An Automated Model Using Electronic Medical Record Data Identifies Patients With Cirrhosis at High Risk for Readmission

被引:67
作者
Singal, Amit G. [1 ,2 ,3 ]
Rahimi, Robert S. [1 ,2 ]
Clark, Christopher [4 ]
Ma, Ying [4 ]
Cuthbert, Jennifer A. [1 ,2 ]
Rockey, Don C. [1 ,2 ,5 ]
Amarasingham, Ruben [2 ,3 ,4 ]
机构
[1] Univ Texas SW Med Ctr Dallas, Div Digest & Liver Dis, Dallas, TX 75390 USA
[2] Univ Texas SW Med Ctr Dallas, Dept Internal Med, Dallas, TX 75390 USA
[3] Univ Texas Southwestern, Dept Clin Sci, Dallas, TX USA
[4] Parkland Hlth & Hosp Syst, Ctr Clin Innovat, Dallas, TX USA
[5] Med Univ S Carolina, Dept Internal Med, Charleston, SC 29425 USA
基金
美国国家卫生研究院;
关键词
Rehospitalization; Risk Model; Liver Disease; Quality of Care; Hepatic Informatics; CONGESTIVE-HEART-FAILURE; QUALITY-OF-CARE; LIVER-DISEASE; MORTALITY; HOSPITALS; PROGRAM; DEATH;
D O I
10.1016/j.cgh.2013.03.022
中图分类号
R57 [消化系及腹部疾病];
学科分类号
摘要
BACKGROUND & AIMS: Patients with cirrhosis have 1-month rates of readmission as high as 35%. Early identification of high-risk patients could permit interventions to reduce readmission. The aim of our study was to construct an automated 30-day readmission risk model for cirrhotic patients using electronic medical record (EMR) data available early during hospitalization. METHODS: We identified patients with cirrhosis admitted to a large safety-net hospital from January 2008 through December 2009. A multiple logistic regression model for 30-day rehospitalization was developed using medical and socioeconomic factors available within 48 hours of admission and tested on a validation cohort. Discrimination was assessed using receiver operator characteristic curve analysis. RESULTS: We identified 836 cirrhotic patients with 1291 unique admission encounters. Rehospitalization occurred within 30 days for 27% of patients. Significant predictors of 30-day readmission included the number of address changes in the prior year (odds ratio [OR], 1.13; 95% confidence interval [CI], 1.05-1.21), number of admissions in the prior year (OR, 1.14; 95% CI, 1.05-1.24), Medicaid insurance (OR, 1.53; 95% CI, 1.10 -2.13), thrombocytopenia (OR, 0.50; 95% CI, 0.35-0.72), low level of alanine aminotransferase (OR, 2.56; 95% CI, 1.09 -6.00), anemia (OR, 1.63; 95% CI, 1.17-2.27), hyponatremia (OR, 1.78; 95% CI, 1.14 -2.80), and Model for End-stage Liver Disease score (OR, 1.04; 95% CI, 1.01-1.06). The risk model predicted 30-day readmission, with c-statistics of 0.68 (95% CI, 0.64 -0.72) and 0.66 (95% CI, 0.59 -0.73) in the derivation and validation cohorts, respectively. CONCLUSIONS: Clinical and social factors available early during admission and extractable from an EMR predicted 30-day readmission in cirrhotic patients with moderate accuracy. Decision support tools that use EMR-automated data are useful for risk stratification of patients with cirrhosis early during hospitalization.
引用
收藏
页码:1335 / +
页数:8
相关论文
共 25 条
[11]   A model to predict survival in patients with end-stage liver disease [J].
Kamath, PS ;
Wiesner, RH ;
Malinchoc, M ;
Kremers, W ;
Therneau, TM ;
Kosberg, CL ;
D'Amico, G ;
Dickson, ER ;
Kim, WR .
HEPATOLOGY, 2001, 33 (02) :464-470
[12]   Increasing Prevalence of HCC and Cirrhosis in Patients With Chronic Hepatitis C Virus Infection [J].
Kanwal, Fasiha ;
Tuyen Hoang ;
Kramer, Jennifer R. ;
Asch, Steven M. ;
Goetz, Matthew Bidwell ;
Zeringue, Angelique ;
Richardson, Peter ;
El-Serag, Hashem B. .
GASTROENTEROLOGY, 2011, 140 (04) :1182-+
[13]   Health-Related Quality of Life Predicts Mortality in Patients With Advanced Chronic Liver Disease [J].
Kanwal, Fasiha ;
Gralnek, Ian M. ;
Hays, Ron D. ;
Zeringue, Angelique ;
Durazo, Francisco ;
Han, Steven B. ;
Saab, Sammy ;
Bolus, Roger ;
Spiegel, Brennan M. R. .
CLINICAL GASTROENTEROLOGY AND HEPATOLOGY, 2009, 7 (07) :793-799
[14]   Readmission after hospitalization for congestive heart failure among Medicare beneficiaries [J].
Krumholz, HM ;
Parent, EM ;
Tu, N ;
Vaccarino, V ;
Wang, Y ;
Radford, MJ ;
Hennen, J .
ARCHIVES OF INTERNAL MEDICINE, 1997, 157 (01) :99-104
[15]   The Association of Alanine Transaminase With Aging, Frailty, and Mortality [J].
Le Couteur, David G. ;
Blyth, Fiona M. ;
Creasey, Helen M. ;
Handelsman, David J. ;
Naganathan, Vasi ;
Sambrook, Philip N. ;
Seibel, Markus J. ;
Waite, Louise M. ;
Cumming, Robert G. .
JOURNALS OF GERONTOLOGY SERIES A-BIOLOGICAL SCIENCES AND MEDICAL SCIENCES, 2010, 65 (07) :712-717
[16]   Prognosis and prognostic research: what, why, and how? [J].
Moons, Karel G. M. ;
Royston, Patrick ;
Vergouwe, Yvonne ;
Grobbee, Diederick E. ;
Altman, Douglas G. .
BMJ-BRITISH MEDICAL JOURNAL, 2009, 338 :1317-1320
[17]   Use of Administrative Claims Data for Identifying Patients with Cirrhosis [J].
Nehra, Mahendra S. ;
Ma, Ying ;
Clark, Christopher ;
Amarasingham, Ruben ;
Rockey, Don C. ;
Singal, Amit G. .
JOURNAL OF CLINICAL GASTROENTEROLOGY, 2013, 47 (05) :E50-E54
[18]   Interventions to prevent readmission for congestive heart failure [J].
Riegel, B ;
Naylor, M ;
Stewart, S ;
McMurray, JJV ;
Rich, MW .
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2004, 291 (23) :2816-2816
[19]   Statistical models and patient predictors of readmission for heart failure - A systematic review [J].
Ross, Joseph S. ;
Mulvey, Gregory K. ;
Stauffer, Brett ;
Patlolla, Vishnu ;
Bernheim, Susannah M. ;
Keenan, Patricia S. ;
Krumholz, Harlan M. .
ARCHIVES OF INTERNAL MEDICINE, 2008, 168 (13) :1371-1386
[20]   Validation, updating and impact of clinical prediction rules: A review [J].
Toll, D. B. ;
Janssen, K. J. M. ;
Vergouwe, Y. ;
Moons, K. G. M. .
JOURNAL OF CLINICAL EPIDEMIOLOGY, 2008, 61 (11) :1085-1094