Predicting the risk of unplanned readmission or death within 30 days of discharge after a heart failure hospitalization
Predicting the risk of unplanned readmission or death within 30 days of discharge after a heart failure hospitalization
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DOI：
10.1016/j.ahj.2012.06.010
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发表时间：
2012-09-01
影响因子：
4.8
通讯作者：
van Walraven, Carl
中科院分区：
文献类型：
--
作者：
Au, Anita G.;McAlister, Finlay A.;van Walraven, Carl
背景目前的模型预测心衰（HF）住院后意外再入院或死亡风险的准确性尚不确定。方法我们将阿尔伯塔的四个管理数据库连接起来，以确定1999年4月至2009年4月间HF住院后存活出院的所有成年人。我们随机选择了一个情节的照顾每一个病人，并评估了五个行政数据为基础的模型（4已经出版，1新）的准确性，用于预测死亡或计划外再入院的风险在30天内discharge.Results超过10年，59652成人（平均年龄76，50%的妇女）出院后，HF住院。出院后30天内，11199例（19%）死亡或计划外再入院。所有5个管理数据模型均表现出该结果的中度歧视（c-统计量在0.57和0.61之间）。医疗保险和医疗补助服务中心（CMS）认可的模型在预测出院后30天死亡或计划外再入院的Charlson评分方面均未表现出实质性改善。然而，一个新的模型，包括索引住院时间，年龄，Charlson评分和急诊室就诊次数在过去6个月，（LaCE指数）表现出20.5%的净重新分类改善（95% CI，18.4%-22.5%），改善19.1%（95%可信区间，17.1%-21.2%）结论没有一个管理数据库模型足够准确，可用于识别哪些HF患者需要额外的资源。在调整“出院后30天内死亡或再入院”结局的风险方面，纳入住院时间的模型（如LaCE）似乎上级于当前CMS认可的模型。(Am Heart J 2012;164：365-72.）
Background The accuracy of current models to predict the risk of unplanned readmission or death after a heart failure (HF) hospitalization is uncertain.Methods We linked four administrative databases in Alberta to identify all adults discharged alive after a HF hospitalization between April 1999 and 2009. We randomly selected one episode of care per patient and evaluated the accuracy of five administrative data-based models (4 already published, 1 new) for predicting risk of death or unplanned readmission within 30 days of discharge.Results Over 10 years, 59652 adults (mean age 76, 50% women) were discharged after a HF hospitalization. Within 30 days of discharge, 11199 (19%) died or had an unplanned readmission. All 5 administrative data models exhibited moderate discrimination for this outcome (c-statistic between 0.57 and 0.61). Neither Centers for Medicare and Medicaid Services (CMS)-endorsed model exhibited substantial improvements over the Charlson score for prediction of 30-day post-discharge death or unplanned readmission. However, a new model incorporating length of index hospital stay, age, Charlson score, and number of emergency room visits in the prior 6 months (the LaCE index) exhibited a 20.5% net reclassification improvement (95% CI, 18.4%-22.5%) over the Charlson score and a 19.1% improvement (95% CI, 17.1%-21.2%) over the CMS readmission model.Conclusions None of the administrative database models are sufficiently accurate to be used to identify which HF patients require extra resources at discharge. Models which incorporate length of stay such as the LaCE appear superior to current CMS-endorsed models for risk adjusting the outcome of "death or readmission within 30 days of discharge". (Am Heart J 2012;164:365-72.)