TY - JOUR
T1 - An algorithm for identifying chronic kidney disease in the French national health insurance claims database
AU - group REDSIAM
AU - Mansouri, Imène
AU - Raffray, Maxime
AU - Lassalle, Mathilde
AU - de Vathaire, Florent
AU - Fresneau, Brice
AU - Fayech, Chiraz
AU - Lazareth, Hélène
AU - Haddy, Nadia
AU - Bayat, Sahar
AU - Couchoud, Cécile
N1 - Publisher Copyright:
© 2022
PY - 2022/7/1
Y1 - 2022/7/1
N2 - 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.
AB - 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.
KW - Algorithms
KW - Chronic kidney disease
KW - Healthcare claims databases
KW - Validation studies
UR - http://www.scopus.com/inward/record.url?scp=85133231815&partnerID=8YFLogxK
U2 - 10.1016/j.nephro.2022.03.003
DO - 10.1016/j.nephro.2022.03.003
M3 - Article
C2 - 35773142
AN - SCOPUS:85133231815
SN - 1769-7255
VL - 18
SP - 255
EP - 262
JO - Nephrologie et Therapeutique
JF - Nephrologie et Therapeutique
IS - 4
ER -