title: Electronic medical record-based multicondition models to predict the risk of 30 day readmission or death among adult medicine patients: validation and comparison to existing models.
abstract: BACKGROUND
  There is increasing interest in using prediction models to identify patients at risk of readmission or death after hospital discharge, but existing models have significant limitations. Electronic medical record (EMR) based models that can be used to predict risk on multiple disease conditions among a wide range of patient demographics early in the hospitalization are needed. The objective of this study was to evaluate the degree to which EMR-based risk models for 30-day readmission or mortality accurately identify high risk patients and to compare these models with published claims-based models.
  
  
  METHODS
  Data were analyzed from all consecutive adult patients admitted to internal medicine services at 7 large hospitals belonging to 3 health systems in Dallas/Fort Worth between November 2009 and October 2010 and split randomly into derivation and validation cohorts. Performance of the model was evaluated against the Canadian LACE mortality or readmission model and the Centers for Medicare and Medicaid Services (CMS) Hospital Wide Readmission model.
  
  
  RESULTS
  Among the 39,604 adults hospitalized for a broad range of medical reasons, 2.8% of patients died, 12.7% were readmitted, and 14.7% were readmitted or died within 30 days after discharge. The electronic multicondition models for the composite outcome of 30-day mortality or readmission had good discrimination using data available within 24 h of admission (C statistic 0.69; 95% CI, 0.68-0.70), or at discharge (0.71; 95% CI, 0.70-0.72), and were significantly better than the LACE model (0.65; 95% CI, 0.64-0.66; P =0.02) with significant NRI (0.16) and IDI (0.039, 95% CI, 0.035-0.044). The electronic multicondition model for 30-day readmission alone had good discrimination using data available within 24 h of admission (C statistic 0.66; 95% CI, 0.65-0.67) or at discharge (0.68; 95% CI, 0.67-0.69), and performed significantly better than the CMS model (0.61; 95% CI, 0.59-0.62; P < 0.01) with significant NRI (0.20) and IDI (0.037, 95% CI, 0.033-0.041).
  
  
  CONCLUSIONS
  A new electronic multicondition model based on information derived from the EMR predicted mortality and readmission at 30 days, and was superior to previously published claims-based models.
authors.affiliation: Parkland Center for Clinical Innovation, 8435 Stemmons Freeway, Suite 1150, Dallas, TX, 75247, USA. ruben.amarasingham@phhs.org.|Division of General Internal Medicine, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, USA. ruben.amarasingham@phhs.org.
authors.forename: Ruben
authors.initials: R
authors.lastname: Amarasingham
authors.affiliation: Texas Health Resources, Dallas, USA.
authors.forename: Ferdinand
authors.initials: F
authors.lastname: Velasco
authors.affiliation: Parkland Center for Clinical Innovation, 8435 Stemmons Freeway, Suite 1150, Dallas, TX, 75247, USA.
authors.forename: Bin
authors.initials: B
authors.lastname: Xie
authors.affiliation: Parkland Center for Clinical Innovation, 8435 Stemmons Freeway, Suite 1150, Dallas, TX, 75247, USA.
authors.forename: Christopher
authors.initials: C
authors.lastname: Clark
authors.affiliation: Parkland Center for Clinical Innovation, 8435 Stemmons Freeway, Suite 1150, Dallas, TX, 75247, USA.
authors.forename: Ying
authors.initials: Y
authors.lastname: Ma
authors.affiliation: Division of Biostatistics, Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, USA.
authors.forename: Song
authors.initials: S
authors.lastname: Zhang
authors.affiliation: Office of Quality Improvement and Safety, University of Southern California Price School of Public Policy, Los Angeles, USA.
authors.forename: Deepa
authors.initials: D
authors.lastname: Bhat
authors.affiliation: Parkland Center for Clinical Innovation, 8435 Stemmons Freeway, Suite 1150, Dallas, TX, 75247, USA.
authors.forename: Brian
authors.initials: B
authors.lastname: Lucena
authors.affiliation: Schaeffer Center for Health Policy & Economics, University of Southern California Price School of Public Policy, Los Angeles, USA.|Department of Community and Family Medicine, Duke University School of Medicine, Durham, USA.|Duke Fuqua School of Business, Durham, USA.
authors.forename: Marco
authors.initials: M
authors.lastname: Huesch
authors.affiliation: Division of General Internal Medicine, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, USA.
authors.forename: Ethan A
authors.initials: EA
authors.lastname: Halm
mesh_terms: D000328:Adult; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006801:Humans; D008962:Models, Theoretical; D009026:Mortality; D010351:Patient Discharge / Q000706:statistics & numerical data*; D010359:Patient Readmission / Q000706:statistics & numerical data*; D018570:Risk Assessment; D013781:Texas