tubular plate (Smith & Nephew) in a traditional AG fashion. The plates were placed at the same location on all tibiae. The proximal hole and the hole closest to the fracture line were filled with 3.5-mm cortical screws, which were long enough to achieve bicortical fixation. No lag screws were placed in this specimen group. In group 2, specimens were fixed with the same plate used in group 1 (Smith & Nephew). In this modified AG (MAG) construct, specimens were fixed identically to group 1 for plate placement and fixation of the 2 proximal screws. In this group, an additional screw was placed perpendicular to the fracture and parallel to the distal tibial articular surface. In both groups (AG and MAG), the plates were not bent before application.

Group 3 consisted of specimens fixed with a 5-hole, precontoured medial malleolar HP (Arthrex). This HP construct was fixed with two 3.5-mm cortical screws long enough to achieve bicortical fixation. The plate also engaged the bone at the tip of the medial malleolus by using 2 sharp prongs. The screws were placed in the most proximal hole and the hole just proximal to the fracture line. No lag screws were placed in the HP construct.

All models were tested in offset-axial loading to replicate a SAD moment similar to previous studies. To test offset-axial loading, a vice held each model identically with a 17º angle from the longitudinal axis (Figure 2). Loading was performed with a material testing system; a material testing system plunger was directed at the inferior articulating cartilage surface of the medial malleolus. The specimens were loaded at a rate of 1 mm/sec until 2 mm of displacement was reached (Figure 3) or catastrophic failure occurred. The raw data analyzed consisted of the initial stiffness of the construct and the overall load-to-failure. The slope of the linear portion of the load-displacement curve of stiffness determined stiffness of the construct.

 

 

 

One-way analysis of variance with post hoc Tukey HSD data analysis was performed to determine if there were statistical differences among the different fixation constructs during load-to-failure. To prevent skewing of results by different values of model elasticity, pretest stiffness was accounted for by calculating a ratio of construct stiffness as a function of pretest model stiffness. Total force-to-failure was the recorded maximum force (in N) to cause failure. A P value of < .05 was set for significance. All data were analyzed using SPSS software (SPSS Version 15.0; SPSS Inc.).

Results

Analysis of pretest stiffness showed no significant difference among models (P = .490). All models failed by a gap of 2 mm at the distal fracture site except for 3 models in the MAG group. These 3 models failed at a much higher load than the remainder of the models and failed by fracture of the models.

The MAG group demonstrated significantly superior stiffness to the 2 other models tested (Figure 4). On average, this group required 753.5 N of force before failure. This was 530 N higher than the HP (P < .05) and 638 N higher than the AG constructs, respectively (P < .05). The HP and AG groups required forces of 223.2 N and 115.5 N for failure, respectively. These numbers were not significant (P= .063).

The absolute construct stiffness and construct stiffness as a function of pretest stiffness of the MAG group was the highest of all groups, 271.7 N/mm and 57.2%, respectively (Figure 5). These numbers showed significance when compared with the values of the HP group (P < .05 for both) and the AG group (P < .05 for both). The average stiffness of the HP group was 159.7 N/mm, which was 36.8% of pretest stiffness.

The AG group had the lowest construct stiffness and percent of pretest stiffness (128.1 N/mm and 29.6%). The HP and AG groups were not statistically different in these comparisons, P = .350 for construct stiffness and P = .395 for percent of pretest stiffness.

Discussion

These results support the use of a one-third tubular plate and lag-screw construct for fixation of vertical shear medial malleolus fractures. This is clinically important because one-third tubular plates with 3.5-mm screws are readily available and cost significantly less than a precountoured anatomic-specific type of fixation. These results are based on the biomechanical properties of the constructs tested in this study.

The previous 2 studies8,9showed conflicting results about the most biomechanically sound fixation for SAD medial malleolar fractures. The study by Toolan and colleagues9 reported that 2 screws placed perpendicular to the fracture demonstrated the strongest overall construct. This study compared 3 separate types of 2-screw–only fixations and 2 plate-and-screw fixations. One construct was similar to the AG group in our study, and the other construct had a lag screw at the apex of the fracture. This previous study,9 however, did not investigate a similar construct to the MAG group that was tested in our study.

According to Dumigan and associates,8 a construct that consisted of a 4-hole plate with 2 screws proximal to the fracture and 2 lag screws showed the strongest fixation. This study, however, did not include a group like our study’s AG group, which is the traditional AG form of fixation.

In our study, we examined the biomechanic properties of a traditional fixation (AG construct), a commonly used fixation (MAG construct), and a newer construct (HP construct). The HP group is unique to this study and, to our knowledge, there is no literature on its use as fixation for this fracture. We did not include a 2-screw–only group, which is a limitation, because this fixation type is not common for the SAD fracture. This study also did not include an HP construct with an additional lag screw, which is an available option as well.

The current investigation used synthetic bone models constructed for biomechanical testing. The models were thought to provide a consistent model for fixation as opposed to using potentially osteopenic cadaveric bone. Each model was the same size and laterality. The stiffness as determined by pretest stiffness was not significantly different among models. Because all models were similar in composition and size, this allowed for more consistent osteotomies and similarly sized malleolar fragments. Theoretically, this allowed a more uniform comparison of all specimens and constructs.

Using models, however, is a limit of this study. While the models were of similar biomechanical quality, it is possible that a model may not reproduce the biology of a cavaderic specimen or the physiology of a construct in vivo. Of the 2 studies that investigated SAD fractures, the Dumigan study8 used cadaveric specimens. The fact that these models were all mildly osteoporotic and were embalmed specimens were study limits. The Toolan study9 used synthetic models. Although these models were consistent, they were models of bones and not intended for biomechanical studies, thereby increasing the potential for skewed results.

 

 

Our study investigated loading only in the offset-axial direction, a difference when compared to the Dumigan and colleagues8 and Toolan and colleagues9 studies. The offest transverse loading previously investigated would most likely represent an external rotation moment. While fixation in vivo could experience an external rotation moment, the specific fracture pattern of interest fails in offset-axial loading. In the original discription of the SAD fracture, Lauge-Hanson7 stated that the talus causes the vertically oriented medial malleolar fracture in the extreme of ankle supination with an adduction moment. Considering this, we investigated failure with a force in the direction that causes this type of fracture.

There are some additional limitations. This study demonstrated superiority of a one-third tubular plate with 2 screws proximally and 1 lag screw. While this was shown in the laboratory under pure offset-axial loading conditions, this may not reproduce daily forces experienced by the constructs. Additionally, this study examined load-to-failure of the constructs and did not investigate cyclic loading that a construct would experience in vivo. Because the testing is not recognizably consistent with day-to-day stresses of these constructs in vivo, this confounds the clinical application of our study.

The stiffness required for clinical healing is undetermined and, therefore, all 3 types of fixation could be adequate clinically. Patients are typically instructed to adhere to weight-bearing limitations on the affected extremity, and casts or splints are applied postoperatively for extended periods of time. Clinical studies would have significant benefit in the evaluation of fixation of vertical shear medial malleolar fractures.

Conclusion

AG plating technique with lag-screw placement is biomechanically superior to the other 2 constructs investigated. The clinical applications of these results are not known, and clinical trials are suggested to determine the best type of fixation for SAD-type medial malleolar fractures.

References

1.    Hak DJ, Egol KA, Gardner MJ, Haskell A. The “not so simple” ankle fracture: avoiding problems and pitfalls to improve patient outcomes. Instr Course Lect. 2011;60:73-88.

2.    Hamilton WC. Supination-adduction injuries. In: Hamilton WC, ed. Traumatic Disorders of the Ankle. 1st ed. New York, NY: Springer-Verlag; 1984:101-112.

3.    McConnell T, Tornetta P. Marginal plafond impaction in association with supination-adduction ankle fractures: a report of eight cases. J Orthop Trauma. 2001;15(6):447-449.

4.    Arimoto HK, Forrester DM. Classification of ankle fractures: an algorithm. AJR Am J Roentgenol. 1980;135(5):1057-1063.

5.    Carr JB. Malleolar fractures and soft tissue injuries of the ankle. In: Browner BD, Jupiter JB, Levine AM, Trafton PG, Krettek C, eds. Skeletal Trauma: Basic Science, Management and Reconstruction. 4th ed. Philadelphia, PA: Saunders Elsevier; 2009:2515-2584.

6.    Davidovitch RI, Egol KA. Ankle fractures. In: Bucholz RW HJ, Court-Brown CM, Tornetta P III, eds. Rockwood and Green’s Fractures in Adults. 7th ed. Philadelphia, PA: Lippincott, Williams, & Wilkins; 2010:1975-2021.

7.    Lauge-Hansen N. Fractures of the ankle. II. Combined experimental-surgical and experimental-roentgenologic investigations. Arch Surg. 1950;60(5):957-985.

8.    Dumigan RM, Bronson DG, Early JS. Analysis of fixation methods for vertical shear fractures of the medial malleolus. J Orthop Trauma. 2006;20(10):687-691.

9.    Toolan BC, Koval KJ, Kummer FJ, Sanders R, Zuckerman JD. Vertical shear fractures of the medial malleolus: a biomechanical study of five internal fixation techniques. Foot Ankle Int. 1994;15(9):483-489.

References

1.    Hak DJ, Egol KA, Gardner MJ, Haskell A. The “not so simple” ankle fracture: avoiding problems and pitfalls to improve patient outcomes. Instr Course Lect. 2011;60:73-88.

2.    Hamilton WC. Supination-adduction injuries. In: Hamilton WC, ed. Traumatic Disorders of the Ankle. 1st ed. New York, NY: Springer-Verlag; 1984:101-112.

3.    McConnell T, Tornetta P. Marginal plafond impaction in association with supination-adduction ankle fractures: a report of eight cases. J Orthop Trauma. 2001;15(6):447-449.

4.    Arimoto HK, Forrester DM. Classification of ankle fractures: an algorithm. AJR Am J Roentgenol. 1980;135(5):1057-1063.

5.    Carr JB. Malleolar fractures and soft tissue injuries of the ankle. In: Browner BD, Jupiter JB, Levine AM, Trafton PG, Krettek C, eds. Skeletal Trauma: Basic Science, Management and Reconstruction. 4th ed. Philadelphia, PA: Saunders Elsevier; 2009:2515-2584.

6.    Davidovitch RI, Egol KA. Ankle fractures. In: Bucholz RW HJ, Court-Brown CM, Tornetta P III, eds. Rockwood and Green’s Fractures in Adults. 7th ed. Philadelphia, PA: Lippincott, Williams, & Wilkins; 2010:1975-2021.

7.    Lauge-Hansen N. Fractures of the ankle. II. Combined experimental-surgical and experimental-roentgenologic investigations. Arch Surg. 1950;60(5):957-985.

8.    Dumigan RM, Bronson DG, Early JS. Analysis of fixation methods for vertical shear fractures of the medial malleolus. J Orthop Trauma. 2006;20(10):687-691.

9.    Toolan BC, Koval KJ, Kummer FJ, Sanders R, Zuckerman JD. Vertical shear fractures of the medial malleolus: a biomechanical study of five internal fixation techniques. Foot Ankle Int. 1994;15(9):483-489.

Issue
The American Journal of Orthopedics - 45(3)
Issue
The American Journal of Orthopedics - 45(3)
Page Number
E98-E102
Page Number
E98-E102
Publications
Publications
Topics
Article Type
Display Headline
Are Hook Plates Advantageous Compared to Antiglide Plates for Vertical Shear Malleolar Fractures?
Display Headline
Are Hook Plates Advantageous Compared to Antiglide Plates for Vertical Shear Malleolar Fractures?
Legacy Keywords
hook plates, hook, antiglide, plates, shear, malleolar fractures, fracture, fracture management, trauma, ankle, ankle fractures, jones, cannada, bledsoe, study, online exclusive
Legacy Keywords
hook plates, hook, antiglide, plates, shear, malleolar fractures, fracture, fracture management, trauma, ankle, ankle fractures, jones, cannada, bledsoe, study, online exclusive
Sections
Article Source

PURLs Copyright

Inside the Article

Article PDF Media