Short Term Survival after Admission for Heart Failure in Sweden: Applying Multilevel Analyses of Discriminatory Accuracy to Evaluate Institutional Performance.
Short Term Survival after Admission for Heart Failure in Sweden: Applying Multilevel Analyses of Discriminatory Accuracy to Evaluate Institutional Performance.
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DOI：
10.1371/journal.pone.0148187
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Merlo J
中科院分区：
文献类型：
--
作者：
Ghith N;Wagner P;Frølich A;Merlo J
医院绩效通常通过分析医院在某些质量指标上的平均差异来评估。结果通常表示为医院方差的质量图表（例如，联赛表、漏斗图）。然而，这些分析很少考虑平均值周围的患者异质性，这对于正确的评估具有根本意义。因此，我们应用一种基于方差分量和判别准确性的创新方法来分析瑞典诊断为心力衰竭（HF）的出院后30天的死亡率。我们分析了2007年至2009年期间在71家医院的565个病房治疗的36，943名45-80岁患者。我们应用单因素和多因素logistic回归分析计算比值比和受试者工作特征下面积（AUC）。我们通过量化类内相关系数（ICC）和通过在多水平回归分析（MLRA）中添加随机效应获得的AUC增量来评估综合医院和病房的影响。最后，将特定病房和医院特征的比值比（OR）与方差的比例变化（PCV）和相反方向的OR比例（POOR）联合解释。总体而言，平均30天死亡率为9%。仅使用关于年龄和先前因不同疾病住院的患者信息，我们获得AUC = 0.727。在加上性别、出生国以及医院和病房级别后，该值几乎没有变化。小型病房和市立医院的平均死亡率较高，但POOR值分别为15%和16%。瑞典的病房和医院总体上表现一致，导致HF后30天死亡率较低。在我们的研究中，对患者既往住院史的了解是30天死亡率的最佳预测因素，而了解患者的性别和出生国或患者接受治疗的地点并不能改善这一信息。
Hospital performance is frequently evaluated by analyzing differences between hospital averages in some quality indicators. The results are often expressed as quality charts of hospital variance (e.g., league tables, funnel plots). However, those analyses seldom consider patients heterogeneity around averages, which is of fundamental relevance for a correct evaluation. Therefore, we apply an innovative methodology based on measures of components of variance and discriminatory accuracy to analyze 30-day mortality after hospital discharge with a diagnosis of Heart Failure (HF) in Sweden. We analyzed 36,943 patients aged 45–80 treated in 565 wards at 71 hospitals during 2007–2009. We applied single and multilevel logistic regression analyses to calculate the odds ratios and the area under the receiver-operating characteristic (AUC). We evaluated general hospital and ward effects by quantifying the intra-class correlation coefficient (ICC) and the increment in the AUC obtained by adding random effects in a multilevel regression analysis (MLRA). Finally, the Odds Ratios (ORs) for specific ward and hospital characteristics were interpreted jointly with the proportional change in variance (PCV) and the proportion of ORs in the opposite direction (POOR). Overall, the average 30-day mortality was 9%. Using only patient information on age and previous hospitalizations for different diseases we obtained an AUC = 0.727. This value was almost unchanged when adding sex, country of birth as well as hospitals and wards levels. Average mortality was higher in small wards and municipal hospitals but the POOR values were 15% and 16% respectively. Swedish wards and hospitals in general performed homogeneously well, resulting in a low 30-day mortality rate after HF. In our study, knowledge on a patient’s previous hospitalizations was the best predictor of 30-day mortality, and this information did not improve by knowing the sex and country of birth of the patient or where the patient was treated.