Diagnosis-specific readmission risk prediction using electronic health data: a retrospective cohort study.

Courtney Hebert,Chaitanya Shivade,Stanley Lemeshow,Caryn Roth,Jared Wasserman,Randi Foraker,Peter Embi,Hagop Mekhjian

doi:10.1186/1472-6947-14-65

Abstract

BackgroundReadmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients. Previous attempts to create universal risk prediction models for readmission have not met with success. In this study we leveraged a comprehensive electronic health record to create readmission-risk models that were institution- and patient- specific in an attempt to improve our ability to predict readmission.MethodsThis is a retrospective cohort study performed at a large midwestern tertiary care medical center. All patients with a primary discharge diagnosis of congestive heart failure, acute myocardial infarction or pneumonia over a two-year time period were included in the analysis.The main outcome was 30-day readmission. Demographic, comorbidity, laboratory, and medication data were collected on all patients from a comprehensive information warehouse. Using multivariable analysis with stepwise removal we created three risk disease-specific risk prediction models and a combined model. These models were then validated on separate cohorts.Results3572 patients were included in the derivation cohort. Overall there was a 16.2% readmission rate. The acute myocardial infarction and pneumonia readmission-risk models performed well on a random sample validation cohort (AUC range 0.73 to 0.76) but less well on a historical validation cohort (AUC 0.66 for both). The congestive heart failure model performed poorly on both validation cohorts (AUC 0.63 and 0.64).ConclusionsThe readmission-risk models for acute myocardial infarction and pneumonia validated well on a contemporary cohort, but not as well on a historical cohort, suggesting that models such as these need to be continuously trained and adjusted to respond to local trends. The poor performance of the congestive heart failure model may suggest that for chronic disease conditions social and behavioral variables are of greater importance and improved documentation of these variables within the electronic health record should be encouraged.

Highlights

Readmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients
Even though high readmission rates are seen in hospitals across the country [4], data suggest that differences may exist between 30-day readmission rates in different settings [2,4,8], indicating that geographic and socioeconomic factors may affect the likelihood of readmission
The Information Warehouse (IW) captures all administrative and clinical data during inpatient hospitalizations. Eligible patients were those admitted to an inpatient service between August 1, 2009 and July 31, 2011 with a primary discharge diagnosis International Classification of Diseases, Version 9 Clinical modification (ICD-9-CM) code of congestive heart failure (CHF), PNA, or AMI, as defined by the Centers for Medicare and Medicaid Services (CMS) [28]

Summary

Introduction

Readmissions after hospital discharge are a common occurrence and are costly for both hospitals and patients. Even though high readmission rates are seen in hospitals across the country [4], data suggest that differences may exist between 30-day readmission rates in different settings [2,4,8], indicating that geographic and socioeconomic factors may affect the likelihood of readmission With this in mind, we created prediction models at our own institution, The Ohio State University Wexner Medical Center (OSUWMC) that are specific to our patients, their context, and specific disease state. We hypothesized that a model tuned to a specific disease state would perform better than a combined model, and that a model created at our own institution would be uniquely suited for our patient population and environment These models are the first step in a plan to embed a tool into our comprehensive electronic health record (EHR) to alert physicians to high-risk patients at the point-of-care

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Discussion

Conclusion