Department of Thoracic Surgery, Dijon University Hospital, Dijon, France.
Department of Thoracic Surgery, CHRU Strasbourg, Strasbourg, France.
PLoS One. 2019 Jul 24;14(7):e0219672. doi: 10.1371/journal.pone.0219672. eCollection 2019.
The national Epithor database was initiated in 2003 in France. Fifteen years on, a quality assessment of the recorded data seemed necessary. This study examines the completeness of the data recorded in Epithor through a comparison with the French PMSI database, which is the national medico-administrative reference database. The aim of this study was to demonstrate the influence of data quality with respect to identifying 30-day mortality hospital outliers.
We used each hospital's individual FINESS code to compare the number of pulmonary resections and deaths recorded in Epithor to the figures found in the PMSI. Centers were classified into either the good-quality data (GQD) group or the low-quality data (LQD) group. To demonstrate the influence of case-mix quality on the ranking of centers with low-quality data, we used 2 methods to estimate the standardized mortality rate (SMR). For the first (SMR1), the expected number of deaths per hospital was estimated with risk-adjustment models fitted with low-quality data. For the second (SMR2), the expected number of deaths per hospital was estimated with a linear predictor for the LQD group using the coefficients of a logistic regression model developed from the GQD group.
Of the hospitals that use Epithor, 25 were classified in the GQD group and 75 in the LQD group. The 30-day mortality rate was 2.8% (n = 300) in the GQD group vs. 1.9% (n = 181) in the LQD group (P <0.0001). The between-hospital differences in SMR1 appeared substantial (interquartile range (IQR) 0-1.036), and they were even higher in SMR2 (IQR 0-1.19). SMR1 identified 7 hospitals as high-mortality outliers. SMR2 identified 4 hospitals as high-mortality outliers. Some hospitals went from non-outlier to high mortality and vice-versa. Kappa values were roughly 0.46 and indicated moderate agreement.
We found that most hospitals provided Epithor with high-quality data, but other hospitals needed to improve the quality of the information provided. Quality control is essential for this type of database and necessary for the unbiased adjustment of regression models.
全国 Epithor 数据库于 2003 年在法国启动。15 年后，对记录数据进行质量评估似乎是必要的。本研究通过与法国 PMSI 数据库（国家医疗管理参考数据库）进行比较，检查了 Epithor 中记录数据的完整性。本研究的目的是通过识别 30 天死亡率的医院异常值，证明数据质量的影响。
我们使用每个医院的个人 FINESS 代码将 Epithor 中记录的肺切除术数量和死亡人数与 PMSI 中的数字进行比较。中心分为高质量数据（GQD）组和低质量数据（LQD）组。为了证明病例组合质量对低质量数据中心排名的影响，我们使用了两种方法来估计标准化死亡率（SMR）。对于第一种（SMR1），使用低质量数据拟合的风险调整模型估计每个医院的预期死亡人数。对于第二种（SMR2），使用 GQD 组开发的逻辑回归模型的系数，为 LQD 组的线性预测器估计每个医院的预期死亡人数。
在使用 Epithor 的医院中，25 家被归类为 GQD 组，75 家被归类为 LQD 组。GQD 组的 30 天死亡率为 2.8%（n=300），而 LQD 组为 1.9%（n=181）（P<0.0001）。SMR1 中的医院间差异似乎很大（四分位距（IQR）0-1.036），在 SMR2 中甚至更高（IQR 0-1.19）。SMR1 将 7 家医院确定为高死亡率异常值。SMR2 将 4 家医院确定为高死亡率异常值。一些医院的死亡率从非异常值变为高死亡率，反之亦然。Kappa 值约为 0.46，表明中度一致性。
我们发现大多数医院为 Epithor 提供了高质量的数据，但其他医院需要提高提供信息的质量。此类数据库的质量控制至关重要，也是回归模型无偏调整所必需的。
