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Development of Machine Learning Model for VO2max 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 (VO2max) 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 VO2max, the VO2max 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 VO2max values measured using a CPET than existing equations. This model may be a promising tool for estimating VO2max 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]

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