A Machine Learning Algorithm to Identify Patients with Possible Non-Dialysis-Dependent CKD

Journal of the American Society of Nephrology, vol. 32, pp. 716–717

Abstract

Background: The DAKOTAH study is a retrospective study of patients with non-dialysis-dependent chronic kidney disease (NDD CKD) in France based on data from the Echantillon Généraliste des Bénéficiaires database. A stepwise machine learning approach was used to identify patients with possible NDD CKD who could not be captured using the NDD CKD case definition (Figure). Methods: First, the ‘potential CKD’ population was designated as patients with a diagnosis of diabetes, cardiovascular disease or hypertension, or with ≥3 prescriptions for antidiabetic and/or antihypertensive drugs, during 2012-2017. Second, an unsupervised algorithm was trained to identify patients very likely to have CKD (‘possible CKD’) in the potential CKD population. Similarity between patients was based on CKD-related variables: sex; number and duration of hospitalizations for renal failure; number of GP visits; medications; and biological exams. A distance metric between patients was defined based on these variables, and patients having similar characteristics were positioned close to one another. The algorithm learned to construct a spherical boundary around the non-CKD population, to create a decision rule for possible CKD versus non-CKD, with outliers considered possible CKD. Results: The algorithm was validated by application to both the potential CKD population and a confirmed CKD patient pool. From the potential and confirmed CKD populations, 21% and 65% of patients were classified as possible CKD, respectively. Similarities were observed between the two groups regarding hospitalizations and selected biological exams. Conclusions: This machine learning-derived decision rule could be a tool to identify undiagnosed patients with NDD CKD. Funding: Commercial Support - Astellas Pharma Inc.

Authors 7

1. Thierry Lobbedez

   Centre Hospitalier Universitaire de Caen Normandie

   Affiliation as printed

   University Hospital, Caen, France;

2. Karim Dardim
   Affiliation as printed

   Association Limousine pour l'Utilisation du Rein Artificiel à Domicile (ALURAD), Limoges, France;

3. Arnaud Panes
   Affiliation as printed

   HEVA, Lyon, France;

4. Gwendoline Poinsot
   Affiliation as printed

   HEVA, Lyon, France;

5. Jérôme Fernandes

   Centre Hospitalier de la Côte Basque

   Affiliation as printed

   Centre Hospitalier de la Côte Basque, Bayonne, France;

6. Laurence Dubel
   Affiliation as printed

   Astellas Pharma Europe, Levallois-Perret, France;

7. Josephine Wolfram Aachen Astellas Pharma Europe B.V

   Astellas Pharma (Netherlands)

   Affiliation as printed

   Astellas Pharma Europe B.V., Leiden, Netherlands