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

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...

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Autores principales: Wan, Elizabeth R., Iancu, Daniela, Ashton, Emma, Siew, Keith, Mohidin, Barian, Sung, Chih-Chien, Nagano, China, Bockenhauer, Detlef, Lin, Shih-Hua, Nozu, Kandai, Walsh, Stephen B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10014379/
https://www.ncbi.nlm.nih.gov/pubmed/36938092
http://dx.doi.org/10.1016/j.ekir.2022.12.008
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author Wan, Elizabeth R.
Iancu, Daniela
Ashton, Emma
Siew, Keith
Mohidin, Barian
Sung, Chih-Chien
Nagano, China
Bockenhauer, Detlef
Lin, Shih-Hua
Nozu, Kandai
Walsh, Stephen B.
author_facet Wan, Elizabeth R.
Iancu, Daniela
Ashton, Emma
Siew, Keith
Mohidin, Barian
Sung, Chih-Chien
Nagano, China
Bockenhauer, Detlef
Lin, Shih-Hua
Nozu, Kandai
Walsh, Stephen B.
author_sort Wan, Elizabeth R.
collection PubMed
description 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(Na)) achieved a classification accuracy of 74%. This was superior to all the single biochemical variables identified previously. 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.
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spelling pubmed-100143792023-03-16 Machine Learning to Identify Genetic Salt-Losing Tubulopathies in Hypokalemic Patients Wan, Elizabeth R. Iancu, Daniela Ashton, Emma Siew, Keith Mohidin, Barian Sung, Chih-Chien Nagano, China Bockenhauer, Detlef Lin, Shih-Hua Nozu, Kandai Walsh, Stephen B. Kidney Int Rep Clinical Research 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(Na)) achieved a classification accuracy of 74%. This was superior to all the single biochemical variables identified previously. 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. Elsevier 2022-12-24 /pmc/articles/PMC10014379/ /pubmed/36938092 http://dx.doi.org/10.1016/j.ekir.2022.12.008 Text en © 2022 International Society of Nephrology. Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Clinical Research
Wan, Elizabeth R.
Iancu, Daniela
Ashton, Emma
Siew, Keith
Mohidin, Barian
Sung, Chih-Chien
Nagano, China
Bockenhauer, Detlef
Lin, Shih-Hua
Nozu, Kandai
Walsh, Stephen B.
Machine Learning to Identify Genetic Salt-Losing Tubulopathies in Hypokalemic Patients
title Machine Learning to Identify Genetic Salt-Losing Tubulopathies in Hypokalemic Patients
title_full Machine Learning to Identify Genetic Salt-Losing Tubulopathies in Hypokalemic Patients
title_fullStr Machine Learning to Identify Genetic Salt-Losing Tubulopathies in Hypokalemic Patients
title_full_unstemmed Machine Learning to Identify Genetic Salt-Losing Tubulopathies in Hypokalemic Patients
title_short Machine Learning to Identify Genetic Salt-Losing Tubulopathies in Hypokalemic Patients
title_sort machine learning to identify genetic salt-losing tubulopathies in hypokalemic patients
topic Clinical Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10014379/
https://www.ncbi.nlm.nih.gov/pubmed/36938092
http://dx.doi.org/10.1016/j.ekir.2022.12.008
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