Shadmi Efrat, Flaks-Manov Natalie, Hoshen Moshe, Goldman Orit, Bitterman Haim, Balicer Ran D
*Faculty of Social Welfare and Health Sciences, University of Haifa, Mount Carmel †Clalit Research Institute, Chief Physician's Office, Clalit Health Services, Tel Aviv ‡Faculty of Medicine, Technion Institute of Technology, Haifa §Epidemiology Department, Faculty of Health Sciences, Ben-Gurion University, Beer-Sheva, Israel.
Med Care. 2015 Mar;53(3):283-9. doi: 10.1097/MLR.0000000000000315.
Readmission prevention should begin as early as possible during the index admission. Early identification may help target patients for within-hospital readmission prevention interventions.
To develop and validate a 30-day readmission prediction model using data from electronic health records available before the index admission.
Retrospective cohort study of admissions between January 1 and March 31, 2010.
Adult enrollees of Clalit Health Services, an integrated delivery system, admitted to an internal medicine ward in any hospital in Israel.
All-cause 30-day emergency readmissions. A prediction score based on before admission electronic health record and administrative data (the Preadmission Readmission Detection Model-PREADM) was developed using a preprocessing variable selection step with decision trees and neural network algorithms. Admissions with a recent prior hospitalization were excluded and automatically flagged as "high-risk." Selected variables were entered into multivariable logistic regression, with a derivation (two-thirds) and a validation cohort (one-third).
The derivation dataset comprised 17,334 admissions, of which 2913 (16.8%) resulted in a 30-day readmission. The PREADM includes 11 variables: chronic conditions, prior health services use, body mass index, and geographical location. The c-statistic was 0.70 in the derivation set and of 0.69 in the validation set. Adding length of stay did not change the discriminatory power of the model.
The PREADM is designed for use by health plans for early high-risk case identification, presenting discriminatory power better than or similar to that of previously reported models, most of which include data available only upon discharge.
再入院预防应在首次入院期间尽早开始。早期识别有助于确定院内再入院预防干预措施的目标患者。
利用首次入院前可用的电子健康记录数据，开发并验证一个30天再入院预测模型。
对2010年1月1日至3月31日期间的入院病例进行回顾性队列研究。
Clalit Health Services综合医疗服务体系的成年参保者，他们入住以色列任何一家医院的内科病房。
全因30天紧急再入院情况。基于入院前电子健康记录和管理数据开发了一个预测评分（入院前再入院检测模型-PREADM），使用决策树和神经网络算法进行预处理变量选择步骤。排除近期有过住院治疗的入院病例，并自动将其标记为“高风险”。将选定变量纳入多变量逻辑回归分析，分为推导队列（三分之二）和验证队列（三分之一）。
推导数据集包括17334例入院病例，其中2913例（16.8%）在30天内再次入院。PREADM包括11个变量：慢性病、先前的医疗服务使用情况、体重指数和地理位置。推导组的c统计量为0.70，验证组为0.69。增加住院时间并未改变模型的辨别能力。
PREADM旨在供健康计划用于早期高风险病例识别，其辨别能力优于或类似于先前报告的模型，其中大多数模型仅包括出院时才可用的数据。
