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PLoS One. 2019 Jul 24;14(7):e0219672. doi: 10.1371/journal.pone.0219672. eCollection 2019.

Comparison of Epithor clinical national database and medico-administrative database to identify the influence of case-mix on the estimation of hospital outliers.

Author information

1
Department of Thoracic Surgery, Dijon University Hospital, Dijon, France.
2
Department of Thoracic Surgery, CHRU Strasbourg, Strasbourg, France.
3
Department of Thoracic Surgery, Hopital-Nord-APHM, Aix-Marseille University, Marseille, France.
4
Department of Thoracic Surgery, Bayonne Hospital, Bayonne, France.
5
Department of Thoracic Surgery, Hopital Larrey, CHU Toulouse, Toulouse, France.
6
Department of Thoracic Surgery, CHU Rouen, Rouen, France.
7
Department of Biostatistics and Epidemiology CHU Besançon, Besançon, France.
8
Department of Biostatistics and Medical Informatics, Dijon University Hospital, Dijon, France.
9
INSERM, CIC 1432, Clinical Investigation Center, clinical epidemiology/clinical trials unit, Dijon University Hospital, University of Burgundy, Dijon, France.
10
INSERM UMR 866, Dijon University Hospital, University of Burgundy, Dijon, France.

Abstract

BACKGROUND:

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.

METHODS:

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.

RESULTS:

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.

CONCLUSION:

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.

Conflict of interest statement

The authors have declared that no competing interests exist.

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