Dataset Information

Development of Machine Learning Model for VO_2max Estimation Using a Patch-Type Single-Lead ECG Monitoring Device in Lung Resection Candidates.

ABSTRACT: A cardiopulmonary exercise test (CPET) is essential for lung resection. However, performing a CPET can be challenging. This study aimed to develop a machine learning model to estimate maximal oxygen consumption (VO_2max) using data collected through a patch-type single-lead electrocardiogram (ECG) monitoring device in candidates for lung resection. This prospective, single-center study included 42 patients who underwent a CPET at a tertiary teaching hospital from October 2021 to July 2022. During the CPET, a single-lead ECG monitoring device was applied to all patients, and the results obtained from the machine-learning algorithm using the information extracted from the ECG patch were compared with the CPET results. According to the Bland-Altman plot of measured and estimated VO_2max, the VO_2max values obtained from the machine learning model and the FRIEND equation showed lower differences from the reference value (bias: -0.33 mL·kg^-1·min^-1, bias: 0.30 mL·kg^-1·min^-1, respectively). In subgroup analysis, the developed model demonstrated greater consistency when applied to different maximal stage levels and sexes. In conclusion, our model provides a closer estimation of VO_2max values measured using a CPET than existing equations. This model may be a promising tool for estimating VO_2max and assessing cardiopulmonary reserve in lung resection candidates when a CPET is not feasible.

SUBMITTER: Lee HA

PROVIDER: S-EPMC10648477 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Development of Machine Learning Model for VO<sub>2max</sub> Estimation Using a Patch-Type Single-Lead ECG Monitoring Device in Lung Resection Candidates.

Lee Hyun Ah HA Yu Woosik W Choi Jong Doo JD Lee Young-Sin YS Park Ji Won JW Jung Yun Jung YJ Sheen Seung Soo SS Jung Junho J Haam Seokjin S Kim Sang Hun SH Park Ji Eun JE

Healthcare (Basel, Switzerland) 20231030 21

A cardiopulmonary exercise test (CPET) is essential for lung resection. However, performing a CPET can be challenging. This study aimed to develop a machine learning model to estimate maximal oxygen consumption (VO<sub>2max</sub>) using data collected through a patch-type single-lead electrocardiogram (ECG) monitoring device in candidates for lung resection. This prospective, single-center study included 42 patients who underwent a CPET at a tertiary teaching hospital from October 2021 to July 2 ...[more]

PMID: 37958007

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Project description:BackgroundCardiorespiratory fitness (VO2max) is an excellent predictor of chronic disease morbidity and mortality risk. Guidelines recommend individuals undertake exercise training to improve VO2max for chronic disease reduction. However, there are large inter-individual differences between exercise training responses. This systematic review is aimed at identifying genetic variants that are associated with VO2max trainability.MethodsPeer-reviewed research papers published up until October 2016 from four databases were examined. Articles were included if they examined genetic variants, incorporated a supervised aerobic exercise intervention; and measured VO2max/VO2peak pre and post-intervention.ResultsThirty-five articles describing 15 cohorts met the criteria for inclusion. The majority of studies used a cross-sectional retrospective design. Thirty-two studies researched candidate genes, two used Genome-Wide Association Studies (GWAS), and one examined mRNA gene expression data, in addition to a GWAS. Across these studies, 97 genes to predict VO2max trainability were identified. Studies found phenotype to be dependent on several of these genotypes/variants, with higher responders to exercise training having more positive response alleles than lower responders (greater gene predictor score). Only 13 genetic variants were reproduced by more than two authors. Several other limitations were noted throughout these studies, including the robustness of significance for identified variants, small sample sizes, limited cohorts focused primarily on Caucasian populations, and minimal baseline data. These factors, along with differences in exercise training programs, diet and other environmental gene expression mediators, likely influence the ideal traits for VO2max trainability.ConclusionNinety-seven genes have been identified as possible predictors of VO2max trainability. To verify the strength of these findings and to identify if there are more genetic variants and/or mediators, further tightly-controlled studies that measure a range of biomarkers across ethnicities are required.

Dataset Information

Development of Machine Learning Model for VO_2max Estimation Using a Patch-Type Single-Lead ECG Monitoring Device in Lung Resection Candidates.

Publications

Development of Machine Learning Model for VO<sub>2max</sub> Estimation Using a Patch-Type Single-Lead ECG Monitoring Device in Lung Resection Candidates.

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Dataset Information

Development of Machine Learning Model for VO2max Estimation Using a Patch-Type Single-Lead ECG Monitoring Device in Lung Resection Candidates.

Publications

Development of Machine Learning Model for VO<sub>2max</sub> Estimation Using a Patch-Type Single-Lead ECG Monitoring Device in Lung Resection Candidates.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Development of Machine Learning Model for VO_2max Estimation Using a Patch-Type Single-Lead ECG Monitoring Device in Lung Resection Candidates.