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Could a Multi-Marker and Machine Learning Approach Help Stratify Patients with Heart Failure?
Half of the patients with heart failure (HF) have preserved ejection fraction (HFpEF). To date, there are no specific markers to distinguish this subgroup. The main objective of this work was to stratify HF patients using current biochemical markers coupled with clinical data. The cohort study inclu...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8538712/ https://www.ncbi.nlm.nih.gov/pubmed/34684033 http://dx.doi.org/10.3390/medicina57100996 |
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author | Lotierzo, Manuela Bruno, Romain Finan-Marchi, Amanda Huet, Fabien Kalmanovich, Eran Rodrigues, Glaucy Dupuy, Anne-Marie Adda, Jérôme Piquemal, David Richard, Sylvain Cristol, Jean-Paul Roubille, François |
author_facet | Lotierzo, Manuela Bruno, Romain Finan-Marchi, Amanda Huet, Fabien Kalmanovich, Eran Rodrigues, Glaucy Dupuy, Anne-Marie Adda, Jérôme Piquemal, David Richard, Sylvain Cristol, Jean-Paul Roubille, François |
author_sort | Lotierzo, Manuela |
collection | PubMed |
description | Half of the patients with heart failure (HF) have preserved ejection fraction (HFpEF). To date, there are no specific markers to distinguish this subgroup. The main objective of this work was to stratify HF patients using current biochemical markers coupled with clinical data. The cohort study included HFpEF (n = 24) and heart failure with reduced ejection fraction (HFrEF) (n = 34) patients as usually considered in clinical practice based on cardiac imaging (EF ≥ 50% for HFpEF; EF < 50% for HFrEF). Routine blood tests consisted of measuring biomarkers of renal and heart functions, inflammation, and iron metabolism. A multi-test approach and analysis of peripheral blood samples aimed to establish a computerized Machine Learning strategy to provide a blood signature to distinguish HFpEF and HFrEF. Based on logistic regression, demographic characteristics and clinical biomarkers showed no statistical significance to differentiate the HFpEF and HFrEF patient subgroups. Hence a multivariate factorial discriminant analysis, performed blindly using the data set, allowed us to stratify the two HF groups. Consequently, a Machine Learning (ML) strategy was developed using the same variables in a genetic algorithm approach. ML provided very encouraging explorative results when considering the small size of the samples applied. The accuracy and the sensitivity were high for both validation and test groups (69% and 100%, 64% and 75%, respectively). Sensitivity was 100% for the validation and 75% for the test group, whereas specificity was 44% and 55% for the validation and test groups because of the small number of samples. Lastly, the precision was acceptable, with 58% in the validation and 60% in the test group. Combining biochemical and clinical markers is an excellent entry to develop a computer classification tool to diagnose HFpEF. This translational approach is a springboard for improving new personalized treatment methods and identifying “high-yield” populations for clinical trials. |
format | Online Article Text |
id | pubmed-8538712 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85387122021-10-24 Could a Multi-Marker and Machine Learning Approach Help Stratify Patients with Heart Failure? Lotierzo, Manuela Bruno, Romain Finan-Marchi, Amanda Huet, Fabien Kalmanovich, Eran Rodrigues, Glaucy Dupuy, Anne-Marie Adda, Jérôme Piquemal, David Richard, Sylvain Cristol, Jean-Paul Roubille, François Medicina (Kaunas) Brief Report Half of the patients with heart failure (HF) have preserved ejection fraction (HFpEF). To date, there are no specific markers to distinguish this subgroup. The main objective of this work was to stratify HF patients using current biochemical markers coupled with clinical data. The cohort study included HFpEF (n = 24) and heart failure with reduced ejection fraction (HFrEF) (n = 34) patients as usually considered in clinical practice based on cardiac imaging (EF ≥ 50% for HFpEF; EF < 50% for HFrEF). Routine blood tests consisted of measuring biomarkers of renal and heart functions, inflammation, and iron metabolism. A multi-test approach and analysis of peripheral blood samples aimed to establish a computerized Machine Learning strategy to provide a blood signature to distinguish HFpEF and HFrEF. Based on logistic regression, demographic characteristics and clinical biomarkers showed no statistical significance to differentiate the HFpEF and HFrEF patient subgroups. Hence a multivariate factorial discriminant analysis, performed blindly using the data set, allowed us to stratify the two HF groups. Consequently, a Machine Learning (ML) strategy was developed using the same variables in a genetic algorithm approach. ML provided very encouraging explorative results when considering the small size of the samples applied. The accuracy and the sensitivity were high for both validation and test groups (69% and 100%, 64% and 75%, respectively). Sensitivity was 100% for the validation and 75% for the test group, whereas specificity was 44% and 55% for the validation and test groups because of the small number of samples. Lastly, the precision was acceptable, with 58% in the validation and 60% in the test group. Combining biochemical and clinical markers is an excellent entry to develop a computer classification tool to diagnose HFpEF. This translational approach is a springboard for improving new personalized treatment methods and identifying “high-yield” populations for clinical trials. MDPI 2021-09-22 /pmc/articles/PMC8538712/ /pubmed/34684033 http://dx.doi.org/10.3390/medicina57100996 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Brief Report Lotierzo, Manuela Bruno, Romain Finan-Marchi, Amanda Huet, Fabien Kalmanovich, Eran Rodrigues, Glaucy Dupuy, Anne-Marie Adda, Jérôme Piquemal, David Richard, Sylvain Cristol, Jean-Paul Roubille, François Could a Multi-Marker and Machine Learning Approach Help Stratify Patients with Heart Failure? |
title | Could a Multi-Marker and Machine Learning Approach Help Stratify Patients with Heart Failure? |
title_full | Could a Multi-Marker and Machine Learning Approach Help Stratify Patients with Heart Failure? |
title_fullStr | Could a Multi-Marker and Machine Learning Approach Help Stratify Patients with Heart Failure? |
title_full_unstemmed | Could a Multi-Marker and Machine Learning Approach Help Stratify Patients with Heart Failure? |
title_short | Could a Multi-Marker and Machine Learning Approach Help Stratify Patients with Heart Failure? |
title_sort | could a multi-marker and machine learning approach help stratify patients with heart failure? |
topic | Brief Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8538712/ https://www.ncbi.nlm.nih.gov/pubmed/34684033 http://dx.doi.org/10.3390/medicina57100996 |
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