The steps of constructing and validating an algorithm to identify chronic kidney disease patients in medical administrative databases
Abstract
Chronic kidney disease (CKD) represents a heavy global health burden associated with increased mortality and morbidity and high economic impact. Chronic kidney disease, which is largely asymptomatic and is diagnosed based on laboratory tests, is particularly difficult to identify in medical-administrative databases in the absence of laboratory results and no specific medications or procedures. The aim of this paper is to describe the progressive stages of constructing and validating an algorithm for targeting chronic kidney disease in the French medical administrative databases SNDS . A consortium of experts in nephrology, kidney epidemiology and healthcare claims databases, referred to as group \"REDSIAM Kidney Disease\", collaborated to design a practical algorithm for assessing the probability of chronic kidney disease cases likelihood through a combination of items associated with the CKD care pathway. The performance of the RENALGO-EXPERT algorithm differs significantly depending on the population and the databases used. Sensitivity tends to improve in more at-risk populations. However, at this stage, the results are not very satisfactory. To improve case detection performance and in the hope of capturing weak signals overlooked by experts, a project using machine learning methods was devised, RENALGO-IA.
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BibTeX
@article{couchoud2026steps,
title = {{The steps of constructing and validating an algorithm to identify chronic kidney disease patients in medical administrative databases}},
author = {Cécile Couchoud and Guillaume Bouzillé and Juliette Piveteau and Mathilde Lassalle and Hélène Lazareth and Aghiles Hamroun and Olivier Moranne and Sahar Bayat},
journal = {Journal of Epidemiology and Population Health},
year = {2026},
month = aug,
doi = {10.1016/j.jeph.2026.203370},
url = {https://hal.science/hal-05547536v1},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - The steps of constructing and validating an algorithm to identify chronic kidney disease patients in medical administrative databases
AU - Cécile Couchoud
AU - Guillaume Bouzillé
AU - Juliette Piveteau
AU - Mathilde Lassalle
AU - Hélène Lazareth
AU - Aghiles Hamroun
AU - Olivier Moranne
AU - Sahar Bayat
PY - 2026
DA - 2026/08/31
JO - Journal of Epidemiology and Population Health
DO - 10.1016/j.jeph.2026.203370
UR - https://hal.science/hal-05547536v1
AB - Chronic kidney disease (CKD) represents a heavy global health burden associated with increased mortality and morbidity and high economic impact. Chronic kidney disease, which is largely asymptomatic and is diagnosed based on laboratory tests, is particularly difficult to identify in medical-administrative databases in the absence of laboratory results and no specific medications or procedures. The aim of this paper is to describe the progressive stages of constructing and validating an algorithm for targeting chronic kidney disease in the French medical administrative databases SNDS . A consortium of experts in nephrology, kidney epidemiology and healthcare claims databases, referred to as group \"REDSIAM Kidney Disease\", collaborated to design a practical algorithm for assessing the probability of chronic kidney disease cases likelihood through a combination of items associated with the CKD care pathway. The performance of the RENALGO-EXPERT algorithm differs significantly depending on the population and the databases used. Sensitivity tends to improve in more at-risk populations. However, at this stage, the results are not very satisfactory. To improve case detection performance and in the hope of capturing weak signals overlooked by experts, a project using machine learning methods was devised, RENALGO-IA.
SN - 2950-4333
SN - 2950-4333
ER -