Evidence map›Paper›PMID 36938092›Full record

ArticleKidney international reports2023

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

Elizabeth R Wan, Daniela Iancu, Emma Ashton, Keith Siew, Barian Mohidin, Chih-Chien Sung, China Nagano, Detlef Bockenhauer, Shih-Hua Lin, Kandai Nozu and 1 more

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In one paragraph

Article in Kidney international reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 87% of its field
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 0 citations in OpenAlex.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

11 authors at 5 institutions in 3 countries.

Elizabeth R WanDepartment of Renal Medicine, University College London, London, UK.
Daniela IancuDepartment of Renal Medicine, University College London, London, UK.
Emma AshtonNorth East Thames Regional Genetics Service Laboratories, Great Ormond Street Hospital for Children National Health Service Foundation Trust, London, UK.
Keith SiewDepartment of Renal Medicine, University College London, London, UK.
Barian MohidinDepartment of Renal Medicine, University College London, London, UK.
Chih-Chien SungDivision of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
China NaganoDepartment of Pediatrics, Kobe University Graduate School of Medicine, Kobe, Japan.
Detlef BockenhauerDepartment of Renal Medicine, University College London, London, UK.
Shih-Hua LinDivision of Nephrology, Department of Medicine, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
Kandai NozuDepartment of Pediatrics, Kobe University Graduate School of Medicine, Kobe, Japan.
Stephen B WalshDepartment of Renal Medicine, University College London, London, UK.
University College London · GBKobe University · JPNational Defense Medical Center · TWNational Health Service · GBTri-Service General Hospital · TW

Funding

Wellcome Trust
6 · The paper itself

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. London and Taipei data sets were combined to train machine learning (ML) algorithms, which were then tested on the Kobe data set. Results: 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 Conclusion: 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.

Indexed as

diagnosisgenotypingmachine learningtubulopathy

Identifiers

PMID36938092
PMCPMC10014379
OpenAlexW4312204531

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LicenceCC BY
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