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An algorithm for identifying chronic kidney disease in the French national health insurance claims database.
Mansouri, Imène; Raffray, Maxime; Lassalle, Mathilde; de Vathaire, Florent; Fresneau, Brice; Fayech, Chiraz; Lazareth, Hélène; Haddy, Nadia; Bayat, Sahar; Couchoud, Cécile.
Afiliación
  • Mansouri I; EPI-PHARE (French National Agency for Medicines and Health Products Safety [ANSM] and French National Health Insurance [CNAM]), Saint-Denis, France; Center for research epidemiology and population health, Radiation epidemiology team, Université Paris-Saclay, Université Paris-Sud, UVSQ, 94805 Villeju
  • Raffray M; University Rennes, EHESP, REPERES (Recherche en pharmaco-épidémiologie et recours aux soins)-EA 7449, 35000 Rennes, France.
  • Lassalle M; REIN registry, Agence de la biomédecine, 1, avenue du Stade de France, 93212 Saint-Denis-La Plaine, France.
  • de Vathaire F; Center for research epidemiology and population health, Radiation epidemiology team, Université Paris-Saclay, Université Paris-Sud, UVSQ, 94805 Villejuif, France; Department of children and adolescent oncology, Gustave-Roussy, Université Paris-Saclay, 94805 Villejuif, France.
  • Fresneau B; Department of children and adolescent oncology, Gustave-Roussy, Université Paris-Saclay, 94805 Villejuif, France; Cancer and Radiation, CESP, Unit 1018 Inserm, Villejuif, France.
  • Fayech C; Department of children and adolescent oncology, Gustave-Roussy, Université Paris-Saclay, 94805 Villejuif, France; Cancer and Radiation, CESP, Unit 1018 Inserm, Villejuif, France.
  • Lazareth H; Service Évaluation et Outils pour la Qualité et la Sécurité des Soins, Direction de l'Amélioration de la Qualité et de la Sécurité des Soins, Haute Autorité de santé, Saint-Denis, France.
  • Haddy N; Center for research epidemiology and population health, Radiation epidemiology team, Université Paris-Saclay, Université Paris-Sud, UVSQ, 94805 Villejuif, France; Department of children and adolescent oncology, Gustave-Roussy, Université Paris-Saclay, 94805 Villejuif, France.
  • Bayat S; University Rennes, EHESP, REPERES (Recherche en pharmaco-épidémiologie et recours aux soins)-EA 7449, 35000 Rennes, France.
  • Couchoud C; University Rennes, EHESP, REPERES (Recherche en pharmaco-épidémiologie et recours aux soins)-EA 7449, 35000 Rennes, France; Université Lyon I, CNRS, UMR 5558, Laboratoire de biométrie et biologie évolutive, équipe biostatistique santé, Villeurbanne, France. Electronic address: cecile.couchoud@biomed
Nephrol Ther ; 18(4): 255-262, 2022 Jul.
Article en En | MEDLINE | ID: mdl-35773142
ABSTRACT

BACKGROUND:

Published algorithms for identifying chronic kidney disease in healthcare claims databases have poor performance except in patients with renal replacement therapy. We propose and describe an algorithm to identify all stage chronic kidney disease in a French healthcare claims databases and assessed its performance by using data from the Renal Epidemiology and Information Network registry and the French Childhood Cancer Survivor Study cohort.

METHODS:

A group of experts met several times to define a list of items and combinations of items that could be related to chronic kidney disease. For the French Childhood Cancer Survivor Study cohort, information on confirmed chronic kidney disease cases extracted from medical records was considered the gold standard (KDIGO definition). Sensitivity, specificity, and positive and negative predictive value and kappa coefficients were estimated. The contribution of each component of the algorithm was assessed for 1 and 2 years before the start of renal replacement therapy for confirmed end-stage kidney disease in the Renal Epidemiology and Information Network registry.

RESULTS:

The algorithm's sensitivity was 78%, specificity 97.4%, negative predictive value 98.4% and positive predictive value 68.7% in French Childhood Cancer Survivor Study cohort and the kappa coefficient was 0.79 for agreement with the gold standard. The algorithm 93.6% and 55.1% of confirmed incident end-stage kidney disease cases from the Renal Epidemiology and Information Network registry when considering 1 year and 2 years, respectively, before renal replacement therapy start.

CONCLUSIONS:

The algorithm showed good performance among younger patients and those with end-stage kidney disease in the twol last years prior to renal replacement therapy. Future research will address the ability of the algorithm to detect early chronic kidney disease stages and to classify the severity of chronic kidney disease.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Insuficiencia Renal Crónica / Fallo Renal Crónico Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Nephrol Ther Asunto de la revista: NEFROLOGIA / TERAPEUTICA Año: 2022 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Insuficiencia Renal Crónica / Fallo Renal Crónico Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Nephrol Ther Asunto de la revista: NEFROLOGIA / TERAPEUTICA Año: 2022 Tipo del documento: Article