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 Clinical decision-making, knowledge support systems, and theory

Ruben Amarasingham, Ferdinand Velasco, Bin Xie, Christopher Clark, Ying Ma, Song Zhang, Deepa Bhat, Brian Lucena, Marco Huesch, Ethan A. Halm

Research output: Contribution to journalArticle

30 Citations (Scopus)

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.

Original languageEnglish (US)
Article number39
JournalBMC medical informatics and decision making
Volume15
Issue number1
DOIs
StatePublished - May 20 2015

Fingerprint

Electronic Health Records
Medicine
Centers for Medicare and Medicaid Services (U.S.)
Mortality
Patient Readmission
Internal Medicine
Hospitalization
Demography
Clinical Decision-Making
Health

All Science Journal Classification (ASJC) codes

  • Health Policy
  • Health Informatics

Cite this

Amarasingham, Ruben ; Velasco, Ferdinand ; Xie, Bin ; Clark, Christopher ; Ma, Ying ; Zhang, Song ; Bhat, Deepa ; Lucena, Brian ; Huesch, Marco ; Halm, Ethan A. / 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 Clinical decision-making, knowledge support systems, and theory. In: BMC medical informatics and decision making. 2015 ; Vol. 15, No. 1.
@article{f1d88289cf2f4bc7a3aeb76e50b3fbc4,
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 Clinical decision-making, knowledge support systems, and theory",
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.",
author = "Ruben Amarasingham and Ferdinand Velasco and Bin Xie and Christopher Clark and Ying Ma and Song Zhang and Deepa Bhat and Brian Lucena and Marco Huesch and Halm, {Ethan A.}",
year = "2015",
month = "5",
day = "20",
doi = "10.1186/s12911-015-0162-6",
language = "English (US)",
volume = "15",
journal = "BMC Medical Informatics and Decision Making",
issn = "1472-6947",
publisher = "BioMed Central",
number = "1",

}

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 Clinical decision-making, knowledge support systems, and theory. / Amarasingham, Ruben; Velasco, Ferdinand; Xie, Bin; Clark, Christopher; Ma, Ying; Zhang, Song; Bhat, Deepa; Lucena, Brian; Huesch, Marco; Halm, Ethan A.

In: BMC medical informatics and decision making, Vol. 15, No. 1, 39, 20.05.2015.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Electronic medical record-based multicondition models to predict the risk of 30 day readmission or death among adult medicine patients

T2 - Validation and comparison to existing models Clinical decision-making, knowledge support systems, and theory

AU - Amarasingham, Ruben

AU - Velasco, Ferdinand

AU - Xie, Bin

AU - Clark, Christopher

AU - Ma, Ying

AU - Zhang, Song

AU - Bhat, Deepa

AU - Lucena, Brian

AU - Huesch, Marco

AU - Halm, Ethan A.

PY - 2015/5/20

Y1 - 2015/5/20

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84931075602&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84931075602&partnerID=8YFLogxK

U2 - 10.1186/s12911-015-0162-6

DO - 10.1186/s12911-015-0162-6

M3 - Article

C2 - 25991003

AN - SCOPUS:84931075602

VL - 15

JO - BMC Medical Informatics and Decision Making

JF - BMC Medical Informatics and Decision Making

SN - 1472-6947

IS - 1

M1 - 39

ER -