Risk prediction models for hospital readmission: a systematic review.
Risk prediction models for hospital readmission: a systematic review.
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DOI：
10.1001/jama.2011.1515
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发表时间：
2011-10-19
影响因子：
120.7
通讯作者：
Kripalani, Sunil
中科院分区：
文献类型：
--
作者：
Kansagara, Devan;Englander, Honora;Salanitro, Amanda;Kagen, David;Theobald, Cecelia;Freeman, Michele;Kripalani, Sunil
预测医院再入院风险对于确定哪些患者将从护理过渡干预中获益最大，以及出于医院比较的目的对再入院率进行风险标准化具有重要意义。总结经验证的再入院风险预测模型，描述其性能，并评估临床或管理使用的适用性。MEDLINE、CINAHL和科克伦图书馆（截至2011年3月），EMBASE（截至2011年8月），手动检索参考文献列表。双重审查，以确定与医学患者测试的预测模型的英语语言研究，推导和验证队列。数据提取的人口，设置，样本量，随访间隔，再入院率，模型的歧视和校准，使用的数据类型，和数据收集的时间。在审查的7，843篇引文中，26个独特模型的30项研究符合标准。最常见的结果是30天再入院;只有一个模型专门针对可预防的再入院。14个依赖于回顾性管理数据的模型可能用于再入院风险和医院比较的标准化;其中，9个模型在美国大规模人群中进行了测试，区分能力较差（c-统计量0.55 - 0.65）。7个模型可用于识别住院期间早期干预的高风险患者（c-统计量0.56 - 0.72），5个模型可用于出院时（c-统计量0.68 - 0.83）。六项研究在同一人群中比较了不同的模型，其中两项发现功能和社会变量改善了模型歧视。虽然大多数模型纳入了医疗合并症和以前的使用变量，很少检查与整体健康和功能，疾病严重程度，或健康的社会决定因素相关的变量。目前大多数再入院风险预测模型，无论是设计用于比较还是临床目的，都表现不佳。虽然在某些情况下，这种模式可能证明是有用的，但随着使用的日益广泛，需要努力提高其性能。
Predicting hospital readmission risk is of great interest to identify which patients would benefit most from care transition interventions, as well as to risk-standardize readmission rates for purposes of hospital comparison. To summarize validated readmission risk prediction models, describe their performance, and assess suitability for clinical or administrative use. MEDLINE, CINAHL, and Cochrane Library through March 2011, EMBASE through August 2011, and hand search of reference lists. Dual review to identify English language studies of prediction models tested with medical patients, with both derivation and validation cohorts. Data were extracted on the population, setting, sample size, follow-up interval, readmission rate, model discrimination and calibration, type of data used, and timing of data collection. Of 7,843 citations reviewed, 30 studies of 26 unique models met criteria. The most common outcome used was 30-day readmission; only one model specifically addressed preventable readmissions. Fourteen models relying on retrospective administrative data could be potentially used for standardization of readmission risk and hospital comparisons; of these, nine were tested in large US populations and had poor discriminative ability (c-statistics 0.55 – 0.65). Seven models could potentially be used to identify high-risk patients for intervention early during a hospitalization (c-statistics 0.56 – 0.72), and five could be used at hospital discharge (c-statistics 0.68 – 0.83). Six studies compared different models in the same population and two of these found that functional and social variables improved model discrimination. Though most models incorporated medical comorbidity and prior utilization variables, few examined variables associated with overall health and function, illness severity, or social determinants of health. Most current readmission risk prediction models, whether designed for comparative or clinical purposes, perform poorly. Though in certain settings such models may prove useful, efforts to improve their performance are needed as use becomes more widespread.
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影响因子：
7.2
作者：
Halfon, P;Eggli, Y;Burnand, B
通讯作者：
Burnand, B
影响因子：
5.7
作者：
Allaudeen, Nazima;Schnipper, Jeffrey L.;Vidyarthi, Arpana R.
通讯作者：
Vidyarthi, Arpana R.
DOI：
10.1111/j.1532-5415.1993.tb06175.x
发表时间：
1993-08-01
影响因子：
6.3
作者：
BOULT, C;DOWD, B;KRULEWITCH, H
通讯作者：
KRULEWITCH, H
影响因子：
3
作者：
Halfon, Patricia;Eggli, Yves;Burnand, Bernard
通讯作者：
Burnand, Bernard
影响因子：
17.3
作者：
Bottle, Alex;Aylin, Paul;Majeed, Azeem
通讯作者：
Majeed, Azeem