Improving cardiovascular risk stratification through multivariate time-series analysis of cardiopulmonary exercise test data
Evangelos Ntalianis,
Nicholas Cauwenberghs,
František Sabovčik,
Everton Santana,
Francois Haddad,
Jomme Claes,
Matthijs Michielsen,
Guido Claessen,
Werner Budts,
Kaatje Goetschalckx,
Véronique Cornelissen,
Tatiana Kuznetsova
Affiliations
Evangelos Ntalianis
Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
Nicholas Cauwenberghs
Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
František Sabovčik
Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
Everton Santana
Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium; Stanford Cardiovascular Institute and Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
Francois Haddad
Stanford Cardiovascular Institute and Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
Jomme Claes
Rehabilitation in Internal Disorders, KU Leuven Department of Rehabilitation Sciences, University of Leuven, Leuven, Belgium
Matthijs Michielsen
Rehabilitation in Internal Disorders, KU Leuven Department of Rehabilitation Sciences, University of Leuven, Leuven, Belgium
Guido Claessen
Department of Cardiology, Hartcentrum, Virga Jessa Hospital, Hasselt, Belgium; Faculty of Medicine and Life Sciences, Hasselt University, Hasselt, Belgium
Werner Budts
Cardiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
Kaatje Goetschalckx
Cardiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium
Véronique Cornelissen
Rehabilitation in Internal Disorders, KU Leuven Department of Rehabilitation Sciences, University of Leuven, Leuven, Belgium
Tatiana Kuznetsova
Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium; Corresponding author
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.
