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

被引:56
作者
Amarasingham, Ruben [1 ,2 ]
Velasco, Ferdinand [3 ]
Xie, Bin [1 ]
Clark, Christopher [1 ]
Ma, Ying [1 ]
Zhang, Song [4 ]
Bhat, Deepa [5 ]
Lucena, Brian [1 ]
Huesch, Marco [6 ,7 ,8 ]
Halm, Ethan A. [2 ]
机构
[1] Parkland Ctr Clin Innovat, Dallas, TX 75247 USA
[2] Univ Texas SW Med Ctr Dallas, Dept Internal Med, Div Gen Internal Med, Dallas, TX 75390 USA
[3] Texas Hlth Resources, Dallas, TX USA
[4] Univ Texas SW Med Ctr Dallas, Dept Clin Sci, Div Biostat, Dallas, TX 75390 USA
[5] Univ So Calif, Price Sch Publ Policy, Off Qual Improvement & Safety, Los Angeles, CA USA
[6] Univ So Calif, Price Sch Publ Policy, Schaeffer Ctr Hlth Policy & Econ, Los Angeles, CA USA
[7] Duke Univ, Sch Med, Dept Community & Family Med, Durham, NC USA
[8] Duke Fuqua Sch Business, Durham, NC USA
来源
BMC MEDICAL INFORMATICS AND DECISION MAKING | 2015年 / 15卷
关键词
Readmission; Predictive model; All-cause readmission; Electronic medical record; HEART-FAILURE PATIENTS; HOSPITAL READMISSION; CARE; METAANALYSIS; PERFORMANCE; OUTCOMES; POLICY; RATES;
D O I
10.1186/s12911-015-0162-6
中图分类号
R-058 [];
学科分类号
摘要
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.
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页数:8
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