Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients Derivation and Validation of a Prediction Model
Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients Derivation and Validation of a Prediction Model
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DOI：
10.1001/jamainternmed.2013.3023
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发表时间：
2013-04-22
影响因子：
39
通讯作者：
Schnipper, Jeffrey L.
中科院分区：
文献类型：
--
作者：
Donze, Jacques;Aujesky, Drahomir;Schnipper, Jeffrey L.
重要性：由于减少再入院的有效干预措施通常实施起来很昂贵，因此预测潜在可避免的再入院的评分可能有助于锁定最有可能受益的患者。目的：利用出院前可获得的行政和临床数据，推导并内部验证一个潜在可避免的30天医院再入院的预测模型。设计：回顾性队列研究。地点：马萨诸塞州波士顿的学术医疗中心。参与者：2009年7月1日至2010年6月30日期间从任何医疗服务机构出院的所有患者。主要结果测量：使用基于管理数据（SQLape）的经过验证的计算机算法确定了伙伴医疗保健网络中3家医院可能避免的30天再入院。使用多变量逻辑回归开发了一个简单的评分，随机选择三分之二的样本作为衍生队列，三分之一作为验证队列。结果：在10731例符合条件的出院患者中，2398例（22.3%）再次入院30天，其中879例（8.5%）被确定为可能避免的出院患者。预测评分确定了7个独立因素，称为HOSPITAL评分：出院时的血红蛋白、肿瘤科出院时的钠水平、指标入院时的程序、指标入院类型、过去12个月的入院次数和住院时间。在验证集中，26.7%的患者被归类为高风险，估计潜在可避免的再入院风险为18.0%（观察值为18.2%）。医院评分具有公平的区分力（C统计量为0.71），具有良好的校正性。结论和意义：这个简单的预测模型确定了出院前可能避免的30天再入院的风险。这个评分有可能很容易地识别出可能需要更多强化过渡护理干预的患者。
Importance: Because effective interventions to reduce hospital readmissions are often expensive to implement, a score to predict potentially avoidable readmissions may help target the patients most likely to benefit.Objective: To derive and internally validate a prediction model for potentially avoidable 30- day hospital readmissions in medical patients using administrative and clinical data readily available prior to discharge.Design: Retrospective cohort study.Setting: Academic medical center in Boston, Massachusetts. Participants: All patient discharges from any medical services between July 1, 2009, and June 30, 2010.Main Outcome Measures: Potentially avoidable 30-day readmissions to 3 hospitals of the Partners Health-Care network were identified using a validated computerized algorithm based on administrative data (SQLape). A simple score was developed using multivariable logistic regression, with two-thirds of the sample randomly selected as the derivation cohort and one-third as the validation cohort.Results: Among 10 731 eligible discharges, 2398 discharges (22.3%) were followed by a 30- day readmission, of which 879 (8.5% of all discharges) were identified as potentially avoidable. The prediction score identified 7 independent factors, referred to as the HOSPITAL score: hemoglobin at discharge, discharge from an oncology service, sodium level at discharge, procedure during the index admission, index type of admission, number of admissions during the last 12 months, and length of stay. In the validation set, 26.7% of the patients were classified as high risk, with an estimated potentially avoidable readmission risk of 18.0% (observed, 18.2%). The HOSPITAL score had fair discriminatory power (C statistic, 0.71) and had good calibration.Conclusions and Relevance: This simple prediction model identifies before discharge the risk of potentially avoidable 30- day readmission in medical patients. This score has potential to easily identify patients who may need more intensive transitional care interventions.