| Autor: |
Evangelos Ntalianis, Nicholas Cauwenberghs, František Sabovčik, Everton Santana, Francois Haddad, Jomme Claes, Matthijs Michielsen, Guido Claessen, Werner Budts, Kaatje Goetschalckx, Véronique Cornelissen, Tatiana Kuznetsova |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
iScience, Vol 27, Iss 9, Pp 110792- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2589-0042 |
| DOI: |
10.1016/j.isci.2024.110792 |
| Popis: |
Summary: Nowadays cardiorespiratory fitness (CRF) is assessed using summary indexes of cardiopulmonary exercise tests (CPETs). Yet, raw time-series CPET recordings may hold additional information with clinical relevance. Therefore, we investigated whether analysis of raw CPET data using dynamic time warping combined with k-medoids could identify distinct CRF phenogroups and improve cardiovascular (CV) risk stratification. CPET recordings from 1,399 participants (mean age, 56.4 years; 37.7% women) were separated into 5 groups with distinct patterns. Cluster 5 was associated with the worst CV profile with higher use of antihypertensive medication and a history of CV disease, while cluster 1 represented the most favorable CV profile. Clusters 4 (hazard ratio: 1.30; p = 0.033) and 5 (hazard ratio: 1.36; p = 0.0088) had a significantly higher risk of incident adverse events compared to clusters 1 and 2. The model evaluation in the external validation cohort revealed similar patterns. Therefore, an integrative CRF profiling might facilitate CV risk stratification and management. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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