Evidence map›Paper›PMID 31339906›Full record

ArticlePloS one2019

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

Alain Bernard, Pierre-Emmanuel Falcoz, Pascal Antoine Thomas, Caroline Rivera, Laurent Brouchet, Jean Marc Baste, Marc Puyraveau, Catherine Quantin, Pierre Benoit Pages, Marcel Dahan

Abstract readComparative Study
In one paragraph

Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Let's talk about complications.Surgical neurology international · 2025
    Article
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  8. Article
  9. Thoracic surgery in France.Journal of thoracic disease · 2022
    Review
  10. Article
  11. Revue des maladies respiratoires · 2020
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Alain BernardDepartment of Thoracic Surgery, Dijon University Hospital, Dijon, France.ORCID 0000-0002-5367-0097
Pierre-Emmanuel FalcozDepartment of Thoracic Surgery, CHRU Strasbourg, Strasbourg, France.
Pascal Antoine ThomasDepartment of Thoracic Surgery, Hopital-Nord-APHM, Aix-Marseille University, Marseille, France.
Caroline RiveraDepartment of Thoracic Surgery, Bayonne Hospital, Bayonne, France.
Laurent BrouchetDepartment of Thoracic Surgery, Hopital Larrey, CHU Toulouse, Toulouse, France.
Jean Marc BasteDepartment of Thoracic Surgery, CHU Rouen, Rouen, France.
Marc PuyraveauDepartment of Biostatistics and Epidemiology CHU Besançon, Besançon, France.
Catherine QuantinDepartment of Biostatistics and Medical Informatics, Dijon University Hospital, Dijon, France.ORCID 0000-0001-5134-9411
Pierre Benoit PagesDepartment of Thoracic Surgery, Dijon University Hospital, Dijon, France.
Marcel DahanDepartment of Thoracic Surgery, Hopital Larrey, CHU Toulouse, Toulouse, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

methodsWe 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.

resultsOf 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.

conclusionWe 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.

Indexed as

Databases, FactualDiagnosis-Related GroupsHospital AdministrationHospitalsOutliers, DRGFemaleHospital MortalityHumansLogistic ModelsMaleModels, TheoreticalRisk Adjustment

Identifiers

PMID31339906
PMCPMC6655697

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.