Oanh Kieu Nguyen1,2, Anil N Makam1,2, Christopher Clark3, Song Zhang4, Bin Xie3, Ferdinand Velasco5, Ruben Amarasingham1,2,3, Ethan A Halm1,2. 1. Division of General Internal Medicine, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas. 2. Division of Outcomes and Health Services Research, Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas. 3. Parkland Center for Clinical Innovation (PCCI), Dallas, Texas. 4. Division of Biostatistics, Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas. 5. Texas Health Resources, Dallas, Texas.
Abstract
BACKGROUND: Incorporating clinical information from the full hospital course may improve prediction of 30-day readmissions. OBJECTIVE: To develop an all-cause readmissions risk-prediction model incorporating electronic health record (EHR) data from the full hospital stay, and to compare "full-stay" model performance to a "first day" and 2 other validated models, LACE (includes Length of stay, Acute [nonelective] admission status, Charlson Comorbidity Index, and Emergency department visits in the past year), and HOSPITAL (includes Hemoglobin at discharge, discharge from Oncology service, Sodium level at discharge, Procedure during index hospitalization, Index hospitalization Type [nonelective], number of Admissions in the past year, and Length of stay). DESIGN: Observational cohort study. SUBJECTS: All medicine discharges between November 2009 and October 2010 from 6 hospitals in North Texas, including safety net, teaching, and nonteaching sites. MEASURES: Thirty-day nonelective readmissions were ascertained from 75 regional hospitals. RESULTS: Among 32,922 admissions (validation = 16,430), 12.7% were readmitted. In addition to many first-day factors, we identified hospital-acquired Clostridium difficile infection (adjusted odds ratio [AOR]: 2.03, 95% confidence interval [CI]: 1.18-3.48), vital sign instability on discharge (AOR: 1.25, 95% CI: 1.15-1.36), hyponatremia on discharge (AOR: 1.34, 95% CI: 1.18-1.51), and length of stay (AOR: 1.06, 95% CI: 1.04-1.07) as significant predictors. The full-stay model had better discrimination than other models though the improvement was modest (C statistic 0.69 vs 0.64-0.67). It was also modestly better in identifying patients at highest risk for readmission (likelihood ratio +2.4 vs. 1.8-2.1) and in reclassifying individuals (net reclassification index 0.02-0.06). CONCLUSIONS: Incorporating clinically granular EHR data from the full hospital stay modestly improves prediction of 30-day readmissions. Given limited improvement in prediction despite incorporation of data on hospital complications, clinical instabilities, and trajectory, our findings suggest that many factors influencing readmissions remain unaccounted for. Further improvements in readmission models will likely require accounting for psychosocial and behavioral factors not currently captured by EHRs. Journal of Hospital Medicine 2016;11:473-480.
BACKGROUND: Incorporating clinical information from the full hospital course may improve prediction of 30-day readmissions. OBJECTIVE: To develop an all-cause readmissions risk-prediction model incorporating electronic health record (EHR) data from the full hospital stay, and to compare "full-stay" model performance to a "first day" and 2 other validated models, LACE (includes Length of stay, Acute [nonelective] admission status, Charlson Comorbidity Index, and Emergency department visits in the past year), and HOSPITAL (includes Hemoglobin at discharge, discharge from Oncology service, Sodium level at discharge, Procedure during index hospitalization, Index hospitalization Type [nonelective], number of Admissions in the past year, and Length of stay). DESIGN: Observational cohort study. SUBJECTS: All medicine discharges between November 2009 and October 2010 from 6 hospitals in North Texas, including safety net, teaching, and nonteaching sites. MEASURES: Thirty-day nonelective readmissions were ascertained from 75 regional hospitals. RESULTS: Among 32,922 admissions (validation = 16,430), 12.7% were readmitted. In addition to many first-day factors, we identified hospital-acquired Clostridium difficileinfection (adjusted odds ratio [AOR]: 2.03, 95% confidence interval [CI]: 1.18-3.48), vital sign instability on discharge (AOR: 1.25, 95% CI: 1.15-1.36), hyponatremia on discharge (AOR: 1.34, 95% CI: 1.18-1.51), and length of stay (AOR: 1.06, 95% CI: 1.04-1.07) as significant predictors. The full-stay model had better discrimination than other models though the improvement was modest (C statistic 0.69 vs 0.64-0.67). It was also modestly better in identifying patients at highest risk for readmission (likelihood ratio +2.4 vs. 1.8-2.1) and in reclassifying individuals (net reclassification index 0.02-0.06). CONCLUSIONS: Incorporating clinically granular EHR data from the full hospital stay modestly improves prediction of 30-day readmissions. Given limited improvement in prediction despite incorporation of data on hospital complications, clinical instabilities, and trajectory, our findings suggest that many factors influencing readmissions remain unaccounted for. Further improvements in readmission models will likely require accounting for psychosocial and behavioral factors not currently captured by EHRs. Journal of Hospital Medicine 2016;11:473-480.
Authors: Salomeh Keyhani; Laura J Myers; Eric Cheng; Paul Hebert; Linda S Williams; Dawn M Bravata Journal: Ann Intern Med Date: 2014-12-02 Impact factor: 25.391
Authors: Amy J H Kind; Steve Jencks; Jane Brock; Menggang Yu; Christie Bartels; William Ehlenbach; Caprice Greenberg; Maureen Smith Journal: Ann Intern Med Date: 2014-12-02 Impact factor: 25.391
Authors: Alice J Watson; Julia O'Rourke; Kamal Jethwani; Aurel Cami; Theodore A Stern; Joseph C Kvedar; Henry C Chueh; Adrian H Zai Journal: Psychosomatics Date: 2011 Jul-Aug Impact factor: 2.386
Authors: Amit G Singal; Robert S Rahimi; Christopher Clark; Ying Ma; Jennifer A Cuthbert; Don C Rockey; Ruben Amarasingham Journal: Clin Gastroenterol Hepatol Date: 2013-04-13 Impact factor: 11.382
Authors: Oanh Kieu Nguyen; Anil N Makam; Christopher Clark; Song Zhang; Bin Xie; Ferdinand Velasco; Ruben Amarasingham; Ethan A Halm Journal: J Gen Intern Med Date: 2016-08-08 Impact factor: 5.128
Authors: Steven P Gerke; Jon D Agley; Cynthia Wilson; Ruth A Gassman; Philip Forys; David W Crabb Journal: Am J Med Qual Date: 2018-01-18 Impact factor: 1.852
Authors: Jeremy Albright; Farwa Batool; Robert K Cleary; Andrew J Mullard; Edward Kreske; Jane Ferraro; Scott E Regenbogen Journal: Surg Endosc Date: 2018-08-27 Impact factor: 4.584