AI Article Synopsis
- Scientists studied exercise tests to see how fit people's hearts and lungs were.
- They divided 1,399 people into 5 groups based on how their bodies responded during these tests.
- Some groups had much better heart health than others, and understanding these patterns can help doctors figure out who might be at risk for heart problems.
Article Abstract
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; = 0.033) and 5 (hazard ratio: 1.36; = 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.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11403400 | PMC |
| http://dx.doi.org/10.1016/j.isci.2024.110792 | DOI Listing |
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