Division for Health Services, Norwegian Institute of Public Health, Oslo, Norway.
Division for Physical and Mental Health, Norwegian Institute of Public Health, Oslo, Norway.
PLoS One. 2018 Apr 13;13(4):e0195248. doi: 10.1371/journal.pone.0195248. eCollection 2018.
A common quality indicator for monitoring and comparing hospitals is based on death within 30 days of admission. An important use is to determine whether a hospital has higher or lower mortality than other hospitals. Thus, the ability to identify such outliers correctly is essential. Two approaches for detection are: 1) calculating the ratio of observed to expected number of deaths (OE) per hospital and 2) including all hospitals in a logistic regression (LR) comparing each hospital to a form of average over all hospitals. The aim of this study was to compare OE and LR with respect to correctly identifying 30-day mortality outliers. Modifications of the methods, i.e., variance corrected approach of OE (OE-Faris), bias corrected LR (LR-Firth), and trimmed mean variants of LR and LR-Firth were also studied.
To study the properties of OE and LR and their variants, we performed a simulation study by generating patient data from hospitals with known outlier status (low mortality, high mortality, non-outlier). Data from simulated scenarios with varying number of hospitals, hospital volume, and mortality outlier status, were analysed by the different methods and compared by level of significance (ability to falsely claim an outlier) and power (ability to reveal an outlier). Moreover, administrative data for patients with acute myocardial infarction (AMI), stroke, and hip fracture from Norwegian hospitals for 2012-2014 were analysed.
None of the methods achieved the nominal (test) level of significance for both low and high mortality outliers. For low mortality outliers, the levels of significance were increased four- to fivefold for OE and OE-Faris. For high mortality outliers, OE and OE-Faris, LR 25% trimmed and LR-Firth 10% and 25% trimmed maintained approximately the nominal level. The methods agreed with respect to outlier status for 94.1% of the AMI hospitals, 98.0% of the stroke, and 97.8% of the hip fracture hospitals.
We recommend, on the balance, LR-Firth 10% or 25% trimmed for detection of both low and high mortality outliers.
监测和比较医院的常用质量指标是基于入院后 30 天内的死亡情况。其一个重要用途是确定医院的死亡率是否高于或低于其他医院。因此，正确识别此类异常值至关重要。两种检测方法是：1）计算每个医院的实际死亡人数与预期死亡人数（OE）的比值；2）将所有医院纳入逻辑回归（LR），将每个医院与所有医院的平均值进行比较。本研究的目的是比较 OE 和 LR 在正确识别 30 天死亡率异常值方面的表现。我们还研究了方法的修改，即 OE 的方差校正方法（OE-Faris）、LR 的偏差校正方法（LR-Firth）以及 LR 和 LR-Firth 的修剪均值变体。
为了研究 OE 和 LR 及其变体的特性，我们通过从已知异常值状态（低死亡率、高死亡率、非异常值）的医院生成患者数据来进行模拟研究。对具有不同医院数量、医院容量和死亡率异常值状态的模拟场景数据进行分析，比较不同方法，并通过显著性水平（错误地声称异常值的能力）和功效（揭示异常值的能力）进行比较。此外，我们还对 2012 年至 2014 年挪威医院的急性心肌梗死（AMI）、中风和髋部骨折患者的行政数据进行了分析。
OE 和 OE-Faris 对低死亡率和高死亡率异常值均未达到名义（检验）显著性水平。对于低死亡率异常值，OE 和 OE-Faris 的显著性水平增加了四倍至五倍。对于高死亡率异常值，OE 和 OE-Faris、LR 25%修剪和 LR-Firth 10%和 25%修剪基本保持了名义水平。对于 AMI 医院，94.1%的方法在异常值状态上达成一致；对于中风，98.0%的方法达成一致；对于髋部骨折，97.8%的方法达成一致。
我们建议，LR-Firth 10%或 25%修剪是检测低死亡率和高死亡率异常值的首选方法。
