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Open Access Highly Accessed Research article

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

Courtney Hebert12*, Chaitanya Shivade3, Randi Foraker4, Jared Wasserman47, Caryn Roth1, Hagop Mekhjian5, Stanley Lemeshow4 and Peter Embi16

Author Affiliations

1 Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA

2 Division of Infectious Diseases, The Ohio State University, Columbus, OH, USA

3 Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA

4 College of Public Health, The Ohio State University, Columbus, OH, USA

5 Division of Gastroenterology, Hepatology & Nutrition, The Ohio State University, Columbus, OH, USA

6 Division of Immunology and Rheumatology, The Ohio State University, Columbus, OH, USA

7 The Dartmouth Institute of Health Policy and Clinical Practice, Lebanon, NH, USA

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BMC Medical Informatics and Decision Making 2014, 14:65  doi:10.1186/1472-6947-14-65

Published: 4 August 2014

Abstract

Background

Readmissions 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.

Methods

This 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.

Results

3572 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).

Conclusions

The 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.

Keywords:
Readmissions; Risk-prediction; Electronic health records