Machine Learning to Identify Genetic Salt-Losing Tubulopathies in Hypokalemic Patients.
diagnosis
genotyping
machine learning
tubulopathy
Journal
Kidney international reports
ISSN: 2468-0249
Titre abrégé: Kidney Int Rep
Pays: United States
ID NLM: 101684752
Informations de publication
Date de publication:
Mar 2023
Mar 2023
Historique:
received:
03
10
2022
revised:
29
11
2022
accepted:
12
12
2022
entrez:
20
3
2023
pubmed:
21
3
2023
medline:
21
3
2023
Statut:
epublish
Résumé
Clinically distinguishing patients with the inherited salt-losing tubulopathies (SLTs), Gitelman or Bartter syndrome (GS or BS) from other causes of hypokalemia (LK) patients is difficult, and genotyping is costly. We decided to identify clinical characteristics that differentiate SLTs from LK. A total of 66 hypokalemic patients with possible SLTs were recruited to a prospective observational cohort study at the University College London Renal Tubular Clinic, London. All patients were genotyped for pathogenic variants in genes which cause SLTs; 39 patients had pathogenic variants in genes causing SLTs. We obtained similar data sets from cohorts in Taipei and Kobe, as follows: the combined data set comprised 419 patients; 291 had genetically confirmed SLT. London and Taipei data sets were combined to train machine learning (ML) algorithms, which were then tested on the Kobe data set. Single biochemical variables (e.g., plasma renin) were significantly, but inconsistently, different between SLTs and LK in all cohorts. A decision table algorithm using serum bicarbonate and urinary sodium excretion (FE ML algorithms can differentiate true SLT in the context of a specialist clinic with some accuracy. However, based on routine biochemistry, the accuracy is insufficient to make genotyping redundant.
Identifiants
pubmed: 36938092
doi: 10.1016/j.ekir.2022.12.008
pii: S2468-0249(22)01904-0
pmc: PMC10014379
doi:
Types de publication
Journal Article
Langues
eng
Pagination
556-565Informations de copyright
© 2022 International Society of Nephrology. Published by Elsevier Inc.
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