Lin Kun-Pei, Chen Pei-Chun, Huang Ling-Ya, Mao Hsiu-Chen, Chan Ding-Cheng Derrick
From the Department of Geriatrics and Gerontology (K-PL, H-CM, D-CC); Department of Internal Medicine (K-PL, D-CC), National Taiwan University Hospital; Institute of Epidemiology and Preventive Medicine (P-CC), College of Public Health, National Taiwan University; Clinical Informatics and Medical Statistics Research Center (P-CC); Department of Neurology (P-CC), Chang Gung University College of Medicine, Chang Gung Memorial Hospital, Chiayi Branch, Chiayi, Taiwan; Chang-Gung University; National Taiwan University Health Data Research Center (L-YH); and National Taiwan University Hospital Chu-Tung Branch (D-CC), Chu-Tung, Taiwan.
Medicine (Baltimore). 2016 Apr;95(16):e3484. doi: 10.1097/MD.0000000000003484.
Recognizing potentially avoidable hospital readmission and admissions are important health care quality issues. We develop prediction models for inpatient readmission and outpatient admission to hospitals for older adults In the retrospective cohort study with 2 million sampling file of the National Health Insurance Research Database in Taiwan, older adults (aged ≥65 y/o) with a first admission in 2008 were enrolled in the inpatient cohort (N = 39,156). The outpatient cohort included subjects who had ≥1 outpatient visit in 2008 (N = 178,286). Each cohort was split into derivation (3/4) and validation (1/4) data set. Primary outcome of the inpatient cohort: 30-day readmission from the date of discharge. The outpatient cohort included hospital admissions within the 1-year follow-up period. Candidate risk factors include demographics, comorbidities, and previous health care utilizations. Series of logistic regression models were applied with area under the receiver operating curves (AUCs) to identify the best model. Roughly 1 of 7 (14.6%) of the inpatients was readmitted within 30 days, and 1 of 5 (19.1%) of the outpatient cohort was admitted within 1 year. Age, education, use of home health care, and selected comorbidities (e.g., cancer with metastasis) were included in the final model. The AUC of the inpatient readmission model was 0.655 (95% confidence interval [CI] 0.646-0.664) and outpatient admission model was 0.642 (95% CI 0.639-0.646). Predictive performance was maintained in both validation data sets. The goodness-to-fit model demonstrated good calibration in both groups. We developed and validated practical clinical prediction models for inpatient readmission and outpatient admissions for general older adults with indicators easily obtained from an administrative data set.
识别潜在可避免的医院再入院和入院情况是重要的医疗质量问题。我们为老年人的住院再入院和门诊入院情况开发了预测模型。在一项回顾性队列研究中，我们使用了台湾国民健康保险研究数据库中的200万份抽样文件，纳入了2008年首次入院的老年人（年龄≥65岁）作为住院队列（N = 39,156）。门诊队列包括在2008年有≥1次门诊就诊的受试者（N = 178,286）。每个队列被分为推导数据集（3/4）和验证数据集（1/4）。住院队列的主要结局：出院日期起30天内的再入院情况。门诊队列包括1年随访期内的医院入院情况。候选风险因素包括人口统计学特征、合并症和既往医疗服务利用情况。应用一系列逻辑回归模型及受试者操作特征曲线下面积（AUC）来确定最佳模型。大约七分之一（14.6%）的住院患者在30天内再次入院，五分之一（19.1%）的门诊队列患者在1年内入院。最终模型纳入了年龄、教育程度、家庭医疗护理的使用情况以及特定合并症（如伴有转移的癌症）。住院再入院模型的AUC为0.655（95%置信区间[CI] 0.646 - 0.664），门诊入院模型的AUC为0.642（95%CI 0.639 - 0.646）。在两个验证数据集中预测性能均得以保持。拟合优度模型在两组中均显示出良好的校准。我们为一般老年人的住院再入院和门诊入院情况开发并验证了实用的临床预测模型，其指标可轻松从管理数据集中获取。
