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

BackgroundThere 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.MethodsData 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.ResultsAmong 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).ConclusionsA 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12911-015-0162-6) contains supplementary material, which is available to authorized users.

Vital Signs Are Still Vital: Instability on Discharge and the Risk of Post-Discharge Adverse Outcomes

“…[19][20][21][22][23][24][25][26][27][28][29] To assess a dose-response relationship between an increasing number of instabilities and adverse outcomes, we used the Cochran-Armitage test for trend. We accounted for clustering of patients by hospital using generalized estimating equations.…”

Predicting 30‐Day Hospital Readmissions in Acute Myocardial Infarction: The AMI “READMITS” (Renal Function, Elevated Brain Natriuretic Peptide, Age, Diabetes Mellitus , Nonmale Sex , Intervention with Timely Percutaneous Coronary Intervention, and Low Systolic Blood Pressure) Score

“…All hospitals used the Epic EHR (Epic Systems Corporation, Verona, WI). Details of this cohort have been previously published 11, 14, 15, 16, 17. We used data from 2009 to 2010, before hospital‐based readmission interventions became widespread, to ensure that AMI cohorts across all 6 hospitals were comparable.…”

“…We included all variables from our previously published multicondition EHR readmission models as candidate predictors, including sociodemographics, prior utilization, Charlson comorbidity index, select laboratory and vital sign abnormalities, length of stay, hospital complications (eg, venous thromboembolism), and disposition status 11, 14, 15. Laboratories and vital signs were categorized and/or dichotomized based on cut points identified in previous studies 11, 14, 15.…”

“…We compared the first‐day and full‐stay AMI models to each other as well as to the corresponding multicondition EHR models our group has separately developed and the CMS AMI model derived from administrative claims data 13, 14, 15. We compared each existing model's performance using the C‐statistic, integrated discrimination index, and net reclassification index (NRI) using the AMI‐specific models as references 23.…”

Incidence, Predictors, and Outcomes of Hospital‐Acquired Anemia

“…Details of this cohort have been published. 11,12 This study included consecutive hospitalizations among adults age 18 years or older who were discharged from a medicine inpatient service with any diagnosis. We excluded hospitalizations by individuals who were anemic within the first 24 hours of admission (hematocrit less than 36% for women and less than 40% for men), were missing a hematocrit value within the first 24 hours of hospitalization or a repeat hematocrit value prior to discharge, had a hospitalization in the preceding 30 days (ie, index hospitalization was considered a readmission), died in the hospital, were transferred to another hospital, or left against medical advice.…”