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Title: | Integrative Interpretation of Cardiopulmonary Exercise Tests for Cardiovascular Outcome Prediction: A Machine Learning Approach | Authors: | Cauwenberghs, Nicholas Sente, Josephine Van Criekinge, Hanne Sabovcik, Frantisek Ntalianis, Evangelos Haddad, Francois Claes , Jomme CLAESSEN, Guido Budts, Werner Goetschalckx, Kaatje Cornelissen, Veronique Kuznetsova, Tatiana |
Issue Date: | 2023 | Publisher: | MDPI | Source: | Diagnostics, 13 (12) (Art N° 2051) | Abstract: | Integrative interpretation of cardiopulmonary exercise tests (CPETs) may improve assessment of cardiovascular (CV) risk. Here, we identified patient phenogroups based on CPET summary metrics and evaluated their predictive value for CV events. We included 2280 patients with diverse CV risk who underwent maximal CPET by cycle ergometry. Key CPET indices and information on incident CV events (median follow-up time: 5.3 years) were derived. Next, we applied unsupervised clustering by Gaussian Mixture modeling to subdivide the cohort into four male and four female phenogroups solely based on differences in CPET metrics. Ten of 18 CPET metrics were used for clustering as eight were removed due to high collinearity. In males and females, the phenogroups differed significantly in age, BMI, blood pressure, disease prevalence, medication intake and spirometry. In males, phenogroups 3 and 4 presented a significantly higher risk for incident CV events than phenogroup 1 (multivariable-adjusted hazard ratio: 1.51 and 2.19; p & LE; 0.048). In females, differences in the risk for future CV events between the phenogroups were not significant after adjustment for clinical covariables. Integrative CPET-based phenogrouping, thus, adequately stratified male patients according to CV risk. CPET phenomapping may facilitate comprehensive evaluation of CPET results and steer CV risk stratification and management. | Notes: | Cauwenberghs, N (corresponding author), Univ Leuven, Dept Cardiovasc Sci, Hypertens & Cardiovasc Epidemiol, B-3000 Leuven, Belgium. nicholas.cauwenberghs@kuleuven.be; veronique.cornelissen@kuleuven.be |
Keywords: | cardiorespiratory fitness;cardiorespiratory fitness;cardiopulmonary exercise test;cardiopulmonary exercise test;cardiovascular risk stratification;cardiovascular risk stratification;cardiopulmonary phenogrouping;cardiopulmonary phenogrouping;machine learning;machine learning | Document URI: | http://hdl.handle.net/1942/40721 | e-ISSN: | 2075-4418 | DOI: | 10.3390/diagnostics13122051 | ISI #: | 001014311600001 | Rights: | 2023 by the authors. 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/). | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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Integrative Interpretation of Cardiopulmonary Exercise Tests for Cardiovascular Outcome Prediction_ A Machine Learning Approach.pdf | Published version | 3.03 MB | Adobe PDF | View/Open |
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