title: Diagnosis-specific readmission risk prediction using electronic health data: a retrospective cohort study.
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.
authors.affiliation: Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA. courtney.hebert@osumc.edu.
authors.forename: Courtney
authors.initials: C
authors.lastname: Hebert
authors.forename: Chaitanya
authors.initials: C
authors.lastname: Shivade
authors.forename: Randi
authors.initials: R
authors.lastname: Foraker
authors.forename: Jared
authors.initials: J
authors.lastname: Wasserman
authors.forename: Caryn
authors.initials: C
authors.lastname: Roth
authors.forename: Hagop
authors.initials: H
authors.lastname: Mekhjian
authors.forename: Stanley
authors.initials: S
authors.lastname: Lemeshow
authors.forename: Peter
authors.initials: P
authors.lastname: Embi
mesh_terms: D000368:Aged; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D005260:Female; D006331:Heart Diseases / Q000628:therapy*; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010359:Patient Readmission / Q000706:statistics & numerical data*; D011014:Pneumonia / Q000628:therapy*; D012189:Retrospective Studies; D018570:Risk Assessment
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