Return visit (RV) rate is a quality measure commonly used in the emergency department (ED) setting. This metric may represent suboptimal care at the index ED visit.^1-5 Although patient- and visit-level factors affecting ED RVs have been evaluated,^1,3,4,6-9 hospital-level factors and factors of a hospital’s patient population that may play roles in ED RV rates have not been examined. Identifying the factors associated with increased RVs may allow resources to be designated to areas that improve emergent care for children.¹⁰

Hospital readmission rates are a closely followed quality measure and are linked to reimbursement by the federal government, but a recent study found the influence a hospital can have on this marker may be mitigated by the impact of the social determinates of health (SDHs) of the hospital’s patient population.¹¹ That study and others have prompted an ongoing debate about adjusting quality measures for SDHs.^12,13 A clearer understanding of these interactions may permit us to focus on factors that can truly lead to improvement in care instead of penalizing practitioners or hospitals that provide care to those most in need.

Prior work has identified several SDHs associated with higher ED RV rates in patient- or visit-level analyses.^3,11,14 We conducted a study of hospital-level characteristics and characteristics of a hospital’s patient population to identify potentially mutable factors associated with increased ED RV rates that, once recognized, may allow for improvement in this quality measure.

This study was not considered human subjects research in accordance with Common Rule 45 CFR§46.104(f) and was evaluated by the Ann and Robert H. Lurie Children’s Hospital and Northwestern University Feinberg School of Medicine Institutional Review Boards and deemed exempt from review.

Study Population and Protocol

Our study had 2 data sources (to be described in detail): the Pediatric Health Information System (PHIS) and a survey of ED medical directors of the hospitals represented within PHIS. Hospitals were eligible for inclusion in the study if their data (1) met PHIS quality control standards for ED patient visits as determined by internal data assurance processes incorporated in PHIS,^3,14,15 (2) included data only from an identifiable single main ED, and (3) completed the ED medical director’s survey.

PHIS Database

PHIS, an administrative database managed by Truven Health Analytics, includes data from ED, ambulatory surgery, observation, and inpatient encounters across Children’s Hospital Association member children’s hospitals in North America. Data are subjected to validity checks before being included in the database.¹⁶ PHIS assigns unique patient identifiers to track individual patient visits within participating institutions over time.

Hospitals were described by percentages of ED patients in several groups: age (<1, 1-4, 5-9, 10-14, and 15-18 years)¹⁷; sex; race/ethnicity; insurance type (commercial, government, other); ED International Classification of Diseases, Ninth Edition (ICD-9) diagnosis code–based severity classification system score (1-2, low severity; 3-5, high severity)¹⁸; complex chronic condition presence at ED visits in prior year^14,19-21; home postal (Zip) code median household income from 2010 US Census data compared with Federal Poverty Level (<1.5, 1.5-2, 2-3, and >3 × FPL)¹⁷; and primary care physician (PCP) density in Federal Health Service Area of patient’s home address as reported by Dartmouth Atlas of Health Care modeled by quartiles.²² Density of PCPs—general pediatricians, family practitioners, general practitioners, and general internists—is calculated as number of PCPs per 100,000 residents. We used PCP density to account for potential care provided by any of the PCPs mentioned. We also assessed, at hospital level, index visit arrival time (8:01 am to 4:00 pm; 4:01 pm to 12:00 am; 12:01 am to 8:00 am) and index visit season.²³

ED Medical Director Survey

A web-based survey was constructed in an iterative process based on literature review and expert opinion to assess hospital-level factors that may impact ED RV rates.^3,7,24-26 The survey was piloted at 3 institutions to refine its structure and content.

The survey included 15 close-ended or multiple-choice questions on ED environment and operations and 2 open-ended questions, “What is the largest barrier to reducing the number of return visits within 72 hours of discharge from a previous ED visit?” and “In your opinion, what is the best way of reducing the number of the return visits within 72 hours of previous ED visit ?” (questionnaire in Supplemental material). Hospital characteristics from the survey included total clinical time allotment, or full-time equivalent (FTE), among all physicians, pediatric emergency medicine (PEM) fellowship-trained physicians, and all other (non-PEM) physicians. The data were standardized across sites by calculating FTE-per-10,000-visits values for each hospital; median duration of ED visit for admitted and discharged patients; median time from arrival to ED physician evaluation; rate of leaving without being seen; discharge educational material authorship and age specificity; follow-up visit scheduling procedure; and percentage of ED patients for whom English was a second language.

Responses to the 2 open-ended questions were independently categorized by Drs. Pittsenbarger and Alpern. Responses could be placed in more than 1 category if multiple answers to the question were included in the response. Categorizations were compared for consistency, and any inconsistencies were resolved by the consensus of the study investigators.

Outcome Measures From PHIS Database

All ED visits within a 12-month period (July 1, 2013–June 30, 2014) by patients younger than 18 years at time of index ED visit were eligible for inclusion in the study. An index visit was defined as any ED visit without another ED visit within the preceding 72 hours. The 72-hour time frame was used because it is the most widely studied time frame for ED RVs.⁵ Index ED visits that led to admission, observation status, death, or transfer were excluded.

The 2 primary outcomes of interest were (1) RVs within 72 hours of index ED visit discharge and (2) RVs within 72 hours that resulted in hospital admission or observation status at the next ED visit (RVA).^7,9,27-30 For patients with multiple ED revisits within 72 hours, only the first was assessed. There was a 72-hour minimum between index visits for the same patient.

Statistical Analyses

To determine hospital groups based on RV and RVA rates, we adjusted RV and RVA rates using generalized linear mixed-effects models, controlling for clustering and allowing for correlated data (within hospitals), nonconstant variability (across hospitals), and non-normally distributed data, as we did in a study of patient-level factors associated with ED RV and RVA.³ For each calculated rate (RV, RVA), the hospitals were then classified into 3 groups based on whether the hospital’s adjusted RV and RVA rates were outside 2 SDs from the mean, below the 5th or above the 95th percentile, or within that range. These groups were labeled lowest outliers, highest outliers, and average-performing hospitals.

After the groups of hospitals were determined, we returned to using unadjusted data to statistically analyze them. We summarized continuous variables using minimum and maximum values, medians, and interquartile ranges (IQRs). We present categorical variables using counts and percentages. To identify hospital characteristics with the most potential to gain from improvement, we also analyzed associations using 2 collapsed groups: hospitals with RV (or RVA) rates included in the average-performing and lowest outlier groups and hospitals within the highest outlier group. Hospital characteristics and hospital’s patient population characteristics from the surveys are summarized based on RV and RVA rate groups. Differences in distributions among continuous variables were assessed by Kruskal-Wallis 1-way analysis of variance. Chi-square tests were used to evaluate differences in proportions among categorical variables. All statistical analyses were performed with SAS Version 9.4 (SAS Institute); 2-sided P < 0.05 was considered statistically significant.

Return Visit Rates and Hospital ED Site Population Characteristics

Twenty-four of 35 (68%) eligible hospitals that met PHIS quality control standards for ED patient visits responded to the ED medical director survey. The included hospitals that both met quality control standards and completed the survey had a total of 1,456,377 patient visits during the study period. Individual sites had annual volumes ranging from 26,627 to 96,637 ED encounters. The mean RV rate across the institutions was 3.7% (range, 3.0%-4.8%), and the mean RVA rate across the hospitals was 0.7% (range, 0.5%-1.1%) (Figure).

There were 5 hospitals with RV rates less than 2 SDs of the mean rate, placing them in the lowest outlier group for RV; 13 hospitals with RV rates within 2 SDs of the mean RV rate, placing them in the average-performing group; and 6 hospitals with RV rates above 2 SDs of the mean, placing them in the highest outlier group. Table 1 lists the hospital ED site population characteristics among the 3 RV rate groups. Hospitals in the highest outlier group served populations with higher proportions of patients with insurance from a government payer, lower proportions of patients covered by a commercial insurance plan, and higher proportion of patients with lower median household incomes.

RV Rates and Hospital-Level Factors Survey Characteristics

Table 2 lists the ED medical director survey hospital-level data among the 3 RV rate groups. There were fewer FTEs by PEM fellowship-trained physicians per 10,000 patient visits at sites with higher RV rates (Table 2). Hospital-level characteristics assessed by the survey were not associated with RVA rates (Supplemental Table 2).

Evaluating characteristics of hospitals with the most potential to gain from improvement, hospitals with the highest RV rates (highest outlier group), compared with hospitals in the lowest outlier and average-performing groups collapsed together, persisted in having fewer PEM fellowship-trained physician FTEs per patient visit (Table 3). A similar collapsed analysis of RVA rates demonstrated that hospitals in the highest outlier group had longer-wait-to-physician time (81 minutes; IQR, 51-105 minutes) compared with hospitals in the other 2 groups (30 minutes; IQR, 19-42.5 minutes) (Table 3).

Other studies have identified patient- and visit-level characteristics associated with higher ED RV and RVA rates.^3,8,9,31 However, as our goal was to identify possible modifiable institutional features, our study examined factors at hospital and population-served levels (instead of patient or visit level) that may impact ED RV and RVA rates. Interestingly, our sample of tertiary-care pediatric center EDs provided evidence of variability in RV and RVA rates. We identified factors associated with RV rates related to the SDHs of the populations served by the ED, which suggests these factors are not modifiable at an institution level. In addition, we found that the increased availability of PEM providers per patient visit correlated with fewer ED RVs.

Hospitals serving ED populations with more government-insured and fewer commercially insured patients had higher rates of return to the ED. Similarly, hospitals with larger proportions of patients from areas with lower median household incomes had higher RV rates. These factors may indicate that patients with limited resources may have more frequent ED RVs,^3,6,32,33 and hospitals that serve them have higher ED RV rates. Our findings complement those of a recent study by Sills et al.,¹¹ who evaluated hospital readmissions and proposed risk adjustment for performance reimbursement. This study found that hospital population-level race, ethnicity, insurance status, and household income were predictors of hospital readmission after discharge.

Of note, our data did not identify similar site-level attributes related to the population served that correlated with RVA rates. We postulate that the need for admission on RV may indicate an inherent clinical urgency or medical need associated with the return to the ED, whereas RV without admission may be related more to patient- or population-level sociodemographic factors than to quality of care and clinical course, which influence ED utilization.^1,3,30 EDs treating higher proportions of patients of minority race or ethnicity, those with fewer financial resources, and those in more need of government health insurance are at higher risk for ED revisits.

We observed that increased PEM fellowship-trained physician staffing was associated with decreased RV rates. The availability of specialty-trained physicians in PEM may allow a larger proportion of patients treated by physicians with honed clinical skills for the patient population. Data from a single pediatric center showed PEM fellowship-trained physicians had admission rates lower than those of their counterparts without subspecialty fellowship training.³⁴ The lower RV rate for this group in our study is especially interesting in light of previously reported lower admission rates at index visit in PEM trained physicians. With lower index admission rates, it may have been assumed that visits associated with PEM trained physician care would have an increased (rather than decreased) chance of RV. In addition, we noted the increased RVA rates were associated with longer waits to see a physician. These measures may indicate the effect of institutional access to robust resources (the ability to hire and support more specialty-trained physicians). These novel findings warrant further evaluation, particularly as our sample included only pediatric centers.

Our survey data demonstrated the impact that access to care has on ED RV rates. The ED medical directors indicated that limited access to outpatient appointments with PCPs and specialists was an important factor increasing ED RVs and a potential avenue for interventions. As the 2 open-ended questions addressed barriers and potential solutions, it is interesting that the respondents cited access to care and discharge instructions as the largest barriers and identified innovations in access to care and discharge education as important potential remedies.

This study demonstrated that, at the hospital level, ED RV quality measures are influenced by complex and varied SDHs that primarily reflect the characteristics of the patient populations served. Prior work has similarly highlighted the importance of gaining a rigorous understanding of other quality measures before widespread use, reporting, and dissemination of results.^11,35-38 With this in mind, as quality measures are developed and implemented, care should be taken to ensure they accurately and appropriately reflect the quality of care provided to the patient and are not more representative of other factors not directly within institutional control. These findings call into question the usefulness of ED RVs as a quality measure for comparing institutions.

Study Limitations

This study had several limitations. The PHIS dataset tracks only patients within each institution and does not include RVs to other EDs, which may account for a proportion of RVs.³⁹ Our survey response rate was 68% among medical directors, excluding 11 hospitals from analysis, which decreased the study’s power to detect differences that may be present between groups. In addition, the generalizability of our findings may be limited to tertiary-care children’s hospitals, as the PHIS dataset included only these types of healthcare facilities. We also included data only from the sites’ main EDs, and therefore cannot know if our results are applicable to satellite EDs. ED staffing of PEM physicians was analyzed using FTEs. However, number of clinical hours in 1 FTE may vary among sites, leading to imprecision in this hospital characteristic.

Hospitals with the highest RV rates served populations with a larger proportion of patients with government insurance and lower household income, and these hospitals had fewer PEM trained physicians. Variation in RV rates among hospitals may be indicative of the SDHs of their unique patient populations. ED revisit rates should be used cautiously in determining the quality of care of hospitals providing care to differing populations.

Disclosure

Nothing to report.

Return visit (RV) rate is a quality measure commonly used in the emergency department (ED) setting. This metric may represent suboptimal care at the index ED visit.^1-5 Although patient- and visit-level factors affecting ED RVs have been evaluated,^1,3,4,6-9 hospital-level factors and factors of a hospital’s patient population that may play roles in ED RV rates have not been examined. Identifying the factors associated with increased RVs may allow resources to be designated to areas that improve emergent care for children.¹⁰

Hospital readmission rates are a closely followed quality measure and are linked to reimbursement by the federal government, but a recent study found the influence a hospital can have on this marker may be mitigated by the impact of the social determinates of health (SDHs) of the hospital’s patient population.¹¹ That study and others have prompted an ongoing debate about adjusting quality measures for SDHs.^12,13 A clearer understanding of these interactions may permit us to focus on factors that can truly lead to improvement in care instead of penalizing practitioners or hospitals that provide care to those most in need.

Prior work has identified several SDHs associated with higher ED RV rates in patient- or visit-level analyses.^3,11,14 We conducted a study of hospital-level characteristics and characteristics of a hospital’s patient population to identify potentially mutable factors associated with increased ED RV rates that, once recognized, may allow for improvement in this quality measure.

This study was not considered human subjects research in accordance with Common Rule 45 CFR§46.104(f) and was evaluated by the Ann and Robert H. Lurie Children’s Hospital and Northwestern University Feinberg School of Medicine Institutional Review Boards and deemed exempt from review.

Study Population and Protocol

Our study had 2 data sources (to be described in detail): the Pediatric Health Information System (PHIS) and a survey of ED medical directors of the hospitals represented within PHIS. Hospitals were eligible for inclusion in the study if their data (1) met PHIS quality control standards for ED patient visits as determined by internal data assurance processes incorporated in PHIS,^3,14,15 (2) included data only from an identifiable single main ED, and (3) completed the ED medical director’s survey.

PHIS Database

PHIS, an administrative database managed by Truven Health Analytics, includes data from ED, ambulatory surgery, observation, and inpatient encounters across Children’s Hospital Association member children’s hospitals in North America. Data are subjected to validity checks before being included in the database.¹⁶ PHIS assigns unique patient identifiers to track individual patient visits within participating institutions over time.

Hospitals were described by percentages of ED patients in several groups: age (<1, 1-4, 5-9, 10-14, and 15-18 years)¹⁷; sex; race/ethnicity; insurance type (commercial, government, other); ED International Classification of Diseases, Ninth Edition (ICD-9) diagnosis code–based severity classification system score (1-2, low severity; 3-5, high severity)¹⁸; complex chronic condition presence at ED visits in prior year^14,19-21; home postal (Zip) code median household income from 2010 US Census data compared with Federal Poverty Level (<1.5, 1.5-2, 2-3, and >3 × FPL)¹⁷; and primary care physician (PCP) density in Federal Health Service Area of patient’s home address as reported by Dartmouth Atlas of Health Care modeled by quartiles.²² Density of PCPs—general pediatricians, family practitioners, general practitioners, and general internists—is calculated as number of PCPs per 100,000 residents. We used PCP density to account for potential care provided by any of the PCPs mentioned. We also assessed, at hospital level, index visit arrival time (8:01 am to 4:00 pm; 4:01 pm to 12:00 am; 12:01 am to 8:00 am) and index visit season.²³

ED Medical Director Survey

A web-based survey was constructed in an iterative process based on literature review and expert opinion to assess hospital-level factors that may impact ED RV rates.^3,7,24-26 The survey was piloted at 3 institutions to refine its structure and content.

The survey included 15 close-ended or multiple-choice questions on ED environment and operations and 2 open-ended questions, “What is the largest barrier to reducing the number of return visits within 72 hours of discharge from a previous ED visit?” and “In your opinion, what is the best way of reducing the number of the return visits within 72 hours of previous ED visit ?” (questionnaire in Supplemental material). Hospital characteristics from the survey included total clinical time allotment, or full-time equivalent (FTE), among all physicians, pediatric emergency medicine (PEM) fellowship-trained physicians, and all other (non-PEM) physicians. The data were standardized across sites by calculating FTE-per-10,000-visits values for each hospital; median duration of ED visit for admitted and discharged patients; median time from arrival to ED physician evaluation; rate of leaving without being seen; discharge educational material authorship and age specificity; follow-up visit scheduling procedure; and percentage of ED patients for whom English was a second language.

Responses to the 2 open-ended questions were independently categorized by Drs. Pittsenbarger and Alpern. Responses could be placed in more than 1 category if multiple answers to the question were included in the response. Categorizations were compared for consistency, and any inconsistencies were resolved by the consensus of the study investigators.

Outcome Measures From PHIS Database

All ED visits within a 12-month period (July 1, 2013–June 30, 2014) by patients younger than 18 years at time of index ED visit were eligible for inclusion in the study. An index visit was defined as any ED visit without another ED visit within the preceding 72 hours. The 72-hour time frame was used because it is the most widely studied time frame for ED RVs.⁵ Index ED visits that led to admission, observation status, death, or transfer were excluded.

The 2 primary outcomes of interest were (1) RVs within 72 hours of index ED visit discharge and (2) RVs within 72 hours that resulted in hospital admission or observation status at the next ED visit (RVA).^7,9,27-30 For patients with multiple ED revisits within 72 hours, only the first was assessed. There was a 72-hour minimum between index visits for the same patient.

Statistical Analyses

To determine hospital groups based on RV and RVA rates, we adjusted RV and RVA rates using generalized linear mixed-effects models, controlling for clustering and allowing for correlated data (within hospitals), nonconstant variability (across hospitals), and non-normally distributed data, as we did in a study of patient-level factors associated with ED RV and RVA.³ For each calculated rate (RV, RVA), the hospitals were then classified into 3 groups based on whether the hospital’s adjusted RV and RVA rates were outside 2 SDs from the mean, below the 5th or above the 95th percentile, or within that range. These groups were labeled lowest outliers, highest outliers, and average-performing hospitals.

After the groups of hospitals were determined, we returned to using unadjusted data to statistically analyze them. We summarized continuous variables using minimum and maximum values, medians, and interquartile ranges (IQRs). We present categorical variables using counts and percentages. To identify hospital characteristics with the most potential to gain from improvement, we also analyzed associations using 2 collapsed groups: hospitals with RV (or RVA) rates included in the average-performing and lowest outlier groups and hospitals within the highest outlier group. Hospital characteristics and hospital’s patient population characteristics from the surveys are summarized based on RV and RVA rate groups. Differences in distributions among continuous variables were assessed by Kruskal-Wallis 1-way analysis of variance. Chi-square tests were used to evaluate differences in proportions among categorical variables. All statistical analyses were performed with SAS Version 9.4 (SAS Institute); 2-sided P < 0.05 was considered statistically significant.

Return Visit Rates and Hospital ED Site Population Characteristics

Twenty-four of 35 (68%) eligible hospitals that met PHIS quality control standards for ED patient visits responded to the ED medical director survey. The included hospitals that both met quality control standards and completed the survey had a total of 1,456,377 patient visits during the study period. Individual sites had annual volumes ranging from 26,627 to 96,637 ED encounters. The mean RV rate across the institutions was 3.7% (range, 3.0%-4.8%), and the mean RVA rate across the hospitals was 0.7% (range, 0.5%-1.1%) (Figure).

There were 5 hospitals with RV rates less than 2 SDs of the mean rate, placing them in the lowest outlier group for RV; 13 hospitals with RV rates within 2 SDs of the mean RV rate, placing them in the average-performing group; and 6 hospitals with RV rates above 2 SDs of the mean, placing them in the highest outlier group. Table 1 lists the hospital ED site population characteristics among the 3 RV rate groups. Hospitals in the highest outlier group served populations with higher proportions of patients with insurance from a government payer, lower proportions of patients covered by a commercial insurance plan, and higher proportion of patients with lower median household incomes.

RV Rates and Hospital-Level Factors Survey Characteristics

Table 2 lists the ED medical director survey hospital-level data among the 3 RV rate groups. There were fewer FTEs by PEM fellowship-trained physicians per 10,000 patient visits at sites with higher RV rates (Table 2). Hospital-level characteristics assessed by the survey were not associated with RVA rates (Supplemental Table 2).

Evaluating characteristics of hospitals with the most potential to gain from improvement, hospitals with the highest RV rates (highest outlier group), compared with hospitals in the lowest outlier and average-performing groups collapsed together, persisted in having fewer PEM fellowship-trained physician FTEs per patient visit (Table 3). A similar collapsed analysis of RVA rates demonstrated that hospitals in the highest outlier group had longer-wait-to-physician time (81 minutes; IQR, 51-105 minutes) compared with hospitals in the other 2 groups (30 minutes; IQR, 19-42.5 minutes) (Table 3).

Other studies have identified patient- and visit-level characteristics associated with higher ED RV and RVA rates.^3,8,9,31 However, as our goal was to identify possible modifiable institutional features, our study examined factors at hospital and population-served levels (instead of patient or visit level) that may impact ED RV and RVA rates. Interestingly, our sample of tertiary-care pediatric center EDs provided evidence of variability in RV and RVA rates. We identified factors associated with RV rates related to the SDHs of the populations served by the ED, which suggests these factors are not modifiable at an institution level. In addition, we found that the increased availability of PEM providers per patient visit correlated with fewer ED RVs.

Hospitals serving ED populations with more government-insured and fewer commercially insured patients had higher rates of return to the ED. Similarly, hospitals with larger proportions of patients from areas with lower median household incomes had higher RV rates. These factors may indicate that patients with limited resources may have more frequent ED RVs,^3,6,32,33 and hospitals that serve them have higher ED RV rates. Our findings complement those of a recent study by Sills et al.,¹¹ who evaluated hospital readmissions and proposed risk adjustment for performance reimbursement. This study found that hospital population-level race, ethnicity, insurance status, and household income were predictors of hospital readmission after discharge.

Of note, our data did not identify similar site-level attributes related to the population served that correlated with RVA rates. We postulate that the need for admission on RV may indicate an inherent clinical urgency or medical need associated with the return to the ED, whereas RV without admission may be related more to patient- or population-level sociodemographic factors than to quality of care and clinical course, which influence ED utilization.^1,3,30 EDs treating higher proportions of patients of minority race or ethnicity, those with fewer financial resources, and those in more need of government health insurance are at higher risk for ED revisits.

We observed that increased PEM fellowship-trained physician staffing was associated with decreased RV rates. The availability of specialty-trained physicians in PEM may allow a larger proportion of patients treated by physicians with honed clinical skills for the patient population. Data from a single pediatric center showed PEM fellowship-trained physicians had admission rates lower than those of their counterparts without subspecialty fellowship training.³⁴ The lower RV rate for this group in our study is especially interesting in light of previously reported lower admission rates at index visit in PEM trained physicians. With lower index admission rates, it may have been assumed that visits associated with PEM trained physician care would have an increased (rather than decreased) chance of RV. In addition, we noted the increased RVA rates were associated with longer waits to see a physician. These measures may indicate the effect of institutional access to robust resources (the ability to hire and support more specialty-trained physicians). These novel findings warrant further evaluation, particularly as our sample included only pediatric centers.

Our survey data demonstrated the impact that access to care has on ED RV rates. The ED medical directors indicated that limited access to outpatient appointments with PCPs and specialists was an important factor increasing ED RVs and a potential avenue for interventions. As the 2 open-ended questions addressed barriers and potential solutions, it is interesting that the respondents cited access to care and discharge instructions as the largest barriers and identified innovations in access to care and discharge education as important potential remedies.

This study demonstrated that, at the hospital level, ED RV quality measures are influenced by complex and varied SDHs that primarily reflect the characteristics of the patient populations served. Prior work has similarly highlighted the importance of gaining a rigorous understanding of other quality measures before widespread use, reporting, and dissemination of results.^11,35-38 With this in mind, as quality measures are developed and implemented, care should be taken to ensure they accurately and appropriately reflect the quality of care provided to the patient and are not more representative of other factors not directly within institutional control. These findings call into question the usefulness of ED RVs as a quality measure for comparing institutions.

Study Limitations

This study had several limitations. The PHIS dataset tracks only patients within each institution and does not include RVs to other EDs, which may account for a proportion of RVs.³⁹ Our survey response rate was 68% among medical directors, excluding 11 hospitals from analysis, which decreased the study’s power to detect differences that may be present between groups. In addition, the generalizability of our findings may be limited to tertiary-care children’s hospitals, as the PHIS dataset included only these types of healthcare facilities. We also included data only from the sites’ main EDs, and therefore cannot know if our results are applicable to satellite EDs. ED staffing of PEM physicians was analyzed using FTEs. However, number of clinical hours in 1 FTE may vary among sites, leading to imprecision in this hospital characteristic.

Hospitals with the highest RV rates served populations with a larger proportion of patients with government insurance and lower household income, and these hospitals had fewer PEM trained physicians. Variation in RV rates among hospitals may be indicative of the SDHs of their unique patient populations. ED revisit rates should be used cautiously in determining the quality of care of hospitals providing care to differing populations.

Disclosure

Nothing to report.

Return visit (RV) rate is a quality measure commonly used in the emergency department (ED) setting. This metric may represent suboptimal care at the index ED visit.^1-5 Although patient- and visit-level factors affecting ED RVs have been evaluated,^1,3,4,6-9 hospital-level factors and factors of a hospital’s patient population that may play roles in ED RV rates have not been examined. Identifying the factors associated with increased RVs may allow resources to be designated to areas that improve emergent care for children.¹⁰

Hospital readmission rates are a closely followed quality measure and are linked to reimbursement by the federal government, but a recent study found the influence a hospital can have on this marker may be mitigated by the impact of the social determinates of health (SDHs) of the hospital’s patient population.¹¹ That study and others have prompted an ongoing debate about adjusting quality measures for SDHs.^12,13 A clearer understanding of these interactions may permit us to focus on factors that can truly lead to improvement in care instead of penalizing practitioners or hospitals that provide care to those most in need.

Prior work has identified several SDHs associated with higher ED RV rates in patient- or visit-level analyses.^3,11,14 We conducted a study of hospital-level characteristics and characteristics of a hospital’s patient population to identify potentially mutable factors associated with increased ED RV rates that, once recognized, may allow for improvement in this quality measure.

This study was not considered human subjects research in accordance with Common Rule 45 CFR§46.104(f) and was evaluated by the Ann and Robert H. Lurie Children’s Hospital and Northwestern University Feinberg School of Medicine Institutional Review Boards and deemed exempt from review.

Study Population and Protocol

Our study had 2 data sources (to be described in detail): the Pediatric Health Information System (PHIS) and a survey of ED medical directors of the hospitals represented within PHIS. Hospitals were eligible for inclusion in the study if their data (1) met PHIS quality control standards for ED patient visits as determined by internal data assurance processes incorporated in PHIS,^3,14,15 (2) included data only from an identifiable single main ED, and (3) completed the ED medical director’s survey.

PHIS Database

PHIS, an administrative database managed by Truven Health Analytics, includes data from ED, ambulatory surgery, observation, and inpatient encounters across Children’s Hospital Association member children’s hospitals in North America. Data are subjected to validity checks before being included in the database.¹⁶ PHIS assigns unique patient identifiers to track individual patient visits within participating institutions over time.

Hospitals were described by percentages of ED patients in several groups: age (<1, 1-4, 5-9, 10-14, and 15-18 years)¹⁷; sex; race/ethnicity; insurance type (commercial, government, other); ED International Classification of Diseases, Ninth Edition (ICD-9) diagnosis code–based severity classification system score (1-2, low severity; 3-5, high severity)¹⁸; complex chronic condition presence at ED visits in prior year^14,19-21; home postal (Zip) code median household income from 2010 US Census data compared with Federal Poverty Level (<1.5, 1.5-2, 2-3, and >3 × FPL)¹⁷; and primary care physician (PCP) density in Federal Health Service Area of patient’s home address as reported by Dartmouth Atlas of Health Care modeled by quartiles.²² Density of PCPs—general pediatricians, family practitioners, general practitioners, and general internists—is calculated as number of PCPs per 100,000 residents. We used PCP density to account for potential care provided by any of the PCPs mentioned. We also assessed, at hospital level, index visit arrival time (8:01 am to 4:00 pm; 4:01 pm to 12:00 am; 12:01 am to 8:00 am) and index visit season.²³

ED Medical Director Survey

A web-based survey was constructed in an iterative process based on literature review and expert opinion to assess hospital-level factors that may impact ED RV rates.^3,7,24-26 The survey was piloted at 3 institutions to refine its structure and content.

The survey included 15 close-ended or multiple-choice questions on ED environment and operations and 2 open-ended questions, “What is the largest barrier to reducing the number of return visits within 72 hours of discharge from a previous ED visit?” and “In your opinion, what is the best way of reducing the number of the return visits within 72 hours of previous ED visit ?” (questionnaire in Supplemental material). Hospital characteristics from the survey included total clinical time allotment, or full-time equivalent (FTE), among all physicians, pediatric emergency medicine (PEM) fellowship-trained physicians, and all other (non-PEM) physicians. The data were standardized across sites by calculating FTE-per-10,000-visits values for each hospital; median duration of ED visit for admitted and discharged patients; median time from arrival to ED physician evaluation; rate of leaving without being seen; discharge educational material authorship and age specificity; follow-up visit scheduling procedure; and percentage of ED patients for whom English was a second language.

Responses to the 2 open-ended questions were independently categorized by Drs. Pittsenbarger and Alpern. Responses could be placed in more than 1 category if multiple answers to the question were included in the response. Categorizations were compared for consistency, and any inconsistencies were resolved by the consensus of the study investigators.

Outcome Measures From PHIS Database

All ED visits within a 12-month period (July 1, 2013–June 30, 2014) by patients younger than 18 years at time of index ED visit were eligible for inclusion in the study. An index visit was defined as any ED visit without another ED visit within the preceding 72 hours. The 72-hour time frame was used because it is the most widely studied time frame for ED RVs.⁵ Index ED visits that led to admission, observation status, death, or transfer were excluded.

The 2 primary outcomes of interest were (1) RVs within 72 hours of index ED visit discharge and (2) RVs within 72 hours that resulted in hospital admission or observation status at the next ED visit (RVA).^7,9,27-30 For patients with multiple ED revisits within 72 hours, only the first was assessed. There was a 72-hour minimum between index visits for the same patient.

Statistical Analyses

To determine hospital groups based on RV and RVA rates, we adjusted RV and RVA rates using generalized linear mixed-effects models, controlling for clustering and allowing for correlated data (within hospitals), nonconstant variability (across hospitals), and non-normally distributed data, as we did in a study of patient-level factors associated with ED RV and RVA.³ For each calculated rate (RV, RVA), the hospitals were then classified into 3 groups based on whether the hospital’s adjusted RV and RVA rates were outside 2 SDs from the mean, below the 5th or above the 95th percentile, or within that range. These groups were labeled lowest outliers, highest outliers, and average-performing hospitals.

After the groups of hospitals were determined, we returned to using unadjusted data to statistically analyze them. We summarized continuous variables using minimum and maximum values, medians, and interquartile ranges (IQRs). We present categorical variables using counts and percentages. To identify hospital characteristics with the most potential to gain from improvement, we also analyzed associations using 2 collapsed groups: hospitals with RV (or RVA) rates included in the average-performing and lowest outlier groups and hospitals within the highest outlier group. Hospital characteristics and hospital’s patient population characteristics from the surveys are summarized based on RV and RVA rate groups. Differences in distributions among continuous variables were assessed by Kruskal-Wallis 1-way analysis of variance. Chi-square tests were used to evaluate differences in proportions among categorical variables. All statistical analyses were performed with SAS Version 9.4 (SAS Institute); 2-sided P < 0.05 was considered statistically significant.

Return Visit Rates and Hospital ED Site Population Characteristics

Twenty-four of 35 (68%) eligible hospitals that met PHIS quality control standards for ED patient visits responded to the ED medical director survey. The included hospitals that both met quality control standards and completed the survey had a total of 1,456,377 patient visits during the study period. Individual sites had annual volumes ranging from 26,627 to 96,637 ED encounters. The mean RV rate across the institutions was 3.7% (range, 3.0%-4.8%), and the mean RVA rate across the hospitals was 0.7% (range, 0.5%-1.1%) (Figure).

There were 5 hospitals with RV rates less than 2 SDs of the mean rate, placing them in the lowest outlier group for RV; 13 hospitals with RV rates within 2 SDs of the mean RV rate, placing them in the average-performing group; and 6 hospitals with RV rates above 2 SDs of the mean, placing them in the highest outlier group. Table 1 lists the hospital ED site population characteristics among the 3 RV rate groups. Hospitals in the highest outlier group served populations with higher proportions of patients with insurance from a government payer, lower proportions of patients covered by a commercial insurance plan, and higher proportion of patients with lower median household incomes.

RV Rates and Hospital-Level Factors Survey Characteristics

Table 2 lists the ED medical director survey hospital-level data among the 3 RV rate groups. There were fewer FTEs by PEM fellowship-trained physicians per 10,000 patient visits at sites with higher RV rates (Table 2). Hospital-level characteristics assessed by the survey were not associated with RVA rates (Supplemental Table 2).

Evaluating characteristics of hospitals with the most potential to gain from improvement, hospitals with the highest RV rates (highest outlier group), compared with hospitals in the lowest outlier and average-performing groups collapsed together, persisted in having fewer PEM fellowship-trained physician FTEs per patient visit (Table 3). A similar collapsed analysis of RVA rates demonstrated that hospitals in the highest outlier group had longer-wait-to-physician time (81 minutes; IQR, 51-105 minutes) compared with hospitals in the other 2 groups (30 minutes; IQR, 19-42.5 minutes) (Table 3).

Other studies have identified patient- and visit-level characteristics associated with higher ED RV and RVA rates.^3,8,9,31 However, as our goal was to identify possible modifiable institutional features, our study examined factors at hospital and population-served levels (instead of patient or visit level) that may impact ED RV and RVA rates. Interestingly, our sample of tertiary-care pediatric center EDs provided evidence of variability in RV and RVA rates. We identified factors associated with RV rates related to the SDHs of the populations served by the ED, which suggests these factors are not modifiable at an institution level. In addition, we found that the increased availability of PEM providers per patient visit correlated with fewer ED RVs.

Hospitals serving ED populations with more government-insured and fewer commercially insured patients had higher rates of return to the ED. Similarly, hospitals with larger proportions of patients from areas with lower median household incomes had higher RV rates. These factors may indicate that patients with limited resources may have more frequent ED RVs,^3,6,32,33 and hospitals that serve them have higher ED RV rates. Our findings complement those of a recent study by Sills et al.,¹¹ who evaluated hospital readmissions and proposed risk adjustment for performance reimbursement. This study found that hospital population-level race, ethnicity, insurance status, and household income were predictors of hospital readmission after discharge.

Of note, our data did not identify similar site-level attributes related to the population served that correlated with RVA rates. We postulate that the need for admission on RV may indicate an inherent clinical urgency or medical need associated with the return to the ED, whereas RV without admission may be related more to patient- or population-level sociodemographic factors than to quality of care and clinical course, which influence ED utilization.^1,3,30 EDs treating higher proportions of patients of minority race or ethnicity, those with fewer financial resources, and those in more need of government health insurance are at higher risk for ED revisits.

We observed that increased PEM fellowship-trained physician staffing was associated with decreased RV rates. The availability of specialty-trained physicians in PEM may allow a larger proportion of patients treated by physicians with honed clinical skills for the patient population. Data from a single pediatric center showed PEM fellowship-trained physicians had admission rates lower than those of their counterparts without subspecialty fellowship training.³⁴ The lower RV rate for this group in our study is especially interesting in light of previously reported lower admission rates at index visit in PEM trained physicians. With lower index admission rates, it may have been assumed that visits associated with PEM trained physician care would have an increased (rather than decreased) chance of RV. In addition, we noted the increased RVA rates were associated with longer waits to see a physician. These measures may indicate the effect of institutional access to robust resources (the ability to hire and support more specialty-trained physicians). These novel findings warrant further evaluation, particularly as our sample included only pediatric centers.

Our survey data demonstrated the impact that access to care has on ED RV rates. The ED medical directors indicated that limited access to outpatient appointments with PCPs and specialists was an important factor increasing ED RVs and a potential avenue for interventions. As the 2 open-ended questions addressed barriers and potential solutions, it is interesting that the respondents cited access to care and discharge instructions as the largest barriers and identified innovations in access to care and discharge education as important potential remedies.

This study demonstrated that, at the hospital level, ED RV quality measures are influenced by complex and varied SDHs that primarily reflect the characteristics of the patient populations served. Prior work has similarly highlighted the importance of gaining a rigorous understanding of other quality measures before widespread use, reporting, and dissemination of results.^11,35-38 With this in mind, as quality measures are developed and implemented, care should be taken to ensure they accurately and appropriately reflect the quality of care provided to the patient and are not more representative of other factors not directly within institutional control. These findings call into question the usefulness of ED RVs as a quality measure for comparing institutions.

Study Limitations

This study had several limitations. The PHIS dataset tracks only patients within each institution and does not include RVs to other EDs, which may account for a proportion of RVs.³⁹ Our survey response rate was 68% among medical directors, excluding 11 hospitals from analysis, which decreased the study’s power to detect differences that may be present between groups. In addition, the generalizability of our findings may be limited to tertiary-care children’s hospitals, as the PHIS dataset included only these types of healthcare facilities. We also included data only from the sites’ main EDs, and therefore cannot know if our results are applicable to satellite EDs. ED staffing of PEM physicians was analyzed using FTEs. However, number of clinical hours in 1 FTE may vary among sites, leading to imprecision in this hospital characteristic.

Hospitals with the highest RV rates served populations with a larger proportion of patients with government insurance and lower household income, and these hospitals had fewer PEM trained physicians. Variation in RV rates among hospitals may be indicative of the SDHs of their unique patient populations. ED revisit rates should be used cautiously in determining the quality of care of hospitals providing care to differing populations.

Disclosure

Nothing to report.

It has been theorized that the physiologic stress that hospitalized patients experience accounts for their transient vulnerability after discharge, or posthospital syndrome.¹ Their acute illness and life-habit changes during hospitalization result in continued impairment of physiologic systems after discharge, and this impairment might leave them more susceptible to new health threats.¹ However, the theory that the stress experienced after a hospitalization might be associated with readmission has never been investigated.

Four biomarkers of the hypothalamic-pituitary-adrenal (HPA) axis may help quantify posthospitalization stress: (1) midregional pro-adrenomedullin (ADM), a precursor reflecting adrenomedullin activity²; (2) copeptin (the C-terminal part of prepro-vasopressin, produced by the hypothalamus in response to stress^3,4), the level of which closely correlates to the vasopressin level but is more stable and lacks circadian rhythm fluctuations^5-7; (3) cortisol, released by the adrenal cortex in response to stress; and (4) prolactin, an indicator of HPA axis activity. These 4 stress biomarkers have been related to the severity, complications, or mortality of several diseases.^3,5,8-17 Besides explaining the hypothetical association between posthospitalization stress and readmission and death, these biomarkers might be valuable in predicting which patients are at higher risk for readmission. Indeed, many prediction models have been developed to identify those patients, but most of these models underperform, target only very specific populations, or have not been externally validated.¹⁸

We hypothesized that the hospitalization stress measured by biomarkers is associated with readmission or death after discharge. In a prospective cohort study, we evaluated the association between 4 stress biomarkers (ADM, copeptin, cortisol, prolactin) and 30-day unplanned readmissions and deaths after an acute-care medical hospitalization, and assessed their additive value to validated readmission prediction scores.

Study Design and Population

Our prospective cohort study included all consecutive patients aged ≥50 years and admitted to the department of general internal medicine at Fribourg Cantonal Hospital in Switzerland between April 8, 2013 and September 23, 2013. Exclusion criteria were discharge on day of admission; death before discharge; discharge to another division, another acute-care hospital, a rehabilitation clinic, or a palliative-care clinic; and refusal or inability to give informed consent. In this hypothesis-generating observational study, we collected data on a convenience sample of patients and did not calculate sample size before data collection. The study was approved by the local ethics committee, and all patients gave informed consent.

Outcomes

The primary outcome was the composite of first unplanned readmission (to any division of any acute-care hospital) or death within 30 days after discharge from index admission. We also included deaths that occurred after discharge, hypothesizing that patients who died may have been readmitted had they lived. The secondary outcome was the same as the primary, but the period was 90 days. Planned readmission was defined as scheduled hospitalization for nonemergent treatment (eg, chemotherapy) or investigation (eg, elective coronarography). All patients were called 6 months after discharge, and readmissions and deaths recorded. If a patient could not be reached directly, we called his or her next of kin, primary care physician, or nursing home, depending on availability. Furthermore, we checked electronic health records for any readmission or death recorded within the Fribourg hospital network, which includes all 3 acute-care hospitals (Fribourg, Riaz, Tavel) in the same canton (state).

Independent Variables

Stress biomarkers. We measured serum levels of 4 stress biomarkers (ADM, copeptin, cortisol, prolactin) at 8 am on an empty stomach on both day of admission and day of discharge. For a patient whose discharge decision was made after 8 hours for the same day, a blood sample was collected as soon as discharge was planned.

Clinical data. Collected data included demographics, history of hospitalization within 6 months before index admission, hospitalization diagnosis, and Charlson Comorbidity Index (CCI), which includes a list of medical conditions that are assigned a number of 1, 2, 3, or 6 points, according to their severity, and which has been associated with mortality.¹⁹

Causes of Admission, Unplanned Readmission, and Death

Causes of index admission, unplanned readmission, and death were obtained from medical records. We used our consensus opinion and a previous analysis²⁰ to classify these causes by body system, and added 2 categories, cancer and infection (both associated with readmission²⁰). The resulting 9 categories were (1) cancer, (2) respiratory disorder, (3) infectious disorder, (4) neurologic disorder (including dementia, psychiatric disorder, alcohol disorder, and intoxication), (5) gastrointestinal disorder, (6) osteoarticular disorder, (7) renal disorder, (8) cardiovascular disorder (including ischemic disease and heart failure), and (9) other.

Additional Performance With Existing Predictive Models

To better define the explanatory power of biomarkers to predict our outcome, we assessed the performance improvement of 2 validated readmission prediction scores by adding the stress biomarkers. As large effect sizes from additional predictors are needed to increase the power discrimination of a model, a significant performance improvement would further support the biomarkers’ important explanatory power. The 2 prediction scores tested were the LACE index (Length of stay, Admission Acuity, CCI, number of Emergency department visits within preceding 6 months²¹) and the HOSPITAL score (Hemoglobin level at discharge, discharge from Oncology service, Sodium level at discharge, any Procedure performed during index hospitalization, Index admission Type, number of Admissions within preceding 12 months, Length of stay). As we did not have an oncology service, we replaced “discharge from oncology service” with “active diagnosis of cancer.” “Length of stay” was tailored to the median in Switzerland (8 days instead of 5 days; Supplement Table 1).^22,23

Data Analysis

Continuous variables were presented as medians with interquartile ranges (IQRs) because of their non-normal distribution, and categorical variables were presented as frequencies and percentages. We compared medians using the nonparametric K-sample test on the equality of medians, and compared frequencies using the Pearson χ² test. The discriminatory power of each biomarker in predicting readmission and death was calculated with the area under the receiver operating characteristic (ROC) curve (AUROC), using serum levels at discharge to better reflect the postdischarge period. Cutoff levels were selected by taking the best compromise between sensitivity and specificity according to the ROC curves (point nearest top left corner).²⁴

Univariate logistic regression analysis was used to test the prediction of 30-day and 90-day unplanned readmission or death by each biomarker. We built 2 different multivariate models: one adjusting for age and LACE index points²¹ and the other adjusting for age and HOSPITAL score.^22,23

To explore any association between reduction of stress during hospitalization and postdischarge outcome, we additionally calculated for each biomarker the difference between admission and discharge serum levels and assessed its association with readmission or death by logistic regression analysis. Because of the modification of cortisol serum levels during corticosteroid therapy, we excluded patients who underwent systemic corticosteroid therapy before or during hospitalization for the cortisol analysis (n = 105/346). Patients with a missing biomarker level were excluded from the respective analyses: discharge (ADM, 28 patients; copeptin, 27; cortisol, 24; prolactin, 24) and admission (ADM, 12 patients; copeptin, 15; cortisol, 8; prolactin, 8).

To assess an additional value of the biomarkers to prediction scores, we assessed the accuracy of the HOSPITAL score and LACE index in their original versions^21,22 and after adding each biomarker. We used AUROC to assess the discriminatory power and used the method of DeLong et al.²⁵ to compare results with and without adding each biomarker. Calibration was evaluated by comparing Hosmer-Lemeshow goodness-of-fit tests (P > 0.05 indicates good fit). Risk reclassification was assessed by Net Reclassification Improvement (NRI),²⁶ quantifying how appropriately a new model reclassifies patients, compared with an old model. Basically, patients without outcome are assigned +1 if correctly reclassified to a lower risk category or –1 if incorrectly reclassified to a higher risk category. NRI_nonevent is the sum of all points/numbers of patients. Conversely, patients with outcome are assigned +1 if correctly reclassified to a higher risk category or –1 if incorrectly reclassified to a lower risk category. NRI_event is the sum of all points/numbers of patients. NRI_overall is the sum of NRI_event and NRI_nonevent ranging from –2 to 2, with a positive value indicating better classification with the new model.

Two-sided P < 0.05 was used for statistical significance. All statistical analyses were performed with Stata Release 13.0 (StataCorp).

Among the 530 patients admitted to the ward, 184 were excluded (120 meeting exclusion criteria, 64 unable to give consent, Figure). Among the 346 patients included, 11.6% (n = 40) had a 30-day unplanned readmission or death (37 were readmitted, 2 died during readmission, 3 died without readmission). Within 90 days, 26.6% (n = 92) had a readmission or death (84 were readmitted, 10 died during or after readmission, 8 died without readmission).

Clinical Characteristics

Table 1 lists the patients’ baseline characteristics. Median age was 73 years (IQR, 64-82 years). Of the 346 patients included, 172 (49.7%) were men. Median CCI was 7 (IQR, 5-9); according to this index, 310 patients (89.6%) had at least 2 comorbidities. Median length of stay was 7 days (IQR, 4-12 days).

Primary Diagnoses of Admission, Unplanned Readmission, and Death

The 3 main causes of index admission were cardiovascular disorder (n = 92), infectious disorder (n = 70), and neurologic disorder (n = 66). Table 2 lists the causes of readmissions and deaths. A same-diagnosis category between index admission and readmission was found in 17 (45.9%) of the 37 readmitted patients and in 3 (60%) of the 5 patients who died.

Biomarkers and 30-Day Unplanned Readmission or Death

AUROC was 0.53 (95% confidence interval [CI], 0.43-0.63) for ADM, 0.60 (95% CI, 0.50-0.70) for copeptin, 0.59 (95% CI, 0.44-0.73) for cortisol, and 0.56 (95% CI, 0.45-0.66) for prolactin. The difference between admission and discharge levels was not associated with unplanned readmission or death for any of the biomarkers (Supplemental Table 2).

ADM and readmission or death. Median ADM level was not different between patients with and without readmission or death (1.0 nmol/L in each case; P = 1.00). The best cutoff level for ADM was 2 nmol/L (sensitivity, 16.7%; specificity, 91.8%). At this level, ADM was associated with a nonstatistically significant 130% increased odds of 30-day readmission or death (P = 0.09; Table 3, Supplemental Table 3). Conversely, the association with the 90-day outcome was significant (P = 0.02; Table 3, Supplemental Table 4).

Copeptin and readmission or death. Patients with 30-day readmission or death had a higher median copeptin level at discharge than patients without (10.4 pmol/L vs 7.3 pmol/L; P = 0.03). At a copeptin level higher than 9 pmol/L (to convert to pg/mL, divide by 0.249; sensitivity, 66.7%; specificity, 59.7%), both 30-day readmission or death (adjusted odds ratio [OR], 2.69; 95% CI, 1.29-5.64; P = 0.009) and 90-day readmission or death (adjusted OR, 2.76; 95% CI, 1.56-4.88; P < 0.001) were nearly 3 times as likely (Table 3, Supplemental Tables 3 and 4).

Cortisol and readmission or death. Median cortisol was not statistically different between patients with and without the primary outcome (431 nmol/L vs 465 nmol/L; P = 0.72). At a cortisol level higher than 590 nmol/L (to convert to μg/dL, divide by 27.59; sensitivity, 54.6%; specificity, 76.4%), 30-day outcome was more than 3 times as likely (adjusted OR, 3.43; 95% CI, 1.36-8.65; P = 0.009; Table 3, Supplemental Table 3). At 90 days, only the model that adjusted for age and LACE index points remained statistically significant (P = 0.02; Table 3, Supplemental Table 4).

Prolactin and readmission or death. Median prolactin was not statistically different between patients with and without the primary outcome (15.1 μg/L vs 14.1 μg/L; P = 0.24). The best cutoff level for prolactin was 23 μg/L (to convert to mIU/L, divide by 0.05; sensitivity, 27.8%; specificity, 82.9%). Prolactin was associated with a nonstatistically significant increased odds of 30-day (P = 0.16) and 90-day (P = 0.24) readmission or death (Table 3, Supplemental Tables 3 and 4).

Additive Value of Biomarkers to HOSPITAL Score and LACE Index

The AUROC for the original HOSPITAL score, 0.70 (95% CI, 0.60-0.80), nonsignificantly increased to 0.76 after adding the biomarkers (P > 0.14). For the LACE index, AUROC was 0.59 (95% CI, 0.49-0.68), with a significant 0.10 increase with cortisol (P = 0.04) and a near significant increase with copeptin (P = 0.08). Calibration remained almost unchanged after adding the biomarkers to both models (Supplemental Table 5). NRI_overall was positive for all biomarkers, with statistical significance for copeptin added to the HOSPITAL score (0.47; 95% CI, 0.13-0.79) and for cortisol added to the LACE index (0.62; 95% CI, 0.15-1.06).

In this prospective cohort study, 30-day and 90-day unplanned readmission or death was nearly 3 times as likely for patients with high copeptin levels on discharge from an acute-care medical hospitalization, and 30-day readmission or death was more than 3 times as likely for patients with high cortisol levels. High ADM and prolactin levels were not consistently associated with readmission or death. Adding such biomarkers to readmission prediction models improved their performance.

These findings support the theory of posthospital syndrome,¹ which describes a period of vulnerability with increased stress after discharge from an acute-care hospitalization, and which may be associated with adverse outcome. The hormones cortisol and copeptin are strongly related to the stress response in humans.^4,5 As copeptin level has been associated with adverse prognosis for several disorders affecting a wide range of physiologic systems,^3,5,15,27 it may be a valuable biomarker of a stressful condition, even independent of the system affected by the acute illness, and its use may be widely generalizable, in contrast to predictive factors identified in other studies.^18,28,29

Although cortisol was independently associated with 30-day readmission or death, and may be an interesting biomarker and less expensive than copeptin, its measurement is limited in patients treated with systemic corticosteroids. Compared with cortisol, copeptin does not undergo diurnal variation, is less affected by corticosteroid therapy, and mirrors stress levels better.^5,7,30,31 Our results showed that, contrary to cortisol, copeptin was also associated with longer term outcome. High ADM level was associated with readmission or death at 90 days only; lack of a significant association at 30 days might be attributable to a lack of power (fewer outcomes at 30 days). Conversely, prolactin level was consistently not associated with outcome. Prolactin may be affected by many drugs that act on the dopaminergic system (eg, domperidone), and therefore its levels may be more difficult to interpret.

Levels of biomarkers were similar to those measured in patients without previously studied conditions (eg, myocardial infarction).^{5,8,10,13,14,16,17} In most previous analyses, levels were measured during a stressful event, whereas we measured them at discharge. Therefore, these biomarkers may constitute sensitive markers of remaining stress at discharge.

Our finding that copeptin level was independently associated with readmission or death supports its relevance as a possible simple measure of the risk of adverse postdischarge outcome, independently of disease type and independently of known predictors. Stress biomarkers may therefore be valuable predictors of which patients are at high risk. All these biomarkers can be measured within 30 minutes, extending their use beyond everyday practice, except for the possible need of an extra blood draw.

The most accurate and validated models are the HOSPITAL score (AUROC range, 0.68-0.77^23,32-36) and the LACE index (AUROC range, 0.56-0.68^23,34,35,37). Adding biomarkers to these models improved overall performance (up to 0.10 increase in AUROC), which is remarkable given that, once a particular level of discriminatory power is reached, extremely large effect sizes from additional markers are needed to increase AUROC.²⁶ Incremental improvement is objectively supported by positive NRI. Our results suggest biomarkers added to prediction models may improve identification of high-risk patients.

We found that less than 50% of the primary diagnoses belonged to the same diagnosis category at readmission and at index admission. This result is in line with previous findings that readmissions were related to the primary diagnosis at index admission in only 22% to 46% of cases,^20,38 and supports our study hypothesis that readmission is related to underlying stress factors often independent of the underlying illness.¹

Study Limitations and Strengths

Our study had some limitations. First, it was a single-center study with a limited sample size. However, we found significant results within the sample. Second, we could not adjust for drugs that were acting on the dopaminergic system and might have affected prolactin levels. However, such interactions would limit the use of this biomarker in clinical practice anyway. Third, we used specific cutoffs, which might decrease analytical power, in comparison with continuous analyses. However, we followed a recognized method²⁴ and found a significant association even with categorized levels. Furthermore, the distribution of biomarkers could not be normalized by logarithmic transformation, and cutoff values have the advantage of being integrable into score point systems (eg, HOSPITAL score, LACE index). Fourth, although in 2 models we found consistent associations with several potential confounders, we could not exclude residual confounding. Fifth, this study was not powered to assess the biomarkers’ predictive value for readmission and death, which might explain the lack of significant differences between AUROC with and without the biomarkers. For all these reasons, this study should be considered hypothesis-generating.

Our study also had its strengths. First, to our knowledge, this is the first study of the association between stress biomarkers at discharge and unplanned readmission or death. Second, the quality of our data was high, with a low percentage of missing biomarker levels. Third, we excluded planned readmissions. Fourth, we used an unselected medical patient population, which had the noteworthy advantage of widening the generalizability of results.

In this prospective cohort study, high copeptin and cortisol levels at discharge were significantly associated with increased odds, ranging from 2-fold to more than 3-fold, of unplanned readmission or death within 30 days after discharge from an internal medicine ward. This finding supports the theory that a physiologic stress that patients experience during hospitalization makes them more susceptible to new health threats (posthospital syndrome). These biomarkers, copeptin in particular, may help us better identify patients at high risk of early unplanned readmission or death.

Acknowledgment

Biomarker measurement was funded by the research fund of the Department of General Internal Medicine, Fribourg Cantonal Hospital, Fribourg, Switzerland.

Disclosure

Nothing to report.

It has been theorized that the physiologic stress that hospitalized patients experience accounts for their transient vulnerability after discharge, or posthospital syndrome.¹ Their acute illness and life-habit changes during hospitalization result in continued impairment of physiologic systems after discharge, and this impairment might leave them more susceptible to new health threats.¹ However, the theory that the stress experienced after a hospitalization might be associated with readmission has never been investigated.

Four biomarkers of the hypothalamic-pituitary-adrenal (HPA) axis may help quantify posthospitalization stress: (1) midregional pro-adrenomedullin (ADM), a precursor reflecting adrenomedullin activity²; (2) copeptin (the C-terminal part of prepro-vasopressin, produced by the hypothalamus in response to stress^3,4), the level of which closely correlates to the vasopressin level but is more stable and lacks circadian rhythm fluctuations^5-7; (3) cortisol, released by the adrenal cortex in response to stress; and (4) prolactin, an indicator of HPA axis activity. These 4 stress biomarkers have been related to the severity, complications, or mortality of several diseases.^3,5,8-17 Besides explaining the hypothetical association between posthospitalization stress and readmission and death, these biomarkers might be valuable in predicting which patients are at higher risk for readmission. Indeed, many prediction models have been developed to identify those patients, but most of these models underperform, target only very specific populations, or have not been externally validated.¹⁸

We hypothesized that the hospitalization stress measured by biomarkers is associated with readmission or death after discharge. In a prospective cohort study, we evaluated the association between 4 stress biomarkers (ADM, copeptin, cortisol, prolactin) and 30-day unplanned readmissions and deaths after an acute-care medical hospitalization, and assessed their additive value to validated readmission prediction scores.

Study Design and Population

Our prospective cohort study included all consecutive patients aged ≥50 years and admitted to the department of general internal medicine at Fribourg Cantonal Hospital in Switzerland between April 8, 2013 and September 23, 2013. Exclusion criteria were discharge on day of admission; death before discharge; discharge to another division, another acute-care hospital, a rehabilitation clinic, or a palliative-care clinic; and refusal or inability to give informed consent. In this hypothesis-generating observational study, we collected data on a convenience sample of patients and did not calculate sample size before data collection. The study was approved by the local ethics committee, and all patients gave informed consent.

Outcomes

The primary outcome was the composite of first unplanned readmission (to any division of any acute-care hospital) or death within 30 days after discharge from index admission. We also included deaths that occurred after discharge, hypothesizing that patients who died may have been readmitted had they lived. The secondary outcome was the same as the primary, but the period was 90 days. Planned readmission was defined as scheduled hospitalization for nonemergent treatment (eg, chemotherapy) or investigation (eg, elective coronarography). All patients were called 6 months after discharge, and readmissions and deaths recorded. If a patient could not be reached directly, we called his or her next of kin, primary care physician, or nursing home, depending on availability. Furthermore, we checked electronic health records for any readmission or death recorded within the Fribourg hospital network, which includes all 3 acute-care hospitals (Fribourg, Riaz, Tavel) in the same canton (state).

Independent Variables

Stress biomarkers. We measured serum levels of 4 stress biomarkers (ADM, copeptin, cortisol, prolactin) at 8 am on an empty stomach on both day of admission and day of discharge. For a patient whose discharge decision was made after 8 hours for the same day, a blood sample was collected as soon as discharge was planned.

Clinical data. Collected data included demographics, history of hospitalization within 6 months before index admission, hospitalization diagnosis, and Charlson Comorbidity Index (CCI), which includes a list of medical conditions that are assigned a number of 1, 2, 3, or 6 points, according to their severity, and which has been associated with mortality.¹⁹

Causes of Admission, Unplanned Readmission, and Death

Causes of index admission, unplanned readmission, and death were obtained from medical records. We used our consensus opinion and a previous analysis²⁰ to classify these causes by body system, and added 2 categories, cancer and infection (both associated with readmission²⁰). The resulting 9 categories were (1) cancer, (2) respiratory disorder, (3) infectious disorder, (4) neurologic disorder (including dementia, psychiatric disorder, alcohol disorder, and intoxication), (5) gastrointestinal disorder, (6) osteoarticular disorder, (7) renal disorder, (8) cardiovascular disorder (including ischemic disease and heart failure), and (9) other.

Additional Performance With Existing Predictive Models

To better define the explanatory power of biomarkers to predict our outcome, we assessed the performance improvement of 2 validated readmission prediction scores by adding the stress biomarkers. As large effect sizes from additional predictors are needed to increase the power discrimination of a model, a significant performance improvement would further support the biomarkers’ important explanatory power. The 2 prediction scores tested were the LACE index (Length of stay, Admission Acuity, CCI, number of Emergency department visits within preceding 6 months²¹) and the HOSPITAL score (Hemoglobin level at discharge, discharge from Oncology service, Sodium level at discharge, any Procedure performed during index hospitalization, Index admission Type, number of Admissions within preceding 12 months, Length of stay). As we did not have an oncology service, we replaced “discharge from oncology service” with “active diagnosis of cancer.” “Length of stay” was tailored to the median in Switzerland (8 days instead of 5 days; Supplement Table 1).^22,23

Data Analysis

Continuous variables were presented as medians with interquartile ranges (IQRs) because of their non-normal distribution, and categorical variables were presented as frequencies and percentages. We compared medians using the nonparametric K-sample test on the equality of medians, and compared frequencies using the Pearson χ² test. The discriminatory power of each biomarker in predicting readmission and death was calculated with the area under the receiver operating characteristic (ROC) curve (AUROC), using serum levels at discharge to better reflect the postdischarge period. Cutoff levels were selected by taking the best compromise between sensitivity and specificity according to the ROC curves (point nearest top left corner).²⁴

Univariate logistic regression analysis was used to test the prediction of 30-day and 90-day unplanned readmission or death by each biomarker. We built 2 different multivariate models: one adjusting for age and LACE index points²¹ and the other adjusting for age and HOSPITAL score.^22,23

To explore any association between reduction of stress during hospitalization and postdischarge outcome, we additionally calculated for each biomarker the difference between admission and discharge serum levels and assessed its association with readmission or death by logistic regression analysis. Because of the modification of cortisol serum levels during corticosteroid therapy, we excluded patients who underwent systemic corticosteroid therapy before or during hospitalization for the cortisol analysis (n = 105/346). Patients with a missing biomarker level were excluded from the respective analyses: discharge (ADM, 28 patients; copeptin, 27; cortisol, 24; prolactin, 24) and admission (ADM, 12 patients; copeptin, 15; cortisol, 8; prolactin, 8).

To assess an additional value of the biomarkers to prediction scores, we assessed the accuracy of the HOSPITAL score and LACE index in their original versions^21,22 and after adding each biomarker. We used AUROC to assess the discriminatory power and used the method of DeLong et al.²⁵ to compare results with and without adding each biomarker. Calibration was evaluated by comparing Hosmer-Lemeshow goodness-of-fit tests (P > 0.05 indicates good fit). Risk reclassification was assessed by Net Reclassification Improvement (NRI),²⁶ quantifying how appropriately a new model reclassifies patients, compared with an old model. Basically, patients without outcome are assigned +1 if correctly reclassified to a lower risk category or –1 if incorrectly reclassified to a higher risk category. NRI_nonevent is the sum of all points/numbers of patients. Conversely, patients with outcome are assigned +1 if correctly reclassified to a higher risk category or –1 if incorrectly reclassified to a lower risk category. NRI_event is the sum of all points/numbers of patients. NRI_overall is the sum of NRI_event and NRI_nonevent ranging from –2 to 2, with a positive value indicating better classification with the new model.

Two-sided P < 0.05 was used for statistical significance. All statistical analyses were performed with Stata Release 13.0 (StataCorp).

Among the 530 patients admitted to the ward, 184 were excluded (120 meeting exclusion criteria, 64 unable to give consent, Figure). Among the 346 patients included, 11.6% (n = 40) had a 30-day unplanned readmission or death (37 were readmitted, 2 died during readmission, 3 died without readmission). Within 90 days, 26.6% (n = 92) had a readmission or death (84 were readmitted, 10 died during or after readmission, 8 died without readmission).

Clinical Characteristics

Table 1 lists the patients’ baseline characteristics. Median age was 73 years (IQR, 64-82 years). Of the 346 patients included, 172 (49.7%) were men. Median CCI was 7 (IQR, 5-9); according to this index, 310 patients (89.6%) had at least 2 comorbidities. Median length of stay was 7 days (IQR, 4-12 days).

Primary Diagnoses of Admission, Unplanned Readmission, and Death

The 3 main causes of index admission were cardiovascular disorder (n = 92), infectious disorder (n = 70), and neurologic disorder (n = 66). Table 2 lists the causes of readmissions and deaths. A same-diagnosis category between index admission and readmission was found in 17 (45.9%) of the 37 readmitted patients and in 3 (60%) of the 5 patients who died.

Biomarkers and 30-Day Unplanned Readmission or Death

AUROC was 0.53 (95% confidence interval [CI], 0.43-0.63) for ADM, 0.60 (95% CI, 0.50-0.70) for copeptin, 0.59 (95% CI, 0.44-0.73) for cortisol, and 0.56 (95% CI, 0.45-0.66) for prolactin. The difference between admission and discharge levels was not associated with unplanned readmission or death for any of the biomarkers (Supplemental Table 2).

ADM and readmission or death. Median ADM level was not different between patients with and without readmission or death (1.0 nmol/L in each case; P = 1.00). The best cutoff level for ADM was 2 nmol/L (sensitivity, 16.7%; specificity, 91.8%). At this level, ADM was associated with a nonstatistically significant 130% increased odds of 30-day readmission or death (P = 0.09; Table 3, Supplemental Table 3). Conversely, the association with the 90-day outcome was significant (P = 0.02; Table 3, Supplemental Table 4).

Copeptin and readmission or death. Patients with 30-day readmission or death had a higher median copeptin level at discharge than patients without (10.4 pmol/L vs 7.3 pmol/L; P = 0.03). At a copeptin level higher than 9 pmol/L (to convert to pg/mL, divide by 0.249; sensitivity, 66.7%; specificity, 59.7%), both 30-day readmission or death (adjusted odds ratio [OR], 2.69; 95% CI, 1.29-5.64; P = 0.009) and 90-day readmission or death (adjusted OR, 2.76; 95% CI, 1.56-4.88; P < 0.001) were nearly 3 times as likely (Table 3, Supplemental Tables 3 and 4).

Cortisol and readmission or death. Median cortisol was not statistically different between patients with and without the primary outcome (431 nmol/L vs 465 nmol/L; P = 0.72). At a cortisol level higher than 590 nmol/L (to convert to μg/dL, divide by 27.59; sensitivity, 54.6%; specificity, 76.4%), 30-day outcome was more than 3 times as likely (adjusted OR, 3.43; 95% CI, 1.36-8.65; P = 0.009; Table 3, Supplemental Table 3). At 90 days, only the model that adjusted for age and LACE index points remained statistically significant (P = 0.02; Table 3, Supplemental Table 4).

Prolactin and readmission or death. Median prolactin was not statistically different between patients with and without the primary outcome (15.1 μg/L vs 14.1 μg/L; P = 0.24). The best cutoff level for prolactin was 23 μg/L (to convert to mIU/L, divide by 0.05; sensitivity, 27.8%; specificity, 82.9%). Prolactin was associated with a nonstatistically significant increased odds of 30-day (P = 0.16) and 90-day (P = 0.24) readmission or death (Table 3, Supplemental Tables 3 and 4).

Additive Value of Biomarkers to HOSPITAL Score and LACE Index

The AUROC for the original HOSPITAL score, 0.70 (95% CI, 0.60-0.80), nonsignificantly increased to 0.76 after adding the biomarkers (P > 0.14). For the LACE index, AUROC was 0.59 (95% CI, 0.49-0.68), with a significant 0.10 increase with cortisol (P = 0.04) and a near significant increase with copeptin (P = 0.08). Calibration remained almost unchanged after adding the biomarkers to both models (Supplemental Table 5). NRI_overall was positive for all biomarkers, with statistical significance for copeptin added to the HOSPITAL score (0.47; 95% CI, 0.13-0.79) and for cortisol added to the LACE index (0.62; 95% CI, 0.15-1.06).

In this prospective cohort study, 30-day and 90-day unplanned readmission or death was nearly 3 times as likely for patients with high copeptin levels on discharge from an acute-care medical hospitalization, and 30-day readmission or death was more than 3 times as likely for patients with high cortisol levels. High ADM and prolactin levels were not consistently associated with readmission or death. Adding such biomarkers to readmission prediction models improved their performance.

These findings support the theory of posthospital syndrome,¹ which describes a period of vulnerability with increased stress after discharge from an acute-care hospitalization, and which may be associated with adverse outcome. The hormones cortisol and copeptin are strongly related to the stress response in humans.^4,5 As copeptin level has been associated with adverse prognosis for several disorders affecting a wide range of physiologic systems,^3,5,15,27 it may be a valuable biomarker of a stressful condition, even independent of the system affected by the acute illness, and its use may be widely generalizable, in contrast to predictive factors identified in other studies.^18,28,29

Although cortisol was independently associated with 30-day readmission or death, and may be an interesting biomarker and less expensive than copeptin, its measurement is limited in patients treated with systemic corticosteroids. Compared with cortisol, copeptin does not undergo diurnal variation, is less affected by corticosteroid therapy, and mirrors stress levels better.^5,7,30,31 Our results showed that, contrary to cortisol, copeptin was also associated with longer term outcome. High ADM level was associated with readmission or death at 90 days only; lack of a significant association at 30 days might be attributable to a lack of power (fewer outcomes at 30 days). Conversely, prolactin level was consistently not associated with outcome. Prolactin may be affected by many drugs that act on the dopaminergic system (eg, domperidone), and therefore its levels may be more difficult to interpret.

Levels of biomarkers were similar to those measured in patients without previously studied conditions (eg, myocardial infarction).^{5,8,10,13,14,16,17} In most previous analyses, levels were measured during a stressful event, whereas we measured them at discharge. Therefore, these biomarkers may constitute sensitive markers of remaining stress at discharge.

Our finding that copeptin level was independently associated with readmission or death supports its relevance as a possible simple measure of the risk of adverse postdischarge outcome, independently of disease type and independently of known predictors. Stress biomarkers may therefore be valuable predictors of which patients are at high risk. All these biomarkers can be measured within 30 minutes, extending their use beyond everyday practice, except for the possible need of an extra blood draw.

The most accurate and validated models are the HOSPITAL score (AUROC range, 0.68-0.77^23,32-36) and the LACE index (AUROC range, 0.56-0.68^23,34,35,37). Adding biomarkers to these models improved overall performance (up to 0.10 increase in AUROC), which is remarkable given that, once a particular level of discriminatory power is reached, extremely large effect sizes from additional markers are needed to increase AUROC.²⁶ Incremental improvement is objectively supported by positive NRI. Our results suggest biomarkers added to prediction models may improve identification of high-risk patients.

We found that less than 50% of the primary diagnoses belonged to the same diagnosis category at readmission and at index admission. This result is in line with previous findings that readmissions were related to the primary diagnosis at index admission in only 22% to 46% of cases,^20,38 and supports our study hypothesis that readmission is related to underlying stress factors often independent of the underlying illness.¹

Study Limitations and Strengths

Our study had some limitations. First, it was a single-center study with a limited sample size. However, we found significant results within the sample. Second, we could not adjust for drugs that were acting on the dopaminergic system and might have affected prolactin levels. However, such interactions would limit the use of this biomarker in clinical practice anyway. Third, we used specific cutoffs, which might decrease analytical power, in comparison with continuous analyses. However, we followed a recognized method²⁴ and found a significant association even with categorized levels. Furthermore, the distribution of biomarkers could not be normalized by logarithmic transformation, and cutoff values have the advantage of being integrable into score point systems (eg, HOSPITAL score, LACE index). Fourth, although in 2 models we found consistent associations with several potential confounders, we could not exclude residual confounding. Fifth, this study was not powered to assess the biomarkers’ predictive value for readmission and death, which might explain the lack of significant differences between AUROC with and without the biomarkers. For all these reasons, this study should be considered hypothesis-generating.

Our study also had its strengths. First, to our knowledge, this is the first study of the association between stress biomarkers at discharge and unplanned readmission or death. Second, the quality of our data was high, with a low percentage of missing biomarker levels. Third, we excluded planned readmissions. Fourth, we used an unselected medical patient population, which had the noteworthy advantage of widening the generalizability of results.

In this prospective cohort study, high copeptin and cortisol levels at discharge were significantly associated with increased odds, ranging from 2-fold to more than 3-fold, of unplanned readmission or death within 30 days after discharge from an internal medicine ward. This finding supports the theory that a physiologic stress that patients experience during hospitalization makes them more susceptible to new health threats (posthospital syndrome). These biomarkers, copeptin in particular, may help us better identify patients at high risk of early unplanned readmission or death.

Acknowledgment

Biomarker measurement was funded by the research fund of the Department of General Internal Medicine, Fribourg Cantonal Hospital, Fribourg, Switzerland.

Disclosure

Nothing to report.

It has been theorized that the physiologic stress that hospitalized patients experience accounts for their transient vulnerability after discharge, or posthospital syndrome.¹ Their acute illness and life-habit changes during hospitalization result in continued impairment of physiologic systems after discharge, and this impairment might leave them more susceptible to new health threats.¹ However, the theory that the stress experienced after a hospitalization might be associated with readmission has never been investigated.

Four biomarkers of the hypothalamic-pituitary-adrenal (HPA) axis may help quantify posthospitalization stress: (1) midregional pro-adrenomedullin (ADM), a precursor reflecting adrenomedullin activity²; (2) copeptin (the C-terminal part of prepro-vasopressin, produced by the hypothalamus in response to stress^3,4), the level of which closely correlates to the vasopressin level but is more stable and lacks circadian rhythm fluctuations^5-7; (3) cortisol, released by the adrenal cortex in response to stress; and (4) prolactin, an indicator of HPA axis activity. These 4 stress biomarkers have been related to the severity, complications, or mortality of several diseases.^3,5,8-17 Besides explaining the hypothetical association between posthospitalization stress and readmission and death, these biomarkers might be valuable in predicting which patients are at higher risk for readmission. Indeed, many prediction models have been developed to identify those patients, but most of these models underperform, target only very specific populations, or have not been externally validated.¹⁸

We hypothesized that the hospitalization stress measured by biomarkers is associated with readmission or death after discharge. In a prospective cohort study, we evaluated the association between 4 stress biomarkers (ADM, copeptin, cortisol, prolactin) and 30-day unplanned readmissions and deaths after an acute-care medical hospitalization, and assessed their additive value to validated readmission prediction scores.

Study Design and Population

Our prospective cohort study included all consecutive patients aged ≥50 years and admitted to the department of general internal medicine at Fribourg Cantonal Hospital in Switzerland between April 8, 2013 and September 23, 2013. Exclusion criteria were discharge on day of admission; death before discharge; discharge to another division, another acute-care hospital, a rehabilitation clinic, or a palliative-care clinic; and refusal or inability to give informed consent. In this hypothesis-generating observational study, we collected data on a convenience sample of patients and did not calculate sample size before data collection. The study was approved by the local ethics committee, and all patients gave informed consent.

Outcomes

The primary outcome was the composite of first unplanned readmission (to any division of any acute-care hospital) or death within 30 days after discharge from index admission. We also included deaths that occurred after discharge, hypothesizing that patients who died may have been readmitted had they lived. The secondary outcome was the same as the primary, but the period was 90 days. Planned readmission was defined as scheduled hospitalization for nonemergent treatment (eg, chemotherapy) or investigation (eg, elective coronarography). All patients were called 6 months after discharge, and readmissions and deaths recorded. If a patient could not be reached directly, we called his or her next of kin, primary care physician, or nursing home, depending on availability. Furthermore, we checked electronic health records for any readmission or death recorded within the Fribourg hospital network, which includes all 3 acute-care hospitals (Fribourg, Riaz, Tavel) in the same canton (state).

Independent Variables

Stress biomarkers. We measured serum levels of 4 stress biomarkers (ADM, copeptin, cortisol, prolactin) at 8 am on an empty stomach on both day of admission and day of discharge. For a patient whose discharge decision was made after 8 hours for the same day, a blood sample was collected as soon as discharge was planned.

Clinical data. Collected data included demographics, history of hospitalization within 6 months before index admission, hospitalization diagnosis, and Charlson Comorbidity Index (CCI), which includes a list of medical conditions that are assigned a number of 1, 2, 3, or 6 points, according to their severity, and which has been associated with mortality.¹⁹

Causes of Admission, Unplanned Readmission, and Death

Causes of index admission, unplanned readmission, and death were obtained from medical records. We used our consensus opinion and a previous analysis²⁰ to classify these causes by body system, and added 2 categories, cancer and infection (both associated with readmission²⁰). The resulting 9 categories were (1) cancer, (2) respiratory disorder, (3) infectious disorder, (4) neurologic disorder (including dementia, psychiatric disorder, alcohol disorder, and intoxication), (5) gastrointestinal disorder, (6) osteoarticular disorder, (7) renal disorder, (8) cardiovascular disorder (including ischemic disease and heart failure), and (9) other.

Additional Performance With Existing Predictive Models

To better define the explanatory power of biomarkers to predict our outcome, we assessed the performance improvement of 2 validated readmission prediction scores by adding the stress biomarkers. As large effect sizes from additional predictors are needed to increase the power discrimination of a model, a significant performance improvement would further support the biomarkers’ important explanatory power. The 2 prediction scores tested were the LACE index (Length of stay, Admission Acuity, CCI, number of Emergency department visits within preceding 6 months²¹) and the HOSPITAL score (Hemoglobin level at discharge, discharge from Oncology service, Sodium level at discharge, any Procedure performed during index hospitalization, Index admission Type, number of Admissions within preceding 12 months, Length of stay). As we did not have an oncology service, we replaced “discharge from oncology service” with “active diagnosis of cancer.” “Length of stay” was tailored to the median in Switzerland (8 days instead of 5 days; Supplement Table 1).^22,23

Data Analysis

Continuous variables were presented as medians with interquartile ranges (IQRs) because of their non-normal distribution, and categorical variables were presented as frequencies and percentages. We compared medians using the nonparametric K-sample test on the equality of medians, and compared frequencies using the Pearson χ² test. The discriminatory power of each biomarker in predicting readmission and death was calculated with the area under the receiver operating characteristic (ROC) curve (AUROC), using serum levels at discharge to better reflect the postdischarge period. Cutoff levels were selected by taking the best compromise between sensitivity and specificity according to the ROC curves (point nearest top left corner).²⁴

Univariate logistic regression analysis was used to test the prediction of 30-day and 90-day unplanned readmission or death by each biomarker. We built 2 different multivariate models: one adjusting for age and LACE index points²¹ and the other adjusting for age and HOSPITAL score.^22,23

To explore any association between reduction of stress during hospitalization and postdischarge outcome, we additionally calculated for each biomarker the difference between admission and discharge serum levels and assessed its association with readmission or death by logistic regression analysis. Because of the modification of cortisol serum levels during corticosteroid therapy, we excluded patients who underwent systemic corticosteroid therapy before or during hospitalization for the cortisol analysis (n = 105/346). Patients with a missing biomarker level were excluded from the respective analyses: discharge (ADM, 28 patients; copeptin, 27; cortisol, 24; prolactin, 24) and admission (ADM, 12 patients; copeptin, 15; cortisol, 8; prolactin, 8).

To assess an additional value of the biomarkers to prediction scores, we assessed the accuracy of the HOSPITAL score and LACE index in their original versions^21,22 and after adding each biomarker. We used AUROC to assess the discriminatory power and used the method of DeLong et al.²⁵ to compare results with and without adding each biomarker. Calibration was evaluated by comparing Hosmer-Lemeshow goodness-of-fit tests (P > 0.05 indicates good fit). Risk reclassification was assessed by Net Reclassification Improvement (NRI),²⁶ quantifying how appropriately a new model reclassifies patients, compared with an old model. Basically, patients without outcome are assigned +1 if correctly reclassified to a lower risk category or –1 if incorrectly reclassified to a higher risk category. NRI_nonevent is the sum of all points/numbers of patients. Conversely, patients with outcome are assigned +1 if correctly reclassified to a higher risk category or –1 if incorrectly reclassified to a lower risk category. NRI_event is the sum of all points/numbers of patients. NRI_overall is the sum of NRI_event and NRI_nonevent ranging from –2 to 2, with a positive value indicating better classification with the new model.

Two-sided P < 0.05 was used for statistical significance. All statistical analyses were performed with Stata Release 13.0 (StataCorp).

Among the 530 patients admitted to the ward, 184 were excluded (120 meeting exclusion criteria, 64 unable to give consent, Figure). Among the 346 patients included, 11.6% (n = 40) had a 30-day unplanned readmission or death (37 were readmitted, 2 died during readmission, 3 died without readmission). Within 90 days, 26.6% (n = 92) had a readmission or death (84 were readmitted, 10 died during or after readmission, 8 died without readmission).

Clinical Characteristics

Table 1 lists the patients’ baseline characteristics. Median age was 73 years (IQR, 64-82 years). Of the 346 patients included, 172 (49.7%) were men. Median CCI was 7 (IQR, 5-9); according to this index, 310 patients (89.6%) had at least 2 comorbidities. Median length of stay was 7 days (IQR, 4-12 days).

Primary Diagnoses of Admission, Unplanned Readmission, and Death

The 3 main causes of index admission were cardiovascular disorder (n = 92), infectious disorder (n = 70), and neurologic disorder (n = 66). Table 2 lists the causes of readmissions and deaths. A same-diagnosis category between index admission and readmission was found in 17 (45.9%) of the 37 readmitted patients and in 3 (60%) of the 5 patients who died.

Biomarkers and 30-Day Unplanned Readmission or Death

AUROC was 0.53 (95% confidence interval [CI], 0.43-0.63) for ADM, 0.60 (95% CI, 0.50-0.70) for copeptin, 0.59 (95% CI, 0.44-0.73) for cortisol, and 0.56 (95% CI, 0.45-0.66) for prolactin. The difference between admission and discharge levels was not associated with unplanned readmission or death for any of the biomarkers (Supplemental Table 2).

ADM and readmission or death. Median ADM level was not different between patients with and without readmission or death (1.0 nmol/L in each case; P = 1.00). The best cutoff level for ADM was 2 nmol/L (sensitivity, 16.7%; specificity, 91.8%). At this level, ADM was associated with a nonstatistically significant 130% increased odds of 30-day readmission or death (P = 0.09; Table 3, Supplemental Table 3). Conversely, the association with the 90-day outcome was significant (P = 0.02; Table 3, Supplemental Table 4).

Copeptin and readmission or death. Patients with 30-day readmission or death had a higher median copeptin level at discharge than patients without (10.4 pmol/L vs 7.3 pmol/L; P = 0.03). At a copeptin level higher than 9 pmol/L (to convert to pg/mL, divide by 0.249; sensitivity, 66.7%; specificity, 59.7%), both 30-day readmission or death (adjusted odds ratio [OR], 2.69; 95% CI, 1.29-5.64; P = 0.009) and 90-day readmission or death (adjusted OR, 2.76; 95% CI, 1.56-4.88; P < 0.001) were nearly 3 times as likely (Table 3, Supplemental Tables 3 and 4).

Cortisol and readmission or death. Median cortisol was not statistically different between patients with and without the primary outcome (431 nmol/L vs 465 nmol/L; P = 0.72). At a cortisol level higher than 590 nmol/L (to convert to μg/dL, divide by 27.59; sensitivity, 54.6%; specificity, 76.4%), 30-day outcome was more than 3 times as likely (adjusted OR, 3.43; 95% CI, 1.36-8.65; P = 0.009; Table 3, Supplemental Table 3). At 90 days, only the model that adjusted for age and LACE index points remained statistically significant (P = 0.02; Table 3, Supplemental Table 4).

Prolactin and readmission or death. Median prolactin was not statistically different between patients with and without the primary outcome (15.1 μg/L vs 14.1 μg/L; P = 0.24). The best cutoff level for prolactin was 23 μg/L (to convert to mIU/L, divide by 0.05; sensitivity, 27.8%; specificity, 82.9%). Prolactin was associated with a nonstatistically significant increased odds of 30-day (P = 0.16) and 90-day (P = 0.24) readmission or death (Table 3, Supplemental Tables 3 and 4).

Additive Value of Biomarkers to HOSPITAL Score and LACE Index

The AUROC for the original HOSPITAL score, 0.70 (95% CI, 0.60-0.80), nonsignificantly increased to 0.76 after adding the biomarkers (P > 0.14). For the LACE index, AUROC was 0.59 (95% CI, 0.49-0.68), with a significant 0.10 increase with cortisol (P = 0.04) and a near significant increase with copeptin (P = 0.08). Calibration remained almost unchanged after adding the biomarkers to both models (Supplemental Table 5). NRI_overall was positive for all biomarkers, with statistical significance for copeptin added to the HOSPITAL score (0.47; 95% CI, 0.13-0.79) and for cortisol added to the LACE index (0.62; 95% CI, 0.15-1.06).

In this prospective cohort study, 30-day and 90-day unplanned readmission or death was nearly 3 times as likely for patients with high copeptin levels on discharge from an acute-care medical hospitalization, and 30-day readmission or death was more than 3 times as likely for patients with high cortisol levels. High ADM and prolactin levels were not consistently associated with readmission or death. Adding such biomarkers to readmission prediction models improved their performance.

These findings support the theory of posthospital syndrome,¹ which describes a period of vulnerability with increased stress after discharge from an acute-care hospitalization, and which may be associated with adverse outcome. The hormones cortisol and copeptin are strongly related to the stress response in humans.^4,5 As copeptin level has been associated with adverse prognosis for several disorders affecting a wide range of physiologic systems,^3,5,15,27 it may be a valuable biomarker of a stressful condition, even independent of the system affected by the acute illness, and its use may be widely generalizable, in contrast to predictive factors identified in other studies.^18,28,29

Although cortisol was independently associated with 30-day readmission or death, and may be an interesting biomarker and less expensive than copeptin, its measurement is limited in patients treated with systemic corticosteroids. Compared with cortisol, copeptin does not undergo diurnal variation, is less affected by corticosteroid therapy, and mirrors stress levels better.^5,7,30,31 Our results showed that, contrary to cortisol, copeptin was also associated with longer term outcome. High ADM level was associated with readmission or death at 90 days only; lack of a significant association at 30 days might be attributable to a lack of power (fewer outcomes at 30 days). Conversely, prolactin level was consistently not associated with outcome. Prolactin may be affected by many drugs that act on the dopaminergic system (eg, domperidone), and therefore its levels may be more difficult to interpret.

Levels of biomarkers were similar to those measured in patients without previously studied conditions (eg, myocardial infarction).^{5,8,10,13,14,16,17} In most previous analyses, levels were measured during a stressful event, whereas we measured them at discharge. Therefore, these biomarkers may constitute sensitive markers of remaining stress at discharge.

Our finding that copeptin level was independently associated with readmission or death supports its relevance as a possible simple measure of the risk of adverse postdischarge outcome, independently of disease type and independently of known predictors. Stress biomarkers may therefore be valuable predictors of which patients are at high risk. All these biomarkers can be measured within 30 minutes, extending their use beyond everyday practice, except for the possible need of an extra blood draw.

The most accurate and validated models are the HOSPITAL score (AUROC range, 0.68-0.77^23,32-36) and the LACE index (AUROC range, 0.56-0.68^23,34,35,37). Adding biomarkers to these models improved overall performance (up to 0.10 increase in AUROC), which is remarkable given that, once a particular level of discriminatory power is reached, extremely large effect sizes from additional markers are needed to increase AUROC.²⁶ Incremental improvement is objectively supported by positive NRI. Our results suggest biomarkers added to prediction models may improve identification of high-risk patients.

We found that less than 50% of the primary diagnoses belonged to the same diagnosis category at readmission and at index admission. This result is in line with previous findings that readmissions were related to the primary diagnosis at index admission in only 22% to 46% of cases,^20,38 and supports our study hypothesis that readmission is related to underlying stress factors often independent of the underlying illness.¹

Study Limitations and Strengths

Our study had some limitations. First, it was a single-center study with a limited sample size. However, we found significant results within the sample. Second, we could not adjust for drugs that were acting on the dopaminergic system and might have affected prolactin levels. However, such interactions would limit the use of this biomarker in clinical practice anyway. Third, we used specific cutoffs, which might decrease analytical power, in comparison with continuous analyses. However, we followed a recognized method²⁴ and found a significant association even with categorized levels. Furthermore, the distribution of biomarkers could not be normalized by logarithmic transformation, and cutoff values have the advantage of being integrable into score point systems (eg, HOSPITAL score, LACE index). Fourth, although in 2 models we found consistent associations with several potential confounders, we could not exclude residual confounding. Fifth, this study was not powered to assess the biomarkers’ predictive value for readmission and death, which might explain the lack of significant differences between AUROC with and without the biomarkers. For all these reasons, this study should be considered hypothesis-generating.

Our study also had its strengths. First, to our knowledge, this is the first study of the association between stress biomarkers at discharge and unplanned readmission or death. Second, the quality of our data was high, with a low percentage of missing biomarker levels. Third, we excluded planned readmissions. Fourth, we used an unselected medical patient population, which had the noteworthy advantage of widening the generalizability of results.

In this prospective cohort study, high copeptin and cortisol levels at discharge were significantly associated with increased odds, ranging from 2-fold to more than 3-fold, of unplanned readmission or death within 30 days after discharge from an internal medicine ward. This finding supports the theory that a physiologic stress that patients experience during hospitalization makes them more susceptible to new health threats (posthospital syndrome). These biomarkers, copeptin in particular, may help us better identify patients at high risk of early unplanned readmission or death.

Acknowledgment

Biomarker measurement was funded by the research fund of the Department of General Internal Medicine, Fribourg Cantonal Hospital, Fribourg, Switzerland.

Disclosure

Nothing to report.

BOSTON – The number of cases of meningitis caused by tuberculosis has fallen dramatically in the United States in recent decades as TB itself has become less common, according to findings from a study presented at the annual meeting of the American Academy of Neurology.

However, these findings from patient hospitalizations during 1993-2013 in the Nationwide Inpatient Sample database also indicate that neurologic complications from TB meningitis are on the rise.

The findings suggest that neurologists need to become involved whenever a patient with TB shows signs of neurologic problems, said study lead author Alexander E. Merkler, MD, of Cornell University, New York, in an interview. “They’re at high risk, and some complications can be life threatening.”

Zerbor/Thinkstock

• Hydrocephalus, from 2.3% (95% confidence interval, 0.5%-4.2%) to 5.4% (95% CI, 2.3%-10.0%).

• Seizure, from 2.9% (95% CI, 0.3%-5.4%) to 14.1% (95% CI, 7.3%-21.0%).

• Stroke, from 2.9% (95% CI, 0.6%-5.3%) to 13.0% (95% CI, 6.3%-19.8%).

• Vision and hearing impairment, from 8.2% (95% CI, 4.8%-11.6%) to 10.9% (95% CI, 4.1%-17.6%), and from 1.1% (95% CI, 0.0%-2.3%) to 3.3% (95% CI, 0.0%-6.9%), respectively.

Dr. Merkler said it’s not clear why these rates are going up, but it may be because patients have more complications as a result of living longer. Another theory is that a form of drug-resistant TB is boosting the level of these complications, Dr. Merkler said, but he’s skeptical of that idea: “I don’t know why drug resistance would lead to more neurological complications.”

The study was funded by the National Institute of Neurological Disorders and Stroke and the Michael Goldberg Stroke Research Fund. Dr. Merkler reported no relevant financial disclosures.

BOSTON – The number of cases of meningitis caused by tuberculosis has fallen dramatically in the United States in recent decades as TB itself has become less common, according to findings from a study presented at the annual meeting of the American Academy of Neurology.

However, these findings from patient hospitalizations during 1993-2013 in the Nationwide Inpatient Sample database also indicate that neurologic complications from TB meningitis are on the rise.

The findings suggest that neurologists need to become involved whenever a patient with TB shows signs of neurologic problems, said study lead author Alexander E. Merkler, MD, of Cornell University, New York, in an interview. “They’re at high risk, and some complications can be life threatening.”

Zerbor/Thinkstock

• Hydrocephalus, from 2.3% (95% confidence interval, 0.5%-4.2%) to 5.4% (95% CI, 2.3%-10.0%).

• Seizure, from 2.9% (95% CI, 0.3%-5.4%) to 14.1% (95% CI, 7.3%-21.0%).

• Stroke, from 2.9% (95% CI, 0.6%-5.3%) to 13.0% (95% CI, 6.3%-19.8%).

• Vision and hearing impairment, from 8.2% (95% CI, 4.8%-11.6%) to 10.9% (95% CI, 4.1%-17.6%), and from 1.1% (95% CI, 0.0%-2.3%) to 3.3% (95% CI, 0.0%-6.9%), respectively.

Dr. Merkler said it’s not clear why these rates are going up, but it may be because patients have more complications as a result of living longer. Another theory is that a form of drug-resistant TB is boosting the level of these complications, Dr. Merkler said, but he’s skeptical of that idea: “I don’t know why drug resistance would lead to more neurological complications.”

The study was funded by the National Institute of Neurological Disorders and Stroke and the Michael Goldberg Stroke Research Fund. Dr. Merkler reported no relevant financial disclosures.

BOSTON – The number of cases of meningitis caused by tuberculosis has fallen dramatically in the United States in recent decades as TB itself has become less common, according to findings from a study presented at the annual meeting of the American Academy of Neurology.

However, these findings from patient hospitalizations during 1993-2013 in the Nationwide Inpatient Sample database also indicate that neurologic complications from TB meningitis are on the rise.

The findings suggest that neurologists need to become involved whenever a patient with TB shows signs of neurologic problems, said study lead author Alexander E. Merkler, MD, of Cornell University, New York, in an interview. “They’re at high risk, and some complications can be life threatening.”

Zerbor/Thinkstock

• Hydrocephalus, from 2.3% (95% confidence interval, 0.5%-4.2%) to 5.4% (95% CI, 2.3%-10.0%).

• Seizure, from 2.9% (95% CI, 0.3%-5.4%) to 14.1% (95% CI, 7.3%-21.0%).

• Stroke, from 2.9% (95% CI, 0.6%-5.3%) to 13.0% (95% CI, 6.3%-19.8%).

• Vision and hearing impairment, from 8.2% (95% CI, 4.8%-11.6%) to 10.9% (95% CI, 4.1%-17.6%), and from 1.1% (95% CI, 0.0%-2.3%) to 3.3% (95% CI, 0.0%-6.9%), respectively.

Dr. Merkler said it’s not clear why these rates are going up, but it may be because patients have more complications as a result of living longer. Another theory is that a form of drug-resistant TB is boosting the level of these complications, Dr. Merkler said, but he’s skeptical of that idea: “I don’t know why drug resistance would lead to more neurological complications.”

The study was funded by the National Institute of Neurological Disorders and Stroke and the Michael Goldberg Stroke Research Fund. Dr. Merkler reported no relevant financial disclosures.

Communication among healthcare professionals is essential for high-quality patient care. However, communication is difficult in hospitals because of heavy workloads, rapidly evolving plans of care, and geographic dispersion of team members. When hospital-based professionals are not in the same place at the same time, they rely on technology to communicate. Pagers have historically been used to support communication in hospitals, but are limited in their capabilities. Several recent small studies have shown that some physicians have started using standard text messaging on smartphones for patient care–related (PCR) messages.^1-3 Although potentially enhancing clinician efficiency, use of standard text messaging for PCR messages raises concern about security risks related to transmission of protected health information. Addressing these concerns are emerging secure mobile messaging applications designed for PCR communication. Although recent studies suggest these applications are well received by users, the adoption rate is largely unknown.^4,5

We conducted a study to see if there was a shift in use of hospital-based communication technologies under way. We surveyed a national sample of hospital-based clinicians to characterize current use of communication technologies, assess potential risks and perceptions related to use of standard text messaging for PCR messages, and characterize the adoption of secure mobile messaging applications designed for PCR communication.

Study Design

The study was a cross-sectional survey of hospitalists—physicians and advanced practice providers whose primary professional focus is care of hospitalized patients. We studied hospitalists because of their role in coordinating care for complex medical patients and because prior studies identified communication as a major component of their work.^6,7 The Northwestern University Institutional Review Board deemed this study exempt.

Survey Instrument

Four investigators (Drs. O’Leary, Liebovitz, Wu, and Reddy) with expertise in interprofessional communication and information technology created a draft survey based in part on results of prior studies assessing clinicians’ use of smartphones and standard text messaging for PCR communication.^1,3 In the first section of the survey, we asked respondents which technologies were provided by their organization and which technologies they used for PCR communication. In the second section, we asked respondents about their use and perceptions of standard text messaging for PCR communication. In the third section, we asked about implementation and adoption of secure mobile messaging applications at their hospital. In the fourth and final section, we asked for demographic information.

We randomly selected 8 attendees of the 2015 Midwest Hospital Medicine Conference and invited them to participate in a focus group that would review a paper version of the draft survey and recommend revisions. Using the group’s feedback, we revised the ordinal response scale for questions related to standard text messaging and made other minor edits. We then created an Internet-based version of the survey and pilot-tested it with 8 hospitalists from 4 diverse hospitalist groups within the Northwestern Medicine Health System. We made additional minor edits based on pilot-test feedback.

Sampling Strategy

We used the largest hospitalist database maintained by the Society of Hospital Medicine (SHM). This database includes information on more than 28,000 individuals, representing SHM members and nonmembers who had participated in organizational events. In addition to clinically active hospitalists, the database includes non-hospitalists and clinically inactive hospitalists. We used this database to try to capture the largest possible number of potentially eligible hospitalists.

Survey Administration

We administered the survey in collaboration with SHM staff. E-mails that included a link to the survey on the Survey Monkey website were sent by SHM staff to individuals within the database. These e-mails were sent through Real Magnet, an e-mail marketing platform⁸ that allowed the SHM staff to determine the number of individuals who received and opened the e-mail and the number who clicked on the survey link. To try to promote participation, we offered respondents the chance to enter a lottery to win one of four $50 gift certificates. The initial e-mail was sent in April 2016, a reminder in May 2016, and a final reminder in July 2016.

Data Analysis

We calculated descriptive statistics of participants’ demographic characteristics. We estimated nonresponse bias by comparing demographic characteristics across waves of respondents using analysis of variance, t tests, and χ² tests. This method is based on the finding that characteristics of late respondents often resemble those of nonrespondents.⁹ We collapsed response categories for communication technologies to simplify interpretation. For example, numeric pagers, alphanumeric pagers, and 2-way pagers were collapsed into a pagers category. We used t tests and χ² tests to assess for associations between receipt of standard text messages for PCR communication and respondents’ age, sex, race, professional type, hospital size, practice location, and hospital teaching status. Similarly, we used t tests and χ² tests to explore associations between implementation of secure mobile messaging application and respondents’ age, sex, race, professional type, hospital size, practice location, and hospital teaching status. All statistical analyses were performed with Stata Release 11.2 (StataCorp).

Participant Characteristics

Overall, the survey link was sent to 28,870 e-mail addresses. Addresses for which e-mails were undeliverable or for which the e-mail was never opened were excluded, yielding a total of 5,786 eligible respondents in the sample. After rejecting 42 clinically inactive individuals, 70 individuals who responded to only the initial item, and 27 duplicates, a total of 620 participant surveys were included in the final analysis. The adjusted response rate was 11.0%.

As shown in Table 1, mean (SD) respondent age was 42.9 (10.0) years, nearly half of the respondents were female, nearly a third were of nonwhite race, an overwhelming majority were physicians, and workplaces were in a variety of hospital settings. The sample size used to calculate demographic characteristics varied from 538 to 549 because of missing data for these items. We found no significant differences in demographic characteristics of respondents across the 3 survey waves, suggesting a lack of survey response bias (Supplemental Table).

Provision and Use of Communication Technologies for PCR Communication

Pagers were provided to the majority of respondents by their hospitals (79.8%, 495/620). Other devices were provided much less frequently, with 21.0% (130/620) reporting their organization provided a smartphone, 20.2% (125/620) a mobile phone, and 4.4% (27/620) a hands-free communication device. Organizations provided no device to 8.2% (51/620) of respondents and an “other” device to 5.5% (34/620).

An overwhelming majority used multiple technologies to receive PCR communication, with 17.7% (110/620) of respondents indicating use of 2 technologies, 22.7% (141/620) use of 3 technologies, and 49.4% (306/620) use of more than 3 technologies. The distribution of the most common ways respondents received PCR communication is shown in Table 2. Pagers were the most common form of technology, with 49.0% (304/620) indicating this was the primary way they received PCR communication. Being called on a mobile phone provided by the organization was the second most common form of receiving PCR communication (11.0%, 68/620), followed by standard text messaging (9.5%, 59/620) and mobile secure messaging using an application approved by the organization (9.0%, 56/620).

Participants’ Experiences With Standard Text Messaging for PCR Communication

Participants’ experiences with standard text messaging for PCR communication are summarized in Table 3. Overall, 65.1% (369/ 567) of respondents reported receiving standard text messages for PCR communication at least once per week when on clinical duty, and 52.9% (300/567) received standard text messages at least once per day.

Overall, 21.5% (122/567) of respondents received standard text messages that included individually identifiable information at least once per day, and 41.3% (234/567) received messages that included some identifiable information (eg, patient initials, room number) at least once per day. About one-fifth of respondents (21.0%, 119/567) indicated receiving standard text messages for urgent clinical issues at least once per day. Receipt of standard text messages for a patient for whom the respondent was no longer providing care, delays in receipt of messages, messages missed because smartphones were set to vibrate, and receipt of messages when not on clinical duty occurred, but less frequently. We found no significant associations between receipt of PCR standard text messages once or more per day and respondents’ age, sex, race, professional type, hospital size, or hospital teaching status. A higher percentage of respondents in the South (63.2%, 96/152) and West (57.9%, 70/121) reported receipt of at least 1 PCR standard text message per day, compared with respondents in the Northeast (51.9%, 54/104), Midwest (45.2%, 61/135), and other (25.0%, 4/16) (P = 0.003).

Senders of PCR standard text messages. Of respondents who received standard text messages for PCR communication at least once per week, a majority reported receiving messages from physicians in the same specialty (88.6%, 327/369) and from physicians in other specialties (71.3%, 263/369). A minority of respondents reported receiving messages from nurses (35.0%, 129/369), social workers (30.6%, 113/369), and pharmacists (27.9%, 103/369).

Perceptions among users. Of respondents who received standard text messages for PCR communication at least once per week, an overwhelming majority agreed or strongly agreed that use of standard text messaging allowed them to provide better care (81.7%, 295/361) and made them more efficient (87.3%, 315/361). A majority also agreed or strongly agreed that standard text messaging posed a risk to the privacy and confidentiality of patient information (56.4%, 203/360), and nearly a third indicated that standard text messaging posed a risk to the timely receipt of messages by the correct individual (27.6%, 100/362). Overall, a large majority agreed or strongly agreed that the benefits of using standard text messaging for PCR communication outweighed the risks (85.0%, 306/360).

Adoption of Organization-Approved Secure Mobile Messaging Applications

Participants’ reported adoption of organization-approved secure mobile messaging applications is shown in the Figure. About one-fourth (26.6%, 146/549) of respondents reported that their organization had implemented a secure messaging application and that some clinicians were using it, whereas relatively few (7.3%, 40/549) reported that their organization had implemented an application that was being used by most clinicians. A substantial portion of respondents (21.3%, 117/549) were not sure whether their organization was planning to implement a secure mobile messaging application for PCR communication. We found no significant associations between partial or nearly full implementation of a secure mobile messaging application and respondents’ age, sex, race, professional type, hospital size, or practice location. A lower percentage of respondents in major teaching hospitals (28.0%, 67/239) reported partial or nearly full implementation of a secure mobile messaging application, compared with respondents from teaching hospitals (39.6%, 74/187) and nonteaching hospitals (39.2%, 40/102) (P = 0.02).

We found that pagers were the technology most commonly used by hospital-based clinicians, but also that a majority have used standard text messaging for PCR communication, and that relatively few hospitals had fully implemented secure mobile messaging applications. Our findings reveal a wide range of technology use and suggest an evolution to support communication among healthcare professionals.

The persistence of pagers as the technology most commonly provided by hospitals and used by clinicians for communication is noteworthy in that pagers are limited in their capabilities, typically not allowing a response to the message sender or the ability to forward a message, and often not allowing the ability to send messages to multiple recipients. The continued heavy use of pagers may be explained by their relatively low cost, especially compared with investment in new technologies, and reliable receipt of messages, even in areas with no cell phone service or WiFi signal. Furthermore, hospitals’ providing pagers allows for oversight, directory creation, and the potential for integration into other information systems. In 2 recent studies, inpatient paging communication was evaluated in depth. Carlile et al.¹⁰ found that the majority of pages requested a response, requiring an interruption in physician workflow to initiate a callback. Kummerow Broman et al.¹¹ similarly found that a majority of pages requested a callback; they also found a high volume of nonurgent messages. With pager use, a high volume of messages, many of which require a response but are nonurgent, makes for a highly interruptive workflow.

That more than half of our hospital-based clinicians received standard text messages for PCR communication once or more per day is consistent with other, smaller studies. Kuhlmann et al.¹ surveyed 97 pediatric hospitalists and found that a majority sent and received work-related text messages. Prochaska et al.² surveyed 131 residents and found that standard text messaging was the communication method preferred by the majority of residents. Similar to these studies, our study found that receipt of standard text messages that included protected health information was fairly common. However, we identified additional risks related to standard text messaging. One-fifth of our respondents received standard text messages for urgent clinical issues once or more per day, and many respondents reported occasional receipt of messages regarding a patient for whom they were no longer providing care and receipt of messages when not on clinical duty. The usual inability to automate forwarding of standard text messages to another clinician creates the potential for clinically important messages to be delayed or missed. These risks have not been reported in the literature, and we think healthcare systems may not be fully aware of them. Our findings suggest that many clinicians have migrated from pagers to standard text messaging for the enhanced efficiency, and they perceive that the benefit of improved efficiency outweighs the risks to protected health information and the delay in receipt of clinically important messages by the correct individual.

Secure mobile messaging applications seem to address the limitations of both pagers and standard text messaging. Secure mobile messaging applications typically allow message response, message forwarding, multiple recipients, directory creation, the potential to create escalation schemes for nonresponse, and integration with other information systems, including electronic health records. Although several hospitals have developed their own systems,^4,12,13 most hospitals likely will purchase a vendor-based system. We found that a minority of hospitals had implemented a secure messaging application, and even fewer had most of their clinicians using it. Although little research has been conducted on these applications, studies suggest they are well received by users.^4,5 Given that paging communication studies have found a large portion of pages are sent by nurses and other non-physician team members, secure mobile messaging applications should allow for direct message exchange with all professionals caring for a patient.^10,11 Furthermore, hospitals will need to ensure adequate cell phone and WiFi signal strength throughout their facilities to ensure reliable and timely delivery of messages.

Our study had several limitations. We used a large database to conduct a national survey but had a low response rate and some drop-off of responses within surveys. Our sample reflected respondent diversity, and our analyses of demographic characteristics found no significant differences across survey response waves. Unfortunately, we did not have nonrespondents’ characteristics and therefore could not compare them with respondents’. It is possible that nonrespondents may have had different practices related to use of communication technology, especially in light of the fact that the survey was conducted by e-mail. However, given our finding that use of standard text messaging was comparable to that in other studies,^1,2 and given the similarity of respondents’ characteristics across response waves, our findings likely were not affected by nonresponse bias.⁹ Last, we used a survey that had not been validated. However, this survey was created by experts in interprofessional collaboration and information technology, was informed by prior studies, and was iteratively refined during pretesting and pilot testing.

Pagers remain the technology most commonly used by hospital-based clinicians, but a majority also use standard text messaging for PCR communication, and relatively few hospitals have fully implemented secure mobile messaging applications. The wide range of technologies used suggests an evolution of methods to support communication among healthcare professionals. An optimized system will improve communication efficiency while ensuring the security of their patients’ information and the timely receipt of that information by the intended clinician.

Acknowledgment

The authors thank the Society of Hospital Medicine and the society staff who helped administer the survey, especially Mr. Ethan Gray.

Disclosure

Nothing to report.

Communication among healthcare professionals is essential for high-quality patient care. However, communication is difficult in hospitals because of heavy workloads, rapidly evolving plans of care, and geographic dispersion of team members. When hospital-based professionals are not in the same place at the same time, they rely on technology to communicate. Pagers have historically been used to support communication in hospitals, but are limited in their capabilities. Several recent small studies have shown that some physicians have started using standard text messaging on smartphones for patient care–related (PCR) messages.^1-3 Although potentially enhancing clinician efficiency, use of standard text messaging for PCR messages raises concern about security risks related to transmission of protected health information. Addressing these concerns are emerging secure mobile messaging applications designed for PCR communication. Although recent studies suggest these applications are well received by users, the adoption rate is largely unknown.^4,5

We conducted a study to see if there was a shift in use of hospital-based communication technologies under way. We surveyed a national sample of hospital-based clinicians to characterize current use of communication technologies, assess potential risks and perceptions related to use of standard text messaging for PCR messages, and characterize the adoption of secure mobile messaging applications designed for PCR communication.

Study Design

The study was a cross-sectional survey of hospitalists—physicians and advanced practice providers whose primary professional focus is care of hospitalized patients. We studied hospitalists because of their role in coordinating care for complex medical patients and because prior studies identified communication as a major component of their work.^6,7 The Northwestern University Institutional Review Board deemed this study exempt.

Survey Instrument

Four investigators (Drs. O’Leary, Liebovitz, Wu, and Reddy) with expertise in interprofessional communication and information technology created a draft survey based in part on results of prior studies assessing clinicians’ use of smartphones and standard text messaging for PCR communication.^1,3 In the first section of the survey, we asked respondents which technologies were provided by their organization and which technologies they used for PCR communication. In the second section, we asked respondents about their use and perceptions of standard text messaging for PCR communication. In the third section, we asked about implementation and adoption of secure mobile messaging applications at their hospital. In the fourth and final section, we asked for demographic information.

We randomly selected 8 attendees of the 2015 Midwest Hospital Medicine Conference and invited them to participate in a focus group that would review a paper version of the draft survey and recommend revisions. Using the group’s feedback, we revised the ordinal response scale for questions related to standard text messaging and made other minor edits. We then created an Internet-based version of the survey and pilot-tested it with 8 hospitalists from 4 diverse hospitalist groups within the Northwestern Medicine Health System. We made additional minor edits based on pilot-test feedback.

Sampling Strategy

We used the largest hospitalist database maintained by the Society of Hospital Medicine (SHM). This database includes information on more than 28,000 individuals, representing SHM members and nonmembers who had participated in organizational events. In addition to clinically active hospitalists, the database includes non-hospitalists and clinically inactive hospitalists. We used this database to try to capture the largest possible number of potentially eligible hospitalists.

Survey Administration

We administered the survey in collaboration with SHM staff. E-mails that included a link to the survey on the Survey Monkey website were sent by SHM staff to individuals within the database. These e-mails were sent through Real Magnet, an e-mail marketing platform⁸ that allowed the SHM staff to determine the number of individuals who received and opened the e-mail and the number who clicked on the survey link. To try to promote participation, we offered respondents the chance to enter a lottery to win one of four $50 gift certificates. The initial e-mail was sent in April 2016, a reminder in May 2016, and a final reminder in July 2016.

Data Analysis

We calculated descriptive statistics of participants’ demographic characteristics. We estimated nonresponse bias by comparing demographic characteristics across waves of respondents using analysis of variance, t tests, and χ² tests. This method is based on the finding that characteristics of late respondents often resemble those of nonrespondents.⁹ We collapsed response categories for communication technologies to simplify interpretation. For example, numeric pagers, alphanumeric pagers, and 2-way pagers were collapsed into a pagers category. We used t tests and χ² tests to assess for associations between receipt of standard text messages for PCR communication and respondents’ age, sex, race, professional type, hospital size, practice location, and hospital teaching status. Similarly, we used t tests and χ² tests to explore associations between implementation of secure mobile messaging application and respondents’ age, sex, race, professional type, hospital size, practice location, and hospital teaching status. All statistical analyses were performed with Stata Release 11.2 (StataCorp).

Participant Characteristics

Overall, the survey link was sent to 28,870 e-mail addresses. Addresses for which e-mails were undeliverable or for which the e-mail was never opened were excluded, yielding a total of 5,786 eligible respondents in the sample. After rejecting 42 clinically inactive individuals, 70 individuals who responded to only the initial item, and 27 duplicates, a total of 620 participant surveys were included in the final analysis. The adjusted response rate was 11.0%.

As shown in Table 1, mean (SD) respondent age was 42.9 (10.0) years, nearly half of the respondents were female, nearly a third were of nonwhite race, an overwhelming majority were physicians, and workplaces were in a variety of hospital settings. The sample size used to calculate demographic characteristics varied from 538 to 549 because of missing data for these items. We found no significant differences in demographic characteristics of respondents across the 3 survey waves, suggesting a lack of survey response bias (Supplemental Table).

Provision and Use of Communication Technologies for PCR Communication

Pagers were provided to the majority of respondents by their hospitals (79.8%, 495/620). Other devices were provided much less frequently, with 21.0% (130/620) reporting their organization provided a smartphone, 20.2% (125/620) a mobile phone, and 4.4% (27/620) a hands-free communication device. Organizations provided no device to 8.2% (51/620) of respondents and an “other” device to 5.5% (34/620).

An overwhelming majority used multiple technologies to receive PCR communication, with 17.7% (110/620) of respondents indicating use of 2 technologies, 22.7% (141/620) use of 3 technologies, and 49.4% (306/620) use of more than 3 technologies. The distribution of the most common ways respondents received PCR communication is shown in Table 2. Pagers were the most common form of technology, with 49.0% (304/620) indicating this was the primary way they received PCR communication. Being called on a mobile phone provided by the organization was the second most common form of receiving PCR communication (11.0%, 68/620), followed by standard text messaging (9.5%, 59/620) and mobile secure messaging using an application approved by the organization (9.0%, 56/620).

Participants’ Experiences With Standard Text Messaging for PCR Communication

Participants’ experiences with standard text messaging for PCR communication are summarized in Table 3. Overall, 65.1% (369/ 567) of respondents reported receiving standard text messages for PCR communication at least once per week when on clinical duty, and 52.9% (300/567) received standard text messages at least once per day.

Overall, 21.5% (122/567) of respondents received standard text messages that included individually identifiable information at least once per day, and 41.3% (234/567) received messages that included some identifiable information (eg, patient initials, room number) at least once per day. About one-fifth of respondents (21.0%, 119/567) indicated receiving standard text messages for urgent clinical issues at least once per day. Receipt of standard text messages for a patient for whom the respondent was no longer providing care, delays in receipt of messages, messages missed because smartphones were set to vibrate, and receipt of messages when not on clinical duty occurred, but less frequently. We found no significant associations between receipt of PCR standard text messages once or more per day and respondents’ age, sex, race, professional type, hospital size, or hospital teaching status. A higher percentage of respondents in the South (63.2%, 96/152) and West (57.9%, 70/121) reported receipt of at least 1 PCR standard text message per day, compared with respondents in the Northeast (51.9%, 54/104), Midwest (45.2%, 61/135), and other (25.0%, 4/16) (P = 0.003).

Senders of PCR standard text messages. Of respondents who received standard text messages for PCR communication at least once per week, a majority reported receiving messages from physicians in the same specialty (88.6%, 327/369) and from physicians in other specialties (71.3%, 263/369). A minority of respondents reported receiving messages from nurses (35.0%, 129/369), social workers (30.6%, 113/369), and pharmacists (27.9%, 103/369).

Perceptions among users. Of respondents who received standard text messages for PCR communication at least once per week, an overwhelming majority agreed or strongly agreed that use of standard text messaging allowed them to provide better care (81.7%, 295/361) and made them more efficient (87.3%, 315/361). A majority also agreed or strongly agreed that standard text messaging posed a risk to the privacy and confidentiality of patient information (56.4%, 203/360), and nearly a third indicated that standard text messaging posed a risk to the timely receipt of messages by the correct individual (27.6%, 100/362). Overall, a large majority agreed or strongly agreed that the benefits of using standard text messaging for PCR communication outweighed the risks (85.0%, 306/360).

Adoption of Organization-Approved Secure Mobile Messaging Applications

Participants’ reported adoption of organization-approved secure mobile messaging applications is shown in the Figure. About one-fourth (26.6%, 146/549) of respondents reported that their organization had implemented a secure messaging application and that some clinicians were using it, whereas relatively few (7.3%, 40/549) reported that their organization had implemented an application that was being used by most clinicians. A substantial portion of respondents (21.3%, 117/549) were not sure whether their organization was planning to implement a secure mobile messaging application for PCR communication. We found no significant associations between partial or nearly full implementation of a secure mobile messaging application and respondents’ age, sex, race, professional type, hospital size, or practice location. A lower percentage of respondents in major teaching hospitals (28.0%, 67/239) reported partial or nearly full implementation of a secure mobile messaging application, compared with respondents from teaching hospitals (39.6%, 74/187) and nonteaching hospitals (39.2%, 40/102) (P = 0.02).

We found that pagers were the technology most commonly used by hospital-based clinicians, but also that a majority have used standard text messaging for PCR communication, and that relatively few hospitals had fully implemented secure mobile messaging applications. Our findings reveal a wide range of technology use and suggest an evolution to support communication among healthcare professionals.

The persistence of pagers as the technology most commonly provided by hospitals and used by clinicians for communication is noteworthy in that pagers are limited in their capabilities, typically not allowing a response to the message sender or the ability to forward a message, and often not allowing the ability to send messages to multiple recipients. The continued heavy use of pagers may be explained by their relatively low cost, especially compared with investment in new technologies, and reliable receipt of messages, even in areas with no cell phone service or WiFi signal. Furthermore, hospitals’ providing pagers allows for oversight, directory creation, and the potential for integration into other information systems. In 2 recent studies, inpatient paging communication was evaluated in depth. Carlile et al.¹⁰ found that the majority of pages requested a response, requiring an interruption in physician workflow to initiate a callback. Kummerow Broman et al.¹¹ similarly found that a majority of pages requested a callback; they also found a high volume of nonurgent messages. With pager use, a high volume of messages, many of which require a response but are nonurgent, makes for a highly interruptive workflow.

That more than half of our hospital-based clinicians received standard text messages for PCR communication once or more per day is consistent with other, smaller studies. Kuhlmann et al.¹ surveyed 97 pediatric hospitalists and found that a majority sent and received work-related text messages. Prochaska et al.² surveyed 131 residents and found that standard text messaging was the communication method preferred by the majority of residents. Similar to these studies, our study found that receipt of standard text messages that included protected health information was fairly common. However, we identified additional risks related to standard text messaging. One-fifth of our respondents received standard text messages for urgent clinical issues once or more per day, and many respondents reported occasional receipt of messages regarding a patient for whom they were no longer providing care and receipt of messages when not on clinical duty. The usual inability to automate forwarding of standard text messages to another clinician creates the potential for clinically important messages to be delayed or missed. These risks have not been reported in the literature, and we think healthcare systems may not be fully aware of them. Our findings suggest that many clinicians have migrated from pagers to standard text messaging for the enhanced efficiency, and they perceive that the benefit of improved efficiency outweighs the risks to protected health information and the delay in receipt of clinically important messages by the correct individual.

Secure mobile messaging applications seem to address the limitations of both pagers and standard text messaging. Secure mobile messaging applications typically allow message response, message forwarding, multiple recipients, directory creation, the potential to create escalation schemes for nonresponse, and integration with other information systems, including electronic health records. Although several hospitals have developed their own systems,^4,12,13 most hospitals likely will purchase a vendor-based system. We found that a minority of hospitals had implemented a secure messaging application, and even fewer had most of their clinicians using it. Although little research has been conducted on these applications, studies suggest they are well received by users.^4,5 Given that paging communication studies have found a large portion of pages are sent by nurses and other non-physician team members, secure mobile messaging applications should allow for direct message exchange with all professionals caring for a patient.^10,11 Furthermore, hospitals will need to ensure adequate cell phone and WiFi signal strength throughout their facilities to ensure reliable and timely delivery of messages.

Our study had several limitations. We used a large database to conduct a national survey but had a low response rate and some drop-off of responses within surveys. Our sample reflected respondent diversity, and our analyses of demographic characteristics found no significant differences across survey response waves. Unfortunately, we did not have nonrespondents’ characteristics and therefore could not compare them with respondents’. It is possible that nonrespondents may have had different practices related to use of communication technology, especially in light of the fact that the survey was conducted by e-mail. However, given our finding that use of standard text messaging was comparable to that in other studies,^1,2 and given the similarity of respondents’ characteristics across response waves, our findings likely were not affected by nonresponse bias.⁹ Last, we used a survey that had not been validated. However, this survey was created by experts in interprofessional collaboration and information technology, was informed by prior studies, and was iteratively refined during pretesting and pilot testing.

Pagers remain the technology most commonly used by hospital-based clinicians, but a majority also use standard text messaging for PCR communication, and relatively few hospitals have fully implemented secure mobile messaging applications. The wide range of technologies used suggests an evolution of methods to support communication among healthcare professionals. An optimized system will improve communication efficiency while ensuring the security of their patients’ information and the timely receipt of that information by the intended clinician.

Acknowledgment

The authors thank the Society of Hospital Medicine and the society staff who helped administer the survey, especially Mr. Ethan Gray.

Disclosure

Nothing to report.

Communication among healthcare professionals is essential for high-quality patient care. However, communication is difficult in hospitals because of heavy workloads, rapidly evolving plans of care, and geographic dispersion of team members. When hospital-based professionals are not in the same place at the same time, they rely on technology to communicate. Pagers have historically been used to support communication in hospitals, but are limited in their capabilities. Several recent small studies have shown that some physicians have started using standard text messaging on smartphones for patient care–related (PCR) messages.^1-3 Although potentially enhancing clinician efficiency, use of standard text messaging for PCR messages raises concern about security risks related to transmission of protected health information. Addressing these concerns are emerging secure mobile messaging applications designed for PCR communication. Although recent studies suggest these applications are well received by users, the adoption rate is largely unknown.^4,5

We conducted a study to see if there was a shift in use of hospital-based communication technologies under way. We surveyed a national sample of hospital-based clinicians to characterize current use of communication technologies, assess potential risks and perceptions related to use of standard text messaging for PCR messages, and characterize the adoption of secure mobile messaging applications designed for PCR communication.

Study Design

The study was a cross-sectional survey of hospitalists—physicians and advanced practice providers whose primary professional focus is care of hospitalized patients. We studied hospitalists because of their role in coordinating care for complex medical patients and because prior studies identified communication as a major component of their work.^6,7 The Northwestern University Institutional Review Board deemed this study exempt.

Survey Instrument

Four investigators (Drs. O’Leary, Liebovitz, Wu, and Reddy) with expertise in interprofessional communication and information technology created a draft survey based in part on results of prior studies assessing clinicians’ use of smartphones and standard text messaging for PCR communication.^1,3 In the first section of the survey, we asked respondents which technologies were provided by their organization and which technologies they used for PCR communication. In the second section, we asked respondents about their use and perceptions of standard text messaging for PCR communication. In the third section, we asked about implementation and adoption of secure mobile messaging applications at their hospital. In the fourth and final section, we asked for demographic information.

We randomly selected 8 attendees of the 2015 Midwest Hospital Medicine Conference and invited them to participate in a focus group that would review a paper version of the draft survey and recommend revisions. Using the group’s feedback, we revised the ordinal response scale for questions related to standard text messaging and made other minor edits. We then created an Internet-based version of the survey and pilot-tested it with 8 hospitalists from 4 diverse hospitalist groups within the Northwestern Medicine Health System. We made additional minor edits based on pilot-test feedback.

Sampling Strategy

We used the largest hospitalist database maintained by the Society of Hospital Medicine (SHM). This database includes information on more than 28,000 individuals, representing SHM members and nonmembers who had participated in organizational events. In addition to clinically active hospitalists, the database includes non-hospitalists and clinically inactive hospitalists. We used this database to try to capture the largest possible number of potentially eligible hospitalists.

Survey Administration

We administered the survey in collaboration with SHM staff. E-mails that included a link to the survey on the Survey Monkey website were sent by SHM staff to individuals within the database. These e-mails were sent through Real Magnet, an e-mail marketing platform⁸ that allowed the SHM staff to determine the number of individuals who received and opened the e-mail and the number who clicked on the survey link. To try to promote participation, we offered respondents the chance to enter a lottery to win one of four $50 gift certificates. The initial e-mail was sent in April 2016, a reminder in May 2016, and a final reminder in July 2016.

Data Analysis

We calculated descriptive statistics of participants’ demographic characteristics. We estimated nonresponse bias by comparing demographic characteristics across waves of respondents using analysis of variance, t tests, and χ² tests. This method is based on the finding that characteristics of late respondents often resemble those of nonrespondents.⁹ We collapsed response categories for communication technologies to simplify interpretation. For example, numeric pagers, alphanumeric pagers, and 2-way pagers were collapsed into a pagers category. We used t tests and χ² tests to assess for associations between receipt of standard text messages for PCR communication and respondents’ age, sex, race, professional type, hospital size, practice location, and hospital teaching status. Similarly, we used t tests and χ² tests to explore associations between implementation of secure mobile messaging application and respondents’ age, sex, race, professional type, hospital size, practice location, and hospital teaching status. All statistical analyses were performed with Stata Release 11.2 (StataCorp).

Participant Characteristics

Overall, the survey link was sent to 28,870 e-mail addresses. Addresses for which e-mails were undeliverable or for which the e-mail was never opened were excluded, yielding a total of 5,786 eligible respondents in the sample. After rejecting 42 clinically inactive individuals, 70 individuals who responded to only the initial item, and 27 duplicates, a total of 620 participant surveys were included in the final analysis. The adjusted response rate was 11.0%.

As shown in Table 1, mean (SD) respondent age was 42.9 (10.0) years, nearly half of the respondents were female, nearly a third were of nonwhite race, an overwhelming majority were physicians, and workplaces were in a variety of hospital settings. The sample size used to calculate demographic characteristics varied from 538 to 549 because of missing data for these items. We found no significant differences in demographic characteristics of respondents across the 3 survey waves, suggesting a lack of survey response bias (Supplemental Table).

Provision and Use of Communication Technologies for PCR Communication

Pagers were provided to the majority of respondents by their hospitals (79.8%, 495/620). Other devices were provided much less frequently, with 21.0% (130/620) reporting their organization provided a smartphone, 20.2% (125/620) a mobile phone, and 4.4% (27/620) a hands-free communication device. Organizations provided no device to 8.2% (51/620) of respondents and an “other” device to 5.5% (34/620).

An overwhelming majority used multiple technologies to receive PCR communication, with 17.7% (110/620) of respondents indicating use of 2 technologies, 22.7% (141/620) use of 3 technologies, and 49.4% (306/620) use of more than 3 technologies. The distribution of the most common ways respondents received PCR communication is shown in Table 2. Pagers were the most common form of technology, with 49.0% (304/620) indicating this was the primary way they received PCR communication. Being called on a mobile phone provided by the organization was the second most common form of receiving PCR communication (11.0%, 68/620), followed by standard text messaging (9.5%, 59/620) and mobile secure messaging using an application approved by the organization (9.0%, 56/620).

Participants’ Experiences With Standard Text Messaging for PCR Communication

Participants’ experiences with standard text messaging for PCR communication are summarized in Table 3. Overall, 65.1% (369/ 567) of respondents reported receiving standard text messages for PCR communication at least once per week when on clinical duty, and 52.9% (300/567) received standard text messages at least once per day.

Overall, 21.5% (122/567) of respondents received standard text messages that included individually identifiable information at least once per day, and 41.3% (234/567) received messages that included some identifiable information (eg, patient initials, room number) at least once per day. About one-fifth of respondents (21.0%, 119/567) indicated receiving standard text messages for urgent clinical issues at least once per day. Receipt of standard text messages for a patient for whom the respondent was no longer providing care, delays in receipt of messages, messages missed because smartphones were set to vibrate, and receipt of messages when not on clinical duty occurred, but less frequently. We found no significant associations between receipt of PCR standard text messages once or more per day and respondents’ age, sex, race, professional type, hospital size, or hospital teaching status. A higher percentage of respondents in the South (63.2%, 96/152) and West (57.9%, 70/121) reported receipt of at least 1 PCR standard text message per day, compared with respondents in the Northeast (51.9%, 54/104), Midwest (45.2%, 61/135), and other (25.0%, 4/16) (P = 0.003).

Senders of PCR standard text messages. Of respondents who received standard text messages for PCR communication at least once per week, a majority reported receiving messages from physicians in the same specialty (88.6%, 327/369) and from physicians in other specialties (71.3%, 263/369). A minority of respondents reported receiving messages from nurses (35.0%, 129/369), social workers (30.6%, 113/369), and pharmacists (27.9%, 103/369).

Perceptions among users. Of respondents who received standard text messages for PCR communication at least once per week, an overwhelming majority agreed or strongly agreed that use of standard text messaging allowed them to provide better care (81.7%, 295/361) and made them more efficient (87.3%, 315/361). A majority also agreed or strongly agreed that standard text messaging posed a risk to the privacy and confidentiality of patient information (56.4%, 203/360), and nearly a third indicated that standard text messaging posed a risk to the timely receipt of messages by the correct individual (27.6%, 100/362). Overall, a large majority agreed or strongly agreed that the benefits of using standard text messaging for PCR communication outweighed the risks (85.0%, 306/360).

Adoption of Organization-Approved Secure Mobile Messaging Applications

Participants’ reported adoption of organization-approved secure mobile messaging applications is shown in the Figure. About one-fourth (26.6%, 146/549) of respondents reported that their organization had implemented a secure messaging application and that some clinicians were using it, whereas relatively few (7.3%, 40/549) reported that their organization had implemented an application that was being used by most clinicians. A substantial portion of respondents (21.3%, 117/549) were not sure whether their organization was planning to implement a secure mobile messaging application for PCR communication. We found no significant associations between partial or nearly full implementation of a secure mobile messaging application and respondents’ age, sex, race, professional type, hospital size, or practice location. A lower percentage of respondents in major teaching hospitals (28.0%, 67/239) reported partial or nearly full implementation of a secure mobile messaging application, compared with respondents from teaching hospitals (39.6%, 74/187) and nonteaching hospitals (39.2%, 40/102) (P = 0.02).

We found that pagers were the technology most commonly used by hospital-based clinicians, but also that a majority have used standard text messaging for PCR communication, and that relatively few hospitals had fully implemented secure mobile messaging applications. Our findings reveal a wide range of technology use and suggest an evolution to support communication among healthcare professionals.

The persistence of pagers as the technology most commonly provided by hospitals and used by clinicians for communication is noteworthy in that pagers are limited in their capabilities, typically not allowing a response to the message sender or the ability to forward a message, and often not allowing the ability to send messages to multiple recipients. The continued heavy use of pagers may be explained by their relatively low cost, especially compared with investment in new technologies, and reliable receipt of messages, even in areas with no cell phone service or WiFi signal. Furthermore, hospitals’ providing pagers allows for oversight, directory creation, and the potential for integration into other information systems. In 2 recent studies, inpatient paging communication was evaluated in depth. Carlile et al.¹⁰ found that the majority of pages requested a response, requiring an interruption in physician workflow to initiate a callback. Kummerow Broman et al.¹¹ similarly found that a majority of pages requested a callback; they also found a high volume of nonurgent messages. With pager use, a high volume of messages, many of which require a response but are nonurgent, makes for a highly interruptive workflow.

That more than half of our hospital-based clinicians received standard text messages for PCR communication once or more per day is consistent with other, smaller studies. Kuhlmann et al.¹ surveyed 97 pediatric hospitalists and found that a majority sent and received work-related text messages. Prochaska et al.² surveyed 131 residents and found that standard text messaging was the communication method preferred by the majority of residents. Similar to these studies, our study found that receipt of standard text messages that included protected health information was fairly common. However, we identified additional risks related to standard text messaging. One-fifth of our respondents received standard text messages for urgent clinical issues once or more per day, and many respondents reported occasional receipt of messages regarding a patient for whom they were no longer providing care and receipt of messages when not on clinical duty. The usual inability to automate forwarding of standard text messages to another clinician creates the potential for clinically important messages to be delayed or missed. These risks have not been reported in the literature, and we think healthcare systems may not be fully aware of them. Our findings suggest that many clinicians have migrated from pagers to standard text messaging for the enhanced efficiency, and they perceive that the benefit of improved efficiency outweighs the risks to protected health information and the delay in receipt of clinically important messages by the correct individual.

Secure mobile messaging applications seem to address the limitations of both pagers and standard text messaging. Secure mobile messaging applications typically allow message response, message forwarding, multiple recipients, directory creation, the potential to create escalation schemes for nonresponse, and integration with other information systems, including electronic health records. Although several hospitals have developed their own systems,^4,12,13 most hospitals likely will purchase a vendor-based system. We found that a minority of hospitals had implemented a secure messaging application, and even fewer had most of their clinicians using it. Although little research has been conducted on these applications, studies suggest they are well received by users.^4,5 Given that paging communication studies have found a large portion of pages are sent by nurses and other non-physician team members, secure mobile messaging applications should allow for direct message exchange with all professionals caring for a patient.^10,11 Furthermore, hospitals will need to ensure adequate cell phone and WiFi signal strength throughout their facilities to ensure reliable and timely delivery of messages.

Our study had several limitations. We used a large database to conduct a national survey but had a low response rate and some drop-off of responses within surveys. Our sample reflected respondent diversity, and our analyses of demographic characteristics found no significant differences across survey response waves. Unfortunately, we did not have nonrespondents’ characteristics and therefore could not compare them with respondents’. It is possible that nonrespondents may have had different practices related to use of communication technology, especially in light of the fact that the survey was conducted by e-mail. However, given our finding that use of standard text messaging was comparable to that in other studies,^1,2 and given the similarity of respondents’ characteristics across response waves, our findings likely were not affected by nonresponse bias.⁹ Last, we used a survey that had not been validated. However, this survey was created by experts in interprofessional collaboration and information technology, was informed by prior studies, and was iteratively refined during pretesting and pilot testing.

Pagers remain the technology most commonly used by hospital-based clinicians, but a majority also use standard text messaging for PCR communication, and relatively few hospitals have fully implemented secure mobile messaging applications. The wide range of technologies used suggests an evolution of methods to support communication among healthcare professionals. An optimized system will improve communication efficiency while ensuring the security of their patients’ information and the timely receipt of that information by the intended clinician.

Acknowledgment

The authors thank the Society of Hospital Medicine and the society staff who helped administer the survey, especially Mr. Ethan Gray.

Disclosure

Nothing to report.

The more vaccines children get at any one office visit between the ages of 4 and 6 years, the more fearful they are of needles later on and the less likely they are to start human papillomavirus vaccine (HPV) as adolescents, according to an investigation of 120 children.

Shot-stacking between ages 4 and 6 years is not uncommon, especially if children might not be back in the office any time soon. Although it’s convenient and often better reimbursed to give all the recommended 4- to 6-year-old shots – MMR, DTaP, varicella, IPV, and maybe flu and hepatitis B vaccines – in one visit, the investigators found a hidden cost.

M. Alexander Otto/Frontline Medical News

[email protected]

The more vaccines children get at any one office visit between the ages of 4 and 6 years, the more fearful they are of needles later on and the less likely they are to start human papillomavirus vaccine (HPV) as adolescents, according to an investigation of 120 children.

Shot-stacking between ages 4 and 6 years is not uncommon, especially if children might not be back in the office any time soon. Although it’s convenient and often better reimbursed to give all the recommended 4- to 6-year-old shots – MMR, DTaP, varicella, IPV, and maybe flu and hepatitis B vaccines – in one visit, the investigators found a hidden cost.

M. Alexander Otto/Frontline Medical News

[email protected]

The more vaccines children get at any one office visit between the ages of 4 and 6 years, the more fearful they are of needles later on and the less likely they are to start human papillomavirus vaccine (HPV) as adolescents, according to an investigation of 120 children.

Shot-stacking between ages 4 and 6 years is not uncommon, especially if children might not be back in the office any time soon. Although it’s convenient and often better reimbursed to give all the recommended 4- to 6-year-old shots – MMR, DTaP, varicella, IPV, and maybe flu and hepatitis B vaccines – in one visit, the investigators found a hidden cost.

M. Alexander Otto/Frontline Medical News

[email protected]

MADRID – A nurse-led program successfully increased the uptake of pneumococcal vaccination among patients with chronic inflammatory rheumatic diseases in a single-center study.

From the start to the end of a 4-month evaluation period, the rate of vaccination of at-risk patients increased from 17.1% (13/76) to 77.6% (59/76; P less than .001).

Sara Freeman/Frontline Medical News

MADRID – A nurse-led program successfully increased the uptake of pneumococcal vaccination among patients with chronic inflammatory rheumatic diseases in a single-center study.

From the start to the end of a 4-month evaluation period, the rate of vaccination of at-risk patients increased from 17.1% (13/76) to 77.6% (59/76; P less than .001).

Sara Freeman/Frontline Medical News

MADRID – A nurse-led program successfully increased the uptake of pneumococcal vaccination among patients with chronic inflammatory rheumatic diseases in a single-center study.

From the start to the end of a 4-month evaluation period, the rate of vaccination of at-risk patients increased from 17.1% (13/76) to 77.6% (59/76; P less than .001).

Sara Freeman/Frontline Medical News

MADRID – Cases of invasive meningococcal disease in English 1 year olds dropped fourfold in the first year after the multicomponent group B meningococcal (MenB) vaccine (4CMenB, Bexsero) was added to the national infant immunization list, Shamez Ladhani, MD, Ph.D, reported at the annual meeting of the European Society for Paediatric Infectious Diseases.

“In 1 year olds, we have seen a significant decrease in the number of cases, compared with the number of cases predicted. I suspect the vaccine effectiveness is close to 94% against meningococcal group B,” said Dr. Ladhani of Public Health England, London.

[email protected]

MADRID – Cases of invasive meningococcal disease in English 1 year olds dropped fourfold in the first year after the multicomponent group B meningococcal (MenB) vaccine (4CMenB, Bexsero) was added to the national infant immunization list, Shamez Ladhani, MD, Ph.D, reported at the annual meeting of the European Society for Paediatric Infectious Diseases.

“In 1 year olds, we have seen a significant decrease in the number of cases, compared with the number of cases predicted. I suspect the vaccine effectiveness is close to 94% against meningococcal group B,” said Dr. Ladhani of Public Health England, London.

[email protected]

MADRID – Cases of invasive meningococcal disease in English 1 year olds dropped fourfold in the first year after the multicomponent group B meningococcal (MenB) vaccine (4CMenB, Bexsero) was added to the national infant immunization list, Shamez Ladhani, MD, Ph.D, reported at the annual meeting of the European Society for Paediatric Infectious Diseases.

“In 1 year olds, we have seen a significant decrease in the number of cases, compared with the number of cases predicted. I suspect the vaccine effectiveness is close to 94% against meningococcal group B,” said Dr. Ladhani of Public Health England, London.

[email protected]

MADRID – A new Finnish study raises a provocative question: Is transient tachypnea of the newborn really transient?

Transient tachypnea of the newborn (TTN) has traditionally been viewed as a benign, self-limited condition involving 1-3 days of respiratory distress. But data from Finland’s comprehensive national health registries indicate that TTN in term babies is associated with significantly increased risk of subsequent bronchiolitis during infancy, Otto Helve, MD, reported at the annual meeting of the European Society for Paediatric Infectious Diseases.

“This association suggests similar pathogenic mechanisms in transient tachypnea of the newborn and bronchiolitis. We suggest that an intrinsic defect in sodium ion–driven pulmonary fluid transport may predispose to clinically significant bronchiolitis during the first year of life,” said Dr. Helve, a pediatrician at the National Institute for Health and Welfare, Helsinki, and the University of Helsinki.

Of more than 1 million term babies born in Finland during 1996-2015, 17,569 were diagnosed with TTN. During the same period, 40,338 infants were hospitalized with a diagnosis of bronchiolitis attributable to respiratory syncytial virus infection.

In a multivariate analysis adjusted for birth year, gender, delivery method, gestational age, and parity, TTN was independently associated with a 1.2-fold increased risk of bronchiolitis during the first year of life.

Dr. Helve reported having no financial conflicts of interest regarding his study.

[email protected]

MADRID – A new Finnish study raises a provocative question: Is transient tachypnea of the newborn really transient?

Transient tachypnea of the newborn (TTN) has traditionally been viewed as a benign, self-limited condition involving 1-3 days of respiratory distress. But data from Finland’s comprehensive national health registries indicate that TTN in term babies is associated with significantly increased risk of subsequent bronchiolitis during infancy, Otto Helve, MD, reported at the annual meeting of the European Society for Paediatric Infectious Diseases.

“This association suggests similar pathogenic mechanisms in transient tachypnea of the newborn and bronchiolitis. We suggest that an intrinsic defect in sodium ion–driven pulmonary fluid transport may predispose to clinically significant bronchiolitis during the first year of life,” said Dr. Helve, a pediatrician at the National Institute for Health and Welfare, Helsinki, and the University of Helsinki.

Of more than 1 million term babies born in Finland during 1996-2015, 17,569 were diagnosed with TTN. During the same period, 40,338 infants were hospitalized with a diagnosis of bronchiolitis attributable to respiratory syncytial virus infection.

In a multivariate analysis adjusted for birth year, gender, delivery method, gestational age, and parity, TTN was independently associated with a 1.2-fold increased risk of bronchiolitis during the first year of life.

Dr. Helve reported having no financial conflicts of interest regarding his study.

[email protected]

MADRID – A new Finnish study raises a provocative question: Is transient tachypnea of the newborn really transient?

Transient tachypnea of the newborn (TTN) has traditionally been viewed as a benign, self-limited condition involving 1-3 days of respiratory distress. But data from Finland’s comprehensive national health registries indicate that TTN in term babies is associated with significantly increased risk of subsequent bronchiolitis during infancy, Otto Helve, MD, reported at the annual meeting of the European Society for Paediatric Infectious Diseases.

“This association suggests similar pathogenic mechanisms in transient tachypnea of the newborn and bronchiolitis. We suggest that an intrinsic defect in sodium ion–driven pulmonary fluid transport may predispose to clinically significant bronchiolitis during the first year of life,” said Dr. Helve, a pediatrician at the National Institute for Health and Welfare, Helsinki, and the University of Helsinki.

Of more than 1 million term babies born in Finland during 1996-2015, 17,569 were diagnosed with TTN. During the same period, 40,338 infants were hospitalized with a diagnosis of bronchiolitis attributable to respiratory syncytial virus infection.

In a multivariate analysis adjusted for birth year, gender, delivery method, gestational age, and parity, TTN was independently associated with a 1.2-fold increased risk of bronchiolitis during the first year of life.

Dr. Helve reported having no financial conflicts of interest regarding his study.

[email protected]

Results of a longitudinal study have confirmed that the recently established EULAR definition of arthralgia suspicious for progression to rheumatoid arthritis can help to distinguish patients who are at highest risk for progression from those who do not progress to RA.

Patients clinically suspected of arthralgia who met the definition for arthralgia suspicious for progression to RA displayed an increased risk for arthritis development, compared with patients who did not meet the EULAR definition (hazard ratio, 2.1; 95% confidence interval, 0.9-4.7). The approach had a sensitivity of 83% and positive predictive value of 30%, study head Annette van der Helm–van Mil, MD, reported at the European Congress of Rheumatology in Madrid.

Results of a longitudinal study have confirmed that the recently established EULAR definition of arthralgia suspicious for progression to rheumatoid arthritis can help to distinguish patients who are at highest risk for progression from those who do not progress to RA.

Patients clinically suspected of arthralgia who met the definition for arthralgia suspicious for progression to RA displayed an increased risk for arthritis development, compared with patients who did not meet the EULAR definition (hazard ratio, 2.1; 95% confidence interval, 0.9-4.7). The approach had a sensitivity of 83% and positive predictive value of 30%, study head Annette van der Helm–van Mil, MD, reported at the European Congress of Rheumatology in Madrid.

Results of a longitudinal study have confirmed that the recently established EULAR definition of arthralgia suspicious for progression to rheumatoid arthritis can help to distinguish patients who are at highest risk for progression from those who do not progress to RA.

Patients clinically suspected of arthralgia who met the definition for arthralgia suspicious for progression to RA displayed an increased risk for arthritis development, compared with patients who did not meet the EULAR definition (hazard ratio, 2.1; 95% confidence interval, 0.9-4.7). The approach had a sensitivity of 83% and positive predictive value of 30%, study head Annette van der Helm–van Mil, MD, reported at the European Congress of Rheumatology in Madrid.

CHICAGO – The clear advantage goes to the second-generation tyrosine kinase inhibitor in a new trial comparing dacomitinib to gefitinib for advanced non–small cell lung cancer.

In a randomized, open-label phase III trial designed as a head-to-head comparison of the two drugs for the first-line treatment of advanced non–small cell lung cancer (NSCLC), “the blinded, independent review showed that we have a median progression-free survival (PFS) of 14.7 months versus 9.2 months,” said first author Tony Mok, MD, professor and chair of the department of clinical oncology at the Chinese University of Hong Kong. This PFS rate, he said, “is among the highest of randomized phase III trials in the first-line setting.”

Two years into the study, those taking dacomitinib had triple the PFS rate of those on gefitinib (30.6% versus 9.6%). The overall hazard ratio (HR) for PFS with dacomitinib compared to gefitinib was 0.59 (95% confidence interval [CI], 0.47-0.74, P less than .0001).

A previous single-arm phase II trial of the drug, ARCHER 2017, showed a response rate of 75.6% and a median PFS of 18.2 months for patients with NSCLC and an EGFR-activating mutation.

“Based on these data, we thought it was likely that we could have a hypothesis for dacomitinib to be superior to gefitinib, a first-generation TKI [tyrosine kinase inhibitor], in terms of progression-free survival,” Dr. Mok said in a press conference at the annual meeting of the American Society of Clinical Oncology. Dacomitinib is a second-generation TKI.

Patients in the new study, ARCHER 1050, had advanced NSCLC with EGFR-activating mutations and no prior systemic treatment for their advanced disease. In addition, patients had good performance status, could not have had prior TKI exposure, and could not have CNS metastases. This last exclusion, explained Dr. Mok, was because investigators were uncertain about dacomitinib’s CNS penetration at the time of study design, and because gefitinib may also not be the best therapeutic choice for CNS metastases.

Patients were randomized 1:1 to receive either dacomitinib 45 mg orally daily (n = 227), or gefitinib 250 mg orally daily (n = 225). Patients were stratified by whether or not they were ethnically Asian, and by whether they had EGFR mutation of exon 19 or exon 21. Patients were balanced in terms of age, gender, ethnicity, smoking, and performance status between arms. About 75% of the patients were Asian, and 65% were nonsmokers.

The international study enrolled patients from 71 centers in Asia and Europe. At the time of the data cutoff, investigators saw PFS events totaling 59.9% in the dacomitinib arm and 79.6% in the gefitinib arm. Patients were followed for PFS for a median of 22.1 months. “We have relatively mature data,” said Dr. Mok, except for overall survival, with only 36.9% of events occurring at the time of the data cutoff.

The primary endpoint in the open-label trial was PFS in the intention-to-treat population, as assessed by an independent, blinded reviewer. Dr. Mok said that the study was powered to see at least 256 PFS events, and to see an improvement in PFS for dacomitinib that equated to an HR of no more than 0.667. This would translate to median PFS for dacomitinib of 14.3 months versus 9.5 months for gefitinib, values Dr. Mok said were “reasonable.” And, he pointed out, the study results fell almost exactly in line with these predictions, though the actual HR was a bit lower than predicted.

An analysis of PFS by subgroup, also conducted by independent review, found that dacomitinib was favored for all subgroups except for non-Asian patients, for whom the HR was 0.89 but did not reach statistical significance. Since these patients made up about one-fourth of the study population, said Dr. Mok, small sample size was a potential issue. “But we have to ask ourselves the question, do they really perform worse than the Asians, if they have a response?”

To attempt to answer this question, the investigators performed an exploratory analysis of the 72 non-Asian patients who had responded to therapy. Among this group, they saw data similar to that of the overall group, with an HR of 0.547 (95% CI, 0.321-0.933, P less than .0123).

Secondary endpoints included investigator-assessed PFS, overall survival, objective response rate, duration of response, quality of life, and safety assessments.

Objective response rates were similar between arms, at 74.9% for dacomitinib and 71.6% for gefitinib (P = .3883). However, the median duration of response was significantly longer for those on dacomitinib (14.8 versus 8.3 months, P less than .0001).

“This may be best explained by looking into the depth of the response,” said Dr. Mok. Patient-level data showed that dacomitinib patients had a larger reduction in target lesion size; “this may reflect a more potent inhibition of EGFR,” he said.

With the more potent inhibition, however, came more frequent grade 3 adverse events involving diarrhea, dermatitis, stomatitis, and paronychia for those on dacomitinib; however, noted Dr. Mok, serious transaminase elevations were more common in the gefitinib group. “There is no new signal” for concerning toxicity, he said. Dose reductions were more common in dacomitinib than in gefitinib (66.1% versus 18%), but there are two tiers of dose reductions permissible with dacomitinib, giving some flexibility.

Dr. Mok reported financial relationships with multiple pharmaceutical companies, including Pfizer and SFJ Pharmaceuticals, which sponsored the study.

[email protected]

On Twitter @karioakes

CHICAGO – The clear advantage goes to the second-generation tyrosine kinase inhibitor in a new trial comparing dacomitinib to gefitinib for advanced non–small cell lung cancer.

In a randomized, open-label phase III trial designed as a head-to-head comparison of the two drugs for the first-line treatment of advanced non–small cell lung cancer (NSCLC), “the blinded, independent review showed that we have a median progression-free survival (PFS) of 14.7 months versus 9.2 months,” said first author Tony Mok, MD, professor and chair of the department of clinical oncology at the Chinese University of Hong Kong. This PFS rate, he said, “is among the highest of randomized phase III trials in the first-line setting.”

Two years into the study, those taking dacomitinib had triple the PFS rate of those on gefitinib (30.6% versus 9.6%). The overall hazard ratio (HR) for PFS with dacomitinib compared to gefitinib was 0.59 (95% confidence interval [CI], 0.47-0.74, P less than .0001).

A previous single-arm phase II trial of the drug, ARCHER 2017, showed a response rate of 75.6% and a median PFS of 18.2 months for patients with NSCLC and an EGFR-activating mutation.

“Based on these data, we thought it was likely that we could have a hypothesis for dacomitinib to be superior to gefitinib, a first-generation TKI [tyrosine kinase inhibitor], in terms of progression-free survival,” Dr. Mok said in a press conference at the annual meeting of the American Society of Clinical Oncology. Dacomitinib is a second-generation TKI.

Patients in the new study, ARCHER 1050, had advanced NSCLC with EGFR-activating mutations and no prior systemic treatment for their advanced disease. In addition, patients had good performance status, could not have had prior TKI exposure, and could not have CNS metastases. This last exclusion, explained Dr. Mok, was because investigators were uncertain about dacomitinib’s CNS penetration at the time of study design, and because gefitinib may also not be the best therapeutic choice for CNS metastases.

Patients were randomized 1:1 to receive either dacomitinib 45 mg orally daily (n = 227), or gefitinib 250 mg orally daily (n = 225). Patients were stratified by whether or not they were ethnically Asian, and by whether they had EGFR mutation of exon 19 or exon 21. Patients were balanced in terms of age, gender, ethnicity, smoking, and performance status between arms. About 75% of the patients were Asian, and 65% were nonsmokers.

The international study enrolled patients from 71 centers in Asia and Europe. At the time of the data cutoff, investigators saw PFS events totaling 59.9% in the dacomitinib arm and 79.6% in the gefitinib arm. Patients were followed for PFS for a median of 22.1 months. “We have relatively mature data,” said Dr. Mok, except for overall survival, with only 36.9% of events occurring at the time of the data cutoff.

The primary endpoint in the open-label trial was PFS in the intention-to-treat population, as assessed by an independent, blinded reviewer. Dr. Mok said that the study was powered to see at least 256 PFS events, and to see an improvement in PFS for dacomitinib that equated to an HR of no more than 0.667. This would translate to median PFS for dacomitinib of 14.3 months versus 9.5 months for gefitinib, values Dr. Mok said were “reasonable.” And, he pointed out, the study results fell almost exactly in line with these predictions, though the actual HR was a bit lower than predicted.

An analysis of PFS by subgroup, also conducted by independent review, found that dacomitinib was favored for all subgroups except for non-Asian patients, for whom the HR was 0.89 but did not reach statistical significance. Since these patients made up about one-fourth of the study population, said Dr. Mok, small sample size was a potential issue. “But we have to ask ourselves the question, do they really perform worse than the Asians, if they have a response?”

To attempt to answer this question, the investigators performed an exploratory analysis of the 72 non-Asian patients who had responded to therapy. Among this group, they saw data similar to that of the overall group, with an HR of 0.547 (95% CI, 0.321-0.933, P less than .0123).

Secondary endpoints included investigator-assessed PFS, overall survival, objective response rate, duration of response, quality of life, and safety assessments.

Objective response rates were similar between arms, at 74.9% for dacomitinib and 71.6% for gefitinib (P = .3883). However, the median duration of response was significantly longer for those on dacomitinib (14.8 versus 8.3 months, P less than .0001).

“This may be best explained by looking into the depth of the response,” said Dr. Mok. Patient-level data showed that dacomitinib patients had a larger reduction in target lesion size; “this may reflect a more potent inhibition of EGFR,” he said.

With the more potent inhibition, however, came more frequent grade 3 adverse events involving diarrhea, dermatitis, stomatitis, and paronychia for those on dacomitinib; however, noted Dr. Mok, serious transaminase elevations were more common in the gefitinib group. “There is no new signal” for concerning toxicity, he said. Dose reductions were more common in dacomitinib than in gefitinib (66.1% versus 18%), but there are two tiers of dose reductions permissible with dacomitinib, giving some flexibility.

Dr. Mok reported financial relationships with multiple pharmaceutical companies, including Pfizer and SFJ Pharmaceuticals, which sponsored the study.

[email protected]

On Twitter @karioakes

CHICAGO – The clear advantage goes to the second-generation tyrosine kinase inhibitor in a new trial comparing dacomitinib to gefitinib for advanced non–small cell lung cancer.

In a randomized, open-label phase III trial designed as a head-to-head comparison of the two drugs for the first-line treatment of advanced non–small cell lung cancer (NSCLC), “the blinded, independent review showed that we have a median progression-free survival (PFS) of 14.7 months versus 9.2 months,” said first author Tony Mok, MD, professor and chair of the department of clinical oncology at the Chinese University of Hong Kong. This PFS rate, he said, “is among the highest of randomized phase III trials in the first-line setting.”

Two years into the study, those taking dacomitinib had triple the PFS rate of those on gefitinib (30.6% versus 9.6%). The overall hazard ratio (HR) for PFS with dacomitinib compared to gefitinib was 0.59 (95% confidence interval [CI], 0.47-0.74, P less than .0001).

A previous single-arm phase II trial of the drug, ARCHER 2017, showed a response rate of 75.6% and a median PFS of 18.2 months for patients with NSCLC and an EGFR-activating mutation.

“Based on these data, we thought it was likely that we could have a hypothesis for dacomitinib to be superior to gefitinib, a first-generation TKI [tyrosine kinase inhibitor], in terms of progression-free survival,” Dr. Mok said in a press conference at the annual meeting of the American Society of Clinical Oncology. Dacomitinib is a second-generation TKI.

Patients in the new study, ARCHER 1050, had advanced NSCLC with EGFR-activating mutations and no prior systemic treatment for their advanced disease. In addition, patients had good performance status, could not have had prior TKI exposure, and could not have CNS metastases. This last exclusion, explained Dr. Mok, was because investigators were uncertain about dacomitinib’s CNS penetration at the time of study design, and because gefitinib may also not be the best therapeutic choice for CNS metastases.

Patients were randomized 1:1 to receive either dacomitinib 45 mg orally daily (n = 227), or gefitinib 250 mg orally daily (n = 225). Patients were stratified by whether or not they were ethnically Asian, and by whether they had EGFR mutation of exon 19 or exon 21. Patients were balanced in terms of age, gender, ethnicity, smoking, and performance status between arms. About 75% of the patients were Asian, and 65% were nonsmokers.

The international study enrolled patients from 71 centers in Asia and Europe. At the time of the data cutoff, investigators saw PFS events totaling 59.9% in the dacomitinib arm and 79.6% in the gefitinib arm. Patients were followed for PFS for a median of 22.1 months. “We have relatively mature data,” said Dr. Mok, except for overall survival, with only 36.9% of events occurring at the time of the data cutoff.

The primary endpoint in the open-label trial was PFS in the intention-to-treat population, as assessed by an independent, blinded reviewer. Dr. Mok said that the study was powered to see at least 256 PFS events, and to see an improvement in PFS for dacomitinib that equated to an HR of no more than 0.667. This would translate to median PFS for dacomitinib of 14.3 months versus 9.5 months for gefitinib, values Dr. Mok said were “reasonable.” And, he pointed out, the study results fell almost exactly in line with these predictions, though the actual HR was a bit lower than predicted.

An analysis of PFS by subgroup, also conducted by independent review, found that dacomitinib was favored for all subgroups except for non-Asian patients, for whom the HR was 0.89 but did not reach statistical significance. Since these patients made up about one-fourth of the study population, said Dr. Mok, small sample size was a potential issue. “But we have to ask ourselves the question, do they really perform worse than the Asians, if they have a response?”

To attempt to answer this question, the investigators performed an exploratory analysis of the 72 non-Asian patients who had responded to therapy. Among this group, they saw data similar to that of the overall group, with an HR of 0.547 (95% CI, 0.321-0.933, P less than .0123).

Secondary endpoints included investigator-assessed PFS, overall survival, objective response rate, duration of response, quality of life, and safety assessments.

Objective response rates were similar between arms, at 74.9% for dacomitinib and 71.6% for gefitinib (P = .3883). However, the median duration of response was significantly longer for those on dacomitinib (14.8 versus 8.3 months, P less than .0001).

“This may be best explained by looking into the depth of the response,” said Dr. Mok. Patient-level data showed that dacomitinib patients had a larger reduction in target lesion size; “this may reflect a more potent inhibition of EGFR,” he said.

With the more potent inhibition, however, came more frequent grade 3 adverse events involving diarrhea, dermatitis, stomatitis, and paronychia for those on dacomitinib; however, noted Dr. Mok, serious transaminase elevations were more common in the gefitinib group. “There is no new signal” for concerning toxicity, he said. Dose reductions were more common in dacomitinib than in gefitinib (66.1% versus 18%), but there are two tiers of dose reductions permissible with dacomitinib, giving some flexibility.

Dr. Mok reported financial relationships with multiple pharmaceutical companies, including Pfizer and SFJ Pharmaceuticals, which sponsored the study.

[email protected]

On Twitter @karioakes