Machine Learning to Identify Genetic Salt-Losing Tubulopathies in Hypokalemic Patients.

ABSTRACT:

Introduction

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.

Methods

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

SUBMITTER: Wan ER 

PROVIDER: S-EPMC10014379 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature