Venous thromboembolism (VTE) affects more than 1 million patients and costs the US healthcare system more than $1.5 billion annually.¹ Inherited and acquired thrombophilias have been perceived as important risk factors in assessing the risk of VTE recurrence and guiding the duration of anticoagulation.

Thrombophilias increase the risk of a first thrombotic event, but existing data have failed to demonstrate the usefulness of routine thrombophilia screening on subsequent management.^2,3 Moreover, thrombophilia testing ordered in the context of an inpatient hospitalization is limited by confounding factors, especially during an acute thrombotic event or in the setting of concurrent anticoagulation.⁴

Recognizing the costliness of routine thrombophilia testing, The American Society of Hematology introduced its Choosing Wisely campaign in 2013 in an effort to reduce test ordering in the setting of provoked VTEs with a major transient risk factor.⁵ In order to define current practice behavior at our institution, we conducted a retrospective study to determine the magnitude and financial impact of potentially inappropriate thrombophilia testing in the inpatient setting.

We performed a retrospective analysis of thrombophilia testing across all inpatient services at a large, quaternary-care academic institution over a 2-year period. Electronic medical record data containing all thrombophilia tests ordered on inpatients from June 2013 to June 2015 were obtained. This study was exempt from institutional review board approval.

Inclusion criteria included any inpatient for which thrombophilia testing occurred. Patients were excluded if testing was ordered in the absence of VTE or arterial thrombosis or if it was ordered as part of a work-up for another medical condition (see Supplementary Material).

Thrombophilia testing was defined as any of the following: inherited thrombophilias (Factor V Leiden or prothrombin 20210 gene mutations, antithrombin, or protein C or S activity levels) or acquired thrombophilias (lupus anticoagulant [Testing refers to the activated partial thromboplastin time lupus assay.], beta-2 glycoprotein 1 immunoglobulins M and G, anticardiolipin immunoglobulins M and G, dilute Russell’s viper venom time, or JAK2 V617F mutations).

Extracted data included patient age, sex, type of thrombophilia test ordered, ordering primary service, admission diagnosis, and objective confirmation of thrombotic events. The indication for test ordering was determined via medical record review of the patient’s corresponding hospitalization. Each test was evaluated in the context of the patient’s presenting history, hospital course, active medications, accompanying laboratory and radiographic studies, and consultant recommendations to arrive at a conclusion regarding both the test’s reason for ordering and whether its indication was “inappropriate,” “appropriate,” or “equivocal.” Cost data were obtained through the Centers for Medicare & Medicaid Services (CMS) Clinical Laboratory Fee Schedule for 2016 (see Supplementary Material).⁶

The criteria for defining test appropriateness were formulated by utilizing a combination of major society guidelines and literature review.^5,7-10 The criteria placed emphasis upon the ordered tests’ clinical relevance and reliability and were subsequently reviewed by a senior hematologist with specific expertise in thrombosis (see Supplementary Material).

Two internal medicine resident physician data reviewers independently evaluated the ordered tests. To ensure consistency between reviewers, a sample of identical test orders was compared for concordance, and a Cohen’s kappa coefficient was calculated. For purposes of analysis, equivocal orders were included under the appropriate category, as this study focused on the quantification of potentially inappropriate ordering practices. Pearson chi-square testing was performed in order to compare ordering practices between services using Stata.¹¹

In total, we reviewed 2179 individual tests, of which 362 (16.6%) were excluded. The remaining 1817 tests involved 299 patients across 26 primary specialties. Fifty-two (2.9% of orders) were ultimately deemed equivocal. The Table illustrates the overall proportion and cost of inappropriate test ordering as well as testing characteristics of the most commonly encountered thrombotic diagnoses. The Figure illustrates the proportion of potentially inappropriate test ordering with its associated cost by test type.

Orders for Factor V Leiden, prothrombin 20210, and protein C and S activity levels were most commonly deemed inappropriate due to the test results’ failure to alter clinical management (97.3%, 99.2%, 99.4% of their inappropriate orders, respectively). Antithrombin testing (59.4%) was deemed inappropriate most commonly in the setting of acute thrombosis. The lupus anticoagulant (82.8%) was inappropriately ordered most frequently in the setting of concurrent anticoagulation.

This retrospective analysis represents one of the largest examinations of inpatient thrombophilia testing practices to date. Our results illustrate the high prevalence and significant financial impact of potentially inappropriate thrombophilia testing conducted in the inpatient setting. The data confirm that, per our defined criteria, more than 90% of inherited thrombophilia testing was potentially inappropriate while the majority of acquired thrombophilia testing was appropriate, with the exception of the lupus anticoagulant.

Even when appropriately ordered, studies suggest that positive thrombophilia screening results fail to impact outcomes in most patients with VTE. In an effort to evaluate positive results’ potential to provide a basis from which to extend the duration of anticoagulation, and therefore reduce the risk of a recurrent VTE, a case-control analysis was performed on a series of patients with a first-VTE event (Multiple Environmental and Genetic Assessment of risk factors for venous thrombosis [MEGA] study).³ In examining the odds ratio (OR) for recurrence between patients who did or did not undergo testing for Factor V Leiden, antithrombin, or protein C or S activity, the data failed to show an impact of testing on the risk of VTE recurrence (OR 1.2; confidence interval, 0.8-1.8). In fact, decision making has increasingly relied on patients’ clinical characteristics rather than thrombophilia test results to guide anticoagulation duration after incident VTEs. A 2017 study illustrated that when using a clinical decision rule (Clinical Decision Rule Validation Study to Predict Low Recurrent Risk in Patients With Unprovoked Venous Thromboembolism [REVERSE criteria]) in patients with a first, unprovoked VTE, routine thrombophilia screening added little to determining the need for prolonged anticoagulation.¹² These findings support the limited clinical utility of current test ordering practices for the prediction and management of recurrent venous thrombosis.

Regarding the acquired thrombophilias, antiphospholipid antibody testing was predominantly ordered in a justified manner, which is consistent with the notion that test results could affect clinical management, such as anticoagulation duration or choice of anticoagulant.¹³ However, the validity of lupus anticoagulant testing was limited by the frequency of patients on concurrent anticoagulation.

Financially, the cumulative cost associated with inappropriate ordering was substantial, regardless of the thrombotic event in question. Moreover, our calculated costs are derived from CMS reimbursement rates and likely underestimate the true financial impact of errant testing given that commercial laboratories frequently charge at rates several-fold higher. On a national scale, prior analyses have suggested that the annual cost of thrombophilia testing, based on typical commercial rates, ranges from $300 million to $672 million.¹⁴

Researchers in prior studies have similarly examined the frequency of inappropriate thrombophilia testing and methods to reduce it. Researchers in a 2014 study demonstrated initially high rates of inappropriate inherited thrombophilia testing, and then showed marked reductions in testing and cost savings across multiple specialties following the introduction of a flowchart on a preprinted order form.¹⁵ Our findings provide motivation to perform similar endeavors.

The proportional difference of inappropriate ordering observed between nonteaching- and teaching-medicine services indicates a potential role for educational interventions. We recently completed a series of lectures on high-value thrombophilia ordering for residents and are actively analyzing its impact on subsequent ordering practices. We are also piloting an electronic best practice advisory for thrombophilia test ordering. Though the advisory may be overridden, providers are asked to provide justification for doing so on a voluntary basis. We plan to evaluate its effect on our findings reported in this study.

We acknowledge that our exclusion criteria resulted in the omission of testing across a spectrum of nonthrombotic clinical conditions, raising the question of selection bias. Because there are no established guidelines to determine the appropriateness of testing in these scenarios, we chose to limit the analysis of errant ordering to the context of thrombotic events. Other limitations of this study include the analysis of equivocal orders as appropriate. However, because equivocal ordering represented less than 3% of all analyzed orders, including these as inappropriate would not have significantly altered our findings.

A review of thrombophilia testing practices at our institution demonstrated that inappropriate testing in the inpatient setting is a frequent phenomenon associated with a significant financial impact. This effect was more pronounced in inherited versus acquired thrombophilia testing. Testing was frequently confounded and often failed to impact patients’ short- or long-term clinical management, regardless of the result.

These findings serve as a strong impetus to reduce the burden of routine thrombophilia testing during hospital admissions. Our data demonstrate a need for institution-wide changes such as implementing best practice advisories, introducing ordering restrictions, and conducting educational interventions in order to reduce unnecessary expenditures and improve patient care.

Disclosure

The authors have nothing to disclose.

Venous thromboembolism (VTE) affects more than 1 million patients and costs the US healthcare system more than $1.5 billion annually.¹ Inherited and acquired thrombophilias have been perceived as important risk factors in assessing the risk of VTE recurrence and guiding the duration of anticoagulation.

Thrombophilias increase the risk of a first thrombotic event, but existing data have failed to demonstrate the usefulness of routine thrombophilia screening on subsequent management.^2,3 Moreover, thrombophilia testing ordered in the context of an inpatient hospitalization is limited by confounding factors, especially during an acute thrombotic event or in the setting of concurrent anticoagulation.⁴

Recognizing the costliness of routine thrombophilia testing, The American Society of Hematology introduced its Choosing Wisely campaign in 2013 in an effort to reduce test ordering in the setting of provoked VTEs with a major transient risk factor.⁵ In order to define current practice behavior at our institution, we conducted a retrospective study to determine the magnitude and financial impact of potentially inappropriate thrombophilia testing in the inpatient setting.

We performed a retrospective analysis of thrombophilia testing across all inpatient services at a large, quaternary-care academic institution over a 2-year period. Electronic medical record data containing all thrombophilia tests ordered on inpatients from June 2013 to June 2015 were obtained. This study was exempt from institutional review board approval.

Inclusion criteria included any inpatient for which thrombophilia testing occurred. Patients were excluded if testing was ordered in the absence of VTE or arterial thrombosis or if it was ordered as part of a work-up for another medical condition (see Supplementary Material).

Thrombophilia testing was defined as any of the following: inherited thrombophilias (Factor V Leiden or prothrombin 20210 gene mutations, antithrombin, or protein C or S activity levels) or acquired thrombophilias (lupus anticoagulant [Testing refers to the activated partial thromboplastin time lupus assay.], beta-2 glycoprotein 1 immunoglobulins M and G, anticardiolipin immunoglobulins M and G, dilute Russell’s viper venom time, or JAK2 V617F mutations).

Extracted data included patient age, sex, type of thrombophilia test ordered, ordering primary service, admission diagnosis, and objective confirmation of thrombotic events. The indication for test ordering was determined via medical record review of the patient’s corresponding hospitalization. Each test was evaluated in the context of the patient’s presenting history, hospital course, active medications, accompanying laboratory and radiographic studies, and consultant recommendations to arrive at a conclusion regarding both the test’s reason for ordering and whether its indication was “inappropriate,” “appropriate,” or “equivocal.” Cost data were obtained through the Centers for Medicare & Medicaid Services (CMS) Clinical Laboratory Fee Schedule for 2016 (see Supplementary Material).⁶

The criteria for defining test appropriateness were formulated by utilizing a combination of major society guidelines and literature review.^5,7-10 The criteria placed emphasis upon the ordered tests’ clinical relevance and reliability and were subsequently reviewed by a senior hematologist with specific expertise in thrombosis (see Supplementary Material).

Two internal medicine resident physician data reviewers independently evaluated the ordered tests. To ensure consistency between reviewers, a sample of identical test orders was compared for concordance, and a Cohen’s kappa coefficient was calculated. For purposes of analysis, equivocal orders were included under the appropriate category, as this study focused on the quantification of potentially inappropriate ordering practices. Pearson chi-square testing was performed in order to compare ordering practices between services using Stata.¹¹

In total, we reviewed 2179 individual tests, of which 362 (16.6%) were excluded. The remaining 1817 tests involved 299 patients across 26 primary specialties. Fifty-two (2.9% of orders) were ultimately deemed equivocal. The Table illustrates the overall proportion and cost of inappropriate test ordering as well as testing characteristics of the most commonly encountered thrombotic diagnoses. The Figure illustrates the proportion of potentially inappropriate test ordering with its associated cost by test type.

Orders for Factor V Leiden, prothrombin 20210, and protein C and S activity levels were most commonly deemed inappropriate due to the test results’ failure to alter clinical management (97.3%, 99.2%, 99.4% of their inappropriate orders, respectively). Antithrombin testing (59.4%) was deemed inappropriate most commonly in the setting of acute thrombosis. The lupus anticoagulant (82.8%) was inappropriately ordered most frequently in the setting of concurrent anticoagulation.

This retrospective analysis represents one of the largest examinations of inpatient thrombophilia testing practices to date. Our results illustrate the high prevalence and significant financial impact of potentially inappropriate thrombophilia testing conducted in the inpatient setting. The data confirm that, per our defined criteria, more than 90% of inherited thrombophilia testing was potentially inappropriate while the majority of acquired thrombophilia testing was appropriate, with the exception of the lupus anticoagulant.

Even when appropriately ordered, studies suggest that positive thrombophilia screening results fail to impact outcomes in most patients with VTE. In an effort to evaluate positive results’ potential to provide a basis from which to extend the duration of anticoagulation, and therefore reduce the risk of a recurrent VTE, a case-control analysis was performed on a series of patients with a first-VTE event (Multiple Environmental and Genetic Assessment of risk factors for venous thrombosis [MEGA] study).³ In examining the odds ratio (OR) for recurrence between patients who did or did not undergo testing for Factor V Leiden, antithrombin, or protein C or S activity, the data failed to show an impact of testing on the risk of VTE recurrence (OR 1.2; confidence interval, 0.8-1.8). In fact, decision making has increasingly relied on patients’ clinical characteristics rather than thrombophilia test results to guide anticoagulation duration after incident VTEs. A 2017 study illustrated that when using a clinical decision rule (Clinical Decision Rule Validation Study to Predict Low Recurrent Risk in Patients With Unprovoked Venous Thromboembolism [REVERSE criteria]) in patients with a first, unprovoked VTE, routine thrombophilia screening added little to determining the need for prolonged anticoagulation.¹² These findings support the limited clinical utility of current test ordering practices for the prediction and management of recurrent venous thrombosis.

Regarding the acquired thrombophilias, antiphospholipid antibody testing was predominantly ordered in a justified manner, which is consistent with the notion that test results could affect clinical management, such as anticoagulation duration or choice of anticoagulant.¹³ However, the validity of lupus anticoagulant testing was limited by the frequency of patients on concurrent anticoagulation.

Financially, the cumulative cost associated with inappropriate ordering was substantial, regardless of the thrombotic event in question. Moreover, our calculated costs are derived from CMS reimbursement rates and likely underestimate the true financial impact of errant testing given that commercial laboratories frequently charge at rates several-fold higher. On a national scale, prior analyses have suggested that the annual cost of thrombophilia testing, based on typical commercial rates, ranges from $300 million to $672 million.¹⁴

Researchers in prior studies have similarly examined the frequency of inappropriate thrombophilia testing and methods to reduce it. Researchers in a 2014 study demonstrated initially high rates of inappropriate inherited thrombophilia testing, and then showed marked reductions in testing and cost savings across multiple specialties following the introduction of a flowchart on a preprinted order form.¹⁵ Our findings provide motivation to perform similar endeavors.

The proportional difference of inappropriate ordering observed between nonteaching- and teaching-medicine services indicates a potential role for educational interventions. We recently completed a series of lectures on high-value thrombophilia ordering for residents and are actively analyzing its impact on subsequent ordering practices. We are also piloting an electronic best practice advisory for thrombophilia test ordering. Though the advisory may be overridden, providers are asked to provide justification for doing so on a voluntary basis. We plan to evaluate its effect on our findings reported in this study.

We acknowledge that our exclusion criteria resulted in the omission of testing across a spectrum of nonthrombotic clinical conditions, raising the question of selection bias. Because there are no established guidelines to determine the appropriateness of testing in these scenarios, we chose to limit the analysis of errant ordering to the context of thrombotic events. Other limitations of this study include the analysis of equivocal orders as appropriate. However, because equivocal ordering represented less than 3% of all analyzed orders, including these as inappropriate would not have significantly altered our findings.

A review of thrombophilia testing practices at our institution demonstrated that inappropriate testing in the inpatient setting is a frequent phenomenon associated with a significant financial impact. This effect was more pronounced in inherited versus acquired thrombophilia testing. Testing was frequently confounded and often failed to impact patients’ short- or long-term clinical management, regardless of the result.

These findings serve as a strong impetus to reduce the burden of routine thrombophilia testing during hospital admissions. Our data demonstrate a need for institution-wide changes such as implementing best practice advisories, introducing ordering restrictions, and conducting educational interventions in order to reduce unnecessary expenditures and improve patient care.

Disclosure

The authors have nothing to disclose.

Venous thromboembolism (VTE) affects more than 1 million patients and costs the US healthcare system more than $1.5 billion annually.¹ Inherited and acquired thrombophilias have been perceived as important risk factors in assessing the risk of VTE recurrence and guiding the duration of anticoagulation.

Thrombophilias increase the risk of a first thrombotic event, but existing data have failed to demonstrate the usefulness of routine thrombophilia screening on subsequent management.^2,3 Moreover, thrombophilia testing ordered in the context of an inpatient hospitalization is limited by confounding factors, especially during an acute thrombotic event or in the setting of concurrent anticoagulation.⁴

Recognizing the costliness of routine thrombophilia testing, The American Society of Hematology introduced its Choosing Wisely campaign in 2013 in an effort to reduce test ordering in the setting of provoked VTEs with a major transient risk factor.⁵ In order to define current practice behavior at our institution, we conducted a retrospective study to determine the magnitude and financial impact of potentially inappropriate thrombophilia testing in the inpatient setting.

We performed a retrospective analysis of thrombophilia testing across all inpatient services at a large, quaternary-care academic institution over a 2-year period. Electronic medical record data containing all thrombophilia tests ordered on inpatients from June 2013 to June 2015 were obtained. This study was exempt from institutional review board approval.

Inclusion criteria included any inpatient for which thrombophilia testing occurred. Patients were excluded if testing was ordered in the absence of VTE or arterial thrombosis or if it was ordered as part of a work-up for another medical condition (see Supplementary Material).

Thrombophilia testing was defined as any of the following: inherited thrombophilias (Factor V Leiden or prothrombin 20210 gene mutations, antithrombin, or protein C or S activity levels) or acquired thrombophilias (lupus anticoagulant [Testing refers to the activated partial thromboplastin time lupus assay.], beta-2 glycoprotein 1 immunoglobulins M and G, anticardiolipin immunoglobulins M and G, dilute Russell’s viper venom time, or JAK2 V617F mutations).

Extracted data included patient age, sex, type of thrombophilia test ordered, ordering primary service, admission diagnosis, and objective confirmation of thrombotic events. The indication for test ordering was determined via medical record review of the patient’s corresponding hospitalization. Each test was evaluated in the context of the patient’s presenting history, hospital course, active medications, accompanying laboratory and radiographic studies, and consultant recommendations to arrive at a conclusion regarding both the test’s reason for ordering and whether its indication was “inappropriate,” “appropriate,” or “equivocal.” Cost data were obtained through the Centers for Medicare & Medicaid Services (CMS) Clinical Laboratory Fee Schedule for 2016 (see Supplementary Material).⁶

The criteria for defining test appropriateness were formulated by utilizing a combination of major society guidelines and literature review.^5,7-10 The criteria placed emphasis upon the ordered tests’ clinical relevance and reliability and were subsequently reviewed by a senior hematologist with specific expertise in thrombosis (see Supplementary Material).

Two internal medicine resident physician data reviewers independently evaluated the ordered tests. To ensure consistency between reviewers, a sample of identical test orders was compared for concordance, and a Cohen’s kappa coefficient was calculated. For purposes of analysis, equivocal orders were included under the appropriate category, as this study focused on the quantification of potentially inappropriate ordering practices. Pearson chi-square testing was performed in order to compare ordering practices between services using Stata.¹¹

In total, we reviewed 2179 individual tests, of which 362 (16.6%) were excluded. The remaining 1817 tests involved 299 patients across 26 primary specialties. Fifty-two (2.9% of orders) were ultimately deemed equivocal. The Table illustrates the overall proportion and cost of inappropriate test ordering as well as testing characteristics of the most commonly encountered thrombotic diagnoses. The Figure illustrates the proportion of potentially inappropriate test ordering with its associated cost by test type.

Orders for Factor V Leiden, prothrombin 20210, and protein C and S activity levels were most commonly deemed inappropriate due to the test results’ failure to alter clinical management (97.3%, 99.2%, 99.4% of their inappropriate orders, respectively). Antithrombin testing (59.4%) was deemed inappropriate most commonly in the setting of acute thrombosis. The lupus anticoagulant (82.8%) was inappropriately ordered most frequently in the setting of concurrent anticoagulation.

This retrospective analysis represents one of the largest examinations of inpatient thrombophilia testing practices to date. Our results illustrate the high prevalence and significant financial impact of potentially inappropriate thrombophilia testing conducted in the inpatient setting. The data confirm that, per our defined criteria, more than 90% of inherited thrombophilia testing was potentially inappropriate while the majority of acquired thrombophilia testing was appropriate, with the exception of the lupus anticoagulant.

Even when appropriately ordered, studies suggest that positive thrombophilia screening results fail to impact outcomes in most patients with VTE. In an effort to evaluate positive results’ potential to provide a basis from which to extend the duration of anticoagulation, and therefore reduce the risk of a recurrent VTE, a case-control analysis was performed on a series of patients with a first-VTE event (Multiple Environmental and Genetic Assessment of risk factors for venous thrombosis [MEGA] study).³ In examining the odds ratio (OR) for recurrence between patients who did or did not undergo testing for Factor V Leiden, antithrombin, or protein C or S activity, the data failed to show an impact of testing on the risk of VTE recurrence (OR 1.2; confidence interval, 0.8-1.8). In fact, decision making has increasingly relied on patients’ clinical characteristics rather than thrombophilia test results to guide anticoagulation duration after incident VTEs. A 2017 study illustrated that when using a clinical decision rule (Clinical Decision Rule Validation Study to Predict Low Recurrent Risk in Patients With Unprovoked Venous Thromboembolism [REVERSE criteria]) in patients with a first, unprovoked VTE, routine thrombophilia screening added little to determining the need for prolonged anticoagulation.¹² These findings support the limited clinical utility of current test ordering practices for the prediction and management of recurrent venous thrombosis.

Regarding the acquired thrombophilias, antiphospholipid antibody testing was predominantly ordered in a justified manner, which is consistent with the notion that test results could affect clinical management, such as anticoagulation duration or choice of anticoagulant.¹³ However, the validity of lupus anticoagulant testing was limited by the frequency of patients on concurrent anticoagulation.

Financially, the cumulative cost associated with inappropriate ordering was substantial, regardless of the thrombotic event in question. Moreover, our calculated costs are derived from CMS reimbursement rates and likely underestimate the true financial impact of errant testing given that commercial laboratories frequently charge at rates several-fold higher. On a national scale, prior analyses have suggested that the annual cost of thrombophilia testing, based on typical commercial rates, ranges from $300 million to $672 million.¹⁴

Researchers in prior studies have similarly examined the frequency of inappropriate thrombophilia testing and methods to reduce it. Researchers in a 2014 study demonstrated initially high rates of inappropriate inherited thrombophilia testing, and then showed marked reductions in testing and cost savings across multiple specialties following the introduction of a flowchart on a preprinted order form.¹⁵ Our findings provide motivation to perform similar endeavors.

The proportional difference of inappropriate ordering observed between nonteaching- and teaching-medicine services indicates a potential role for educational interventions. We recently completed a series of lectures on high-value thrombophilia ordering for residents and are actively analyzing its impact on subsequent ordering practices. We are also piloting an electronic best practice advisory for thrombophilia test ordering. Though the advisory may be overridden, providers are asked to provide justification for doing so on a voluntary basis. We plan to evaluate its effect on our findings reported in this study.

We acknowledge that our exclusion criteria resulted in the omission of testing across a spectrum of nonthrombotic clinical conditions, raising the question of selection bias. Because there are no established guidelines to determine the appropriateness of testing in these scenarios, we chose to limit the analysis of errant ordering to the context of thrombotic events. Other limitations of this study include the analysis of equivocal orders as appropriate. However, because equivocal ordering represented less than 3% of all analyzed orders, including these as inappropriate would not have significantly altered our findings.

A review of thrombophilia testing practices at our institution demonstrated that inappropriate testing in the inpatient setting is a frequent phenomenon associated with a significant financial impact. This effect was more pronounced in inherited versus acquired thrombophilia testing. Testing was frequently confounded and often failed to impact patients’ short- or long-term clinical management, regardless of the result.

These findings serve as a strong impetus to reduce the burden of routine thrombophilia testing during hospital admissions. Our data demonstrate a need for institution-wide changes such as implementing best practice advisories, introducing ordering restrictions, and conducting educational interventions in order to reduce unnecessary expenditures and improve patient care.

Disclosure

The authors have nothing to disclose.

As the number of intensive care unit (ICU) survivors has steadily increased over the past few decades, there is growing awareness of the long-term physical, cognitive, and psychological impairments after ICU hospitalization, collectively known as post–intensive care syndrome (PICS).¹ Systematic reviews based mostly on research studies suggest that the prevalence of depressive symptoms 2-12 months after ICU discharge is nearly 30%.^2-5 Due to the scarcity of established models of care for ICU survivors, there is limited characterization of depressive symptoms and antidepressant regimens in this clinical population. The Critical Care Recovery Center (CCRC) at Eskenazi Hospital is one of the first ICU survivor clinics in the United States and targets a racially diverse, underserved population in the Indianapolis metropolitan area.⁶ In this study, we examined whether patients had depressive symptoms at their initial CCRC visit, and whether the risk factors for depressive symptoms differed if they were on an antidepressant at their initial CCRC visit.

Referral criteria to the CCRC were 18 years or older, admitted to the Eskenazi ICU, were on mechanical ventilation or delirious for ≥48 hours (major risk factors for the development of PICS), and recommended for follow-up by a critical care physician. The exclusion criterion included was enrollment in hospice or palliative care services. Institutional review board approval was obtained to conduct retrospective analyses of de-identified clinical data. Medical history and medication lists were collected from patients, informal caregivers, and electronic medical records.

Two hundred thirty-three patients were seen in the CCRC from July 2011 to August 2016. Two hundred four patients rated symptoms of depression with either the Patient Health Questionnaire (PHQ-9; N = 99) or Geriatric Depression Scale (GDS-30; N = 105) at their initial visit to the CCRC prior to receiving any treatment at the CCRC. Twenty-nine patients who did not complete depression questionnaires were excluded from the analyses. Patients with PHQ-9 score ≥10 or GDS score ≥20 were categorized as having moderate to severe depressive symptoms.^7,8

Electronic medical records were reviewed to determine whether patients were on an antidepressant at hospital admission, hospital discharge, and the initial CCRC visit prior to any treatment in the CCRC. Patients who were on a tricyclic antidepressant, selective serotonin reuptake inhibitor, selective serotonin-norepinephrine reuptake inhibitor, noradrenergic and specific serotonergic antidepressant (eg, mirtazapine), or norepinephrine and dopaminergic reuptake inhibitor (eg, bupropion) at any dose were designated as being on an antidepressant. Prescribers of antidepressants included primary care providers, clinical providers during their hospital stay, and various outpatient subspecialists other than those in the CCRC.

We then examined whether the risk factors for depressive symptoms differed if patients were on an antidepressant at their initial CCRC visit. We compared demographic and clinical characteristics between depressed and nondepressed patients not on an antidepressant. We repeated these analyses for those on an antidepressant. Dichotomous outcomes were compared using chi-square testing, and two-way Student t tests for continuous outcomes. Demographic and clinical variables with P < 0.1 were included as covariates in a logistic regression model for depressive symptoms separately for those not an antidepressant and those on an antidepressant. History of depression was not included as a covariate because it is highly collinear with post-ICU depression.

Two hundred four ICU survivors in this study reflected a racially diverse and underserved population (monthly income $745.3 ± $931.5). Although most had respiratory failure and/or delirium during their hospital stay, 94.1% (N = 160) mostly lived independently after discharge. Nearly one-third of patients (N = 69) were on at least 1 antidepressant at their initial CCRC visit. Of these 69 patients, 60.9% (N = 42) had an antidepressant prescription on hospital admission, and 60.9% (N = 42) had an antidepressant prescription on hospital discharge.

We first compared the demographic and clinical characteristics of patients with and without depressive symptoms at their initial CCRC visit. Patients with depressive symptoms were younger, less likely to have cardiac disease, more likely to have a history of depression, more likely to have been prescribed an antidepressant on hospital admission, more likely to be prescribed an antidepressant on hospital discharge, and more likely to be on an antidepressant at their initial CCRC visit (Table 1).

Our study demonstrated that about one-third of our ICU survivor clinical cohort had untreated or inadequately treated depressive symptoms at their CCRC initial visit. Many patients with depressive symptoms had a history of depression and/or antidepressant prescription on hospital admission. This suggests that pre-ICU depression is a major contributor to post-ICU depression. These findings are consistent with the results of a large retrospective analysis of Danish ICU survivors that found that patients were more likely to have premorbid psychiatric diagnoses, compared with the general population.⁹ Another ICU survivor research study that excluded patients who were on antidepressants prior to ICU hospitalization found that 49% of these patients were on an antidepressant after their ICU stay.¹⁰ Our much lower rate of patients on an antidepressant after their ICU stay may reflect the differences between patient populations, differences in healthcare systems, and differences in clinician prescribing practices.

Younger age was associated with a higher likelihood of depressive symptoms independent of antidepressant status. Findings about the relationship between age and post-ICU depression have varied. The Bringing to Light the Risk Factors and Incidence of Neuropsychological Dysfunction in ICU Survivors group found that older age was associated with more depressive symptoms at 12 months postdischarge.¹¹ On the other hand, a systematic review of post-ICU depression did not find any relationship between age and post-ICU depression.^2,3 These differences may be due in part to demographic variations in cohorts.

Our logistic regression models suggest that there may also be different risk factors in patients who had untreated vs inadequately treated depressive symptoms. Patients who were not on an antidepressant at their initial CCRC visit were more likely to have a lower level of education. This is consistent with the Medical Expenditure Panel Surveys study, which showed that adults with less than a high school education were less likely to receive depression treatment.¹² In patients who were on antidepressants at their initial CCRC visit, African Americans were more likely to have depressive symptoms. Possible reasons may include differences in receiving guideline-concordant antidepressant medication treatment, access to mental health subspecialty services, higher prevalence of treatment refractory depression, and differences in responses to antidepressant treatments.^13,14

Strengths of our study include detailed characterization for a fairly large ICU survivor clinic population and a racially diverse cohort. To the best of our knowledge, our study is also the first to examine whether there may be different risk factors for depressive symptoms based on antidepressant status. Limitations include the lack of information about nonpharmacologic antidepressant treatment and the inability to assess whether noncompliance, insufficient dose, or insufficient time on antidepressants contributed to inadequate antidepressant treatment. Antidepressants may have also been prescribed for other purposes such as smoking cessation, neuropathic pain, and migraine headaches. However, because 72.4% of patients on antidepressants had a history of depression, it is likely that most of them were on antidepressants to treat depression.

Other limitations include potential biases in our clinical cohort. Over the last 5 years, the CCRC has provided care to more than 200 ICU survivors. With 1100 mechanically ventilated admissions per year, only 1.8% of survivors are seen. The referral criteria for the CCRC is a major source of selection bias, which likely overrepresents PICS. Because patients are seen in the CCRC about 3 months after hospital discharge, there is also informant censoring due to death. Physically sicker survivors in nursing home facilities were less likely to be included. Finally, the small cohort size may have resulted in an underpowered study.

Future studies will need to confirm our findings about the high prevalence of post-ICU depression and different responses to antidepressant medications by certain groups. Pre-ICU depression, lack of antidepressant treatment, and inadequate antidepressant treatment are major causes of post-ICU depression. Currently, the CCRC offers pharmacotherapy, problem-solving therapy, or referral to mental health specialists to treat patients with depressive symptoms. ICU survivor clinics, such as the CCRC, may become important settings that allow for increased access to depression treatment for those at higher risk for post-ICU depression as well as the testing of new antidepressant regimens for those with inadequately treated depression.

Acknowledgments

The authors thank Dr. Adil Sheikh for assistance with data entry and Cynthia Reynolds for her clinical services. Grant support: The Critical Care Recovery Center (CCRC) is supported by Eskenazi Health Services. SW is supported by NIA 2P30AG010133. SG is supported by NIA 2P30AG010133 and NIA 5R01AG045350. SK is supported by NHBLI 5T32HL091816-07. MB is supported by NIA R01 AG040220-05, AHRQ P30 HS024384-02, CMS 1 L1 CMS331444-02-00 and NIA R01 AG030618-05A1. BK is supported by NIA K23-AG043476 and NHLBI R01HL131730.

Disclosure

There are no conflicts of interest. None of the above NIH grants supported the CCRC or this work.

As the number of intensive care unit (ICU) survivors has steadily increased over the past few decades, there is growing awareness of the long-term physical, cognitive, and psychological impairments after ICU hospitalization, collectively known as post–intensive care syndrome (PICS).¹ Systematic reviews based mostly on research studies suggest that the prevalence of depressive symptoms 2-12 months after ICU discharge is nearly 30%.^2-5 Due to the scarcity of established models of care for ICU survivors, there is limited characterization of depressive symptoms and antidepressant regimens in this clinical population. The Critical Care Recovery Center (CCRC) at Eskenazi Hospital is one of the first ICU survivor clinics in the United States and targets a racially diverse, underserved population in the Indianapolis metropolitan area.⁶ In this study, we examined whether patients had depressive symptoms at their initial CCRC visit, and whether the risk factors for depressive symptoms differed if they were on an antidepressant at their initial CCRC visit.

Referral criteria to the CCRC were 18 years or older, admitted to the Eskenazi ICU, were on mechanical ventilation or delirious for ≥48 hours (major risk factors for the development of PICS), and recommended for follow-up by a critical care physician. The exclusion criterion included was enrollment in hospice or palliative care services. Institutional review board approval was obtained to conduct retrospective analyses of de-identified clinical data. Medical history and medication lists were collected from patients, informal caregivers, and electronic medical records.

Two hundred thirty-three patients were seen in the CCRC from July 2011 to August 2016. Two hundred four patients rated symptoms of depression with either the Patient Health Questionnaire (PHQ-9; N = 99) or Geriatric Depression Scale (GDS-30; N = 105) at their initial visit to the CCRC prior to receiving any treatment at the CCRC. Twenty-nine patients who did not complete depression questionnaires were excluded from the analyses. Patients with PHQ-9 score ≥10 or GDS score ≥20 were categorized as having moderate to severe depressive symptoms.^7,8

Electronic medical records were reviewed to determine whether patients were on an antidepressant at hospital admission, hospital discharge, and the initial CCRC visit prior to any treatment in the CCRC. Patients who were on a tricyclic antidepressant, selective serotonin reuptake inhibitor, selective serotonin-norepinephrine reuptake inhibitor, noradrenergic and specific serotonergic antidepressant (eg, mirtazapine), or norepinephrine and dopaminergic reuptake inhibitor (eg, bupropion) at any dose were designated as being on an antidepressant. Prescribers of antidepressants included primary care providers, clinical providers during their hospital stay, and various outpatient subspecialists other than those in the CCRC.

We then examined whether the risk factors for depressive symptoms differed if patients were on an antidepressant at their initial CCRC visit. We compared demographic and clinical characteristics between depressed and nondepressed patients not on an antidepressant. We repeated these analyses for those on an antidepressant. Dichotomous outcomes were compared using chi-square testing, and two-way Student t tests for continuous outcomes. Demographic and clinical variables with P < 0.1 were included as covariates in a logistic regression model for depressive symptoms separately for those not an antidepressant and those on an antidepressant. History of depression was not included as a covariate because it is highly collinear with post-ICU depression.

Two hundred four ICU survivors in this study reflected a racially diverse and underserved population (monthly income $745.3 ± $931.5). Although most had respiratory failure and/or delirium during their hospital stay, 94.1% (N = 160) mostly lived independently after discharge. Nearly one-third of patients (N = 69) were on at least 1 antidepressant at their initial CCRC visit. Of these 69 patients, 60.9% (N = 42) had an antidepressant prescription on hospital admission, and 60.9% (N = 42) had an antidepressant prescription on hospital discharge.

We first compared the demographic and clinical characteristics of patients with and without depressive symptoms at their initial CCRC visit. Patients with depressive symptoms were younger, less likely to have cardiac disease, more likely to have a history of depression, more likely to have been prescribed an antidepressant on hospital admission, more likely to be prescribed an antidepressant on hospital discharge, and more likely to be on an antidepressant at their initial CCRC visit (Table 1).

Our study demonstrated that about one-third of our ICU survivor clinical cohort had untreated or inadequately treated depressive symptoms at their CCRC initial visit. Many patients with depressive symptoms had a history of depression and/or antidepressant prescription on hospital admission. This suggests that pre-ICU depression is a major contributor to post-ICU depression. These findings are consistent with the results of a large retrospective analysis of Danish ICU survivors that found that patients were more likely to have premorbid psychiatric diagnoses, compared with the general population.⁹ Another ICU survivor research study that excluded patients who were on antidepressants prior to ICU hospitalization found that 49% of these patients were on an antidepressant after their ICU stay.¹⁰ Our much lower rate of patients on an antidepressant after their ICU stay may reflect the differences between patient populations, differences in healthcare systems, and differences in clinician prescribing practices.

Younger age was associated with a higher likelihood of depressive symptoms independent of antidepressant status. Findings about the relationship between age and post-ICU depression have varied. The Bringing to Light the Risk Factors and Incidence of Neuropsychological Dysfunction in ICU Survivors group found that older age was associated with more depressive symptoms at 12 months postdischarge.¹¹ On the other hand, a systematic review of post-ICU depression did not find any relationship between age and post-ICU depression.^2,3 These differences may be due in part to demographic variations in cohorts.

Our logistic regression models suggest that there may also be different risk factors in patients who had untreated vs inadequately treated depressive symptoms. Patients who were not on an antidepressant at their initial CCRC visit were more likely to have a lower level of education. This is consistent with the Medical Expenditure Panel Surveys study, which showed that adults with less than a high school education were less likely to receive depression treatment.¹² In patients who were on antidepressants at their initial CCRC visit, African Americans were more likely to have depressive symptoms. Possible reasons may include differences in receiving guideline-concordant antidepressant medication treatment, access to mental health subspecialty services, higher prevalence of treatment refractory depression, and differences in responses to antidepressant treatments.^13,14

Strengths of our study include detailed characterization for a fairly large ICU survivor clinic population and a racially diverse cohort. To the best of our knowledge, our study is also the first to examine whether there may be different risk factors for depressive symptoms based on antidepressant status. Limitations include the lack of information about nonpharmacologic antidepressant treatment and the inability to assess whether noncompliance, insufficient dose, or insufficient time on antidepressants contributed to inadequate antidepressant treatment. Antidepressants may have also been prescribed for other purposes such as smoking cessation, neuropathic pain, and migraine headaches. However, because 72.4% of patients on antidepressants had a history of depression, it is likely that most of them were on antidepressants to treat depression.

Other limitations include potential biases in our clinical cohort. Over the last 5 years, the CCRC has provided care to more than 200 ICU survivors. With 1100 mechanically ventilated admissions per year, only 1.8% of survivors are seen. The referral criteria for the CCRC is a major source of selection bias, which likely overrepresents PICS. Because patients are seen in the CCRC about 3 months after hospital discharge, there is also informant censoring due to death. Physically sicker survivors in nursing home facilities were less likely to be included. Finally, the small cohort size may have resulted in an underpowered study.

Future studies will need to confirm our findings about the high prevalence of post-ICU depression and different responses to antidepressant medications by certain groups. Pre-ICU depression, lack of antidepressant treatment, and inadequate antidepressant treatment are major causes of post-ICU depression. Currently, the CCRC offers pharmacotherapy, problem-solving therapy, or referral to mental health specialists to treat patients with depressive symptoms. ICU survivor clinics, such as the CCRC, may become important settings that allow for increased access to depression treatment for those at higher risk for post-ICU depression as well as the testing of new antidepressant regimens for those with inadequately treated depression.

Acknowledgments

The authors thank Dr. Adil Sheikh for assistance with data entry and Cynthia Reynolds for her clinical services. Grant support: The Critical Care Recovery Center (CCRC) is supported by Eskenazi Health Services. SW is supported by NIA 2P30AG010133. SG is supported by NIA 2P30AG010133 and NIA 5R01AG045350. SK is supported by NHBLI 5T32HL091816-07. MB is supported by NIA R01 AG040220-05, AHRQ P30 HS024384-02, CMS 1 L1 CMS331444-02-00 and NIA R01 AG030618-05A1. BK is supported by NIA K23-AG043476 and NHLBI R01HL131730.

Disclosure

There are no conflicts of interest. None of the above NIH grants supported the CCRC or this work.

As the number of intensive care unit (ICU) survivors has steadily increased over the past few decades, there is growing awareness of the long-term physical, cognitive, and psychological impairments after ICU hospitalization, collectively known as post–intensive care syndrome (PICS).¹ Systematic reviews based mostly on research studies suggest that the prevalence of depressive symptoms 2-12 months after ICU discharge is nearly 30%.^2-5 Due to the scarcity of established models of care for ICU survivors, there is limited characterization of depressive symptoms and antidepressant regimens in this clinical population. The Critical Care Recovery Center (CCRC) at Eskenazi Hospital is one of the first ICU survivor clinics in the United States and targets a racially diverse, underserved population in the Indianapolis metropolitan area.⁶ In this study, we examined whether patients had depressive symptoms at their initial CCRC visit, and whether the risk factors for depressive symptoms differed if they were on an antidepressant at their initial CCRC visit.

Referral criteria to the CCRC were 18 years or older, admitted to the Eskenazi ICU, were on mechanical ventilation or delirious for ≥48 hours (major risk factors for the development of PICS), and recommended for follow-up by a critical care physician. The exclusion criterion included was enrollment in hospice or palliative care services. Institutional review board approval was obtained to conduct retrospective analyses of de-identified clinical data. Medical history and medication lists were collected from patients, informal caregivers, and electronic medical records.

Two hundred thirty-three patients were seen in the CCRC from July 2011 to August 2016. Two hundred four patients rated symptoms of depression with either the Patient Health Questionnaire (PHQ-9; N = 99) or Geriatric Depression Scale (GDS-30; N = 105) at their initial visit to the CCRC prior to receiving any treatment at the CCRC. Twenty-nine patients who did not complete depression questionnaires were excluded from the analyses. Patients with PHQ-9 score ≥10 or GDS score ≥20 were categorized as having moderate to severe depressive symptoms.^7,8

Electronic medical records were reviewed to determine whether patients were on an antidepressant at hospital admission, hospital discharge, and the initial CCRC visit prior to any treatment in the CCRC. Patients who were on a tricyclic antidepressant, selective serotonin reuptake inhibitor, selective serotonin-norepinephrine reuptake inhibitor, noradrenergic and specific serotonergic antidepressant (eg, mirtazapine), or norepinephrine and dopaminergic reuptake inhibitor (eg, bupropion) at any dose were designated as being on an antidepressant. Prescribers of antidepressants included primary care providers, clinical providers during their hospital stay, and various outpatient subspecialists other than those in the CCRC.

We then examined whether the risk factors for depressive symptoms differed if patients were on an antidepressant at their initial CCRC visit. We compared demographic and clinical characteristics between depressed and nondepressed patients not on an antidepressant. We repeated these analyses for those on an antidepressant. Dichotomous outcomes were compared using chi-square testing, and two-way Student t tests for continuous outcomes. Demographic and clinical variables with P < 0.1 were included as covariates in a logistic regression model for depressive symptoms separately for those not an antidepressant and those on an antidepressant. History of depression was not included as a covariate because it is highly collinear with post-ICU depression.

Two hundred four ICU survivors in this study reflected a racially diverse and underserved population (monthly income $745.3 ± $931.5). Although most had respiratory failure and/or delirium during their hospital stay, 94.1% (N = 160) mostly lived independently after discharge. Nearly one-third of patients (N = 69) were on at least 1 antidepressant at their initial CCRC visit. Of these 69 patients, 60.9% (N = 42) had an antidepressant prescription on hospital admission, and 60.9% (N = 42) had an antidepressant prescription on hospital discharge.

We first compared the demographic and clinical characteristics of patients with and without depressive symptoms at their initial CCRC visit. Patients with depressive symptoms were younger, less likely to have cardiac disease, more likely to have a history of depression, more likely to have been prescribed an antidepressant on hospital admission, more likely to be prescribed an antidepressant on hospital discharge, and more likely to be on an antidepressant at their initial CCRC visit (Table 1).

Our study demonstrated that about one-third of our ICU survivor clinical cohort had untreated or inadequately treated depressive symptoms at their CCRC initial visit. Many patients with depressive symptoms had a history of depression and/or antidepressant prescription on hospital admission. This suggests that pre-ICU depression is a major contributor to post-ICU depression. These findings are consistent with the results of a large retrospective analysis of Danish ICU survivors that found that patients were more likely to have premorbid psychiatric diagnoses, compared with the general population.⁹ Another ICU survivor research study that excluded patients who were on antidepressants prior to ICU hospitalization found that 49% of these patients were on an antidepressant after their ICU stay.¹⁰ Our much lower rate of patients on an antidepressant after their ICU stay may reflect the differences between patient populations, differences in healthcare systems, and differences in clinician prescribing practices.

Younger age was associated with a higher likelihood of depressive symptoms independent of antidepressant status. Findings about the relationship between age and post-ICU depression have varied. The Bringing to Light the Risk Factors and Incidence of Neuropsychological Dysfunction in ICU Survivors group found that older age was associated with more depressive symptoms at 12 months postdischarge.¹¹ On the other hand, a systematic review of post-ICU depression did not find any relationship between age and post-ICU depression.^2,3 These differences may be due in part to demographic variations in cohorts.

Our logistic regression models suggest that there may also be different risk factors in patients who had untreated vs inadequately treated depressive symptoms. Patients who were not on an antidepressant at their initial CCRC visit were more likely to have a lower level of education. This is consistent with the Medical Expenditure Panel Surveys study, which showed that adults with less than a high school education were less likely to receive depression treatment.¹² In patients who were on antidepressants at their initial CCRC visit, African Americans were more likely to have depressive symptoms. Possible reasons may include differences in receiving guideline-concordant antidepressant medication treatment, access to mental health subspecialty services, higher prevalence of treatment refractory depression, and differences in responses to antidepressant treatments.^13,14

Strengths of our study include detailed characterization for a fairly large ICU survivor clinic population and a racially diverse cohort. To the best of our knowledge, our study is also the first to examine whether there may be different risk factors for depressive symptoms based on antidepressant status. Limitations include the lack of information about nonpharmacologic antidepressant treatment and the inability to assess whether noncompliance, insufficient dose, or insufficient time on antidepressants contributed to inadequate antidepressant treatment. Antidepressants may have also been prescribed for other purposes such as smoking cessation, neuropathic pain, and migraine headaches. However, because 72.4% of patients on antidepressants had a history of depression, it is likely that most of them were on antidepressants to treat depression.

Other limitations include potential biases in our clinical cohort. Over the last 5 years, the CCRC has provided care to more than 200 ICU survivors. With 1100 mechanically ventilated admissions per year, only 1.8% of survivors are seen. The referral criteria for the CCRC is a major source of selection bias, which likely overrepresents PICS. Because patients are seen in the CCRC about 3 months after hospital discharge, there is also informant censoring due to death. Physically sicker survivors in nursing home facilities were less likely to be included. Finally, the small cohort size may have resulted in an underpowered study.

Future studies will need to confirm our findings about the high prevalence of post-ICU depression and different responses to antidepressant medications by certain groups. Pre-ICU depression, lack of antidepressant treatment, and inadequate antidepressant treatment are major causes of post-ICU depression. Currently, the CCRC offers pharmacotherapy, problem-solving therapy, or referral to mental health specialists to treat patients with depressive symptoms. ICU survivor clinics, such as the CCRC, may become important settings that allow for increased access to depression treatment for those at higher risk for post-ICU depression as well as the testing of new antidepressant regimens for those with inadequately treated depression.

Acknowledgments

The authors thank Dr. Adil Sheikh for assistance with data entry and Cynthia Reynolds for her clinical services. Grant support: The Critical Care Recovery Center (CCRC) is supported by Eskenazi Health Services. SW is supported by NIA 2P30AG010133. SG is supported by NIA 2P30AG010133 and NIA 5R01AG045350. SK is supported by NHBLI 5T32HL091816-07. MB is supported by NIA R01 AG040220-05, AHRQ P30 HS024384-02, CMS 1 L1 CMS331444-02-00 and NIA R01 AG030618-05A1. BK is supported by NIA K23-AG043476 and NHLBI R01HL131730.

Disclosure

There are no conflicts of interest. None of the above NIH grants supported the CCRC or this work.

Medication reconciliation at hospital discharge is a critical component of the posthospital transition of care.¹ Effective reconciliation involves a clear process for documenting a current medication list, identifying and resolving discrepancies, and then documenting decisions and instructions around which medications should be continued, modified, or stopped.² Existing studies^3-5 suggest that medication discrepancies are common during hospital discharge transitions of care and lead to preventable adverse drug events, patient disability, and increased healthcare utilization following hospital discharge, including physician office visits, emergency department (ED) visits, and hospitalizations.^6-8

While the majority of studies of medication discrepancies have been conducted in general medical patients, few have examined how gaps in discharge medication reconciliation might affect surgical patients.^9,10 Two prior studies^9,10 suggest that medication discrepancies may occur more frequently for surgical patients, compared with medical patients, particularly discrepancies in reordering home medications postoperatively, raising patient safety concerns for more than 50 million patients hospitalized for surgery each year.¹¹ In particular, little is known about the appropriate discharge reconciliation of chronic cardiovascular medications, such as beta-blockers, renin-angiotensin system inhibitors, and statins in surgical patients, despite perioperative practice guidelines recommending continuation or rapid reinitiation of these medications after noncardiac surgery.¹² Problems with chronic cardiovascular medications have been implicated as major contributors to ED visits and hospitalizations for adverse drug events,^13,14 further highlighting the importance of safe and appropriate management of these medications.

To better understand the current state and impact of postoperative discharge medication reconciliation of chronic cardiovascular medications in surgical patients, we examined (1) the appropriate discharge reconciliation of 4 cardiovascular medication classes, and (2) the associations between the appropriate discharge reconciliation of these medication classes and postdischarge acute hospital and ambulatory visits in patients hospitalized for elective noncardiac surgery at an academic medical center.

Study Design and Patient Selection

We performed a retrospective analysis of data collected as part of a cohort study of hospitalized surgical patients admitted between January 2007 and December 2011. The original study was designed to assess the impact of a social marketing intervention on guideline-appropriate perioperative beta-blocker use in surgical patients. The study was conducted at 1 academic medical center that had 2 campuses with full noncardiac operative facilities and a 600-bed total capacity. Both sites had preoperative clinics, and patients were recruited by review of preoperative clinic records. Institutional review boards responsible for all sites approved the study.

For this analysis, we included adults (age >18 years) undergoing elective noncardiac surgery, who were expected to remain hospitalized for at least 1 day and were taking antiplatelet agents (aspirin, aspirin-dipyridamole, or clopidogrel), beta-blockers, renin-angiotensin system inhibitors (angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers), or statin lipid-lowering agents.

Data Collection

Data Sources. We collected data from a structured review of medical records as well as from discharge abstract information obtained from administrative data systems. Data regarding patient demographics (age, sex, and race/ethnicity), medical history, preoperative cardiovascular medications, surgical procedure and service, and attending surgeon were obtained from a medical record review of comprehensive preoperative clinic evaluations. Data regarding complications during hospitalization were obtained from medical record review and administrative data (Supplement for International Classification of Diseases, Ninth Revision codes).¹⁵ Research assistants abstracting data were trained by using a comprehensive reference manual providing specific criteria for classifying chart abstraction data. Research assistants also were directly observed during initial chart abstractions and underwent random chart validation audits by a senior investigator to ensure consistency. Any discrepancies in coding were resolved by consensus among senior investigators.

Definition of Key Predictor: Appropriate Reconciliation. We abstracted discharge medication lists from the electronic medical record. We defined the appropriate reconciliation of cardiovascular medications at discharge as documentation in discharge instructions, medication reconciliation tools, or discharge summaries that a preadmission cardiovascular medication was being continued at discharge, or, if the medication was not continued, documentation of a new contraindication to the medication or complication precluding its use during hospitalization. Medication continuity was considered appropriate if it was continued at discharge irrespective of changes in dosage. By using this measure for individual medications, we also assessed appropriate reconciliation as an “all-or-none” complete versus incomplete measure (appropriate reconciliation of all preoperative cardiovascular medication classes the patient was taking).¹⁶

Definition of Outcomes. Our coprimary outcomes were acute hospital visits (ED visits or hospitalizations) and unplanned ambulatory visits (primary care or surgical) at 30 days after surgery. Postoperative ambulatory visits that were not planned prior to surgery were defined as unplanned. Outcomes were ascertained by patient reports during follow-up telephone questionnaires administered by trained research staff and verified by medical record review.

Definition of Covariates. Using these data, we calculated a Revised Cardiac Risk Index (RCRI) score,¹⁷ which estimates the risk of perioperative cardiac complications in patients undergoing surgery. Through chart abstraction data supplemented by diagnosis codes from administrative data, we also constructed variables indicating occurrences of postoperative complications anytime during hospitalization that might pose contraindications to continuation of the 4 cardiovascular medication classes studied. For example, if a chart indicated that the patient had an acute rise in creatinine (elevation of baseline creatinine by 50% or absolute rise of 1 mg/dL in patients with baseline creatinine greater than 3 mg/dL) during hospitalization and a preoperative renin-angiotensin system inhibitor was not prescribed at discharge, we would have considered discontinuation appropriate. Other complications we abstracted were hypotension (systolic blood pressure less than 90 mmHg) for beta-blockers and renin-angiotensin system inhibitors, bradycardia (heart rate less than 50 bpm) for beta-blockers, acute kidney injury (defined above) and hyperkalemia for renin-angiotensin system inhibitors, and bleeding (any site) for antiplatelet agents.

Statistical Analysis

We used χ² and Kruskal-Wallis tests to compare baseline patient characteristics. To assess associations between appropriate medication reconciliation and patient outcomes, we used multilevel mixed-effects logistic regression to account for the clustering of patients by the attending surgeon. We adjusted for baseline patient demographics, surgical service, the number of baseline cardiovascular medications, and individual RCRI criteria. We constructed separate models for all-or-none appropriate reconciliation and for each individual medication class.

As a sensitivity analysis, we constructed similar models by using a simplified definition of appropriate reconciliation based entirely on medication continuity (continued or not continued at discharge) without taking potential contraindications during hospitalization into account. For complete versus incomplete reconciliation, we also constructed models with an interaction term between the number of baseline cardiovascular medications and appropriate medication reconciliation to test the hypothesis that inappropriate reconciliation would be more likely with an increasing number of preoperative cardiovascular medications. Because this interaction term was not statistically significant, we did not include it in the final models for ease of reporting and interpretability. We performed all statistical analyses using Stata 14 (StataCorp, LLC, College Station, Texas), and used 2-sided statistical tests and a P value of less than .05 to define statistical significance.

Patient Characteristics

A total of 849 patients were enrolled, of which 752 (88.6%) were taking at least 1 of the specified cardiovascular medications in the preoperative period. Their mean age was 61.5; 50.9% were male, 72.6% were non-Hispanic white, and 89.4% had RCRI scores of 0 or 1 (Table 1). The majority (63.8%) were undergoing general surgery, orthopedic surgery, or neurosurgery procedures. In the preoperative period, 327 (43.5%) patients were taking antiplatelet agents, 624 (83.0%) were taking beta-blockers, 361 (48.0%) were taking renin-angiotensin system inhibitors, and 406 (54.0%) were taking statins (Table 2). Among patients taking antiplatelet agents, 271 (82.9%) were taking aspirin alone, 21 (6.4%) were taking clopidogrel alone, and 35 (10.7%) were taking dual antiplatelet therapy with aspirin and clopidogrel. Nearly three-quarters of the patients (551, 73.3%) were taking medications from 2 or more classes, and the proportion of patients with inappropriate reconciliation increased with the number of preoperative cardiovascular medications.

Patients with and without appropriate reconciliation of all preoperative cardiovascular medications were similar in age, sex, and race/ethnicity (Table 1). Patients with inappropriate reconciliation of at least 1 medication were more likely to be on the urology and renal/liver transplant surgical services, have higher RCRI scores, and be taking antiplatelet agents, statins, renin-angiotensin system inhibitors, and 3 or more cardiovascular medications in the preoperative period.

Appropriate Medication Reconciliation

Four hundred thirty-six patients (58.0%) had their baseline cardiovascular medications appropriately reconciled. Among all patients with appropriately reconciled medications, 1 (0.2%) had beta-blockers discontinued due to a documented episode of hypotension; 17 (3.9%) had renin-angiotensin system inhibitors discontinued due to episodes of acute kidney injury, hypotension, or hyperkalemia; and 1 (0.2%) had antiplatelet agents discontinued due to bleeding. For individual medications, appropriate reconciliation between the preoperative and discharge periods occurred for 156 of the 327 patients on antiplatelet agents (47.7%), 507 of the 624 patients on beta-blockers (81.3%), 259 of the 361 patients on renin-angiotensin system inhibitors (71.8%), and 302 of the 406 patients on statins (74.4%; Table 2).

Associations Between Medication Reconciliation and Outcomes

Thirty-day outcome data on acute hospital visits were available for 679 (90.3%) patients. Of these, 146 (21.5%) were seen in the ED or were hospitalized, and 111 (16.3%) were seen for an unplanned primary care or surgical outpatient visit at 30 days after surgery. Patients with incomplete outcome data were more likely to have complete medication reconciliation compared with those with complete outcome data (71.2% vs 56.6%, P = 0.02). As shown in Table 3, the proportion of patients with 30-day acute hospital visits was nonstatistically significantly lower in patients with complete medication reconciliation (20.8% vs 22.4%, P = 0.63) and the appropriate reconciliation of beta-blockers (21.9% vs 23.6%, P = 0.71) and renin-angiotensin system inhibitors (19.6% vs 20.0%, P = 0.93), and nonsignificantly higher with the appropriate reconciliation of antiplatelet agents (23.9% vs 19.9%, P = 0.40). Acute hospital visits were statistically significantly lower with the appropriate reconciliation of statins (17.9% vs 31.9%, P = 0.004).

Sensitivity Analysis

Overall, 430 (57.2%) patients had complete cardiovascular medication continuity without considering potential contraindications during hospitalization. Associations between medication continuity and acute hospital and ambulatory visits were similar to the primary analyses.

In this study of 752 patients hospitalized for elective noncardiac surgery, we found significant gaps in the appropriate reconciliation of commonly prescribed cardiovascular medications, with inappropriate discontinuation ranging from 18.8% to 52.3% for individual medications. Unplanned postdischarge healthcare utilization was high, with acute hospital visits documented in 21.5% of patients and unplanned ambulatory visits in 16.3% at 30 days after surgery. However, medication reconciliation gaps were not consistently associated with ED visits, hospitalizations, or unplanned ambulatory visits.

Our finding of large gaps in postoperative medication reconciliation is consistent with existing studies of medication reconciliation in surgical patients.^9,10,18 One study found medication discrepancies in 40.2% of postoperative patients receiving usual care and discrepancies judged to have the potential to cause harm (such as the omission of beta-blockers) in 29.9%.⁹ Consistent with our findings, this study also found that most postoperative medication discrepancies were omissions in reordering home medications, though at a rate somewhat higher than those seen in medical patients at discharge.⁵ While hospitalization by itself increases the risk of unintentional discontinuation of chronic medications,³ our results, along with existing literature, suggest that the risk for omission of chronic medications is unacceptably high.

We also found significant variation in reconciliation among cardiovascular medications, with appropriate reconciliation occurring least frequently for antiplatelet agents and most frequently for beta-blockers. The low rates of appropriate reconciliation for antiplatelet agents may be attributable to deliberate withholding of antiplatelet therapy in the postoperative period based on clinical assessments of surgical bleeding risk in the absence of active bleeding. Perioperative management of antiplatelet agents for noncardiac surgery remains an unclear and controversial topic, which may also contribute to the variation noted.¹⁹ Conversely, beta-blockers demonstrated high rates of preoperative use (over 80% of patients) and appropriate reconciliation. Both findings are likely attributable in part to the timing of the study, which began prior to the publication of the Perioperative Ischemic Evaluation trial, which more definitively demonstrated the potential harms of perioperative beta-blocker therapy.²⁰

Despite a high proportion of patients with discontinuous medications at discharge, we found no associations between the appropriate reconciliation of beta-blockers, renin-angiotensin system inhibitors, and antiplatelet agents and acute hospital or ambulatory visits in the first 30 days after discharge. One explanation for this discrepancy is that, although we focused on cardiovascular medications commonly implicated in acute hospital visits, the vast majority of patients in our study had low perioperative cardiovascular risk as assessed by the RCRI. Previous studies have demonstrated that the benefit of perioperative beta-blocker therapy is predominantly in patients with moderate to high perioperative cardiovascular risk.^21,22 It is possible that the detrimental effects of the discontinuation of chronic cardiovascular medications are more prominent in populations at a higher risk of perioperative cardiovascular complications or that complications will occur later than 30 days after discharge. Similarly, while the benefits of continuation of renin-angiotensin system inhibitors are less clear,²³ few patients in our cohort had a history of congestive heart failure (6.3%) or coronary artery disease (13.0%), 2 conditions in which the impact of perioperative discontinuation of renin-angiotensin inhibitor or beta-blocker therapy would likely be more pronounced.^24,25 An additional explanation for the lack of associations is that, while multiple studies have demonstrated that medication errors are common, the proportion of errors with the potential for harm is much lower, and the proportion that causes actual harm is lower still.^5,26,27 Thus, while we likely captured high-severity medication errors leading to acute hospital or unplanned ambulatory visits, we would not have captured medication errors with lower severity clinical consequences that did not result in medical encounters.

We did find an association between the continuation of statin therapy and reduced ED visits and hospitalizations. This finding is supported by previous studies of patients undergoing noncardiac surgery, including 1 demonstrating an association between immediate postoperative statin therapy and reduced in-hospital mortality²⁸ and another study demonstrating an association between postoperative statin therapy and reductions in a composite endpoint of 30-day mortality, atrial fibrillation, and nonfatal myocardial infarction.²⁹ Alternatively, this finding could reflect the effects of unaddressed confounding by factors contributing to statin discontinuation and poor health outcomes leading to acute hospital visits, such as acute elevations in liver enzymes.

Our study has important implications for patients undergoing elective noncardiac surgery and the healthcare providers caring for them. First, inappropriate omissions of chronic cardiovascular medications at discharge are common; clinicians should increase their general awareness and focus on appropriately reconciling these medications, for even if our results do not connect medication discontinuity to readmissions or unexpected clinical encounters, their impact on patients’ understanding of their medications remains a potential concern. Second, the overall high rates of unplanned postdischarge healthcare utilization in this study highlight the need for close postdischarge monitoring of patients undergoing elective surgical procedures and for further research to identify preventable etiologies of postdischarge healthcare utilization in this population. Third, further study is needed to identify specific patient populations and medication classes, in which appropriate reconciliation is associated with patient outcomes that may benefit from more intensive discharge medication reconciliation interventions.

Our study has limitations. First, the majority of patients in this single-center study were at low risk of perioperative cardiovascular events, and our results may not be generalizable to higher-risk patients undergoing elective surgery. Second, discharge reconciliation was based on documentation of medication reconciliation and not on patient-reported medication adherence. In addition, the ability to judge the accuracy of discharge medication reconciliation is in part dependent on the accuracy of the admission medication reconciliation. Thus, although we used preoperative medication regimens documented during preadmission visits to comprehensive preoperative clinics for comparison, discrepancies in these preoperative regimens could have affected our analysis of appropriate discharge reconciliation. Third, inadequate documentation of clinical reasons for discontinuing medications may have led to residual confounding by indication in our observational study. Finally, the outcomes available to us may have been relatively insensitive to other adverse effects of medication discontinuity, such as patient symptoms (eg, angina severity), patient awareness of medications, or work placed on primary care physicians needing to “clean up” erroneous medication lists.

In conclusion, gaps in appropriate discharge reconciliation of chronic cardiovascular medications were common but not consistently associated with postdischarge acute hospital or unplanned ambulatory visits in a relatively low-risk cohort of patients undergoing elective surgery. While appropriate medication reconciliation should always be a priority, further study is needed to identify medication reconciliation approaches associated with postdischarge healthcare utilization and other patient outcomes.

Disclosure

Dr. Lee reports receiving grant support from the Health Resources and Services Administration (T32HP19025). Dr. Vittinghoff reports receiving grant support from the Agency for Healthcare Research and Quality. Dr. Auerbach and Dr. Fleischmann report receiving grant support from the National Institutes of Health. Dr. Auerbach also reports receiving honorarium as Editor-in-Chief of the Journal of Hospital Medicine. Dr. Corbett reports receiving grant and travel support from Simon Fraser University. The remaining authors have no disclosures to report.

Medication reconciliation at hospital discharge is a critical component of the posthospital transition of care.¹ Effective reconciliation involves a clear process for documenting a current medication list, identifying and resolving discrepancies, and then documenting decisions and instructions around which medications should be continued, modified, or stopped.² Existing studies^3-5 suggest that medication discrepancies are common during hospital discharge transitions of care and lead to preventable adverse drug events, patient disability, and increased healthcare utilization following hospital discharge, including physician office visits, emergency department (ED) visits, and hospitalizations.^6-8

While the majority of studies of medication discrepancies have been conducted in general medical patients, few have examined how gaps in discharge medication reconciliation might affect surgical patients.^9,10 Two prior studies^9,10 suggest that medication discrepancies may occur more frequently for surgical patients, compared with medical patients, particularly discrepancies in reordering home medications postoperatively, raising patient safety concerns for more than 50 million patients hospitalized for surgery each year.¹¹ In particular, little is known about the appropriate discharge reconciliation of chronic cardiovascular medications, such as beta-blockers, renin-angiotensin system inhibitors, and statins in surgical patients, despite perioperative practice guidelines recommending continuation or rapid reinitiation of these medications after noncardiac surgery.¹² Problems with chronic cardiovascular medications have been implicated as major contributors to ED visits and hospitalizations for adverse drug events,^13,14 further highlighting the importance of safe and appropriate management of these medications.

To better understand the current state and impact of postoperative discharge medication reconciliation of chronic cardiovascular medications in surgical patients, we examined (1) the appropriate discharge reconciliation of 4 cardiovascular medication classes, and (2) the associations between the appropriate discharge reconciliation of these medication classes and postdischarge acute hospital and ambulatory visits in patients hospitalized for elective noncardiac surgery at an academic medical center.

Study Design and Patient Selection

We performed a retrospective analysis of data collected as part of a cohort study of hospitalized surgical patients admitted between January 2007 and December 2011. The original study was designed to assess the impact of a social marketing intervention on guideline-appropriate perioperative beta-blocker use in surgical patients. The study was conducted at 1 academic medical center that had 2 campuses with full noncardiac operative facilities and a 600-bed total capacity. Both sites had preoperative clinics, and patients were recruited by review of preoperative clinic records. Institutional review boards responsible for all sites approved the study.

For this analysis, we included adults (age >18 years) undergoing elective noncardiac surgery, who were expected to remain hospitalized for at least 1 day and were taking antiplatelet agents (aspirin, aspirin-dipyridamole, or clopidogrel), beta-blockers, renin-angiotensin system inhibitors (angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers), or statin lipid-lowering agents.

Data Collection

Data Sources. We collected data from a structured review of medical records as well as from discharge abstract information obtained from administrative data systems. Data regarding patient demographics (age, sex, and race/ethnicity), medical history, preoperative cardiovascular medications, surgical procedure and service, and attending surgeon were obtained from a medical record review of comprehensive preoperative clinic evaluations. Data regarding complications during hospitalization were obtained from medical record review and administrative data (Supplement for International Classification of Diseases, Ninth Revision codes).¹⁵ Research assistants abstracting data were trained by using a comprehensive reference manual providing specific criteria for classifying chart abstraction data. Research assistants also were directly observed during initial chart abstractions and underwent random chart validation audits by a senior investigator to ensure consistency. Any discrepancies in coding were resolved by consensus among senior investigators.

Definition of Key Predictor: Appropriate Reconciliation. We abstracted discharge medication lists from the electronic medical record. We defined the appropriate reconciliation of cardiovascular medications at discharge as documentation in discharge instructions, medication reconciliation tools, or discharge summaries that a preadmission cardiovascular medication was being continued at discharge, or, if the medication was not continued, documentation of a new contraindication to the medication or complication precluding its use during hospitalization. Medication continuity was considered appropriate if it was continued at discharge irrespective of changes in dosage. By using this measure for individual medications, we also assessed appropriate reconciliation as an “all-or-none” complete versus incomplete measure (appropriate reconciliation of all preoperative cardiovascular medication classes the patient was taking).¹⁶

Definition of Outcomes. Our coprimary outcomes were acute hospital visits (ED visits or hospitalizations) and unplanned ambulatory visits (primary care or surgical) at 30 days after surgery. Postoperative ambulatory visits that were not planned prior to surgery were defined as unplanned. Outcomes were ascertained by patient reports during follow-up telephone questionnaires administered by trained research staff and verified by medical record review.

Definition of Covariates. Using these data, we calculated a Revised Cardiac Risk Index (RCRI) score,¹⁷ which estimates the risk of perioperative cardiac complications in patients undergoing surgery. Through chart abstraction data supplemented by diagnosis codes from administrative data, we also constructed variables indicating occurrences of postoperative complications anytime during hospitalization that might pose contraindications to continuation of the 4 cardiovascular medication classes studied. For example, if a chart indicated that the patient had an acute rise in creatinine (elevation of baseline creatinine by 50% or absolute rise of 1 mg/dL in patients with baseline creatinine greater than 3 mg/dL) during hospitalization and a preoperative renin-angiotensin system inhibitor was not prescribed at discharge, we would have considered discontinuation appropriate. Other complications we abstracted were hypotension (systolic blood pressure less than 90 mmHg) for beta-blockers and renin-angiotensin system inhibitors, bradycardia (heart rate less than 50 bpm) for beta-blockers, acute kidney injury (defined above) and hyperkalemia for renin-angiotensin system inhibitors, and bleeding (any site) for antiplatelet agents.

Statistical Analysis

We used χ² and Kruskal-Wallis tests to compare baseline patient characteristics. To assess associations between appropriate medication reconciliation and patient outcomes, we used multilevel mixed-effects logistic regression to account for the clustering of patients by the attending surgeon. We adjusted for baseline patient demographics, surgical service, the number of baseline cardiovascular medications, and individual RCRI criteria. We constructed separate models for all-or-none appropriate reconciliation and for each individual medication class.

As a sensitivity analysis, we constructed similar models by using a simplified definition of appropriate reconciliation based entirely on medication continuity (continued or not continued at discharge) without taking potential contraindications during hospitalization into account. For complete versus incomplete reconciliation, we also constructed models with an interaction term between the number of baseline cardiovascular medications and appropriate medication reconciliation to test the hypothesis that inappropriate reconciliation would be more likely with an increasing number of preoperative cardiovascular medications. Because this interaction term was not statistically significant, we did not include it in the final models for ease of reporting and interpretability. We performed all statistical analyses using Stata 14 (StataCorp, LLC, College Station, Texas), and used 2-sided statistical tests and a P value of less than .05 to define statistical significance.

Patient Characteristics

A total of 849 patients were enrolled, of which 752 (88.6%) were taking at least 1 of the specified cardiovascular medications in the preoperative period. Their mean age was 61.5; 50.9% were male, 72.6% were non-Hispanic white, and 89.4% had RCRI scores of 0 or 1 (Table 1). The majority (63.8%) were undergoing general surgery, orthopedic surgery, or neurosurgery procedures. In the preoperative period, 327 (43.5%) patients were taking antiplatelet agents, 624 (83.0%) were taking beta-blockers, 361 (48.0%) were taking renin-angiotensin system inhibitors, and 406 (54.0%) were taking statins (Table 2). Among patients taking antiplatelet agents, 271 (82.9%) were taking aspirin alone, 21 (6.4%) were taking clopidogrel alone, and 35 (10.7%) were taking dual antiplatelet therapy with aspirin and clopidogrel. Nearly three-quarters of the patients (551, 73.3%) were taking medications from 2 or more classes, and the proportion of patients with inappropriate reconciliation increased with the number of preoperative cardiovascular medications.

Patients with and without appropriate reconciliation of all preoperative cardiovascular medications were similar in age, sex, and race/ethnicity (Table 1). Patients with inappropriate reconciliation of at least 1 medication were more likely to be on the urology and renal/liver transplant surgical services, have higher RCRI scores, and be taking antiplatelet agents, statins, renin-angiotensin system inhibitors, and 3 or more cardiovascular medications in the preoperative period.

Appropriate Medication Reconciliation

Four hundred thirty-six patients (58.0%) had their baseline cardiovascular medications appropriately reconciled. Among all patients with appropriately reconciled medications, 1 (0.2%) had beta-blockers discontinued due to a documented episode of hypotension; 17 (3.9%) had renin-angiotensin system inhibitors discontinued due to episodes of acute kidney injury, hypotension, or hyperkalemia; and 1 (0.2%) had antiplatelet agents discontinued due to bleeding. For individual medications, appropriate reconciliation between the preoperative and discharge periods occurred for 156 of the 327 patients on antiplatelet agents (47.7%), 507 of the 624 patients on beta-blockers (81.3%), 259 of the 361 patients on renin-angiotensin system inhibitors (71.8%), and 302 of the 406 patients on statins (74.4%; Table 2).

Associations Between Medication Reconciliation and Outcomes

Thirty-day outcome data on acute hospital visits were available for 679 (90.3%) patients. Of these, 146 (21.5%) were seen in the ED or were hospitalized, and 111 (16.3%) were seen for an unplanned primary care or surgical outpatient visit at 30 days after surgery. Patients with incomplete outcome data were more likely to have complete medication reconciliation compared with those with complete outcome data (71.2% vs 56.6%, P = 0.02). As shown in Table 3, the proportion of patients with 30-day acute hospital visits was nonstatistically significantly lower in patients with complete medication reconciliation (20.8% vs 22.4%, P = 0.63) and the appropriate reconciliation of beta-blockers (21.9% vs 23.6%, P = 0.71) and renin-angiotensin system inhibitors (19.6% vs 20.0%, P = 0.93), and nonsignificantly higher with the appropriate reconciliation of antiplatelet agents (23.9% vs 19.9%, P = 0.40). Acute hospital visits were statistically significantly lower with the appropriate reconciliation of statins (17.9% vs 31.9%, P = 0.004).

Sensitivity Analysis

Overall, 430 (57.2%) patients had complete cardiovascular medication continuity without considering potential contraindications during hospitalization. Associations between medication continuity and acute hospital and ambulatory visits were similar to the primary analyses.

In this study of 752 patients hospitalized for elective noncardiac surgery, we found significant gaps in the appropriate reconciliation of commonly prescribed cardiovascular medications, with inappropriate discontinuation ranging from 18.8% to 52.3% for individual medications. Unplanned postdischarge healthcare utilization was high, with acute hospital visits documented in 21.5% of patients and unplanned ambulatory visits in 16.3% at 30 days after surgery. However, medication reconciliation gaps were not consistently associated with ED visits, hospitalizations, or unplanned ambulatory visits.

Our finding of large gaps in postoperative medication reconciliation is consistent with existing studies of medication reconciliation in surgical patients.^9,10,18 One study found medication discrepancies in 40.2% of postoperative patients receiving usual care and discrepancies judged to have the potential to cause harm (such as the omission of beta-blockers) in 29.9%.⁹ Consistent with our findings, this study also found that most postoperative medication discrepancies were omissions in reordering home medications, though at a rate somewhat higher than those seen in medical patients at discharge.⁵ While hospitalization by itself increases the risk of unintentional discontinuation of chronic medications,³ our results, along with existing literature, suggest that the risk for omission of chronic medications is unacceptably high.

We also found significant variation in reconciliation among cardiovascular medications, with appropriate reconciliation occurring least frequently for antiplatelet agents and most frequently for beta-blockers. The low rates of appropriate reconciliation for antiplatelet agents may be attributable to deliberate withholding of antiplatelet therapy in the postoperative period based on clinical assessments of surgical bleeding risk in the absence of active bleeding. Perioperative management of antiplatelet agents for noncardiac surgery remains an unclear and controversial topic, which may also contribute to the variation noted.¹⁹ Conversely, beta-blockers demonstrated high rates of preoperative use (over 80% of patients) and appropriate reconciliation. Both findings are likely attributable in part to the timing of the study, which began prior to the publication of the Perioperative Ischemic Evaluation trial, which more definitively demonstrated the potential harms of perioperative beta-blocker therapy.²⁰

Despite a high proportion of patients with discontinuous medications at discharge, we found no associations between the appropriate reconciliation of beta-blockers, renin-angiotensin system inhibitors, and antiplatelet agents and acute hospital or ambulatory visits in the first 30 days after discharge. One explanation for this discrepancy is that, although we focused on cardiovascular medications commonly implicated in acute hospital visits, the vast majority of patients in our study had low perioperative cardiovascular risk as assessed by the RCRI. Previous studies have demonstrated that the benefit of perioperative beta-blocker therapy is predominantly in patients with moderate to high perioperative cardiovascular risk.^21,22 It is possible that the detrimental effects of the discontinuation of chronic cardiovascular medications are more prominent in populations at a higher risk of perioperative cardiovascular complications or that complications will occur later than 30 days after discharge. Similarly, while the benefits of continuation of renin-angiotensin system inhibitors are less clear,²³ few patients in our cohort had a history of congestive heart failure (6.3%) or coronary artery disease (13.0%), 2 conditions in which the impact of perioperative discontinuation of renin-angiotensin inhibitor or beta-blocker therapy would likely be more pronounced.^24,25 An additional explanation for the lack of associations is that, while multiple studies have demonstrated that medication errors are common, the proportion of errors with the potential for harm is much lower, and the proportion that causes actual harm is lower still.^5,26,27 Thus, while we likely captured high-severity medication errors leading to acute hospital or unplanned ambulatory visits, we would not have captured medication errors with lower severity clinical consequences that did not result in medical encounters.

We did find an association between the continuation of statin therapy and reduced ED visits and hospitalizations. This finding is supported by previous studies of patients undergoing noncardiac surgery, including 1 demonstrating an association between immediate postoperative statin therapy and reduced in-hospital mortality²⁸ and another study demonstrating an association between postoperative statin therapy and reductions in a composite endpoint of 30-day mortality, atrial fibrillation, and nonfatal myocardial infarction.²⁹ Alternatively, this finding could reflect the effects of unaddressed confounding by factors contributing to statin discontinuation and poor health outcomes leading to acute hospital visits, such as acute elevations in liver enzymes.

Our study has important implications for patients undergoing elective noncardiac surgery and the healthcare providers caring for them. First, inappropriate omissions of chronic cardiovascular medications at discharge are common; clinicians should increase their general awareness and focus on appropriately reconciling these medications, for even if our results do not connect medication discontinuity to readmissions or unexpected clinical encounters, their impact on patients’ understanding of their medications remains a potential concern. Second, the overall high rates of unplanned postdischarge healthcare utilization in this study highlight the need for close postdischarge monitoring of patients undergoing elective surgical procedures and for further research to identify preventable etiologies of postdischarge healthcare utilization in this population. Third, further study is needed to identify specific patient populations and medication classes, in which appropriate reconciliation is associated with patient outcomes that may benefit from more intensive discharge medication reconciliation interventions.

Our study has limitations. First, the majority of patients in this single-center study were at low risk of perioperative cardiovascular events, and our results may not be generalizable to higher-risk patients undergoing elective surgery. Second, discharge reconciliation was based on documentation of medication reconciliation and not on patient-reported medication adherence. In addition, the ability to judge the accuracy of discharge medication reconciliation is in part dependent on the accuracy of the admission medication reconciliation. Thus, although we used preoperative medication regimens documented during preadmission visits to comprehensive preoperative clinics for comparison, discrepancies in these preoperative regimens could have affected our analysis of appropriate discharge reconciliation. Third, inadequate documentation of clinical reasons for discontinuing medications may have led to residual confounding by indication in our observational study. Finally, the outcomes available to us may have been relatively insensitive to other adverse effects of medication discontinuity, such as patient symptoms (eg, angina severity), patient awareness of medications, or work placed on primary care physicians needing to “clean up” erroneous medication lists.

In conclusion, gaps in appropriate discharge reconciliation of chronic cardiovascular medications were common but not consistently associated with postdischarge acute hospital or unplanned ambulatory visits in a relatively low-risk cohort of patients undergoing elective surgery. While appropriate medication reconciliation should always be a priority, further study is needed to identify medication reconciliation approaches associated with postdischarge healthcare utilization and other patient outcomes.

Disclosure

Dr. Lee reports receiving grant support from the Health Resources and Services Administration (T32HP19025). Dr. Vittinghoff reports receiving grant support from the Agency for Healthcare Research and Quality. Dr. Auerbach and Dr. Fleischmann report receiving grant support from the National Institutes of Health. Dr. Auerbach also reports receiving honorarium as Editor-in-Chief of the Journal of Hospital Medicine. Dr. Corbett reports receiving grant and travel support from Simon Fraser University. The remaining authors have no disclosures to report.

Medication reconciliation at hospital discharge is a critical component of the posthospital transition of care.¹ Effective reconciliation involves a clear process for documenting a current medication list, identifying and resolving discrepancies, and then documenting decisions and instructions around which medications should be continued, modified, or stopped.² Existing studies^3-5 suggest that medication discrepancies are common during hospital discharge transitions of care and lead to preventable adverse drug events, patient disability, and increased healthcare utilization following hospital discharge, including physician office visits, emergency department (ED) visits, and hospitalizations.^6-8

While the majority of studies of medication discrepancies have been conducted in general medical patients, few have examined how gaps in discharge medication reconciliation might affect surgical patients.^9,10 Two prior studies^9,10 suggest that medication discrepancies may occur more frequently for surgical patients, compared with medical patients, particularly discrepancies in reordering home medications postoperatively, raising patient safety concerns for more than 50 million patients hospitalized for surgery each year.¹¹ In particular, little is known about the appropriate discharge reconciliation of chronic cardiovascular medications, such as beta-blockers, renin-angiotensin system inhibitors, and statins in surgical patients, despite perioperative practice guidelines recommending continuation or rapid reinitiation of these medications after noncardiac surgery.¹² Problems with chronic cardiovascular medications have been implicated as major contributors to ED visits and hospitalizations for adverse drug events,^13,14 further highlighting the importance of safe and appropriate management of these medications.

To better understand the current state and impact of postoperative discharge medication reconciliation of chronic cardiovascular medications in surgical patients, we examined (1) the appropriate discharge reconciliation of 4 cardiovascular medication classes, and (2) the associations between the appropriate discharge reconciliation of these medication classes and postdischarge acute hospital and ambulatory visits in patients hospitalized for elective noncardiac surgery at an academic medical center.

Study Design and Patient Selection

We performed a retrospective analysis of data collected as part of a cohort study of hospitalized surgical patients admitted between January 2007 and December 2011. The original study was designed to assess the impact of a social marketing intervention on guideline-appropriate perioperative beta-blocker use in surgical patients. The study was conducted at 1 academic medical center that had 2 campuses with full noncardiac operative facilities and a 600-bed total capacity. Both sites had preoperative clinics, and patients were recruited by review of preoperative clinic records. Institutional review boards responsible for all sites approved the study.

For this analysis, we included adults (age >18 years) undergoing elective noncardiac surgery, who were expected to remain hospitalized for at least 1 day and were taking antiplatelet agents (aspirin, aspirin-dipyridamole, or clopidogrel), beta-blockers, renin-angiotensin system inhibitors (angiotensin-converting-enzyme inhibitors or angiotensin-receptor blockers), or statin lipid-lowering agents.

Data Collection

Data Sources. We collected data from a structured review of medical records as well as from discharge abstract information obtained from administrative data systems. Data regarding patient demographics (age, sex, and race/ethnicity), medical history, preoperative cardiovascular medications, surgical procedure and service, and attending surgeon were obtained from a medical record review of comprehensive preoperative clinic evaluations. Data regarding complications during hospitalization were obtained from medical record review and administrative data (Supplement for International Classification of Diseases, Ninth Revision codes).¹⁵ Research assistants abstracting data were trained by using a comprehensive reference manual providing specific criteria for classifying chart abstraction data. Research assistants also were directly observed during initial chart abstractions and underwent random chart validation audits by a senior investigator to ensure consistency. Any discrepancies in coding were resolved by consensus among senior investigators.

Definition of Key Predictor: Appropriate Reconciliation. We abstracted discharge medication lists from the electronic medical record. We defined the appropriate reconciliation of cardiovascular medications at discharge as documentation in discharge instructions, medication reconciliation tools, or discharge summaries that a preadmission cardiovascular medication was being continued at discharge, or, if the medication was not continued, documentation of a new contraindication to the medication or complication precluding its use during hospitalization. Medication continuity was considered appropriate if it was continued at discharge irrespective of changes in dosage. By using this measure for individual medications, we also assessed appropriate reconciliation as an “all-or-none” complete versus incomplete measure (appropriate reconciliation of all preoperative cardiovascular medication classes the patient was taking).¹⁶

Definition of Outcomes. Our coprimary outcomes were acute hospital visits (ED visits or hospitalizations) and unplanned ambulatory visits (primary care or surgical) at 30 days after surgery. Postoperative ambulatory visits that were not planned prior to surgery were defined as unplanned. Outcomes were ascertained by patient reports during follow-up telephone questionnaires administered by trained research staff and verified by medical record review.

Definition of Covariates. Using these data, we calculated a Revised Cardiac Risk Index (RCRI) score,¹⁷ which estimates the risk of perioperative cardiac complications in patients undergoing surgery. Through chart abstraction data supplemented by diagnosis codes from administrative data, we also constructed variables indicating occurrences of postoperative complications anytime during hospitalization that might pose contraindications to continuation of the 4 cardiovascular medication classes studied. For example, if a chart indicated that the patient had an acute rise in creatinine (elevation of baseline creatinine by 50% or absolute rise of 1 mg/dL in patients with baseline creatinine greater than 3 mg/dL) during hospitalization and a preoperative renin-angiotensin system inhibitor was not prescribed at discharge, we would have considered discontinuation appropriate. Other complications we abstracted were hypotension (systolic blood pressure less than 90 mmHg) for beta-blockers and renin-angiotensin system inhibitors, bradycardia (heart rate less than 50 bpm) for beta-blockers, acute kidney injury (defined above) and hyperkalemia for renin-angiotensin system inhibitors, and bleeding (any site) for antiplatelet agents.

Statistical Analysis

We used χ² and Kruskal-Wallis tests to compare baseline patient characteristics. To assess associations between appropriate medication reconciliation and patient outcomes, we used multilevel mixed-effects logistic regression to account for the clustering of patients by the attending surgeon. We adjusted for baseline patient demographics, surgical service, the number of baseline cardiovascular medications, and individual RCRI criteria. We constructed separate models for all-or-none appropriate reconciliation and for each individual medication class.

As a sensitivity analysis, we constructed similar models by using a simplified definition of appropriate reconciliation based entirely on medication continuity (continued or not continued at discharge) without taking potential contraindications during hospitalization into account. For complete versus incomplete reconciliation, we also constructed models with an interaction term between the number of baseline cardiovascular medications and appropriate medication reconciliation to test the hypothesis that inappropriate reconciliation would be more likely with an increasing number of preoperative cardiovascular medications. Because this interaction term was not statistically significant, we did not include it in the final models for ease of reporting and interpretability. We performed all statistical analyses using Stata 14 (StataCorp, LLC, College Station, Texas), and used 2-sided statistical tests and a P value of less than .05 to define statistical significance.

Patient Characteristics

A total of 849 patients were enrolled, of which 752 (88.6%) were taking at least 1 of the specified cardiovascular medications in the preoperative period. Their mean age was 61.5; 50.9% were male, 72.6% were non-Hispanic white, and 89.4% had RCRI scores of 0 or 1 (Table 1). The majority (63.8%) were undergoing general surgery, orthopedic surgery, or neurosurgery procedures. In the preoperative period, 327 (43.5%) patients were taking antiplatelet agents, 624 (83.0%) were taking beta-blockers, 361 (48.0%) were taking renin-angiotensin system inhibitors, and 406 (54.0%) were taking statins (Table 2). Among patients taking antiplatelet agents, 271 (82.9%) were taking aspirin alone, 21 (6.4%) were taking clopidogrel alone, and 35 (10.7%) were taking dual antiplatelet therapy with aspirin and clopidogrel. Nearly three-quarters of the patients (551, 73.3%) were taking medications from 2 or more classes, and the proportion of patients with inappropriate reconciliation increased with the number of preoperative cardiovascular medications.

Patients with and without appropriate reconciliation of all preoperative cardiovascular medications were similar in age, sex, and race/ethnicity (Table 1). Patients with inappropriate reconciliation of at least 1 medication were more likely to be on the urology and renal/liver transplant surgical services, have higher RCRI scores, and be taking antiplatelet agents, statins, renin-angiotensin system inhibitors, and 3 or more cardiovascular medications in the preoperative period.

Appropriate Medication Reconciliation

Four hundred thirty-six patients (58.0%) had their baseline cardiovascular medications appropriately reconciled. Among all patients with appropriately reconciled medications, 1 (0.2%) had beta-blockers discontinued due to a documented episode of hypotension; 17 (3.9%) had renin-angiotensin system inhibitors discontinued due to episodes of acute kidney injury, hypotension, or hyperkalemia; and 1 (0.2%) had antiplatelet agents discontinued due to bleeding. For individual medications, appropriate reconciliation between the preoperative and discharge periods occurred for 156 of the 327 patients on antiplatelet agents (47.7%), 507 of the 624 patients on beta-blockers (81.3%), 259 of the 361 patients on renin-angiotensin system inhibitors (71.8%), and 302 of the 406 patients on statins (74.4%; Table 2).

Associations Between Medication Reconciliation and Outcomes

Thirty-day outcome data on acute hospital visits were available for 679 (90.3%) patients. Of these, 146 (21.5%) were seen in the ED or were hospitalized, and 111 (16.3%) were seen for an unplanned primary care or surgical outpatient visit at 30 days after surgery. Patients with incomplete outcome data were more likely to have complete medication reconciliation compared with those with complete outcome data (71.2% vs 56.6%, P = 0.02). As shown in Table 3, the proportion of patients with 30-day acute hospital visits was nonstatistically significantly lower in patients with complete medication reconciliation (20.8% vs 22.4%, P = 0.63) and the appropriate reconciliation of beta-blockers (21.9% vs 23.6%, P = 0.71) and renin-angiotensin system inhibitors (19.6% vs 20.0%, P = 0.93), and nonsignificantly higher with the appropriate reconciliation of antiplatelet agents (23.9% vs 19.9%, P = 0.40). Acute hospital visits were statistically significantly lower with the appropriate reconciliation of statins (17.9% vs 31.9%, P = 0.004).

Sensitivity Analysis

Overall, 430 (57.2%) patients had complete cardiovascular medication continuity without considering potential contraindications during hospitalization. Associations between medication continuity and acute hospital and ambulatory visits were similar to the primary analyses.

In this study of 752 patients hospitalized for elective noncardiac surgery, we found significant gaps in the appropriate reconciliation of commonly prescribed cardiovascular medications, with inappropriate discontinuation ranging from 18.8% to 52.3% for individual medications. Unplanned postdischarge healthcare utilization was high, with acute hospital visits documented in 21.5% of patients and unplanned ambulatory visits in 16.3% at 30 days after surgery. However, medication reconciliation gaps were not consistently associated with ED visits, hospitalizations, or unplanned ambulatory visits.

Our finding of large gaps in postoperative medication reconciliation is consistent with existing studies of medication reconciliation in surgical patients.^9,10,18 One study found medication discrepancies in 40.2% of postoperative patients receiving usual care and discrepancies judged to have the potential to cause harm (such as the omission of beta-blockers) in 29.9%.⁹ Consistent with our findings, this study also found that most postoperative medication discrepancies were omissions in reordering home medications, though at a rate somewhat higher than those seen in medical patients at discharge.⁵ While hospitalization by itself increases the risk of unintentional discontinuation of chronic medications,³ our results, along with existing literature, suggest that the risk for omission of chronic medications is unacceptably high.

We also found significant variation in reconciliation among cardiovascular medications, with appropriate reconciliation occurring least frequently for antiplatelet agents and most frequently for beta-blockers. The low rates of appropriate reconciliation for antiplatelet agents may be attributable to deliberate withholding of antiplatelet therapy in the postoperative period based on clinical assessments of surgical bleeding risk in the absence of active bleeding. Perioperative management of antiplatelet agents for noncardiac surgery remains an unclear and controversial topic, which may also contribute to the variation noted.¹⁹ Conversely, beta-blockers demonstrated high rates of preoperative use (over 80% of patients) and appropriate reconciliation. Both findings are likely attributable in part to the timing of the study, which began prior to the publication of the Perioperative Ischemic Evaluation trial, which more definitively demonstrated the potential harms of perioperative beta-blocker therapy.²⁰

Despite a high proportion of patients with discontinuous medications at discharge, we found no associations between the appropriate reconciliation of beta-blockers, renin-angiotensin system inhibitors, and antiplatelet agents and acute hospital or ambulatory visits in the first 30 days after discharge. One explanation for this discrepancy is that, although we focused on cardiovascular medications commonly implicated in acute hospital visits, the vast majority of patients in our study had low perioperative cardiovascular risk as assessed by the RCRI. Previous studies have demonstrated that the benefit of perioperative beta-blocker therapy is predominantly in patients with moderate to high perioperative cardiovascular risk.^21,22 It is possible that the detrimental effects of the discontinuation of chronic cardiovascular medications are more prominent in populations at a higher risk of perioperative cardiovascular complications or that complications will occur later than 30 days after discharge. Similarly, while the benefits of continuation of renin-angiotensin system inhibitors are less clear,²³ few patients in our cohort had a history of congestive heart failure (6.3%) or coronary artery disease (13.0%), 2 conditions in which the impact of perioperative discontinuation of renin-angiotensin inhibitor or beta-blocker therapy would likely be more pronounced.^24,25 An additional explanation for the lack of associations is that, while multiple studies have demonstrated that medication errors are common, the proportion of errors with the potential for harm is much lower, and the proportion that causes actual harm is lower still.^5,26,27 Thus, while we likely captured high-severity medication errors leading to acute hospital or unplanned ambulatory visits, we would not have captured medication errors with lower severity clinical consequences that did not result in medical encounters.

We did find an association between the continuation of statin therapy and reduced ED visits and hospitalizations. This finding is supported by previous studies of patients undergoing noncardiac surgery, including 1 demonstrating an association between immediate postoperative statin therapy and reduced in-hospital mortality²⁸ and another study demonstrating an association between postoperative statin therapy and reductions in a composite endpoint of 30-day mortality, atrial fibrillation, and nonfatal myocardial infarction.²⁹ Alternatively, this finding could reflect the effects of unaddressed confounding by factors contributing to statin discontinuation and poor health outcomes leading to acute hospital visits, such as acute elevations in liver enzymes.

Our study has important implications for patients undergoing elective noncardiac surgery and the healthcare providers caring for them. First, inappropriate omissions of chronic cardiovascular medications at discharge are common; clinicians should increase their general awareness and focus on appropriately reconciling these medications, for even if our results do not connect medication discontinuity to readmissions or unexpected clinical encounters, their impact on patients’ understanding of their medications remains a potential concern. Second, the overall high rates of unplanned postdischarge healthcare utilization in this study highlight the need for close postdischarge monitoring of patients undergoing elective surgical procedures and for further research to identify preventable etiologies of postdischarge healthcare utilization in this population. Third, further study is needed to identify specific patient populations and medication classes, in which appropriate reconciliation is associated with patient outcomes that may benefit from more intensive discharge medication reconciliation interventions.

Our study has limitations. First, the majority of patients in this single-center study were at low risk of perioperative cardiovascular events, and our results may not be generalizable to higher-risk patients undergoing elective surgery. Second, discharge reconciliation was based on documentation of medication reconciliation and not on patient-reported medication adherence. In addition, the ability to judge the accuracy of discharge medication reconciliation is in part dependent on the accuracy of the admission medication reconciliation. Thus, although we used preoperative medication regimens documented during preadmission visits to comprehensive preoperative clinics for comparison, discrepancies in these preoperative regimens could have affected our analysis of appropriate discharge reconciliation. Third, inadequate documentation of clinical reasons for discontinuing medications may have led to residual confounding by indication in our observational study. Finally, the outcomes available to us may have been relatively insensitive to other adverse effects of medication discontinuity, such as patient symptoms (eg, angina severity), patient awareness of medications, or work placed on primary care physicians needing to “clean up” erroneous medication lists.

In conclusion, gaps in appropriate discharge reconciliation of chronic cardiovascular medications were common but not consistently associated with postdischarge acute hospital or unplanned ambulatory visits in a relatively low-risk cohort of patients undergoing elective surgery. While appropriate medication reconciliation should always be a priority, further study is needed to identify medication reconciliation approaches associated with postdischarge healthcare utilization and other patient outcomes.

Disclosure

Dr. Lee reports receiving grant support from the Health Resources and Services Administration (T32HP19025). Dr. Vittinghoff reports receiving grant support from the Agency for Healthcare Research and Quality. Dr. Auerbach and Dr. Fleischmann report receiving grant support from the National Institutes of Health. Dr. Auerbach also reports receiving honorarium as Editor-in-Chief of the Journal of Hospital Medicine. Dr. Corbett reports receiving grant and travel support from Simon Fraser University. The remaining authors have no disclosures to report.

Influenza-associated morbidity poses a significant hospital burden.¹ A study from the United States estimated that seasonal influenza is responsible for 3.1 million hospitalization days per year.²

Assessment of hospital burden during influenza seasons presents a challenge due to several possible factors, such as inaccurate recording of diagnosis³ and incomplete age group data. Although great emphasis has historically been placed on older age groups, a study from England and Wales showed that the number of hospitalizations and deaths resulting from influenza was significantly higher in children as compared with adults.⁴ Moreover, excess visits to emergency departments in New York City because of fever and respiratory morbidity during influenza seasons were found mostly among school-age children, whereas in adults, the surplus was small to nonexistent.⁵

Studies examining influenza-related hospitalizations evaluated numbers and rates of hospitalization.^6-11 However, information regarding length of hospitalizations, hospitalizations during the influenza season that were not influenza related, or comparisons between influenza seasons and summer seasons is scarce. These determinants are of great importance for hospital preparedness towards influenza seasons. The aim of the current study was to estimate excess hospitalizations and length of hospitalization during influenza seasons, as compared with the summer, in different age groups and selected diagnoses in Israel.

Data Sources

Hospitalization data of internal medicine and pediatric departments in 28 acute care hospitals in Israel between 2005 and 2013 were obtained from the National Hospital Discharges Database managed by the Health Information Division (HID) in the Israel Ministry of Health (MOH). The information included number of discharges (including in-hospital deaths), number of hospitalization days, and the mean length of stay (LOS) per discharge for all diagnoses and for primary or secondary diagnoses of respiratory/cardiovascular disease (ICD9 390-519) and influenza/pneumonia (ICD9 480-487).

Bed occupancy rates for internal medicine and pediatric departments were based on the National Patient Flow Database managed by the HID.

The 2009-2010 pandemic influenza season was excluded from analysis due to different morbidity patterns and timing (April 2009 until August 2010) as compared with seasonal influenza.

Data Classification

Hospitalizations data were analyzed for all ages, for specific age groups (the first year of life [0], ages 1-4, 5-14, 15-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84, and 85 years and older), for all diagnoses, and for primary or secondary discharge diagnosis of respiratory/cardiovascular disease (ICD9 390-519) and influenza/pneumonia (ICD9 480-487).

Duration of Influenza Season

The beginning and the end of the influenza season were determined by the National Influenza surveillance program, which includes on average 22 community sentinel clinics, throughout Israel, each influenza season. These clinics send nose-throat samples from a convenience sample of patients with influenza-like illness (ILI), from week 40 of each year until the end of the influenza season in the subsequent year. These samples are analyzed for the presence of influenza virus by real-time reverse transcription polymerase chain reaction (RT-PCR) at the Central Virology Laboratory of Israel. Based on influenza virus detection in nose-throat samples from patients with ILI attending the community sentinel clinics, we determined the first and last month of each influenza season. The first month in which positive influenza samples were identified in sequence was defined as the first month of the season. The month in which the sequence of positive influenza samples stopped was defined as the last month of the season.

The 2009-2010 pandemic influenza season was excluded from analysis due to different morbidity patterns and timing (April 2009 until August 2010) as compared with seasonal influenza.

Data Analysis

Rates. Rates of monthly hospitalizations and monthly hospitalization days were calculated per 100,000 residents for all ages and for the specific age groups. Estimated average population sizes in different years for all ages and for specific age groups were obtained from the Central Bureau of Statistics (http://www.cbs.gov.il/reader/shnaton/templ_shnaton.html?num_tab=st02_01&CYear=2014). Monthly LOS was not converted to rates.

Hospitalizations. Mean monthly rate of hospitalizations during influenza and summer seasons was calculated by dividing the sum of hospital discharge rates during influenza/summer seasons of the entire evaluation period (2005/2006 to 2012/2013) by the number of influenza/summer activity months of that period.

Hospitalization Days. The measure “hospitalization days” refers to the hospitalization days of all patients who were discharged during influenza seasons. Mean monthly rate of hospitalization days during the influenza season and summer season was calculated using the procedure described for monthly mean rate of hospitalizations.

Length of Stay. The measure “length of stay” refers to the number of days that individual patients stayed in the hospital during an admission in the evaluated seasons.

Mean monthly LOS during the influenza and summer seasons for all patients (in both internal medicine and pediatric departments) and by age group was calculated by dividing the sum of monthly LOS during influenza seasons/summer season of the entire evaluation period (2005/2006 to 2012/2013 except for the 2009/2010 season) by the number of influenza/summer activity months of that period.

LOS for each specific month of the evaluation period for a single patient was calculated by dividing the number of hospitalization days of all patients that were discharged that month (stratified by age group) by the number of discharges in the same month.

Bed Occupancy. Bed occupancy rates for internal medicine and pediatric departments of the seasons evaluated were computed as a weighted rate based on the hospitalization days and licensed inpatient beds for the period of each influenza and summer season. The calculation took into account the number of days of each month and was based on the monthly reporting of hospital inpatient days in these departments and on the number of inpatient beds according to standard license documents issued by the MOH for each hospital.

Difference Between Influenza and Summer Seasons. Differences in mean monthly rates of hospitalizations, mean monthly rate of hospitalization days, and LOS during influenza seasons and the preceding summer were calculated as absolute numbers per month and as a percentage. The difference between bed occupancy during the influenza seasons and the preceding summers was expressed in percentage. Differences were computed for all diagnoses and for ICD9 480-487 and 390-519.

Statistical Analysis

Mean and standard deviation for monthly hospitalization rates, rates of monthly hospitalization days, and for LOS were calculated for all the influenza and summer seasons that were evaluated. Differences and statistical significance for these parameters were evaluated using a two-tailed Wilcoxon-Mann-Whitney test adjusted for ties, with 95% confidence interval for mean locations. The null hypothesis of the Wilcoxon test used was that the mean ranks of the influenza and summer season observations were equal.

Mean of bed occupancy percentage was calculated for influenza and summer seasons, with the difference and statistical significance being evaluated using a χ² test. P value of < 0.05 was considered statistically significant. SAS Version 9.1 and R program version 3.3.1 software were used for analysis.

Influenza Seasons

The length of influenza seasons varied, with the shortest season lasting 3 months (2006-2007) and the longest season lasting six months (2010-2011 and 2011-2012; Table 1). Of the 14 first and last months of the 7 influenza seasons, 9 had influenza activity throughout the month, 2 had 3 weeks of influenza activity, and 3 had 2 weeks of influenza activity (Table 1).

Hospitalizations

A total of 452,209 hospital discharges occurred in pediatric and internal medicine departments during the influenza seasons that were evaluated. The mean monthly rate of hospitalizations (as defined in Methods) for all patients was 4.8% higher during influenza seasons as compared with the preceding summer seasons (panel A in Figure; Supplementary Table 1). Analysis by age groups revealed a statistically significant increase during influenza seasons in the younger and older age groups (panel A in Figure; Supplementary Table 1). Specifically, the increase for all diagnoses was 32.8%, 16.7%, and 14.1% for infants <1 year, children aged 1-4 years, and adults ≥85 years, respectively (panel A in Figure; Supplementary Table 1).

The mean monthly rate of hospitalizations for all ages due to the diagnosis of respiratory/cardiovascular diseases and influenza/pneumonia was 18.6% and 60.8% higher, respectively, during influenza seasons compared with the preceding summers (panels B and C in Figure). These differences were statistically significant (panels B and C in Figure; Supplementary Table 1).

Hospitalization Days

The mean monthly rate of hospitalization days per 100,000 residents showed a similar trend to that of the hospitalization rates (panels A, B, and C in Figure; Supplementary Table 2), with the most prominent increases observed among infants and children <5 years and adults ≥65 years.

The mean monthly rate of hospitalization days per 100,000 during influenza seasons for all ages due to all diagnoses was 8% higher (P < 0.001) as compared with the summer seasons (panel A in Figure; Supplementary Table 2). Statistically significant increases were also found among patients diagnosed with respiratory/cardiovascular diseases and for influenza/pneumonia (panels B and C in Figure; Supplementary Table 2).

Hospital Length of Stay

The longest mean monthly LOS due to all diagnoses (for both influenza and summer seasons) was observed in adults ≥65 years of age (Table 2). The longest mean monthly LOS due to influenza/pneumonia (for both influenza and summer seasons) was observed in adults ≥55 years or older, and for the diagnosis of respiratory/cardiovascular diseases, infants <1 year and adults ≥55 years had the longest LOS.

The differences between influenza and summer seasons in mean monthly LOS were mostly small or not observed in any of the diagnostic categories examined. The mean monthly LOS due to a diagnosis of influenza/pneumonia was shorter during the influenza seasons than summer seasons in most age groups. These differences were statistically significant in children <5 years and adults ≥45 years (Table 2).

The mean LOS due to respiratory/cardiovascular diseases was significantly shorter during influenza seasons than summer seasons in children under 5.

Bed Occupancy

Mean bed occupancy was significantly higher during influenza seasons compared with the preceding summer seasons, both in internal medicine and pediatric departments (Table 3). The differences were higher in pediatric departments as compared with internal medicine departments for most years evaluated.

Our study demonstrates trends of excess hospitalizations during influenza as compared with summer seasons and identifies patient groups that contribute mostly to changes in hospital burden between these seasons.

Overall, the present study demonstrates differences between influenza and summer seasons for all measures tested: hospitalizations, hospitalization days, LOS, and bed occupancy. These differences were due primarily to excess number of hospitalizations and hospitalization days, rather than to longer LOS.

Our results concerning hospitalizations for all diagnoses are consistent with a United States report showing about 5% more hospitalizations following emergency department visits during winter compared with summer.¹²

The increase in hospitalizations and total hospitalization days in older age groups reflects the probability of severe diseases in a population with multiple comorbidities, and is consistent with a 90% influenza-related mortality due to respiratory and cardiovascular diseases reported in patients 65 and older.¹³ The increase in hospitalization and total hospitalization days in the age groups <5 years during influenza seasons are consistent with studies showing that the risk of children to contract influenza is higher than that of adults surrounding them. In this regard, outbreak investigations during the 2009 influenza pandemic showed that influenza attack rates in children were higher than those of adults.¹⁴

Nationwide studies from Singapore and Taiwan also showed more hospitalizations related to influenza in young children and older adults.^15,16

The increase in hospitalization days for all patients should be interpreted while taking into account the mean monthly LOS per patient (Table 2). In most age groups, a small decrease in the mean LOS for individual patients with the diagnosis of influenza/pneumonia was observed (Table 2). This decrease may suggest a need to shorten hospitalization slightly in order to accommodate new patients. Similarly, the decrease in hospitalization rates from all diagnoses during influenza seasons in the 5-54 years age groups (Figure) may stem, at least in part, from the shortage of available hospital beds due to patient overload. Additional study is required to further explore these decreases and their possible effects on morbidity and mortality.

Influenza vaccine guidelines in Israel following the 2009 influenza pandemic recommend influenza vaccination for all individuals age 6 months and older. However, influenza vaccination in Israel has remained low. Specifically, vaccination rates among children below the age of 5 years have been approximately 21%, as compared with 60%-65% in adults 65 years and over.¹⁷ Given the low rate of vaccination in children, we believe that there would be minimal or no difference in hospitalization of children under the age of 5 years, between the pre- and postpandemic years. Israel has started a school-based influenza vaccination program for the 2016-2017 influenza season in an effort to increase childhood influenza vaccination. It would be important to see if the expansion and continuation of the program would have an effect on influenza season hospitalizations.

Our study has several advantages. To the best of our knowledge, it is the first study examining differences in hospital burden between influenza and summer seasons on a national level. As such, it constitutes one of the largest studies on the subject. In addition, our study relies on original data, rather than estimates. Analysis of specific months of each year in which influenza virus circulates provides a targeted analysis of influenza seasons, rather than the entire winter season. The comparison with summer months is of great importance for preparatory plans by health systems, as it takes into account the degree of variation between the seasons. The analysis of 6 influenza seasons in our study intended to take into account season-to-season disease variability. Such variability among influenza seasons has been described previously due to changes in the virus itself, the population immune status, and the weather.¹⁸

We used several diagnosis categories to evaluate different aspects of hospital burden. Although the category of “all diagnoses” provided a broad assessment of hospital burden, influenza/pneumonia or pulmonary/cardiovascular disease constituted a more specific measure of influenza-associated burden.

Evaluating LOS added to the accuracy of hospital burden estimates, and our age-group analysis highlighted the specific age groups responsible for changes in hospital burden. Thus, the use of several measures to assess influenza season morbidity provides a comprehensive picture of the hospitalization dynamics between influenza and summer seasons. In this regard, the trends observed in our study for hospitalizations and total hospitalization days correspond to those observed in bed occupancy, especially for hospitalization rates due to all causes.

Our study has several limitations. We did not rely on laboratory diagnosis of influenza to determine burden. Because obtaining specimens for viral detection is usually based on individual clinical judgement, and patients hospitalized with influenza-related complications can often test negative for the virus due to time elapsed from disease onset, relying on a laboratory-based analysis may lead to underestimation of hospital burden. On the other hand, it is possible that patients with morbidity not specifically related to influenza were included in our analysis. Respiratory syncytial virus (RSV), for example, can also cause respiratory illness during the fall and winter.¹⁹ However, in Israel, RSV epidemic usually occurs before the influenza epidemic.^17,20 Thus, it is expected that only a small percentage of hospital admissions due to RSV would occur during the influenza season. Another limitation of our study relates to the small number of months in the beginning and end of influenza seasons in which influenza activity was recorded only during part of the month. Thus, hospital burden may have been underestimated during these “incomplete” months. Future studies using time series analysis methods will contribute to a more accurate estimation of such differences, as well as account for variability in influenza activity.

Our results clearly highlight the issues that challenge hospitals in Israel, and possibly other countries, during influenza seasons, such as the most affected age groups and the shortening of hospital stay. Thus, our findings are most relevant for hospital preparedness towards influenza seasons, particularly in terms of the need for additional hospital beds and personnel.

Acknowledgment

We would like to thank Anneke ifrah for English language editing

Disclosure

All authors report no conflict of interest relevant to this article. No financial support was provided relevant to this article.

Influenza-associated morbidity poses a significant hospital burden.¹ A study from the United States estimated that seasonal influenza is responsible for 3.1 million hospitalization days per year.²

Assessment of hospital burden during influenza seasons presents a challenge due to several possible factors, such as inaccurate recording of diagnosis³ and incomplete age group data. Although great emphasis has historically been placed on older age groups, a study from England and Wales showed that the number of hospitalizations and deaths resulting from influenza was significantly higher in children as compared with adults.⁴ Moreover, excess visits to emergency departments in New York City because of fever and respiratory morbidity during influenza seasons were found mostly among school-age children, whereas in adults, the surplus was small to nonexistent.⁵

Studies examining influenza-related hospitalizations evaluated numbers and rates of hospitalization.^6-11 However, information regarding length of hospitalizations, hospitalizations during the influenza season that were not influenza related, or comparisons between influenza seasons and summer seasons is scarce. These determinants are of great importance for hospital preparedness towards influenza seasons. The aim of the current study was to estimate excess hospitalizations and length of hospitalization during influenza seasons, as compared with the summer, in different age groups and selected diagnoses in Israel.

Data Sources

Hospitalization data of internal medicine and pediatric departments in 28 acute care hospitals in Israel between 2005 and 2013 were obtained from the National Hospital Discharges Database managed by the Health Information Division (HID) in the Israel Ministry of Health (MOH). The information included number of discharges (including in-hospital deaths), number of hospitalization days, and the mean length of stay (LOS) per discharge for all diagnoses and for primary or secondary diagnoses of respiratory/cardiovascular disease (ICD9 390-519) and influenza/pneumonia (ICD9 480-487).

Bed occupancy rates for internal medicine and pediatric departments were based on the National Patient Flow Database managed by the HID.

The 2009-2010 pandemic influenza season was excluded from analysis due to different morbidity patterns and timing (April 2009 until August 2010) as compared with seasonal influenza.

Data Classification

Hospitalizations data were analyzed for all ages, for specific age groups (the first year of life [0], ages 1-4, 5-14, 15-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84, and 85 years and older), for all diagnoses, and for primary or secondary discharge diagnosis of respiratory/cardiovascular disease (ICD9 390-519) and influenza/pneumonia (ICD9 480-487).

Duration of Influenza Season

The beginning and the end of the influenza season were determined by the National Influenza surveillance program, which includes on average 22 community sentinel clinics, throughout Israel, each influenza season. These clinics send nose-throat samples from a convenience sample of patients with influenza-like illness (ILI), from week 40 of each year until the end of the influenza season in the subsequent year. These samples are analyzed for the presence of influenza virus by real-time reverse transcription polymerase chain reaction (RT-PCR) at the Central Virology Laboratory of Israel. Based on influenza virus detection in nose-throat samples from patients with ILI attending the community sentinel clinics, we determined the first and last month of each influenza season. The first month in which positive influenza samples were identified in sequence was defined as the first month of the season. The month in which the sequence of positive influenza samples stopped was defined as the last month of the season.

The 2009-2010 pandemic influenza season was excluded from analysis due to different morbidity patterns and timing (April 2009 until August 2010) as compared with seasonal influenza.

Data Analysis

Rates. Rates of monthly hospitalizations and monthly hospitalization days were calculated per 100,000 residents for all ages and for the specific age groups. Estimated average population sizes in different years for all ages and for specific age groups were obtained from the Central Bureau of Statistics (http://www.cbs.gov.il/reader/shnaton/templ_shnaton.html?num_tab=st02_01&CYear=2014). Monthly LOS was not converted to rates.

Hospitalizations. Mean monthly rate of hospitalizations during influenza and summer seasons was calculated by dividing the sum of hospital discharge rates during influenza/summer seasons of the entire evaluation period (2005/2006 to 2012/2013) by the number of influenza/summer activity months of that period.

Hospitalization Days. The measure “hospitalization days” refers to the hospitalization days of all patients who were discharged during influenza seasons. Mean monthly rate of hospitalization days during the influenza season and summer season was calculated using the procedure described for monthly mean rate of hospitalizations.

Length of Stay. The measure “length of stay” refers to the number of days that individual patients stayed in the hospital during an admission in the evaluated seasons.

Mean monthly LOS during the influenza and summer seasons for all patients (in both internal medicine and pediatric departments) and by age group was calculated by dividing the sum of monthly LOS during influenza seasons/summer season of the entire evaluation period (2005/2006 to 2012/2013 except for the 2009/2010 season) by the number of influenza/summer activity months of that period.

LOS for each specific month of the evaluation period for a single patient was calculated by dividing the number of hospitalization days of all patients that were discharged that month (stratified by age group) by the number of discharges in the same month.

Bed Occupancy. Bed occupancy rates for internal medicine and pediatric departments of the seasons evaluated were computed as a weighted rate based on the hospitalization days and licensed inpatient beds for the period of each influenza and summer season. The calculation took into account the number of days of each month and was based on the monthly reporting of hospital inpatient days in these departments and on the number of inpatient beds according to standard license documents issued by the MOH for each hospital.

Difference Between Influenza and Summer Seasons. Differences in mean monthly rates of hospitalizations, mean monthly rate of hospitalization days, and LOS during influenza seasons and the preceding summer were calculated as absolute numbers per month and as a percentage. The difference between bed occupancy during the influenza seasons and the preceding summers was expressed in percentage. Differences were computed for all diagnoses and for ICD9 480-487 and 390-519.

Statistical Analysis

Mean and standard deviation for monthly hospitalization rates, rates of monthly hospitalization days, and for LOS were calculated for all the influenza and summer seasons that were evaluated. Differences and statistical significance for these parameters were evaluated using a two-tailed Wilcoxon-Mann-Whitney test adjusted for ties, with 95% confidence interval for mean locations. The null hypothesis of the Wilcoxon test used was that the mean ranks of the influenza and summer season observations were equal.

Mean of bed occupancy percentage was calculated for influenza and summer seasons, with the difference and statistical significance being evaluated using a χ² test. P value of < 0.05 was considered statistically significant. SAS Version 9.1 and R program version 3.3.1 software were used for analysis.

Influenza Seasons

The length of influenza seasons varied, with the shortest season lasting 3 months (2006-2007) and the longest season lasting six months (2010-2011 and 2011-2012; Table 1). Of the 14 first and last months of the 7 influenza seasons, 9 had influenza activity throughout the month, 2 had 3 weeks of influenza activity, and 3 had 2 weeks of influenza activity (Table 1).

Hospitalizations

A total of 452,209 hospital discharges occurred in pediatric and internal medicine departments during the influenza seasons that were evaluated. The mean monthly rate of hospitalizations (as defined in Methods) for all patients was 4.8% higher during influenza seasons as compared with the preceding summer seasons (panel A in Figure; Supplementary Table 1). Analysis by age groups revealed a statistically significant increase during influenza seasons in the younger and older age groups (panel A in Figure; Supplementary Table 1). Specifically, the increase for all diagnoses was 32.8%, 16.7%, and 14.1% for infants <1 year, children aged 1-4 years, and adults ≥85 years, respectively (panel A in Figure; Supplementary Table 1).

The mean monthly rate of hospitalizations for all ages due to the diagnosis of respiratory/cardiovascular diseases and influenza/pneumonia was 18.6% and 60.8% higher, respectively, during influenza seasons compared with the preceding summers (panels B and C in Figure). These differences were statistically significant (panels B and C in Figure; Supplementary Table 1).

Hospitalization Days

The mean monthly rate of hospitalization days per 100,000 residents showed a similar trend to that of the hospitalization rates (panels A, B, and C in Figure; Supplementary Table 2), with the most prominent increases observed among infants and children <5 years and adults ≥65 years.

The mean monthly rate of hospitalization days per 100,000 during influenza seasons for all ages due to all diagnoses was 8% higher (P < 0.001) as compared with the summer seasons (panel A in Figure; Supplementary Table 2). Statistically significant increases were also found among patients diagnosed with respiratory/cardiovascular diseases and for influenza/pneumonia (panels B and C in Figure; Supplementary Table 2).

Hospital Length of Stay

The longest mean monthly LOS due to all diagnoses (for both influenza and summer seasons) was observed in adults ≥65 years of age (Table 2). The longest mean monthly LOS due to influenza/pneumonia (for both influenza and summer seasons) was observed in adults ≥55 years or older, and for the diagnosis of respiratory/cardiovascular diseases, infants <1 year and adults ≥55 years had the longest LOS.

The differences between influenza and summer seasons in mean monthly LOS were mostly small or not observed in any of the diagnostic categories examined. The mean monthly LOS due to a diagnosis of influenza/pneumonia was shorter during the influenza seasons than summer seasons in most age groups. These differences were statistically significant in children <5 years and adults ≥45 years (Table 2).

The mean LOS due to respiratory/cardiovascular diseases was significantly shorter during influenza seasons than summer seasons in children under 5.

Bed Occupancy

Mean bed occupancy was significantly higher during influenza seasons compared with the preceding summer seasons, both in internal medicine and pediatric departments (Table 3). The differences were higher in pediatric departments as compared with internal medicine departments for most years evaluated.

Our study demonstrates trends of excess hospitalizations during influenza as compared with summer seasons and identifies patient groups that contribute mostly to changes in hospital burden between these seasons.

Overall, the present study demonstrates differences between influenza and summer seasons for all measures tested: hospitalizations, hospitalization days, LOS, and bed occupancy. These differences were due primarily to excess number of hospitalizations and hospitalization days, rather than to longer LOS.

Our results concerning hospitalizations for all diagnoses are consistent with a United States report showing about 5% more hospitalizations following emergency department visits during winter compared with summer.¹²

The increase in hospitalizations and total hospitalization days in older age groups reflects the probability of severe diseases in a population with multiple comorbidities, and is consistent with a 90% influenza-related mortality due to respiratory and cardiovascular diseases reported in patients 65 and older.¹³ The increase in hospitalization and total hospitalization days in the age groups <5 years during influenza seasons are consistent with studies showing that the risk of children to contract influenza is higher than that of adults surrounding them. In this regard, outbreak investigations during the 2009 influenza pandemic showed that influenza attack rates in children were higher than those of adults.¹⁴

Nationwide studies from Singapore and Taiwan also showed more hospitalizations related to influenza in young children and older adults.^15,16

The increase in hospitalization days for all patients should be interpreted while taking into account the mean monthly LOS per patient (Table 2). In most age groups, a small decrease in the mean LOS for individual patients with the diagnosis of influenza/pneumonia was observed (Table 2). This decrease may suggest a need to shorten hospitalization slightly in order to accommodate new patients. Similarly, the decrease in hospitalization rates from all diagnoses during influenza seasons in the 5-54 years age groups (Figure) may stem, at least in part, from the shortage of available hospital beds due to patient overload. Additional study is required to further explore these decreases and their possible effects on morbidity and mortality.

Influenza vaccine guidelines in Israel following the 2009 influenza pandemic recommend influenza vaccination for all individuals age 6 months and older. However, influenza vaccination in Israel has remained low. Specifically, vaccination rates among children below the age of 5 years have been approximately 21%, as compared with 60%-65% in adults 65 years and over.¹⁷ Given the low rate of vaccination in children, we believe that there would be minimal or no difference in hospitalization of children under the age of 5 years, between the pre- and postpandemic years. Israel has started a school-based influenza vaccination program for the 2016-2017 influenza season in an effort to increase childhood influenza vaccination. It would be important to see if the expansion and continuation of the program would have an effect on influenza season hospitalizations.

Our study has several advantages. To the best of our knowledge, it is the first study examining differences in hospital burden between influenza and summer seasons on a national level. As such, it constitutes one of the largest studies on the subject. In addition, our study relies on original data, rather than estimates. Analysis of specific months of each year in which influenza virus circulates provides a targeted analysis of influenza seasons, rather than the entire winter season. The comparison with summer months is of great importance for preparatory plans by health systems, as it takes into account the degree of variation between the seasons. The analysis of 6 influenza seasons in our study intended to take into account season-to-season disease variability. Such variability among influenza seasons has been described previously due to changes in the virus itself, the population immune status, and the weather.¹⁸

We used several diagnosis categories to evaluate different aspects of hospital burden. Although the category of “all diagnoses” provided a broad assessment of hospital burden, influenza/pneumonia or pulmonary/cardiovascular disease constituted a more specific measure of influenza-associated burden.

Evaluating LOS added to the accuracy of hospital burden estimates, and our age-group analysis highlighted the specific age groups responsible for changes in hospital burden. Thus, the use of several measures to assess influenza season morbidity provides a comprehensive picture of the hospitalization dynamics between influenza and summer seasons. In this regard, the trends observed in our study for hospitalizations and total hospitalization days correspond to those observed in bed occupancy, especially for hospitalization rates due to all causes.

Our study has several limitations. We did not rely on laboratory diagnosis of influenza to determine burden. Because obtaining specimens for viral detection is usually based on individual clinical judgement, and patients hospitalized with influenza-related complications can often test negative for the virus due to time elapsed from disease onset, relying on a laboratory-based analysis may lead to underestimation of hospital burden. On the other hand, it is possible that patients with morbidity not specifically related to influenza were included in our analysis. Respiratory syncytial virus (RSV), for example, can also cause respiratory illness during the fall and winter.¹⁹ However, in Israel, RSV epidemic usually occurs before the influenza epidemic.^17,20 Thus, it is expected that only a small percentage of hospital admissions due to RSV would occur during the influenza season. Another limitation of our study relates to the small number of months in the beginning and end of influenza seasons in which influenza activity was recorded only during part of the month. Thus, hospital burden may have been underestimated during these “incomplete” months. Future studies using time series analysis methods will contribute to a more accurate estimation of such differences, as well as account for variability in influenza activity.

Our results clearly highlight the issues that challenge hospitals in Israel, and possibly other countries, during influenza seasons, such as the most affected age groups and the shortening of hospital stay. Thus, our findings are most relevant for hospital preparedness towards influenza seasons, particularly in terms of the need for additional hospital beds and personnel.

Acknowledgment

We would like to thank Anneke ifrah for English language editing

Disclosure

All authors report no conflict of interest relevant to this article. No financial support was provided relevant to this article.

Influenza-associated morbidity poses a significant hospital burden.¹ A study from the United States estimated that seasonal influenza is responsible for 3.1 million hospitalization days per year.²

Assessment of hospital burden during influenza seasons presents a challenge due to several possible factors, such as inaccurate recording of diagnosis³ and incomplete age group data. Although great emphasis has historically been placed on older age groups, a study from England and Wales showed that the number of hospitalizations and deaths resulting from influenza was significantly higher in children as compared with adults.⁴ Moreover, excess visits to emergency departments in New York City because of fever and respiratory morbidity during influenza seasons were found mostly among school-age children, whereas in adults, the surplus was small to nonexistent.⁵

Studies examining influenza-related hospitalizations evaluated numbers and rates of hospitalization.^6-11 However, information regarding length of hospitalizations, hospitalizations during the influenza season that were not influenza related, or comparisons between influenza seasons and summer seasons is scarce. These determinants are of great importance for hospital preparedness towards influenza seasons. The aim of the current study was to estimate excess hospitalizations and length of hospitalization during influenza seasons, as compared with the summer, in different age groups and selected diagnoses in Israel.

Data Sources

Hospitalization data of internal medicine and pediatric departments in 28 acute care hospitals in Israel between 2005 and 2013 were obtained from the National Hospital Discharges Database managed by the Health Information Division (HID) in the Israel Ministry of Health (MOH). The information included number of discharges (including in-hospital deaths), number of hospitalization days, and the mean length of stay (LOS) per discharge for all diagnoses and for primary or secondary diagnoses of respiratory/cardiovascular disease (ICD9 390-519) and influenza/pneumonia (ICD9 480-487).

Bed occupancy rates for internal medicine and pediatric departments were based on the National Patient Flow Database managed by the HID.

The 2009-2010 pandemic influenza season was excluded from analysis due to different morbidity patterns and timing (April 2009 until August 2010) as compared with seasonal influenza.

Data Classification

Hospitalizations data were analyzed for all ages, for specific age groups (the first year of life [0], ages 1-4, 5-14, 15-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84, and 85 years and older), for all diagnoses, and for primary or secondary discharge diagnosis of respiratory/cardiovascular disease (ICD9 390-519) and influenza/pneumonia (ICD9 480-487).

Duration of Influenza Season

The beginning and the end of the influenza season were determined by the National Influenza surveillance program, which includes on average 22 community sentinel clinics, throughout Israel, each influenza season. These clinics send nose-throat samples from a convenience sample of patients with influenza-like illness (ILI), from week 40 of each year until the end of the influenza season in the subsequent year. These samples are analyzed for the presence of influenza virus by real-time reverse transcription polymerase chain reaction (RT-PCR) at the Central Virology Laboratory of Israel. Based on influenza virus detection in nose-throat samples from patients with ILI attending the community sentinel clinics, we determined the first and last month of each influenza season. The first month in which positive influenza samples were identified in sequence was defined as the first month of the season. The month in which the sequence of positive influenza samples stopped was defined as the last month of the season.

The 2009-2010 pandemic influenza season was excluded from analysis due to different morbidity patterns and timing (April 2009 until August 2010) as compared with seasonal influenza.

Data Analysis

Rates. Rates of monthly hospitalizations and monthly hospitalization days were calculated per 100,000 residents for all ages and for the specific age groups. Estimated average population sizes in different years for all ages and for specific age groups were obtained from the Central Bureau of Statistics (http://www.cbs.gov.il/reader/shnaton/templ_shnaton.html?num_tab=st02_01&CYear=2014). Monthly LOS was not converted to rates.

Hospitalizations. Mean monthly rate of hospitalizations during influenza and summer seasons was calculated by dividing the sum of hospital discharge rates during influenza/summer seasons of the entire evaluation period (2005/2006 to 2012/2013) by the number of influenza/summer activity months of that period.

Hospitalization Days. The measure “hospitalization days” refers to the hospitalization days of all patients who were discharged during influenza seasons. Mean monthly rate of hospitalization days during the influenza season and summer season was calculated using the procedure described for monthly mean rate of hospitalizations.

Length of Stay. The measure “length of stay” refers to the number of days that individual patients stayed in the hospital during an admission in the evaluated seasons.

Mean monthly LOS during the influenza and summer seasons for all patients (in both internal medicine and pediatric departments) and by age group was calculated by dividing the sum of monthly LOS during influenza seasons/summer season of the entire evaluation period (2005/2006 to 2012/2013 except for the 2009/2010 season) by the number of influenza/summer activity months of that period.

LOS for each specific month of the evaluation period for a single patient was calculated by dividing the number of hospitalization days of all patients that were discharged that month (stratified by age group) by the number of discharges in the same month.

Bed Occupancy. Bed occupancy rates for internal medicine and pediatric departments of the seasons evaluated were computed as a weighted rate based on the hospitalization days and licensed inpatient beds for the period of each influenza and summer season. The calculation took into account the number of days of each month and was based on the monthly reporting of hospital inpatient days in these departments and on the number of inpatient beds according to standard license documents issued by the MOH for each hospital.

Difference Between Influenza and Summer Seasons. Differences in mean monthly rates of hospitalizations, mean monthly rate of hospitalization days, and LOS during influenza seasons and the preceding summer were calculated as absolute numbers per month and as a percentage. The difference between bed occupancy during the influenza seasons and the preceding summers was expressed in percentage. Differences were computed for all diagnoses and for ICD9 480-487 and 390-519.

Statistical Analysis

Mean and standard deviation for monthly hospitalization rates, rates of monthly hospitalization days, and for LOS were calculated for all the influenza and summer seasons that were evaluated. Differences and statistical significance for these parameters were evaluated using a two-tailed Wilcoxon-Mann-Whitney test adjusted for ties, with 95% confidence interval for mean locations. The null hypothesis of the Wilcoxon test used was that the mean ranks of the influenza and summer season observations were equal.

Mean of bed occupancy percentage was calculated for influenza and summer seasons, with the difference and statistical significance being evaluated using a χ² test. P value of < 0.05 was considered statistically significant. SAS Version 9.1 and R program version 3.3.1 software were used for analysis.

Influenza Seasons

The length of influenza seasons varied, with the shortest season lasting 3 months (2006-2007) and the longest season lasting six months (2010-2011 and 2011-2012; Table 1). Of the 14 first and last months of the 7 influenza seasons, 9 had influenza activity throughout the month, 2 had 3 weeks of influenza activity, and 3 had 2 weeks of influenza activity (Table 1).

Hospitalizations

A total of 452,209 hospital discharges occurred in pediatric and internal medicine departments during the influenza seasons that were evaluated. The mean monthly rate of hospitalizations (as defined in Methods) for all patients was 4.8% higher during influenza seasons as compared with the preceding summer seasons (panel A in Figure; Supplementary Table 1). Analysis by age groups revealed a statistically significant increase during influenza seasons in the younger and older age groups (panel A in Figure; Supplementary Table 1). Specifically, the increase for all diagnoses was 32.8%, 16.7%, and 14.1% for infants <1 year, children aged 1-4 years, and adults ≥85 years, respectively (panel A in Figure; Supplementary Table 1).

The mean monthly rate of hospitalizations for all ages due to the diagnosis of respiratory/cardiovascular diseases and influenza/pneumonia was 18.6% and 60.8% higher, respectively, during influenza seasons compared with the preceding summers (panels B and C in Figure). These differences were statistically significant (panels B and C in Figure; Supplementary Table 1).

Hospitalization Days

The mean monthly rate of hospitalization days per 100,000 residents showed a similar trend to that of the hospitalization rates (panels A, B, and C in Figure; Supplementary Table 2), with the most prominent increases observed among infants and children <5 years and adults ≥65 years.

The mean monthly rate of hospitalization days per 100,000 during influenza seasons for all ages due to all diagnoses was 8% higher (P < 0.001) as compared with the summer seasons (panel A in Figure; Supplementary Table 2). Statistically significant increases were also found among patients diagnosed with respiratory/cardiovascular diseases and for influenza/pneumonia (panels B and C in Figure; Supplementary Table 2).

Hospital Length of Stay

The longest mean monthly LOS due to all diagnoses (for both influenza and summer seasons) was observed in adults ≥65 years of age (Table 2). The longest mean monthly LOS due to influenza/pneumonia (for both influenza and summer seasons) was observed in adults ≥55 years or older, and for the diagnosis of respiratory/cardiovascular diseases, infants <1 year and adults ≥55 years had the longest LOS.

The differences between influenza and summer seasons in mean monthly LOS were mostly small or not observed in any of the diagnostic categories examined. The mean monthly LOS due to a diagnosis of influenza/pneumonia was shorter during the influenza seasons than summer seasons in most age groups. These differences were statistically significant in children <5 years and adults ≥45 years (Table 2).

The mean LOS due to respiratory/cardiovascular diseases was significantly shorter during influenza seasons than summer seasons in children under 5.

Bed Occupancy

Mean bed occupancy was significantly higher during influenza seasons compared with the preceding summer seasons, both in internal medicine and pediatric departments (Table 3). The differences were higher in pediatric departments as compared with internal medicine departments for most years evaluated.

Our study demonstrates trends of excess hospitalizations during influenza as compared with summer seasons and identifies patient groups that contribute mostly to changes in hospital burden between these seasons.

Overall, the present study demonstrates differences between influenza and summer seasons for all measures tested: hospitalizations, hospitalization days, LOS, and bed occupancy. These differences were due primarily to excess number of hospitalizations and hospitalization days, rather than to longer LOS.

Our results concerning hospitalizations for all diagnoses are consistent with a United States report showing about 5% more hospitalizations following emergency department visits during winter compared with summer.¹²

The increase in hospitalizations and total hospitalization days in older age groups reflects the probability of severe diseases in a population with multiple comorbidities, and is consistent with a 90% influenza-related mortality due to respiratory and cardiovascular diseases reported in patients 65 and older.¹³ The increase in hospitalization and total hospitalization days in the age groups <5 years during influenza seasons are consistent with studies showing that the risk of children to contract influenza is higher than that of adults surrounding them. In this regard, outbreak investigations during the 2009 influenza pandemic showed that influenza attack rates in children were higher than those of adults.¹⁴

Nationwide studies from Singapore and Taiwan also showed more hospitalizations related to influenza in young children and older adults.^15,16

The increase in hospitalization days for all patients should be interpreted while taking into account the mean monthly LOS per patient (Table 2). In most age groups, a small decrease in the mean LOS for individual patients with the diagnosis of influenza/pneumonia was observed (Table 2). This decrease may suggest a need to shorten hospitalization slightly in order to accommodate new patients. Similarly, the decrease in hospitalization rates from all diagnoses during influenza seasons in the 5-54 years age groups (Figure) may stem, at least in part, from the shortage of available hospital beds due to patient overload. Additional study is required to further explore these decreases and their possible effects on morbidity and mortality.

Influenza vaccine guidelines in Israel following the 2009 influenza pandemic recommend influenza vaccination for all individuals age 6 months and older. However, influenza vaccination in Israel has remained low. Specifically, vaccination rates among children below the age of 5 years have been approximately 21%, as compared with 60%-65% in adults 65 years and over.¹⁷ Given the low rate of vaccination in children, we believe that there would be minimal or no difference in hospitalization of children under the age of 5 years, between the pre- and postpandemic years. Israel has started a school-based influenza vaccination program for the 2016-2017 influenza season in an effort to increase childhood influenza vaccination. It would be important to see if the expansion and continuation of the program would have an effect on influenza season hospitalizations.

Our study has several advantages. To the best of our knowledge, it is the first study examining differences in hospital burden between influenza and summer seasons on a national level. As such, it constitutes one of the largest studies on the subject. In addition, our study relies on original data, rather than estimates. Analysis of specific months of each year in which influenza virus circulates provides a targeted analysis of influenza seasons, rather than the entire winter season. The comparison with summer months is of great importance for preparatory plans by health systems, as it takes into account the degree of variation between the seasons. The analysis of 6 influenza seasons in our study intended to take into account season-to-season disease variability. Such variability among influenza seasons has been described previously due to changes in the virus itself, the population immune status, and the weather.¹⁸

We used several diagnosis categories to evaluate different aspects of hospital burden. Although the category of “all diagnoses” provided a broad assessment of hospital burden, influenza/pneumonia or pulmonary/cardiovascular disease constituted a more specific measure of influenza-associated burden.

Evaluating LOS added to the accuracy of hospital burden estimates, and our age-group analysis highlighted the specific age groups responsible for changes in hospital burden. Thus, the use of several measures to assess influenza season morbidity provides a comprehensive picture of the hospitalization dynamics between influenza and summer seasons. In this regard, the trends observed in our study for hospitalizations and total hospitalization days correspond to those observed in bed occupancy, especially for hospitalization rates due to all causes.

Our study has several limitations. We did not rely on laboratory diagnosis of influenza to determine burden. Because obtaining specimens for viral detection is usually based on individual clinical judgement, and patients hospitalized with influenza-related complications can often test negative for the virus due to time elapsed from disease onset, relying on a laboratory-based analysis may lead to underestimation of hospital burden. On the other hand, it is possible that patients with morbidity not specifically related to influenza were included in our analysis. Respiratory syncytial virus (RSV), for example, can also cause respiratory illness during the fall and winter.¹⁹ However, in Israel, RSV epidemic usually occurs before the influenza epidemic.^17,20 Thus, it is expected that only a small percentage of hospital admissions due to RSV would occur during the influenza season. Another limitation of our study relates to the small number of months in the beginning and end of influenza seasons in which influenza activity was recorded only during part of the month. Thus, hospital burden may have been underestimated during these “incomplete” months. Future studies using time series analysis methods will contribute to a more accurate estimation of such differences, as well as account for variability in influenza activity.

Our results clearly highlight the issues that challenge hospitals in Israel, and possibly other countries, during influenza seasons, such as the most affected age groups and the shortening of hospital stay. Thus, our findings are most relevant for hospital preparedness towards influenza seasons, particularly in terms of the need for additional hospital beds and personnel.

Acknowledgment

We would like to thank Anneke ifrah for English language editing

Disclosure

All authors report no conflict of interest relevant to this article. No financial support was provided relevant to this article.

Thrombophilia is a prothrombotic state, either acquired or inherited, leading to a thrombotic predisposition.¹ The most common heritable thrombophilias include factor V Leiden (FVL) and prothrombin G20210A. The most common acquired thrombophilia is the presence of phospholipid antibodies.¹ Thrombotic risk varies with thrombophilia type. For example, deficiencies of antithrombin, protein C and protein S, and the presence of phospholipid antibodies, confer higher risk than FVL and prothrombin G20210A.^2-5 Other thrombophilias (eg, methylenetetrahydrofolate reductase mutation, increased factor VIII activity) are relatively uncommon and/or their impact on thrombosis risk appears to be either minimal or unknown.^1-6 There is little clinical evidence that testing for thrombophilia impacts subsequent thrombosis prevention.^5,7,8 Multiple clinical guidelines and medical societies recommend against the routine and indiscriminate use of thrombophilia testing.^8-13 In general, thrombophilia testing should be considered only if the result would lead to changes in anticoagulant initiation, intensity, and/or duration, or might inform interventions to prevent thrombosis in asymptomatic family members.^8-13 However, thrombophilia testing rarely changes the acute management of a thrombotic event and may have harmful effects on patients and their family members because positive results may unnecessarily increase anxiety and negative results may provide false reassurance.^6,14-18 The cost-effectiveness of thrombophilia testing is unknown. Economic models have sought to quantify cost-effectiveness, but conclusions from these studies are limited.⁷

The utility of thrombophilia testing in emergency department (ED) and inpatient settings is further limited because patients are often treated and discharged before thrombophilia test results are available. Additionally, in these settings, multiple factors increase the risk of false-positive or false-negative results (eg, acute thrombosis, acute illness, pregnancy, and anticoagulant therapy).^19,20 The purpose of this study was to systematically assess thrombophilia testing patterns in the ED and hospitalized patients at an academic medical center and to quantify the proportion of tests associated with minimal clinical utility. We hypothesize that the majority of thrombophilia tests completed in the inpatient setting are associated with minimal clinical utility.

Setting and Patients

This study was conducted at University of Utah Health Care (UUHC) University Hospital, a 488-bed academic medical center with a level I trauma center, primary stroke center, and 50-bed ED. Laboratory services for UUHC, including thrombophilia testing, are provided by a national reference laboratory, Associated Regional and University Pathologists Laboratories. This study included patients ≥18 years of age who received thrombophilia testing (Supplementary Table 1) during an ED visit or inpatient admission at University Hospital between July 1, 2014 and December 31, 2014. There were no exclusion criteria. An institutional electronic data repository was used to identify patients matching inclusion criteria. All study activities were reviewed and approved by the UUHC Institutional Review Board with a waiver of informed consent.

Outcomes

An electronic database query was used to identify patients, collect patient demographic information, and collect test characteristics. Each patient’s electronic medical record was manually reviewed to collect all other outcomes. Indication for thrombophilia testing was identified by manual review of provider notes. Thrombophilia tests occurring in situations associated with minimal clinical utility were defined as tests meeting at least one of the following criteria: patient discharged before test results were available for review; test type not recommended by published guidelines or by UUHC Thrombosis Service physicians for thrombophilia testing (Supplementary Table 2); test performed in situations associated with decreased accuracy; test was a duplicate test as a result of different thrombophilia panels containing identical tests; and test followed a provoked venous thromboembolism (VTE). Testing in situations associated with decreased accuracy are summarized in Supplementary Table 3 and included at least one of the following at the time of the test: anticoagulant therapy, acute thrombosis, pregnant or <8 weeks postpartum, and receiving estrogen-containing medications. Only test types known to be affected by the respective situation were included. Testing following a provoked VTE was defined as testing prompted by an acute thrombosis and performed within 3 months following major surgery (defined administratively as any surgery performed in an operating room), during pregnancy, <8 weeks postpartum, or while on estrogen-containing medications. Thrombophilia testing during anticoagulant therapy was defined as testing within 4 half-lives of anticoagulant administration based on medication administration records. Anticoagulant therapy changes were identified by comparing prior-to-admission and discharge medication lists.

Data Analysis

Patient and laboratory characteristics were summarized using descriptive statistics, including mean and standard deviation (SD) for continuous variables and proportions for categorical variables. Data analysis was performed using Excel (Version 2013, Microsoft Corporation. Redmond, Washington).

During the 6-month study period, 163 patients received at least 1 thrombophilia test during an ED visit or inpatient admission. Patient characteristics are summarized in Table 1. Tested patients were most commonly inpatients (96%) and female (71%). A total of 1451 thrombophilia tests were performed with a mean (± SD) of 8.9 ± 6.0 tests per patient. Testing characteristics are summarized in Table 2. Of the 39 different test types performed, the most commonly ordered were cardiolipin IgG and IgM antibodies (9% each), lupus anticoagulant (9%), and β₂-glycoprotein 1 IgG and IgM antibodies (8% each). When combined with testing for phosphatidyl antibodies, antiphospholipid tests accounted for 70% of all tests. Overall, 134 (9%) test results were positive. The mean time for results to become available was 2.2 ± 2.5 days. The frequency of test types with corresponding positivity rates and mean time for results to become available are summarized in Supplementary Table 4.

The indications for thrombophilia testing are summarized in Table 3. Ischemic stroke was the most common indication for testing (50% of tests; 35% of patients), followed by VTE (21% of tests; 21% of patients), and pregnancy-related conditions (eg, preeclampsia, intrauterine fetal demise; 15% of tests; 25% of patients). Overall, 911 tests (63%) occurred in situations associated with minimal clinical utility, with 126 patients (77%) receiving at least one of these tests (Table 4).

In this retrospective analysis, 1451 thrombophilia tests were performed in 163 patients over 6 months. Tested patients were relatively young, which is likely explained by the number of patients tested for pregnancy-related conditions and the fact that a stroke or VTE in younger patients more frequently prompted providers to suspect thrombophilia. Nearly three-fourths of patients were female, which is likely due to testing for pregnancy-related conditions and possibly diagnostic suspicion bias given the comparative predilection of antiphospholipid syndrome for women. The patient characteristics in our study are consistent with other studies evaluating thrombophilia testing.^21,22

Thrombophilia testing was most frequently prompted by stroke, VTE, and pregnancy-related conditions. Only 26% of patients had acute thrombosis identified during the admission, primarily because of the high proportion of tests for cryptogenic strokes and pregnancy-related conditions. Thrombophilia testing is recommended in patients who have had a stroke when the stroke is considered to be cryptogenic after a standard stroke evaluation.²³ Thrombophilia testing in pregnancy-related conditions is controversial but is often considered in situations such as stillbirths with severe placental pathology and/or significant growth restriction, or in mothers with a personal or family history of thrombosis.²⁴ The proportion of testing for pregnancy-related conditions may be greater than at other institutions because UUHC Maternal Fetal Medicine is a referral center for women with conditions associated with hypercoagulability. Anticoagulant therapy was initiated in 21% of patients, but specifically in response to thrombophilia testing in only 2 instances; in most cases, anticoagulant therapy was initiated regardless of thrombophilia test results.

The results of this study confirm our hypothesis because the majority of thrombophilia tests occurred in situations associated with minimal clinical utility. Testing in these situations was not isolated to specific patients or medical services because 77% of tested patients received at least 1 test associated with minimal clinical utility. Our study took a conservative approach in defining scenarios associated with minimal clinical utility because other situations can also affect testing accuracy (eg, hepatic disease, nephrotic syndrome) but were not included in our analysis of this outcome.

The results of this study highlight opportunities to improve thrombophilia testing practices at our institution and may be generalizable to institutions with similar testing patterns. Because multiple medical services order thrombophilia tests, strategies to improve testing practices are still being determined. The results of this study can serve as a baseline for comparison after strategies are implemented. The most common situation associated with minimal clinical utility was the use of test types not generally recommended by guidelines or UUHC Thrombosis Service physicians for thrombophilia testing (eg, β₂-glycoprotein 1 IgA antibodies, phosphatidyl antibodies). We intend to require a hematology or thrombosis specialty consult prior to ordering these tests. This intervention alone could potentially decrease unnecessary testing by a third. Another consideration is to require a specialty consult prior to any inpatient thrombophilia testing. This strategy has been found to decrease inappropriate testing at other institutions.²¹ We also intend to streamline available thrombophilia testing panels because a poorly designed panel could lead to ordering of multiple tests associated with minimal clinical utility. At least 12 different thrombophilia panels are currently available in our computerized physician order entry system (see Supplementary Table 5). We hypothesize that current panel designs contribute to providers inadvertently ordering unintended or duplicate tests and that reducing the number of available panels and clearly delineating what tests are contained in each panel is likely to reduce unnecessary testing. Other strategies being considered include using electronic clinical decision support tools, implementing strict ordering criteria for all inpatient testing, and establishing a thrombosis stewardship program.

Our study was unique in at least 2 ways. First, previous studies describing thrombophilia testing have described testing patterns for patients with specific indications (eg, VTE), whereas our study described all thrombophilia tests regardless of indication. This allows for testing pattern comparisons across indications and medical services, increasing the generalizability of our results. Second, this study quantifies tests occurring in situations associated with a practical definition of minimal clinical utility.

Our study has several limitations: (1) Many variables were reliant on provider notes and other documentation, which allows for potential misclassification of variables. (2) It was not always possible to determine the ultimate utility of each test in clinical management decisions, and our study did not investigate the impact of thrombophilia testing on duration of anticoagulant therapy. Additionally, select situations could benefit from testing regardless if anticoagulant therapy is altered (eg, informing contraceptive choices). (3) Testing performed following a provoked acute thrombosis was defined as testing within 3 months following administratively defined major surgery. This definition could have included some minor procedures that do not substantially increase VTE risk, resulting in underestimated clinical utility. (4) The UUHC University Hospital serves as a referral hospital for a large geographical area, and investigators did not have access to outpatient records for a large proportion of discharged patients. As a result, frequency of repeat testing could not be assessed, possibly resulting in overestimated clinical utility. (5) In categorizing indications for testing, testing for CVST was subcategorized under testing for ischemic stroke based on presenting symptoms rather than on underlying pathophysiology. The rationale for this categorization is that patients with CVST were often tested based on presenting symptoms. Additionally, tests for CVST were ordered by the neurology service, which also ordered tests for all other ischemic stroke indications. (6) The purpose of our study was to investigate the subset of the hospital’s patient population that received thrombophilia testing, and patients were identified by tests received and not by diagnosis codes. As a result, we are unable to provide the proportion of total patients treated at the hospital for specific conditions who were tested (eg, the proportion of stroke patients that received thrombophilia testing). (7) Current practice guidelines do not recommend testing for phosphatidyl antibodies, even when traditional antiphospholipid testing is negative.^25-27 Although expert panels continue to explore associations between phosphatidyl antibodies and pregnancy morbidity and thrombotic events, the low level of evidence is insufficient to guide clinical management.²⁸ Therefore, we categorized all phosphatidyl testing as associated with minimal clinical utility.

In a large academic medical center, the majority of tests occurred in situations associated with minimal clinical utility. Strategies to improve thrombophilia testing practices are needed in order to minimize potentially inappropriate testing, provide more cost-effective care, and promote value-driven outcomes.

Disclosure

S.W. received financial support for this submitted work via a Bristol-Myers-Squibb grant. G.F. received financial support from Portola Pharmaceuticals for consulting and lectures that were not related to this submitted work.

Thrombophilia is a prothrombotic state, either acquired or inherited, leading to a thrombotic predisposition.¹ The most common heritable thrombophilias include factor V Leiden (FVL) and prothrombin G20210A. The most common acquired thrombophilia is the presence of phospholipid antibodies.¹ Thrombotic risk varies with thrombophilia type. For example, deficiencies of antithrombin, protein C and protein S, and the presence of phospholipid antibodies, confer higher risk than FVL and prothrombin G20210A.^2-5 Other thrombophilias (eg, methylenetetrahydrofolate reductase mutation, increased factor VIII activity) are relatively uncommon and/or their impact on thrombosis risk appears to be either minimal or unknown.^1-6 There is little clinical evidence that testing for thrombophilia impacts subsequent thrombosis prevention.^5,7,8 Multiple clinical guidelines and medical societies recommend against the routine and indiscriminate use of thrombophilia testing.^8-13 In general, thrombophilia testing should be considered only if the result would lead to changes in anticoagulant initiation, intensity, and/or duration, or might inform interventions to prevent thrombosis in asymptomatic family members.^8-13 However, thrombophilia testing rarely changes the acute management of a thrombotic event and may have harmful effects on patients and their family members because positive results may unnecessarily increase anxiety and negative results may provide false reassurance.^6,14-18 The cost-effectiveness of thrombophilia testing is unknown. Economic models have sought to quantify cost-effectiveness, but conclusions from these studies are limited.⁷

The utility of thrombophilia testing in emergency department (ED) and inpatient settings is further limited because patients are often treated and discharged before thrombophilia test results are available. Additionally, in these settings, multiple factors increase the risk of false-positive or false-negative results (eg, acute thrombosis, acute illness, pregnancy, and anticoagulant therapy).^19,20 The purpose of this study was to systematically assess thrombophilia testing patterns in the ED and hospitalized patients at an academic medical center and to quantify the proportion of tests associated with minimal clinical utility. We hypothesize that the majority of thrombophilia tests completed in the inpatient setting are associated with minimal clinical utility.

Setting and Patients

This study was conducted at University of Utah Health Care (UUHC) University Hospital, a 488-bed academic medical center with a level I trauma center, primary stroke center, and 50-bed ED. Laboratory services for UUHC, including thrombophilia testing, are provided by a national reference laboratory, Associated Regional and University Pathologists Laboratories. This study included patients ≥18 years of age who received thrombophilia testing (Supplementary Table 1) during an ED visit or inpatient admission at University Hospital between July 1, 2014 and December 31, 2014. There were no exclusion criteria. An institutional electronic data repository was used to identify patients matching inclusion criteria. All study activities were reviewed and approved by the UUHC Institutional Review Board with a waiver of informed consent.

Outcomes

An electronic database query was used to identify patients, collect patient demographic information, and collect test characteristics. Each patient’s electronic medical record was manually reviewed to collect all other outcomes. Indication for thrombophilia testing was identified by manual review of provider notes. Thrombophilia tests occurring in situations associated with minimal clinical utility were defined as tests meeting at least one of the following criteria: patient discharged before test results were available for review; test type not recommended by published guidelines or by UUHC Thrombosis Service physicians for thrombophilia testing (Supplementary Table 2); test performed in situations associated with decreased accuracy; test was a duplicate test as a result of different thrombophilia panels containing identical tests; and test followed a provoked venous thromboembolism (VTE). Testing in situations associated with decreased accuracy are summarized in Supplementary Table 3 and included at least one of the following at the time of the test: anticoagulant therapy, acute thrombosis, pregnant or <8 weeks postpartum, and receiving estrogen-containing medications. Only test types known to be affected by the respective situation were included. Testing following a provoked VTE was defined as testing prompted by an acute thrombosis and performed within 3 months following major surgery (defined administratively as any surgery performed in an operating room), during pregnancy, <8 weeks postpartum, or while on estrogen-containing medications. Thrombophilia testing during anticoagulant therapy was defined as testing within 4 half-lives of anticoagulant administration based on medication administration records. Anticoagulant therapy changes were identified by comparing prior-to-admission and discharge medication lists.

Data Analysis

Patient and laboratory characteristics were summarized using descriptive statistics, including mean and standard deviation (SD) for continuous variables and proportions for categorical variables. Data analysis was performed using Excel (Version 2013, Microsoft Corporation. Redmond, Washington).

During the 6-month study period, 163 patients received at least 1 thrombophilia test during an ED visit or inpatient admission. Patient characteristics are summarized in Table 1. Tested patients were most commonly inpatients (96%) and female (71%). A total of 1451 thrombophilia tests were performed with a mean (± SD) of 8.9 ± 6.0 tests per patient. Testing characteristics are summarized in Table 2. Of the 39 different test types performed, the most commonly ordered were cardiolipin IgG and IgM antibodies (9% each), lupus anticoagulant (9%), and β₂-glycoprotein 1 IgG and IgM antibodies (8% each). When combined with testing for phosphatidyl antibodies, antiphospholipid tests accounted for 70% of all tests. Overall, 134 (9%) test results were positive. The mean time for results to become available was 2.2 ± 2.5 days. The frequency of test types with corresponding positivity rates and mean time for results to become available are summarized in Supplementary Table 4.

The indications for thrombophilia testing are summarized in Table 3. Ischemic stroke was the most common indication for testing (50% of tests; 35% of patients), followed by VTE (21% of tests; 21% of patients), and pregnancy-related conditions (eg, preeclampsia, intrauterine fetal demise; 15% of tests; 25% of patients). Overall, 911 tests (63%) occurred in situations associated with minimal clinical utility, with 126 patients (77%) receiving at least one of these tests (Table 4).

In this retrospective analysis, 1451 thrombophilia tests were performed in 163 patients over 6 months. Tested patients were relatively young, which is likely explained by the number of patients tested for pregnancy-related conditions and the fact that a stroke or VTE in younger patients more frequently prompted providers to suspect thrombophilia. Nearly three-fourths of patients were female, which is likely due to testing for pregnancy-related conditions and possibly diagnostic suspicion bias given the comparative predilection of antiphospholipid syndrome for women. The patient characteristics in our study are consistent with other studies evaluating thrombophilia testing.^21,22

Thrombophilia testing was most frequently prompted by stroke, VTE, and pregnancy-related conditions. Only 26% of patients had acute thrombosis identified during the admission, primarily because of the high proportion of tests for cryptogenic strokes and pregnancy-related conditions. Thrombophilia testing is recommended in patients who have had a stroke when the stroke is considered to be cryptogenic after a standard stroke evaluation.²³ Thrombophilia testing in pregnancy-related conditions is controversial but is often considered in situations such as stillbirths with severe placental pathology and/or significant growth restriction, or in mothers with a personal or family history of thrombosis.²⁴ The proportion of testing for pregnancy-related conditions may be greater than at other institutions because UUHC Maternal Fetal Medicine is a referral center for women with conditions associated with hypercoagulability. Anticoagulant therapy was initiated in 21% of patients, but specifically in response to thrombophilia testing in only 2 instances; in most cases, anticoagulant therapy was initiated regardless of thrombophilia test results.

The results of this study confirm our hypothesis because the majority of thrombophilia tests occurred in situations associated with minimal clinical utility. Testing in these situations was not isolated to specific patients or medical services because 77% of tested patients received at least 1 test associated with minimal clinical utility. Our study took a conservative approach in defining scenarios associated with minimal clinical utility because other situations can also affect testing accuracy (eg, hepatic disease, nephrotic syndrome) but were not included in our analysis of this outcome.

The results of this study highlight opportunities to improve thrombophilia testing practices at our institution and may be generalizable to institutions with similar testing patterns. Because multiple medical services order thrombophilia tests, strategies to improve testing practices are still being determined. The results of this study can serve as a baseline for comparison after strategies are implemented. The most common situation associated with minimal clinical utility was the use of test types not generally recommended by guidelines or UUHC Thrombosis Service physicians for thrombophilia testing (eg, β₂-glycoprotein 1 IgA antibodies, phosphatidyl antibodies). We intend to require a hematology or thrombosis specialty consult prior to ordering these tests. This intervention alone could potentially decrease unnecessary testing by a third. Another consideration is to require a specialty consult prior to any inpatient thrombophilia testing. This strategy has been found to decrease inappropriate testing at other institutions.²¹ We also intend to streamline available thrombophilia testing panels because a poorly designed panel could lead to ordering of multiple tests associated with minimal clinical utility. At least 12 different thrombophilia panels are currently available in our computerized physician order entry system (see Supplementary Table 5). We hypothesize that current panel designs contribute to providers inadvertently ordering unintended or duplicate tests and that reducing the number of available panels and clearly delineating what tests are contained in each panel is likely to reduce unnecessary testing. Other strategies being considered include using electronic clinical decision support tools, implementing strict ordering criteria for all inpatient testing, and establishing a thrombosis stewardship program.

Our study was unique in at least 2 ways. First, previous studies describing thrombophilia testing have described testing patterns for patients with specific indications (eg, VTE), whereas our study described all thrombophilia tests regardless of indication. This allows for testing pattern comparisons across indications and medical services, increasing the generalizability of our results. Second, this study quantifies tests occurring in situations associated with a practical definition of minimal clinical utility.

Our study has several limitations: (1) Many variables were reliant on provider notes and other documentation, which allows for potential misclassification of variables. (2) It was not always possible to determine the ultimate utility of each test in clinical management decisions, and our study did not investigate the impact of thrombophilia testing on duration of anticoagulant therapy. Additionally, select situations could benefit from testing regardless if anticoagulant therapy is altered (eg, informing contraceptive choices). (3) Testing performed following a provoked acute thrombosis was defined as testing within 3 months following administratively defined major surgery. This definition could have included some minor procedures that do not substantially increase VTE risk, resulting in underestimated clinical utility. (4) The UUHC University Hospital serves as a referral hospital for a large geographical area, and investigators did not have access to outpatient records for a large proportion of discharged patients. As a result, frequency of repeat testing could not be assessed, possibly resulting in overestimated clinical utility. (5) In categorizing indications for testing, testing for CVST was subcategorized under testing for ischemic stroke based on presenting symptoms rather than on underlying pathophysiology. The rationale for this categorization is that patients with CVST were often tested based on presenting symptoms. Additionally, tests for CVST were ordered by the neurology service, which also ordered tests for all other ischemic stroke indications. (6) The purpose of our study was to investigate the subset of the hospital’s patient population that received thrombophilia testing, and patients were identified by tests received and not by diagnosis codes. As a result, we are unable to provide the proportion of total patients treated at the hospital for specific conditions who were tested (eg, the proportion of stroke patients that received thrombophilia testing). (7) Current practice guidelines do not recommend testing for phosphatidyl antibodies, even when traditional antiphospholipid testing is negative.^25-27 Although expert panels continue to explore associations between phosphatidyl antibodies and pregnancy morbidity and thrombotic events, the low level of evidence is insufficient to guide clinical management.²⁸ Therefore, we categorized all phosphatidyl testing as associated with minimal clinical utility.

In a large academic medical center, the majority of tests occurred in situations associated with minimal clinical utility. Strategies to improve thrombophilia testing practices are needed in order to minimize potentially inappropriate testing, provide more cost-effective care, and promote value-driven outcomes.

Disclosure

S.W. received financial support for this submitted work via a Bristol-Myers-Squibb grant. G.F. received financial support from Portola Pharmaceuticals for consulting and lectures that were not related to this submitted work.

Thrombophilia is a prothrombotic state, either acquired or inherited, leading to a thrombotic predisposition.¹ The most common heritable thrombophilias include factor V Leiden (FVL) and prothrombin G20210A. The most common acquired thrombophilia is the presence of phospholipid antibodies.¹ Thrombotic risk varies with thrombophilia type. For example, deficiencies of antithrombin, protein C and protein S, and the presence of phospholipid antibodies, confer higher risk than FVL and prothrombin G20210A.^2-5 Other thrombophilias (eg, methylenetetrahydrofolate reductase mutation, increased factor VIII activity) are relatively uncommon and/or their impact on thrombosis risk appears to be either minimal or unknown.^1-6 There is little clinical evidence that testing for thrombophilia impacts subsequent thrombosis prevention.^5,7,8 Multiple clinical guidelines and medical societies recommend against the routine and indiscriminate use of thrombophilia testing.^8-13 In general, thrombophilia testing should be considered only if the result would lead to changes in anticoagulant initiation, intensity, and/or duration, or might inform interventions to prevent thrombosis in asymptomatic family members.^8-13 However, thrombophilia testing rarely changes the acute management of a thrombotic event and may have harmful effects on patients and their family members because positive results may unnecessarily increase anxiety and negative results may provide false reassurance.^6,14-18 The cost-effectiveness of thrombophilia testing is unknown. Economic models have sought to quantify cost-effectiveness, but conclusions from these studies are limited.⁷

The utility of thrombophilia testing in emergency department (ED) and inpatient settings is further limited because patients are often treated and discharged before thrombophilia test results are available. Additionally, in these settings, multiple factors increase the risk of false-positive or false-negative results (eg, acute thrombosis, acute illness, pregnancy, and anticoagulant therapy).^19,20 The purpose of this study was to systematically assess thrombophilia testing patterns in the ED and hospitalized patients at an academic medical center and to quantify the proportion of tests associated with minimal clinical utility. We hypothesize that the majority of thrombophilia tests completed in the inpatient setting are associated with minimal clinical utility.

Setting and Patients

This study was conducted at University of Utah Health Care (UUHC) University Hospital, a 488-bed academic medical center with a level I trauma center, primary stroke center, and 50-bed ED. Laboratory services for UUHC, including thrombophilia testing, are provided by a national reference laboratory, Associated Regional and University Pathologists Laboratories. This study included patients ≥18 years of age who received thrombophilia testing (Supplementary Table 1) during an ED visit or inpatient admission at University Hospital between July 1, 2014 and December 31, 2014. There were no exclusion criteria. An institutional electronic data repository was used to identify patients matching inclusion criteria. All study activities were reviewed and approved by the UUHC Institutional Review Board with a waiver of informed consent.

Outcomes

An electronic database query was used to identify patients, collect patient demographic information, and collect test characteristics. Each patient’s electronic medical record was manually reviewed to collect all other outcomes. Indication for thrombophilia testing was identified by manual review of provider notes. Thrombophilia tests occurring in situations associated with minimal clinical utility were defined as tests meeting at least one of the following criteria: patient discharged before test results were available for review; test type not recommended by published guidelines or by UUHC Thrombosis Service physicians for thrombophilia testing (Supplementary Table 2); test performed in situations associated with decreased accuracy; test was a duplicate test as a result of different thrombophilia panels containing identical tests; and test followed a provoked venous thromboembolism (VTE). Testing in situations associated with decreased accuracy are summarized in Supplementary Table 3 and included at least one of the following at the time of the test: anticoagulant therapy, acute thrombosis, pregnant or <8 weeks postpartum, and receiving estrogen-containing medications. Only test types known to be affected by the respective situation were included. Testing following a provoked VTE was defined as testing prompted by an acute thrombosis and performed within 3 months following major surgery (defined administratively as any surgery performed in an operating room), during pregnancy, <8 weeks postpartum, or while on estrogen-containing medications. Thrombophilia testing during anticoagulant therapy was defined as testing within 4 half-lives of anticoagulant administration based on medication administration records. Anticoagulant therapy changes were identified by comparing prior-to-admission and discharge medication lists.

Data Analysis

Patient and laboratory characteristics were summarized using descriptive statistics, including mean and standard deviation (SD) for continuous variables and proportions for categorical variables. Data analysis was performed using Excel (Version 2013, Microsoft Corporation. Redmond, Washington).

During the 6-month study period, 163 patients received at least 1 thrombophilia test during an ED visit or inpatient admission. Patient characteristics are summarized in Table 1. Tested patients were most commonly inpatients (96%) and female (71%). A total of 1451 thrombophilia tests were performed with a mean (± SD) of 8.9 ± 6.0 tests per patient. Testing characteristics are summarized in Table 2. Of the 39 different test types performed, the most commonly ordered were cardiolipin IgG and IgM antibodies (9% each), lupus anticoagulant (9%), and β₂-glycoprotein 1 IgG and IgM antibodies (8% each). When combined with testing for phosphatidyl antibodies, antiphospholipid tests accounted for 70% of all tests. Overall, 134 (9%) test results were positive. The mean time for results to become available was 2.2 ± 2.5 days. The frequency of test types with corresponding positivity rates and mean time for results to become available are summarized in Supplementary Table 4.

The indications for thrombophilia testing are summarized in Table 3. Ischemic stroke was the most common indication for testing (50% of tests; 35% of patients), followed by VTE (21% of tests; 21% of patients), and pregnancy-related conditions (eg, preeclampsia, intrauterine fetal demise; 15% of tests; 25% of patients). Overall, 911 tests (63%) occurred in situations associated with minimal clinical utility, with 126 patients (77%) receiving at least one of these tests (Table 4).

In this retrospective analysis, 1451 thrombophilia tests were performed in 163 patients over 6 months. Tested patients were relatively young, which is likely explained by the number of patients tested for pregnancy-related conditions and the fact that a stroke or VTE in younger patients more frequently prompted providers to suspect thrombophilia. Nearly three-fourths of patients were female, which is likely due to testing for pregnancy-related conditions and possibly diagnostic suspicion bias given the comparative predilection of antiphospholipid syndrome for women. The patient characteristics in our study are consistent with other studies evaluating thrombophilia testing.^21,22

Thrombophilia testing was most frequently prompted by stroke, VTE, and pregnancy-related conditions. Only 26% of patients had acute thrombosis identified during the admission, primarily because of the high proportion of tests for cryptogenic strokes and pregnancy-related conditions. Thrombophilia testing is recommended in patients who have had a stroke when the stroke is considered to be cryptogenic after a standard stroke evaluation.²³ Thrombophilia testing in pregnancy-related conditions is controversial but is often considered in situations such as stillbirths with severe placental pathology and/or significant growth restriction, or in mothers with a personal or family history of thrombosis.²⁴ The proportion of testing for pregnancy-related conditions may be greater than at other institutions because UUHC Maternal Fetal Medicine is a referral center for women with conditions associated with hypercoagulability. Anticoagulant therapy was initiated in 21% of patients, but specifically in response to thrombophilia testing in only 2 instances; in most cases, anticoagulant therapy was initiated regardless of thrombophilia test results.

The results of this study confirm our hypothesis because the majority of thrombophilia tests occurred in situations associated with minimal clinical utility. Testing in these situations was not isolated to specific patients or medical services because 77% of tested patients received at least 1 test associated with minimal clinical utility. Our study took a conservative approach in defining scenarios associated with minimal clinical utility because other situations can also affect testing accuracy (eg, hepatic disease, nephrotic syndrome) but were not included in our analysis of this outcome.

The results of this study highlight opportunities to improve thrombophilia testing practices at our institution and may be generalizable to institutions with similar testing patterns. Because multiple medical services order thrombophilia tests, strategies to improve testing practices are still being determined. The results of this study can serve as a baseline for comparison after strategies are implemented. The most common situation associated with minimal clinical utility was the use of test types not generally recommended by guidelines or UUHC Thrombosis Service physicians for thrombophilia testing (eg, β₂-glycoprotein 1 IgA antibodies, phosphatidyl antibodies). We intend to require a hematology or thrombosis specialty consult prior to ordering these tests. This intervention alone could potentially decrease unnecessary testing by a third. Another consideration is to require a specialty consult prior to any inpatient thrombophilia testing. This strategy has been found to decrease inappropriate testing at other institutions.²¹ We also intend to streamline available thrombophilia testing panels because a poorly designed panel could lead to ordering of multiple tests associated with minimal clinical utility. At least 12 different thrombophilia panels are currently available in our computerized physician order entry system (see Supplementary Table 5). We hypothesize that current panel designs contribute to providers inadvertently ordering unintended or duplicate tests and that reducing the number of available panels and clearly delineating what tests are contained in each panel is likely to reduce unnecessary testing. Other strategies being considered include using electronic clinical decision support tools, implementing strict ordering criteria for all inpatient testing, and establishing a thrombosis stewardship program.

Our study was unique in at least 2 ways. First, previous studies describing thrombophilia testing have described testing patterns for patients with specific indications (eg, VTE), whereas our study described all thrombophilia tests regardless of indication. This allows for testing pattern comparisons across indications and medical services, increasing the generalizability of our results. Second, this study quantifies tests occurring in situations associated with a practical definition of minimal clinical utility.

Our study has several limitations: (1) Many variables were reliant on provider notes and other documentation, which allows for potential misclassification of variables. (2) It was not always possible to determine the ultimate utility of each test in clinical management decisions, and our study did not investigate the impact of thrombophilia testing on duration of anticoagulant therapy. Additionally, select situations could benefit from testing regardless if anticoagulant therapy is altered (eg, informing contraceptive choices). (3) Testing performed following a provoked acute thrombosis was defined as testing within 3 months following administratively defined major surgery. This definition could have included some minor procedures that do not substantially increase VTE risk, resulting in underestimated clinical utility. (4) The UUHC University Hospital serves as a referral hospital for a large geographical area, and investigators did not have access to outpatient records for a large proportion of discharged patients. As a result, frequency of repeat testing could not be assessed, possibly resulting in overestimated clinical utility. (5) In categorizing indications for testing, testing for CVST was subcategorized under testing for ischemic stroke based on presenting symptoms rather than on underlying pathophysiology. The rationale for this categorization is that patients with CVST were often tested based on presenting symptoms. Additionally, tests for CVST were ordered by the neurology service, which also ordered tests for all other ischemic stroke indications. (6) The purpose of our study was to investigate the subset of the hospital’s patient population that received thrombophilia testing, and patients were identified by tests received and not by diagnosis codes. As a result, we are unable to provide the proportion of total patients treated at the hospital for specific conditions who were tested (eg, the proportion of stroke patients that received thrombophilia testing). (7) Current practice guidelines do not recommend testing for phosphatidyl antibodies, even when traditional antiphospholipid testing is negative.^25-27 Although expert panels continue to explore associations between phosphatidyl antibodies and pregnancy morbidity and thrombotic events, the low level of evidence is insufficient to guide clinical management.²⁸ Therefore, we categorized all phosphatidyl testing as associated with minimal clinical utility.

In a large academic medical center, the majority of tests occurred in situations associated with minimal clinical utility. Strategies to improve thrombophilia testing practices are needed in order to minimize potentially inappropriate testing, provide more cost-effective care, and promote value-driven outcomes.

Disclosure

S.W. received financial support for this submitted work via a Bristol-Myers-Squibb grant. G.F. received financial support from Portola Pharmaceuticals for consulting and lectures that were not related to this submitted work.

Discussions about cardiopulmonary resuscitation (CPR) can be difficult due to their association with end of life. The Patient Self Determination Act (H.R.4449 — 101st Congress [1989-1990]) and institutional standards mandate collaboration between care providers and patients regarding goals of care in emergency situations such as cardiopulmonary arrest. The default option is to provide CPR, which may involve chest compressions, intubation, and/or defibrillation. Yet numerous studies show that a significant number of patients have no code preference documented in their medical chart, and even fewer report a conversation with their care provider about their wishes regarding CPR.^1-3 CPR is an invasive and potentially painful procedure with a higher chance of failure than success⁴, and yet many patients report that their provider did not discuss with them the risks and benefits of resuscitation.^5,6 Further highlighting the importance of individual discussions about CPR preferences is the reality that factors such as age and disease burden further skew the likelihood of survival after cardiopulmonary arrest.⁷

Complicating the lack of appropriate provider and patient discussion of the risks and benefits of resuscitation are significant misunderstandings about CPR in the lay population. Patients routinely overestimate the likelihood of survival following CPR.^8,9 This may be partially due to the portrayal of CPR in the lay media as highly efficacious.¹⁰ Other factors known to prevent effective provider-and-patient discussions about CPR preferences are providers’ discomfort with the subject¹¹ and perceived time constraints.¹²

Informational videos have been developed to assist patients with decision making about CPR and have been shown to impact patients’ choices in the setting of life-limiting diseases such as advanced cancer,^13-14 serious illness with a prognosis of less than 1 year,¹⁵ and dementia.¹⁶ While discussion of code status is vitally important in end-of-life planning for seriously ill individuals, delayed discussion of CPR preferences is associated with a significant increase in the number of invasive procedures performed at the end of life, increased length of stay in the hospital, and increased medical cost.¹⁷ Despite clear evidence that earlier discussion of resuscitation options are valuable, no studies have examined the impact of a video about code status options in the general patient population.

Here we present our findings of a randomized trial in patients hospitalized on the general medicine wards who were 65 years of age or older, regardless of illness severity or diagnosis. The video tool was a supplement for, rather than a replacement of, standard provider and patient communication about code preferences, and we compared patients who watched the video against controls who had standard discussions with their providers. Our video detailed the process of chest compressions and intubation during CPR and explained the differences between the code statuses: full code, do not resuscitate (DNR), and do not resuscitate/do not intubate (DNR/DNI). We found a significant difference between the 2 groups, with significantly more individuals in the video group choosing DNR/DNI. These findings suggest that video support tools may be a useful supplement to traditional provider discussions about code preferences in the general patient population.

We enrolled patients from the general medicine wards at the Minneapolis VA Hospital from September 28, 2015 to October 23, 2015. Eligibility criteria included age 65 years or older, ability to provide informed consent, and ability to communicate in English. Study recruitment and data collection were performed by a study coordinator who was a house staff physician and had no role in the care of the participants. The medical charts of all general medicine patients were reviewed to determine if they met the age criteria. The physician of record for potential participants was contacted to assess if the patient was able to provide informed consent and communicate in English. Eligible patients were approached and informed consent was obtained from those who chose to participate in the study. After obtaining informed consent, patients were randomized using a random number generator to the intervention or usual-care arm of the study.

Those who were assigned to the intervention arm watched a 6-minute long video explaining the code-preference choices of full code, DNR, or DNR/DNI. Full code was described as possibly including CPR, intubation, and/or defibrillation depending on the clinical situation. Do not resuscitate was described as meaning no CPR or defibrillation but possible intubation in the case of respiratory failure. Do not resuscitate/do not intubate was explained as meaning no CPR, no defibrillation, and no intubation but rather permitting “natural death” to occur. The video showed a mock code with chest compressions, defibrillation, and intubation on a mannequin as well as palliative care specialists who discussed potential complications and survival rates of inhospital resuscitation.

The video was created at the University of Minnesota with the departments of palliative care and internal medicine (www.mmcgmeservices.org/codestat.html). After viewing the video, participants in the intervention arm filled out a questionnaire designed to assess their knowledge and beliefs about CPR and trust in their medical care providers. They were asked to circle their code preference. The participants’ medical teams were made aware of the code preferences and were counseled to discuss code preferences further if it was different from their previously documented code preference.

Participants in the control arm were assigned to usual care. At the institution where this study occurred, a discussion about code preferences between the patient and their medical team is considered the standard of care. After informed consent was obtained, participants filled out the same questionnaire as the participants in the intervention arm. They were asked to circle their code status preference. If they chose to ask questions about resuscitation, these were answered, but the study coordinator did not volunteer information about resuscitation or intervene in the medical care of the participants in any way.

Study Participants

A total of 273 potentially eligible patients were approached to participate and 119 (44%) enrolled. (Figure 1). Of the 154 patients that were deemed eligible after initial screening, 42 patients were unable to give consent due to the severity of their illness or because of their mental status. Another 112 patients declined participation in the study, citing reasons such as disinterest in the consent paperwork, desire to spend time with visitors, and unease with the subject matter. Patients who declined participation did not differ significantly by age, sex, or race from those enrolled in the study.

Among the 119 participants, 60 were randomized to the control arm, and 59 were randomized to the intervention arm. Participants in the 2 arms did not differ significantly in age, sex, or race (P > 0.05), although all 4 women in the study were randomized to the intervention arm. Eighty-seven percent of the study population identified as white with the remainder including black, Asian, Pacific Islander, Native American, or declining to answer. The mean age was 75.8 years in the control arm vs. 75.2 years in the intervention arm.

Primary diagnoses in the study group ranged widely from relatively minor skin infections to acute pancreatitis. The control arm and the intervention arm did not differ significantly in the incidence of heart failure, pulmonary disease, renal dialysis, cirrhosis, stroke, or active cancer (P > 0.05). Patients were considered as having a stroke if they had suffered a stroke during their hospital admission or if they had long-term sequelae of prior stroke. Patients were considered as having active cancer if they were currently undergoing treatment or had metastases. Participants were considered as having multiple morbidities if they possessed 2 or more of the listed conditions. Between the control arm and the intervention arm, there was no significant difference in the number of participants with multiple morbidities (27% in the control group and 24% in the video group).

Code Status Preference

There was a significant difference in the code status preferences of the intervention arm and the control arm (P < 0.00001; Figure 2). In the control arm, 71% of participants chose full code, 12% chose DNR, and 17% chose DNR/DNI. In the intervention arm, only 37% chose full code, 7% chose DNR, and 56% chose DNR/DNI.

Secondary outcomes

Participants in the control and intervention arms were asked about their trust in their medical team (Question 1, Figure 3). There was no significant difference, but a trend towards less trust in the intervention group (P = 0.083) was seen with 93% of the control arm and 76% of the intervention arm agreeing with the statement “My doctors and healthcare team want what is best for me.”

Question 2, “If I choose to avoid resuscitation efforts, I will not receive care,” was designed to assess participants’ knowledge and perception about the care they would receive if they chose DNR/DNI as their code status. No significant difference was seen between the control and the interventions arms, with 28% of the control group agreeing with the statement, compared to 22% of the video group.

For question 3, participants were asked to respond to the statement “I would like to live as long as possible, even if I never leave the hospital.” No significant differences were seen between the control and the intervention arms, with 22% of both groups agreeing with the statement.

When we examined participant responses by the code status chosen, a significantly higher percentage of participants who chose full code agreed with the statement in question 3 (P = 0.0133). Of participants who chose full code, 27% agreed with the statement, compared to 18% of participants who chose DNR and 12% of participants who chose DNR/DNI. There was no significant difference (P > 0.05) between participant code status choice and either Question 1 or 2.

This study examined the effect of watching a video about CPR and intubation on the code status preferences of hospitalized patients. Participants who viewed a video about CPR and intubation were more likely to choose to forgo these treatments. Participants who chose CPR and intubation were more likely to agree that they would want to live as long as possible even if that time were spent in a medical setting.

To our knowledge, this is the first study to examine the role of a video decision support tool about code choices in the general hospital population, regardless of prognosis. Previous work has trialed the use of video support tools in hospitalized patients with a prognosis of less than 1 year,¹⁵ patients admitted to the ICU,¹⁸ and outpatients with cancer¹⁸ and those with dementia.¹⁶ Unlike previous studies, our study included a variety of illness severity.

Discussions about resuscitation are important for all adults admitted to the hospital because of the unpredictable nature of illness and the importance of providing high-quality care at the end of life.  A recent study indicates that in-hospital cardiopulmonary arrest occurs in almost 1 per 1000 hospital days.¹⁹ These discussions are particularly salient for patients 65 years and older because of the higher incidence of death in this group. Inpatient admission is often a result of a change in health status, making it an important time for patients to reassess their resuscitation preferences based on their physical state and known comorbidities.

Video tools supplement the traditional code status discussion in several key ways. They provide a visual simulation of the procedures that occur during a typical resuscitation. These tools can help patients understand what CPR and intubation entail and transmit information that might be missed in verbal discussions. Visual media is now a common way for patients to obtain medical information^20-22 and may be particularly helpful to patients who have low health literacy.²³Video tools also help ensure that patients receive all the facts about resuscitation irrespective of how busy their provider may be or how comfortable the provider is with the topic. Lastly, video tools can reinforce information that is shared in the initial code status discussion. Given the significant differences in code status preference between our control and video arms, it is clear that the video tool has a significant impact on patient choices.

While we feel that our study clearly indicates the utility of video tools in code status discussion in hospitalized patients, there are some limitations. The current study enrolled participants who were predominantly white and male. All participants were recruited from the Minneapolis Veterans Affairs Health Care System, Minnesota. The relatively homogenous study population may impact the study’s generalizability. Another potential limitation of our study was the large number of eligible participants who declined to participate (41%), with many citing that they did not want to sign the consent paperwork. Additionally, the study coordinator was not blinded to the randomization of the participants, which could result in ascertainment bias. Also of concern was a trend, albeit nonsignificant, towards less trust in the healthcare team in the video group. Because the study was not designed to assess trust in the healthcare team both before and after the intervention, it is unclear if this difference was a result of the video.

Another area of potential concern is that visual images can be edited to sway viewers’ opinions based on the way content is presented. In our video, we included input from palliative care and internal medicine specialists. Cardiopulmonary resuscitation and intubation were performed on a CPR mannequin. The risks and benefits of CPR and intubation were discussed, as were the implications of choosing DNR or DNR/DNI code statuses.

The questionnaire that we used to assess participants’ knowledge and beliefs about resuscitation showed no differences between the control and the intervention arms of the study. We were surprised that a significant number of participants in the intervention group agreed with the statement, “If I choose to avoid resuscitation efforts, I will not receive care.” Our video specifically addressed the common belief that choosing DNR/DNI or DNR code statuses means that a patient will not continue to receive medical care. It is possible that participants were confused by the way the question was worded or that they understood the question to apply only to care received after a cardiopulmonary arrest had occurred.

This study and several others^14-16 show that the use of video tools impacts participants’ code status preferences. There is clinical and humanistic importance in helping patients make informed decisions regarding whether or not they would want CPR and/or intubation if their heart were to stop or if they were to stop breathing. The data suggest that video tools are an efficient way to improve patient care and should be made widely available.

Disclosures: The authors report no conflicts of interest.

Discussions about cardiopulmonary resuscitation (CPR) can be difficult due to their association with end of life. The Patient Self Determination Act (H.R.4449 — 101st Congress [1989-1990]) and institutional standards mandate collaboration between care providers and patients regarding goals of care in emergency situations such as cardiopulmonary arrest. The default option is to provide CPR, which may involve chest compressions, intubation, and/or defibrillation. Yet numerous studies show that a significant number of patients have no code preference documented in their medical chart, and even fewer report a conversation with their care provider about their wishes regarding CPR.^1-3 CPR is an invasive and potentially painful procedure with a higher chance of failure than success⁴, and yet many patients report that their provider did not discuss with them the risks and benefits of resuscitation.^5,6 Further highlighting the importance of individual discussions about CPR preferences is the reality that factors such as age and disease burden further skew the likelihood of survival after cardiopulmonary arrest.⁷

Complicating the lack of appropriate provider and patient discussion of the risks and benefits of resuscitation are significant misunderstandings about CPR in the lay population. Patients routinely overestimate the likelihood of survival following CPR.^8,9 This may be partially due to the portrayal of CPR in the lay media as highly efficacious.¹⁰ Other factors known to prevent effective provider-and-patient discussions about CPR preferences are providers’ discomfort with the subject¹¹ and perceived time constraints.¹²

Informational videos have been developed to assist patients with decision making about CPR and have been shown to impact patients’ choices in the setting of life-limiting diseases such as advanced cancer,^13-14 serious illness with a prognosis of less than 1 year,¹⁵ and dementia.¹⁶ While discussion of code status is vitally important in end-of-life planning for seriously ill individuals, delayed discussion of CPR preferences is associated with a significant increase in the number of invasive procedures performed at the end of life, increased length of stay in the hospital, and increased medical cost.¹⁷ Despite clear evidence that earlier discussion of resuscitation options are valuable, no studies have examined the impact of a video about code status options in the general patient population.

Here we present our findings of a randomized trial in patients hospitalized on the general medicine wards who were 65 years of age or older, regardless of illness severity or diagnosis. The video tool was a supplement for, rather than a replacement of, standard provider and patient communication about code preferences, and we compared patients who watched the video against controls who had standard discussions with their providers. Our video detailed the process of chest compressions and intubation during CPR and explained the differences between the code statuses: full code, do not resuscitate (DNR), and do not resuscitate/do not intubate (DNR/DNI). We found a significant difference between the 2 groups, with significantly more individuals in the video group choosing DNR/DNI. These findings suggest that video support tools may be a useful supplement to traditional provider discussions about code preferences in the general patient population.

We enrolled patients from the general medicine wards at the Minneapolis VA Hospital from September 28, 2015 to October 23, 2015. Eligibility criteria included age 65 years or older, ability to provide informed consent, and ability to communicate in English. Study recruitment and data collection were performed by a study coordinator who was a house staff physician and had no role in the care of the participants. The medical charts of all general medicine patients were reviewed to determine if they met the age criteria. The physician of record for potential participants was contacted to assess if the patient was able to provide informed consent and communicate in English. Eligible patients were approached and informed consent was obtained from those who chose to participate in the study. After obtaining informed consent, patients were randomized using a random number generator to the intervention or usual-care arm of the study.

Those who were assigned to the intervention arm watched a 6-minute long video explaining the code-preference choices of full code, DNR, or DNR/DNI. Full code was described as possibly including CPR, intubation, and/or defibrillation depending on the clinical situation. Do not resuscitate was described as meaning no CPR or defibrillation but possible intubation in the case of respiratory failure. Do not resuscitate/do not intubate was explained as meaning no CPR, no defibrillation, and no intubation but rather permitting “natural death” to occur. The video showed a mock code with chest compressions, defibrillation, and intubation on a mannequin as well as palliative care specialists who discussed potential complications and survival rates of inhospital resuscitation.

The video was created at the University of Minnesota with the departments of palliative care and internal medicine (www.mmcgmeservices.org/codestat.html). After viewing the video, participants in the intervention arm filled out a questionnaire designed to assess their knowledge and beliefs about CPR and trust in their medical care providers. They were asked to circle their code preference. The participants’ medical teams were made aware of the code preferences and were counseled to discuss code preferences further if it was different from their previously documented code preference.

Participants in the control arm were assigned to usual care. At the institution where this study occurred, a discussion about code preferences between the patient and their medical team is considered the standard of care. After informed consent was obtained, participants filled out the same questionnaire as the participants in the intervention arm. They were asked to circle their code status preference. If they chose to ask questions about resuscitation, these were answered, but the study coordinator did not volunteer information about resuscitation or intervene in the medical care of the participants in any way.

Study Participants

A total of 273 potentially eligible patients were approached to participate and 119 (44%) enrolled. (Figure 1). Of the 154 patients that were deemed eligible after initial screening, 42 patients were unable to give consent due to the severity of their illness or because of their mental status. Another 112 patients declined participation in the study, citing reasons such as disinterest in the consent paperwork, desire to spend time with visitors, and unease with the subject matter. Patients who declined participation did not differ significantly by age, sex, or race from those enrolled in the study.

Among the 119 participants, 60 were randomized to the control arm, and 59 were randomized to the intervention arm. Participants in the 2 arms did not differ significantly in age, sex, or race (P > 0.05), although all 4 women in the study were randomized to the intervention arm. Eighty-seven percent of the study population identified as white with the remainder including black, Asian, Pacific Islander, Native American, or declining to answer. The mean age was 75.8 years in the control arm vs. 75.2 years in the intervention arm.

Primary diagnoses in the study group ranged widely from relatively minor skin infections to acute pancreatitis. The control arm and the intervention arm did not differ significantly in the incidence of heart failure, pulmonary disease, renal dialysis, cirrhosis, stroke, or active cancer (P > 0.05). Patients were considered as having a stroke if they had suffered a stroke during their hospital admission or if they had long-term sequelae of prior stroke. Patients were considered as having active cancer if they were currently undergoing treatment or had metastases. Participants were considered as having multiple morbidities if they possessed 2 or more of the listed conditions. Between the control arm and the intervention arm, there was no significant difference in the number of participants with multiple morbidities (27% in the control group and 24% in the video group).

Code Status Preference

There was a significant difference in the code status preferences of the intervention arm and the control arm (P < 0.00001; Figure 2). In the control arm, 71% of participants chose full code, 12% chose DNR, and 17% chose DNR/DNI. In the intervention arm, only 37% chose full code, 7% chose DNR, and 56% chose DNR/DNI.

Secondary outcomes

Participants in the control and intervention arms were asked about their trust in their medical team (Question 1, Figure 3). There was no significant difference, but a trend towards less trust in the intervention group (P = 0.083) was seen with 93% of the control arm and 76% of the intervention arm agreeing with the statement “My doctors and healthcare team want what is best for me.”

Question 2, “If I choose to avoid resuscitation efforts, I will not receive care,” was designed to assess participants’ knowledge and perception about the care they would receive if they chose DNR/DNI as their code status. No significant difference was seen between the control and the interventions arms, with 28% of the control group agreeing with the statement, compared to 22% of the video group.

For question 3, participants were asked to respond to the statement “I would like to live as long as possible, even if I never leave the hospital.” No significant differences were seen between the control and the intervention arms, with 22% of both groups agreeing with the statement.

When we examined participant responses by the code status chosen, a significantly higher percentage of participants who chose full code agreed with the statement in question 3 (P = 0.0133). Of participants who chose full code, 27% agreed with the statement, compared to 18% of participants who chose DNR and 12% of participants who chose DNR/DNI. There was no significant difference (P > 0.05) between participant code status choice and either Question 1 or 2.

This study examined the effect of watching a video about CPR and intubation on the code status preferences of hospitalized patients. Participants who viewed a video about CPR and intubation were more likely to choose to forgo these treatments. Participants who chose CPR and intubation were more likely to agree that they would want to live as long as possible even if that time were spent in a medical setting.

To our knowledge, this is the first study to examine the role of a video decision support tool about code choices in the general hospital population, regardless of prognosis. Previous work has trialed the use of video support tools in hospitalized patients with a prognosis of less than 1 year,¹⁵ patients admitted to the ICU,¹⁸ and outpatients with cancer¹⁸ and those with dementia.¹⁶ Unlike previous studies, our study included a variety of illness severity.

Discussions about resuscitation are important for all adults admitted to the hospital because of the unpredictable nature of illness and the importance of providing high-quality care at the end of life.  A recent study indicates that in-hospital cardiopulmonary arrest occurs in almost 1 per 1000 hospital days.¹⁹ These discussions are particularly salient for patients 65 years and older because of the higher incidence of death in this group. Inpatient admission is often a result of a change in health status, making it an important time for patients to reassess their resuscitation preferences based on their physical state and known comorbidities.

Video tools supplement the traditional code status discussion in several key ways. They provide a visual simulation of the procedures that occur during a typical resuscitation. These tools can help patients understand what CPR and intubation entail and transmit information that might be missed in verbal discussions. Visual media is now a common way for patients to obtain medical information^20-22 and may be particularly helpful to patients who have low health literacy.²³Video tools also help ensure that patients receive all the facts about resuscitation irrespective of how busy their provider may be or how comfortable the provider is with the topic. Lastly, video tools can reinforce information that is shared in the initial code status discussion. Given the significant differences in code status preference between our control and video arms, it is clear that the video tool has a significant impact on patient choices.

While we feel that our study clearly indicates the utility of video tools in code status discussion in hospitalized patients, there are some limitations. The current study enrolled participants who were predominantly white and male. All participants were recruited from the Minneapolis Veterans Affairs Health Care System, Minnesota. The relatively homogenous study population may impact the study’s generalizability. Another potential limitation of our study was the large number of eligible participants who declined to participate (41%), with many citing that they did not want to sign the consent paperwork. Additionally, the study coordinator was not blinded to the randomization of the participants, which could result in ascertainment bias. Also of concern was a trend, albeit nonsignificant, towards less trust in the healthcare team in the video group. Because the study was not designed to assess trust in the healthcare team both before and after the intervention, it is unclear if this difference was a result of the video.

Another area of potential concern is that visual images can be edited to sway viewers’ opinions based on the way content is presented. In our video, we included input from palliative care and internal medicine specialists. Cardiopulmonary resuscitation and intubation were performed on a CPR mannequin. The risks and benefits of CPR and intubation were discussed, as were the implications of choosing DNR or DNR/DNI code statuses.

The questionnaire that we used to assess participants’ knowledge and beliefs about resuscitation showed no differences between the control and the intervention arms of the study. We were surprised that a significant number of participants in the intervention group agreed with the statement, “If I choose to avoid resuscitation efforts, I will not receive care.” Our video specifically addressed the common belief that choosing DNR/DNI or DNR code statuses means that a patient will not continue to receive medical care. It is possible that participants were confused by the way the question was worded or that they understood the question to apply only to care received after a cardiopulmonary arrest had occurred.

This study and several others^14-16 show that the use of video tools impacts participants’ code status preferences. There is clinical and humanistic importance in helping patients make informed decisions regarding whether or not they would want CPR and/or intubation if their heart were to stop or if they were to stop breathing. The data suggest that video tools are an efficient way to improve patient care and should be made widely available.

Disclosures: The authors report no conflicts of interest.

Discussions about cardiopulmonary resuscitation (CPR) can be difficult due to their association with end of life. The Patient Self Determination Act (H.R.4449 — 101st Congress [1989-1990]) and institutional standards mandate collaboration between care providers and patients regarding goals of care in emergency situations such as cardiopulmonary arrest. The default option is to provide CPR, which may involve chest compressions, intubation, and/or defibrillation. Yet numerous studies show that a significant number of patients have no code preference documented in their medical chart, and even fewer report a conversation with their care provider about their wishes regarding CPR.^1-3 CPR is an invasive and potentially painful procedure with a higher chance of failure than success⁴, and yet many patients report that their provider did not discuss with them the risks and benefits of resuscitation.^5,6 Further highlighting the importance of individual discussions about CPR preferences is the reality that factors such as age and disease burden further skew the likelihood of survival after cardiopulmonary arrest.⁷

Complicating the lack of appropriate provider and patient discussion of the risks and benefits of resuscitation are significant misunderstandings about CPR in the lay population. Patients routinely overestimate the likelihood of survival following CPR.^8,9 This may be partially due to the portrayal of CPR in the lay media as highly efficacious.¹⁰ Other factors known to prevent effective provider-and-patient discussions about CPR preferences are providers’ discomfort with the subject¹¹ and perceived time constraints.¹²

Informational videos have been developed to assist patients with decision making about CPR and have been shown to impact patients’ choices in the setting of life-limiting diseases such as advanced cancer,^13-14 serious illness with a prognosis of less than 1 year,¹⁵ and dementia.¹⁶ While discussion of code status is vitally important in end-of-life planning for seriously ill individuals, delayed discussion of CPR preferences is associated with a significant increase in the number of invasive procedures performed at the end of life, increased length of stay in the hospital, and increased medical cost.¹⁷ Despite clear evidence that earlier discussion of resuscitation options are valuable, no studies have examined the impact of a video about code status options in the general patient population.

Here we present our findings of a randomized trial in patients hospitalized on the general medicine wards who were 65 years of age or older, regardless of illness severity or diagnosis. The video tool was a supplement for, rather than a replacement of, standard provider and patient communication about code preferences, and we compared patients who watched the video against controls who had standard discussions with their providers. Our video detailed the process of chest compressions and intubation during CPR and explained the differences between the code statuses: full code, do not resuscitate (DNR), and do not resuscitate/do not intubate (DNR/DNI). We found a significant difference between the 2 groups, with significantly more individuals in the video group choosing DNR/DNI. These findings suggest that video support tools may be a useful supplement to traditional provider discussions about code preferences in the general patient population.

We enrolled patients from the general medicine wards at the Minneapolis VA Hospital from September 28, 2015 to October 23, 2015. Eligibility criteria included age 65 years or older, ability to provide informed consent, and ability to communicate in English. Study recruitment and data collection were performed by a study coordinator who was a house staff physician and had no role in the care of the participants. The medical charts of all general medicine patients were reviewed to determine if they met the age criteria. The physician of record for potential participants was contacted to assess if the patient was able to provide informed consent and communicate in English. Eligible patients were approached and informed consent was obtained from those who chose to participate in the study. After obtaining informed consent, patients were randomized using a random number generator to the intervention or usual-care arm of the study.

Those who were assigned to the intervention arm watched a 6-minute long video explaining the code-preference choices of full code, DNR, or DNR/DNI. Full code was described as possibly including CPR, intubation, and/or defibrillation depending on the clinical situation. Do not resuscitate was described as meaning no CPR or defibrillation but possible intubation in the case of respiratory failure. Do not resuscitate/do not intubate was explained as meaning no CPR, no defibrillation, and no intubation but rather permitting “natural death” to occur. The video showed a mock code with chest compressions, defibrillation, and intubation on a mannequin as well as palliative care specialists who discussed potential complications and survival rates of inhospital resuscitation.

The video was created at the University of Minnesota with the departments of palliative care and internal medicine (www.mmcgmeservices.org/codestat.html). After viewing the video, participants in the intervention arm filled out a questionnaire designed to assess their knowledge and beliefs about CPR and trust in their medical care providers. They were asked to circle their code preference. The participants’ medical teams were made aware of the code preferences and were counseled to discuss code preferences further if it was different from their previously documented code preference.

Participants in the control arm were assigned to usual care. At the institution where this study occurred, a discussion about code preferences between the patient and their medical team is considered the standard of care. After informed consent was obtained, participants filled out the same questionnaire as the participants in the intervention arm. They were asked to circle their code status preference. If they chose to ask questions about resuscitation, these were answered, but the study coordinator did not volunteer information about resuscitation or intervene in the medical care of the participants in any way.

Study Participants

A total of 273 potentially eligible patients were approached to participate and 119 (44%) enrolled. (Figure 1). Of the 154 patients that were deemed eligible after initial screening, 42 patients were unable to give consent due to the severity of their illness or because of their mental status. Another 112 patients declined participation in the study, citing reasons such as disinterest in the consent paperwork, desire to spend time with visitors, and unease with the subject matter. Patients who declined participation did not differ significantly by age, sex, or race from those enrolled in the study.

Among the 119 participants, 60 were randomized to the control arm, and 59 were randomized to the intervention arm. Participants in the 2 arms did not differ significantly in age, sex, or race (P > 0.05), although all 4 women in the study were randomized to the intervention arm. Eighty-seven percent of the study population identified as white with the remainder including black, Asian, Pacific Islander, Native American, or declining to answer. The mean age was 75.8 years in the control arm vs. 75.2 years in the intervention arm.

Primary diagnoses in the study group ranged widely from relatively minor skin infections to acute pancreatitis. The control arm and the intervention arm did not differ significantly in the incidence of heart failure, pulmonary disease, renal dialysis, cirrhosis, stroke, or active cancer (P > 0.05). Patients were considered as having a stroke if they had suffered a stroke during their hospital admission or if they had long-term sequelae of prior stroke. Patients were considered as having active cancer if they were currently undergoing treatment or had metastases. Participants were considered as having multiple morbidities if they possessed 2 or more of the listed conditions. Between the control arm and the intervention arm, there was no significant difference in the number of participants with multiple morbidities (27% in the control group and 24% in the video group).

Code Status Preference

There was a significant difference in the code status preferences of the intervention arm and the control arm (P < 0.00001; Figure 2). In the control arm, 71% of participants chose full code, 12% chose DNR, and 17% chose DNR/DNI. In the intervention arm, only 37% chose full code, 7% chose DNR, and 56% chose DNR/DNI.

Secondary outcomes

Participants in the control and intervention arms were asked about their trust in their medical team (Question 1, Figure 3). There was no significant difference, but a trend towards less trust in the intervention group (P = 0.083) was seen with 93% of the control arm and 76% of the intervention arm agreeing with the statement “My doctors and healthcare team want what is best for me.”

Question 2, “If I choose to avoid resuscitation efforts, I will not receive care,” was designed to assess participants’ knowledge and perception about the care they would receive if they chose DNR/DNI as their code status. No significant difference was seen between the control and the interventions arms, with 28% of the control group agreeing with the statement, compared to 22% of the video group.

For question 3, participants were asked to respond to the statement “I would like to live as long as possible, even if I never leave the hospital.” No significant differences were seen between the control and the intervention arms, with 22% of both groups agreeing with the statement.

When we examined participant responses by the code status chosen, a significantly higher percentage of participants who chose full code agreed with the statement in question 3 (P = 0.0133). Of participants who chose full code, 27% agreed with the statement, compared to 18% of participants who chose DNR and 12% of participants who chose DNR/DNI. There was no significant difference (P > 0.05) between participant code status choice and either Question 1 or 2.

This study examined the effect of watching a video about CPR and intubation on the code status preferences of hospitalized patients. Participants who viewed a video about CPR and intubation were more likely to choose to forgo these treatments. Participants who chose CPR and intubation were more likely to agree that they would want to live as long as possible even if that time were spent in a medical setting.

To our knowledge, this is the first study to examine the role of a video decision support tool about code choices in the general hospital population, regardless of prognosis. Previous work has trialed the use of video support tools in hospitalized patients with a prognosis of less than 1 year,¹⁵ patients admitted to the ICU,¹⁸ and outpatients with cancer¹⁸ and those with dementia.¹⁶ Unlike previous studies, our study included a variety of illness severity.

Discussions about resuscitation are important for all adults admitted to the hospital because of the unpredictable nature of illness and the importance of providing high-quality care at the end of life.  A recent study indicates that in-hospital cardiopulmonary arrest occurs in almost 1 per 1000 hospital days.¹⁹ These discussions are particularly salient for patients 65 years and older because of the higher incidence of death in this group. Inpatient admission is often a result of a change in health status, making it an important time for patients to reassess their resuscitation preferences based on their physical state and known comorbidities.

Video tools supplement the traditional code status discussion in several key ways. They provide a visual simulation of the procedures that occur during a typical resuscitation. These tools can help patients understand what CPR and intubation entail and transmit information that might be missed in verbal discussions. Visual media is now a common way for patients to obtain medical information^20-22 and may be particularly helpful to patients who have low health literacy.²³Video tools also help ensure that patients receive all the facts about resuscitation irrespective of how busy their provider may be or how comfortable the provider is with the topic. Lastly, video tools can reinforce information that is shared in the initial code status discussion. Given the significant differences in code status preference between our control and video arms, it is clear that the video tool has a significant impact on patient choices.

While we feel that our study clearly indicates the utility of video tools in code status discussion in hospitalized patients, there are some limitations. The current study enrolled participants who were predominantly white and male. All participants were recruited from the Minneapolis Veterans Affairs Health Care System, Minnesota. The relatively homogenous study population may impact the study’s generalizability. Another potential limitation of our study was the large number of eligible participants who declined to participate (41%), with many citing that they did not want to sign the consent paperwork. Additionally, the study coordinator was not blinded to the randomization of the participants, which could result in ascertainment bias. Also of concern was a trend, albeit nonsignificant, towards less trust in the healthcare team in the video group. Because the study was not designed to assess trust in the healthcare team both before and after the intervention, it is unclear if this difference was a result of the video.

Another area of potential concern is that visual images can be edited to sway viewers’ opinions based on the way content is presented. In our video, we included input from palliative care and internal medicine specialists. Cardiopulmonary resuscitation and intubation were performed on a CPR mannequin. The risks and benefits of CPR and intubation were discussed, as were the implications of choosing DNR or DNR/DNI code statuses.

The questionnaire that we used to assess participants’ knowledge and beliefs about resuscitation showed no differences between the control and the intervention arms of the study. We were surprised that a significant number of participants in the intervention group agreed with the statement, “If I choose to avoid resuscitation efforts, I will not receive care.” Our video specifically addressed the common belief that choosing DNR/DNI or DNR code statuses means that a patient will not continue to receive medical care. It is possible that participants were confused by the way the question was worded or that they understood the question to apply only to care received after a cardiopulmonary arrest had occurred.

This study and several others^14-16 show that the use of video tools impacts participants’ code status preferences. There is clinical and humanistic importance in helping patients make informed decisions regarding whether or not they would want CPR and/or intubation if their heart were to stop or if they were to stop breathing. The data suggest that video tools are an efficient way to improve patient care and should be made widely available.

Disclosures: The authors report no conflicts of interest.

BACKGROUND: There is increasing recognition that patients have critical insights into care experiences, including about breakdowns in care. Harnessing patient perspectives for hospital improvement requires an in-depth understanding of the types of breakdowns patients identify and the impact of these events.

Also in JHM

Excess readmission vs. excess penalties: Maximum readmission penalties as a function of socioeconomics and geography

AUTHORS: Chris M. Caracciolo, MPH, Devin M. Parker, MS, Emily Marshall, MS, Jeremiah R. Brown, PhD, MS

Impact of a safety huddle-based intervention on monitor alarm rates in low-acuity pediatric intensive care unit patientsAUTHORS: Maya Dewan, MD, MPH, Heather Wolfe, MD, MSHP, Richard Lin, MD, Eileen Ware, RN, Michelle Weiss, RN, Lihai Song, MS, Matthew MacMurchy, BA, Daniela Davis, MD, MSCE, Christopher P. Bonafide MD MSCE

Perspectives of clinicians at skilled nursing facilities on 30-day hospital readmissions: A qualitative studyAUTHORS: Bennett W. Clark, MD, Katelyn Baron, MSIOP, Kathleen Tynan-McKiernan, RN, MSN, Meredith Campbell Britton, LMSW, Karl E. Minges, PhD, Sarwat I. Chaudhry, MD

Use of postacute facility care in children hospitalized with acute respiratory illnessAUTHORS: Jay G. Berry, MD, MPH, Karen M. Wilson, MD, MPH, FAAP, Helene Dumas, PT, MS, Edwin Simpser, MD, Jane O’Brien, MD, Kathleen Whitford, PNP, Rachna May, MD, Vineeta Mittal, MD, Nancy Murphy, MD, David Steinhorn, MD, Rishi Agrawal, MD, MPH, Kris Rehm, MD, Michelle Marks, DO, FAAP, SFHM, Christine Traul, MD, Michael Dribbon, PhD, Christopher J. Haines, DO, MBA, FAAP, FACEP, Matt Hall, PhD

A contemporary assessment of mechanical complication rates and trainee perceptions of central venous catheter insertionAUTHORS: Lauren Heidemann, MD, Niket Nathani, MD, Rommel Sagana, MD, Vineet Chopra, MD, MSc, Michael Heung, MD, MS

For more articles and subscription information, visit www.journalofhospitalmedicine.com.

BACKGROUND: There is increasing recognition that patients have critical insights into care experiences, including about breakdowns in care. Harnessing patient perspectives for hospital improvement requires an in-depth understanding of the types of breakdowns patients identify and the impact of these events.

Also in JHM

Excess readmission vs. excess penalties: Maximum readmission penalties as a function of socioeconomics and geography

AUTHORS: Chris M. Caracciolo, MPH, Devin M. Parker, MS, Emily Marshall, MS, Jeremiah R. Brown, PhD, MS

Impact of a safety huddle-based intervention on monitor alarm rates in low-acuity pediatric intensive care unit patientsAUTHORS: Maya Dewan, MD, MPH, Heather Wolfe, MD, MSHP, Richard Lin, MD, Eileen Ware, RN, Michelle Weiss, RN, Lihai Song, MS, Matthew MacMurchy, BA, Daniela Davis, MD, MSCE, Christopher P. Bonafide MD MSCE

Perspectives of clinicians at skilled nursing facilities on 30-day hospital readmissions: A qualitative studyAUTHORS: Bennett W. Clark, MD, Katelyn Baron, MSIOP, Kathleen Tynan-McKiernan, RN, MSN, Meredith Campbell Britton, LMSW, Karl E. Minges, PhD, Sarwat I. Chaudhry, MD

Use of postacute facility care in children hospitalized with acute respiratory illnessAUTHORS: Jay G. Berry, MD, MPH, Karen M. Wilson, MD, MPH, FAAP, Helene Dumas, PT, MS, Edwin Simpser, MD, Jane O’Brien, MD, Kathleen Whitford, PNP, Rachna May, MD, Vineeta Mittal, MD, Nancy Murphy, MD, David Steinhorn, MD, Rishi Agrawal, MD, MPH, Kris Rehm, MD, Michelle Marks, DO, FAAP, SFHM, Christine Traul, MD, Michael Dribbon, PhD, Christopher J. Haines, DO, MBA, FAAP, FACEP, Matt Hall, PhD

A contemporary assessment of mechanical complication rates and trainee perceptions of central venous catheter insertionAUTHORS: Lauren Heidemann, MD, Niket Nathani, MD, Rommel Sagana, MD, Vineet Chopra, MD, MSc, Michael Heung, MD, MS

For more articles and subscription information, visit www.journalofhospitalmedicine.com.

BACKGROUND: There is increasing recognition that patients have critical insights into care experiences, including about breakdowns in care. Harnessing patient perspectives for hospital improvement requires an in-depth understanding of the types of breakdowns patients identify and the impact of these events.

Also in JHM

Excess readmission vs. excess penalties: Maximum readmission penalties as a function of socioeconomics and geography

AUTHORS: Chris M. Caracciolo, MPH, Devin M. Parker, MS, Emily Marshall, MS, Jeremiah R. Brown, PhD, MS

Impact of a safety huddle-based intervention on monitor alarm rates in low-acuity pediatric intensive care unit patientsAUTHORS: Maya Dewan, MD, MPH, Heather Wolfe, MD, MSHP, Richard Lin, MD, Eileen Ware, RN, Michelle Weiss, RN, Lihai Song, MS, Matthew MacMurchy, BA, Daniela Davis, MD, MSCE, Christopher P. Bonafide MD MSCE

Perspectives of clinicians at skilled nursing facilities on 30-day hospital readmissions: A qualitative studyAUTHORS: Bennett W. Clark, MD, Katelyn Baron, MSIOP, Kathleen Tynan-McKiernan, RN, MSN, Meredith Campbell Britton, LMSW, Karl E. Minges, PhD, Sarwat I. Chaudhry, MD

Use of postacute facility care in children hospitalized with acute respiratory illnessAUTHORS: Jay G. Berry, MD, MPH, Karen M. Wilson, MD, MPH, FAAP, Helene Dumas, PT, MS, Edwin Simpser, MD, Jane O’Brien, MD, Kathleen Whitford, PNP, Rachna May, MD, Vineeta Mittal, MD, Nancy Murphy, MD, David Steinhorn, MD, Rishi Agrawal, MD, MPH, Kris Rehm, MD, Michelle Marks, DO, FAAP, SFHM, Christine Traul, MD, Michael Dribbon, PhD, Christopher J. Haines, DO, MBA, FAAP, FACEP, Matt Hall, PhD

A contemporary assessment of mechanical complication rates and trainee perceptions of central venous catheter insertionAUTHORS: Lauren Heidemann, MD, Niket Nathani, MD, Rommel Sagana, MD, Vineet Chopra, MD, MSc, Michael Heung, MD, MS

For more articles and subscription information, visit www.journalofhospitalmedicine.com.

“A good understanding of the diverse veteran populations is imperative if the VA is to genuinely resolve the inequities for those at high risk and with the most need,” says the introduction to the first-ever National Veteran Health Equity Report. The report, which contains demographic information on veterans who received VHA care in Fiscal Year (FY) 2013, is designed to provide that comparative information on minorities, women, and other veteran groups. For example:

• Although women represented only about 7% of patients in FY2013, their numbers in VHA have more than doubled since 2000—140% growth, far outstripping the 63% growth among men over the same period;
• In FY2013, 46% of veterans were aged ≥ 65 years;
• More than one-third of veterans lived in rural areas;
• Almost one-half of VHA patients had a service-connected disability. All racial/ethnic minority patient groups, compared with whites, were more likely to have a service-connected disability;

• A higher proportion of women had a service-connected disability, and women are twice as likely to be diagnosed with depression;
• Overall, 33% of VHA patients had ≥ 1 mental health diagnoses. Women and blacks/African Americans were “over-represented” among patients diagnosed with serious mental illness;

• Veterans with serious mental illness also had higher diagnosis rates for musculoskeletal disorders (60% vs 43%) and gastrointestinal conditions (48% vs 30%). In fact, among the top 20 diagnosed conditions, rates for the SMI group exceeded that for the veterans with no mental health diagnosis for 17 conditions by a margin of > 10% for 7. The largest disparities were in tobacco use disorder, psychosocial factors, spine disorders, and housing insufficiency; and
• The only condition in which the diagnosed rate in a racial/ethnic group exceeded that for whites by a margin of 10% was PTSD, diagnosed in 21% of American Indian/Alaska Natives, versus 11% of whites.

Although the report targeted  6 million veterans accessing VA care in FY13, the estimated number of living veterans is about 22 million. It is a “starting place,” the developers promise. Next iterations will continue to evolve to meet the unique needs of diverse veterans.

“A good understanding of the diverse veteran populations is imperative if the VA is to genuinely resolve the inequities for those at high risk and with the most need,” says the introduction to the first-ever National Veteran Health Equity Report. The report, which contains demographic information on veterans who received VHA care in Fiscal Year (FY) 2013, is designed to provide that comparative information on minorities, women, and other veteran groups. For example:

• Although women represented only about 7% of patients in FY2013, their numbers in VHA have more than doubled since 2000—140% growth, far outstripping the 63% growth among men over the same period;
• In FY2013, 46% of veterans were aged ≥ 65 years;
• More than one-third of veterans lived in rural areas;
• Almost one-half of VHA patients had a service-connected disability. All racial/ethnic minority patient groups, compared with whites, were more likely to have a service-connected disability;

• A higher proportion of women had a service-connected disability, and women are twice as likely to be diagnosed with depression;
• Overall, 33% of VHA patients had ≥ 1 mental health diagnoses. Women and blacks/African Americans were “over-represented” among patients diagnosed with serious mental illness;

• Veterans with serious mental illness also had higher diagnosis rates for musculoskeletal disorders (60% vs 43%) and gastrointestinal conditions (48% vs 30%). In fact, among the top 20 diagnosed conditions, rates for the SMI group exceeded that for the veterans with no mental health diagnosis for 17 conditions by a margin of > 10% for 7. The largest disparities were in tobacco use disorder, psychosocial factors, spine disorders, and housing insufficiency; and
• The only condition in which the diagnosed rate in a racial/ethnic group exceeded that for whites by a margin of 10% was PTSD, diagnosed in 21% of American Indian/Alaska Natives, versus 11% of whites.

Although the report targeted  6 million veterans accessing VA care in FY13, the estimated number of living veterans is about 22 million. It is a “starting place,” the developers promise. Next iterations will continue to evolve to meet the unique needs of diverse veterans.

“A good understanding of the diverse veteran populations is imperative if the VA is to genuinely resolve the inequities for those at high risk and with the most need,” says the introduction to the first-ever National Veteran Health Equity Report. The report, which contains demographic information on veterans who received VHA care in Fiscal Year (FY) 2013, is designed to provide that comparative information on minorities, women, and other veteran groups. For example:

• Although women represented only about 7% of patients in FY2013, their numbers in VHA have more than doubled since 2000—140% growth, far outstripping the 63% growth among men over the same period;
• In FY2013, 46% of veterans were aged ≥ 65 years;
• More than one-third of veterans lived in rural areas;
• Almost one-half of VHA patients had a service-connected disability. All racial/ethnic minority patient groups, compared with whites, were more likely to have a service-connected disability;

• A higher proportion of women had a service-connected disability, and women are twice as likely to be diagnosed with depression;
• Overall, 33% of VHA patients had ≥ 1 mental health diagnoses. Women and blacks/African Americans were “over-represented” among patients diagnosed with serious mental illness;

• Veterans with serious mental illness also had higher diagnosis rates for musculoskeletal disorders (60% vs 43%) and gastrointestinal conditions (48% vs 30%). In fact, among the top 20 diagnosed conditions, rates for the SMI group exceeded that for the veterans with no mental health diagnosis for 17 conditions by a margin of > 10% for 7. The largest disparities were in tobacco use disorder, psychosocial factors, spine disorders, and housing insufficiency; and
• The only condition in which the diagnosed rate in a racial/ethnic group exceeded that for whites by a margin of 10% was PTSD, diagnosed in 21% of American Indian/Alaska Natives, versus 11% of whites.

Although the report targeted  6 million veterans accessing VA care in FY13, the estimated number of living veterans is about 22 million. It is a “starting place,” the developers promise. Next iterations will continue to evolve to meet the unique needs of diverse veterans.

Stack of money

Reference pricing effectively encourages patients to spend significantly less on prescription drugs, according to research published in NEJM.

Under the reference pricing strategy, the insurer or employer establishes its maximum contribution toward the price of therapeutically similar drugs, and the patient must pay the remainder out of pocket.

The insurer/employer contribution is based on the price of the lowest-priced drug in the therapeutic class, called the reference drug.

“If the patient chooses a cheap or moderately priced option, the employer’s contribution will cover most of the cost,” said study author James C. Robinson, PhD, of the University of California at Berkeley.

“However, if the patient insists on a particularly high-priced option, he or she will need to make a meaningful payment from personal resources.”

It has been theorized that this policy would encourage patients to save money by selecting cheaper drugs. However, little is known about how the policy has actually influenced patient spending.

The new study showed that reference pricing was associated with significant changes in drug selection and spending for patients covered by employment-based insurance in the US.

Researchers analyzed changes in prescriptions and pricing for 1302 drugs in 78 therapeutic classes in the US, before and after an alliance of private employers began using reference pricing.

The trends were compared to a cohort without reference pricing. The study’s dataset included 1.1 million prescriptions reimbursed from 2010 to 2014.

Implementation of reference pricing was associated with a 7% increase in prescriptions filled for the low-price reference drug within its therapeutic class, a 14% decrease in average price paid, and a 5% increase in consumer cost-sharing.

In the first 18 months after implementation, employers’ spending dropped $1.34 million, and employees’ cost-sharing increased $120,000.

Based on these findings, Dr Robinson and his colleagues concluded that reference pricing may be one instrument for influencing patients’ drug choices and drug prices paid by employers and insurers. The team believes that, in the future, pharmaceutical companies charging “premium prices” may need to demonstrate that their drugs provide “premium performance.”

“There is huge and unjustified variation within and across geographic areas in the prices charged for almost every test and treatment, drug and device, office visit and hospitalization,” Dr Robinson said.

“It’s not a surprise when one considers that most patients are covered by health insurance and, hence, do not shop among competing providers on the basis of price. Some providers look at price-unconscious consumer demand and ask themselves, ‘Why don’t we raise our prices?’”

This research was funded by the US Agency for Healthcare Research and Quality and the Genentech Foundation.

Stack of money

Reference pricing effectively encourages patients to spend significantly less on prescription drugs, according to research published in NEJM.

Under the reference pricing strategy, the insurer or employer establishes its maximum contribution toward the price of therapeutically similar drugs, and the patient must pay the remainder out of pocket.

The insurer/employer contribution is based on the price of the lowest-priced drug in the therapeutic class, called the reference drug.

“If the patient chooses a cheap or moderately priced option, the employer’s contribution will cover most of the cost,” said study author James C. Robinson, PhD, of the University of California at Berkeley.

“However, if the patient insists on a particularly high-priced option, he or she will need to make a meaningful payment from personal resources.”

It has been theorized that this policy would encourage patients to save money by selecting cheaper drugs. However, little is known about how the policy has actually influenced patient spending.

The new study showed that reference pricing was associated with significant changes in drug selection and spending for patients covered by employment-based insurance in the US.

Researchers analyzed changes in prescriptions and pricing for 1302 drugs in 78 therapeutic classes in the US, before and after an alliance of private employers began using reference pricing.

The trends were compared to a cohort without reference pricing. The study’s dataset included 1.1 million prescriptions reimbursed from 2010 to 2014.

Implementation of reference pricing was associated with a 7% increase in prescriptions filled for the low-price reference drug within its therapeutic class, a 14% decrease in average price paid, and a 5% increase in consumer cost-sharing.

In the first 18 months after implementation, employers’ spending dropped $1.34 million, and employees’ cost-sharing increased $120,000.

Based on these findings, Dr Robinson and his colleagues concluded that reference pricing may be one instrument for influencing patients’ drug choices and drug prices paid by employers and insurers. The team believes that, in the future, pharmaceutical companies charging “premium prices” may need to demonstrate that their drugs provide “premium performance.”

“There is huge and unjustified variation within and across geographic areas in the prices charged for almost every test and treatment, drug and device, office visit and hospitalization,” Dr Robinson said.

“It’s not a surprise when one considers that most patients are covered by health insurance and, hence, do not shop among competing providers on the basis of price. Some providers look at price-unconscious consumer demand and ask themselves, ‘Why don’t we raise our prices?’”

This research was funded by the US Agency for Healthcare Research and Quality and the Genentech Foundation.

Stack of money

Reference pricing effectively encourages patients to spend significantly less on prescription drugs, according to research published in NEJM.

Under the reference pricing strategy, the insurer or employer establishes its maximum contribution toward the price of therapeutically similar drugs, and the patient must pay the remainder out of pocket.

The insurer/employer contribution is based on the price of the lowest-priced drug in the therapeutic class, called the reference drug.

“If the patient chooses a cheap or moderately priced option, the employer’s contribution will cover most of the cost,” said study author James C. Robinson, PhD, of the University of California at Berkeley.

“However, if the patient insists on a particularly high-priced option, he or she will need to make a meaningful payment from personal resources.”

It has been theorized that this policy would encourage patients to save money by selecting cheaper drugs. However, little is known about how the policy has actually influenced patient spending.

The new study showed that reference pricing was associated with significant changes in drug selection and spending for patients covered by employment-based insurance in the US.

Researchers analyzed changes in prescriptions and pricing for 1302 drugs in 78 therapeutic classes in the US, before and after an alliance of private employers began using reference pricing.

The trends were compared to a cohort without reference pricing. The study’s dataset included 1.1 million prescriptions reimbursed from 2010 to 2014.

Implementation of reference pricing was associated with a 7% increase in prescriptions filled for the low-price reference drug within its therapeutic class, a 14% decrease in average price paid, and a 5% increase in consumer cost-sharing.

In the first 18 months after implementation, employers’ spending dropped $1.34 million, and employees’ cost-sharing increased $120,000.

Based on these findings, Dr Robinson and his colleagues concluded that reference pricing may be one instrument for influencing patients’ drug choices and drug prices paid by employers and insurers. The team believes that, in the future, pharmaceutical companies charging “premium prices” may need to demonstrate that their drugs provide “premium performance.”

“There is huge and unjustified variation within and across geographic areas in the prices charged for almost every test and treatment, drug and device, office visit and hospitalization,” Dr Robinson said.

“It’s not a surprise when one considers that most patients are covered by health insurance and, hence, do not shop among competing providers on the basis of price. Some providers look at price-unconscious consumer demand and ask themselves, ‘Why don’t we raise our prices?’”

This research was funded by the US Agency for Healthcare Research and Quality and the Genentech Foundation.

Lab mouse

A 3-drug combination has demonstrated long-term efficacy against acute lymphoblastic leukemia (ALL) in mice, according to research published in Nature Communications.

Researchers found that when the production of nucleotides is stopped, a DNA replication stress response is activated, and this allows ALL cells to survive.

The team used these findings to devise a 3-drug regimen that blocks both of the nucleotide biosynthetic pathways and inhibits the replication stress response.

One of the drugs is triapine (3-AP), which inhibits ribonucleotide reductase (RNR) and enhances salvage nucleotide biosynthesis.

Another drug is DI-82, which inhibits the nucleoside salvage kinase deoxycytidine kinase (dCK).

And the third drug is VE-822, which inhibits the replication stress-sensing kinase ataxia telangiectasia and Rad3-related protein (ATR).

The researchers tested these 3 drugs in a mouse model of pre-B-ALL. Starting on day 7 after inoculation of pre-B-ALL, the mice received 3-AP and DI-82 twice a day and VE-822 once a day.

The team said mice in the control group succumbed to their disease within 17 days. However, all of the mice that received the 3-drug combination were still disease-free 442 days after they had stopped treatment (on day 42).

The researchers said the combination was well-tolerated, as mice maintained their body weight and enjoyed long-term survival without any detectable pathology.

The team also tested a dosing regimen in which all 3 drugs were given once a day.

Although this was less effective than the first regimen, 4 of 5 mice had no detectable disease 313 days after stopping treatment.

Two of the researchers involved in this study are co-founders and equity holders of Trethera Corp, a company developing a dCK inhibitor known as TRE-515.

The University of California (where most of the study authors work) has patented intellectual property for small-molecule dCK inhibitors, and it was licensed by Trethera.

Lab mouse

A 3-drug combination has demonstrated long-term efficacy against acute lymphoblastic leukemia (ALL) in mice, according to research published in Nature Communications.

Researchers found that when the production of nucleotides is stopped, a DNA replication stress response is activated, and this allows ALL cells to survive.

The team used these findings to devise a 3-drug regimen that blocks both of the nucleotide biosynthetic pathways and inhibits the replication stress response.

One of the drugs is triapine (3-AP), which inhibits ribonucleotide reductase (RNR) and enhances salvage nucleotide biosynthesis.

Another drug is DI-82, which inhibits the nucleoside salvage kinase deoxycytidine kinase (dCK).

And the third drug is VE-822, which inhibits the replication stress-sensing kinase ataxia telangiectasia and Rad3-related protein (ATR).

The researchers tested these 3 drugs in a mouse model of pre-B-ALL. Starting on day 7 after inoculation of pre-B-ALL, the mice received 3-AP and DI-82 twice a day and VE-822 once a day.

The team said mice in the control group succumbed to their disease within 17 days. However, all of the mice that received the 3-drug combination were still disease-free 442 days after they had stopped treatment (on day 42).

The researchers said the combination was well-tolerated, as mice maintained their body weight and enjoyed long-term survival without any detectable pathology.

The team also tested a dosing regimen in which all 3 drugs were given once a day.

Although this was less effective than the first regimen, 4 of 5 mice had no detectable disease 313 days after stopping treatment.

Two of the researchers involved in this study are co-founders and equity holders of Trethera Corp, a company developing a dCK inhibitor known as TRE-515.

The University of California (where most of the study authors work) has patented intellectual property for small-molecule dCK inhibitors, and it was licensed by Trethera.

Lab mouse

A 3-drug combination has demonstrated long-term efficacy against acute lymphoblastic leukemia (ALL) in mice, according to research published in Nature Communications.

Researchers found that when the production of nucleotides is stopped, a DNA replication stress response is activated, and this allows ALL cells to survive.

The team used these findings to devise a 3-drug regimen that blocks both of the nucleotide biosynthetic pathways and inhibits the replication stress response.

One of the drugs is triapine (3-AP), which inhibits ribonucleotide reductase (RNR) and enhances salvage nucleotide biosynthesis.

Another drug is DI-82, which inhibits the nucleoside salvage kinase deoxycytidine kinase (dCK).

And the third drug is VE-822, which inhibits the replication stress-sensing kinase ataxia telangiectasia and Rad3-related protein (ATR).

The researchers tested these 3 drugs in a mouse model of pre-B-ALL. Starting on day 7 after inoculation of pre-B-ALL, the mice received 3-AP and DI-82 twice a day and VE-822 once a day.

The team said mice in the control group succumbed to their disease within 17 days. However, all of the mice that received the 3-drug combination were still disease-free 442 days after they had stopped treatment (on day 42).

The researchers said the combination was well-tolerated, as mice maintained their body weight and enjoyed long-term survival without any detectable pathology.

The team also tested a dosing regimen in which all 3 drugs were given once a day.

Although this was less effective than the first regimen, 4 of 5 mice had no detectable disease 313 days after stopping treatment.

Two of the researchers involved in this study are co-founders and equity holders of Trethera Corp, a company developing a dCK inhibitor known as TRE-515.

The University of California (where most of the study authors work) has patented intellectual property for small-molecule dCK inhibitors, and it was licensed by Trethera.