Nguyen Oanh Kieu, Makam Anil N, Clark Christopher, Zhang Song, Xie Bin, Velasco Ferdinand, Amarasingham Ruben, Halm Ethan A
Division of General Internal Medicine, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, Texas.
Division of Outcomes and Health Services Research, Department of Clinical Sciences, UT Southwestern Medical Center, Dallas, Texas.
J Hosp Med. 2016 Jul;11(7):473-80. doi: 10.1002/jhm.2568. Epub 2016 Feb 29.
Incorporating clinical information from the full hospital course may improve prediction of 30-day readmissions.
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).
Observational cohort study.
All medicine discharges between November 2009 and October 2010 from 6 hospitals in North Texas, including safety net, teaching, and nonteaching sites.
Thirty-day nonelective readmissions were ascertained from 75 regional hospitals.
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).
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. © 2016 Society of Hospital Medicine.
纳入整个住院过程中的临床信息可能会改善对30天再入院情况的预测。
开发一个纳入整个住院期间电子健康记录（EHR）数据的全因再入院风险预测模型，并将“全程”模型的性能与“首日”模型以及另外两个经过验证的模型（LACE模型，包括住院时间、急性（非选择性）入院状态、Charlson合并症指数和过去一年的急诊科就诊次数；HOSPITAL模型，包括出院时血红蛋白、肿瘤科出院、出院时血钠水平、本次住院期间的手术、本次住院类型（非选择性）、过去一年的入院次数和住院时间）进行比较。
观察性队列研究。
2009年11月至2010年10月期间，来自北德克萨斯州6家医院（包括安全网医院、教学医院和非教学医院）的所有内科出院患者。
从75家地区医院确定30天非选择性再入院情况。
在32922例入院患者中（验证组 = 16430例），12.7%的患者再次入院。除了许多首日因素外，我们还确定医院获得性艰难梭菌感染（调整后比值比[AOR]：2.03，95%置信区间[CI]：1.18 - 3.48）、出院时生命体征不稳定（AOR：1.25，95%CI：1.15 - 1.36）、出院时低钠血症（AOR：1.34，95%CI：1.18 - 1.51）和住院时间（AOR：1.06，95%CI：1.04 - 1.07）是显著的预测因素。尽管改善幅度不大，但“全程”模型的区分度优于其他模型（C统计量为0.69，而其他模型为0.64 - 0.67）。在识别再入院风险最高的患者方面（似然比为 +2.4，而其他模型为1.8 - 2.1）以及对个体进行重新分类方面（净重新分类指数为0.02 - 0.06），该模型也略胜一筹。
纳入整个住院期间详细的临床EHR数据能适度改善对30天再入院情况的预测。尽管纳入了关于医院并发症、临床不稳定情况和病程的数据，但预测方面的改善有限，我们的研究结果表明，许多影响再入院的因素仍未得到考虑。再入院模型的进一步改进可能需要考虑目前EHR未涵盖的社会心理和行为因素。《医院医学杂志》2016年；11：473 - 480。©2016医院医学协会。
