Division of General Internal Medicine, University of Alberta, Edmonton, Canada.
Am Heart J. 2012 Sep;164(3):365-72. doi: 10.1016/j.ahj.2012.06.010. Epub 2012 Aug 17.
The accuracy of current models to predict the risk of unplanned readmission or death after a heart failure (HF) hospitalization is uncertain.
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.
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.
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".
目前的模型预测心力衰竭(HF)住院后计划外再入院或死亡风险的准确性尚不确定。
我们将艾伯塔省的四个行政数据库联系起来,以确定在 1999 年 4 月至 2009 年期间,所有 HF 住院后存活出院的成年人。我们为每位患者随机选择一次护理,并评估五个基于行政数据的模型(已发表的 4 个,新的 1 个)预测出院后 30 天内死亡或计划外再入院风险的准确性。
在 10 年内,59652 名成年人(平均年龄 76 岁,50%为女性)在 HF 住院后出院。出院后 30 天内,11199 人(19%)死亡或计划外再入院。所有 5 种行政数据模型在该结果上均具有中等的区分能力(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)认可的模型均未在预测 30 天内出院后死亡或计划外再入院方面,对 Charlson 评分有显著改善。然而,一种新的模型,纳入了指数住院期间的长度、年龄、Charlson 评分以及过去 6 个月内急诊就诊次数(LaCE 指数),在 Charlson 评分方面的净重新分类改善了 20.5%(95%置信区间,18.4%-22.5%),在 CMS 再入院模型方面改善了 19.1%(95%置信区间,17.1%-21.2%)。
没有一个行政数据库模型足够准确,可以用来识别哪些 HF 患者在出院时需要额外的资源。纳入住院时间的模型(如 LaCE)似乎优于当前 CMS 认可的模型,用于对“出院后 30 天内死亡或再入院”的结果进行风险调整。