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Contribution of medico-administrative data to the development of a comorbidity score to predict mortality in End-Stage Renal Disease patients

  • Pladys, Adélaïde1
  • Vigneau, Cécile2, 3
  • Raffray, Maxime1
  • Sautenet, Bénédicte4
  • Gentile, Stéphanie5
  • Couchoud, Cécile6
  • Bayat, Sahar1
  • 1 Univ Rennes, EHESP, REPERES (Recherche en pharmaco-épidémiologie et recours aux soins) – EA 7449, Rennes, F-35000, France , Rennes (France)
  • 2 University of Rennes 1, INSERM, Rennes, U1085-IRSET, France , Rennes (France)
  • 3 CHU Pontchaillou, Department of Nephrology, Rennes, France , Rennes (France)
  • 4 Tours Hospital, Department of Nephrology-Hypertension, Dialysis, Kidney Transplantation, Tours University, INSERM UMR, Tours, 1246 SPHERE, France , Tours (France)
  • 5 University of Aix-Marseille, CEReSS (Centre d’Études et de Recherche sur les Services de Santé et la Qualité de Vie) - EA 3279, Marseille, France , Marseille (France)
  • 6 Renal Epidemiology and Information Network (REIN), Biomedecine Agency, Saint Denis La Plaine, France , Saint Denis La Plaine (France)
Published Article
Scientific Reports
Springer Nature
Publication Date
May 22, 2020
DOI: 10.1038/s41598-020-65612-x
Springer Nature


Comorbidity scores to predict mortality are very useful to facilitate decision-making for personalized patient management. This study aim was to assess the contribution of medico-administrative data in addition to French Renal Epidemiology and Information Network (REIN) data to the development of a risk score to predict the 1-year all-cause mortality in patients with End Stage Renal Disease (ESRD), and to compare it with previous scores. Data from a derivation sample (n = 6336 patients who started dialysis in 2015 in France) obtained by linking the REIN and the French National Health Insurance Information System databases were analyzed with multivariate Cox models to select risk factors to establish the score. A randomly chosen validation sample (n = 2716 patients who started dialysis in 2015) was used to validate the score and to compare it with the comorbidity indexes developed by Wright and Charlson. The ability to predict one-year mortality of the score constructed using REIN data linked to the medico-administrative database was not higher than that of the score constructed using only REIN data (i.e., Rennes score). The Rennes score included five comorbidities, albumin, and age. This score (AUC = 0.794, 95%CI: 0.768–0.821) outperformed both the Wright (AUC = 0.631, 95%CI: 0.621–0.639; p < 0.001) and Charlson (AUC = 0.703, 95%CI: 0.689–0.716; p < 0.001) indexes. Data from the REIN registry alone, collected at dialysis start, are sufficient to develop a risk score that can predict the one-year mortality in patients with ESRD. This simple score might help identifying high risk patients and proposing the most adapted care.

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