Common sampling and modeling approaches to analyzing readmission risk that ignore clustering produce misleading results.
Common sampling and modeling approaches to analyzing readmission risk that ignore clustering produce misleading results.
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DOI：
10.1186/s12874-020-01162-0
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发表时间：
2020-11-25
影响因子：
4
通讯作者：
Rubin DJ
中科院分区：
文献类型：
--
作者：
Zhao H;Tanner S;Golden SH;Fisher SG;Rubin DJ
关于如何对住院病例进行抽样和分析多个变量以模拟再入院风险，几乎没有共识。本研究的目的是比较再入院率和预测模型的准确性，基于不同的采样和多变量建模方法。我们对2004年1月1日至2012年12月31日期间从城市学术医疗中心出院的17，284例成人糖尿病患者（44，203例）进行了回顾性队列研究。全因30天再入院的模型由四种策略开发：使用每例患者首次出院（LR-第一）的逻辑回归，使用所有出院（LR-所有）的逻辑回归，使用所有出院的广义估计方程（GEE），和使用所有出院的聚类加权（CWGEE）。开发了多组模型，并在一系列样本量范围内进行了内部验证。再入院率为10.2%之间的第一次放电和20.3%之间的所有放电，揭示了采样只有第一次放电低估了人口的再入院率。出院次数与再入院次数高度相关（r = 0.87，P < 0.001）。考虑到GEE和CWGEE的聚类，模型性能的估计值比LR-all更保守。LR首先产生了错误乐观的Brier分数。模型性能在6000-8000次放电的样品下不稳定，在较大的样品中稳定。GEE和CWGEE在较大样本中的表现优于较小样本。医院再入院风险模型应基于所有出院，而不仅仅是每个患者的第一次出院，并利用考虑聚类数据的方法。在线版本包含补充材料，可通过10.1186/s12874-020-01162-0获得。
There is little consensus on how to sample hospitalizations and analyze multiple variables to model readmission risk. The purpose of this study was to compare readmission rates and the accuracy of predictive models based on different sampling and multivariable modeling approaches. We conducted a retrospective cohort study of 17,284 adult diabetes patients with 44,203 discharges from an urban academic medical center between 1/1/2004 and 12/31/2012. Models for all-cause 30-day readmission were developed by four strategies: logistic regression using the first discharge per patient (LR-first), logistic regression using all discharges (LR-all), generalized estimating equations (GEE) using all discharges, and cluster-weighted (CWGEE) using all discharges. Multiple sets of models were developed and internally validated across a range of sample sizes. The readmission rate was 10.2% among first discharges and 20.3% among all discharges, revealing that sampling only first discharges underestimates a population’s readmission rate. Number of discharges was highly correlated with number of readmissions (r = 0.87, P < 0.001). Accounting for clustering with GEE and CWGEE yielded more conservative estimates of model performance than LR-all. LR-first produced falsely optimistic Brier scores. Model performance was unstable below samples of 6000–8000 discharges and stable in larger samples. GEE and CWGEE performed better in larger samples than in smaller samples. Hospital readmission risk models should be based on all discharges as opposed to just the first discharge per patient and utilize methods that account for clustered data. The online version contains supplementary material available at 10.1186/s12874-020-01162-0.
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影响因子：
4.9
作者：
Bennett, Kevin J.;Probst, Janice C.;Glover, Saundra H.
通讯作者：
Glover, Saundra H.
DOI：
10.1056/nejmsa1702321
发表时间：
2017-09-14
期刊：
The New England journal of medicine
影响因子：
--
作者：
Krumholz HM;Wang K;Lin Z;Dharmarajan K;Horwitz LI;Ross JS;Drye EE;Bernheim SM;Normand ST
通讯作者：
Normand ST
影响因子：
4
作者：
Bouwmeester W;Twisk JW;Kappen TH;van Klei WA;Moons KG;Vergouwe Y
通讯作者：
Vergouwe Y
影响因子：
39
作者：
Donze, Jacques;Aujesky, Drahomir;Schnipper, Jeffrey L.
通讯作者：
Schnipper, Jeffrey L.
影响因子：
5
作者：
Hanley, JA;Negassa, A;Forrester, JE
通讯作者：
Forrester, JE