Multivariate time-series clustering of cardiopulmonary exercise test data for cardiovascular risk stratification

Abstract Aims Current clinical practice assesses cardiorespiratory fitness (CRF) by evaluating summary indexes of cardiopulmonary exercise tests (CPET). Yet, the raw time series recordings may hold additional information with clinical relevance. In this study, we investigated if the interpretation of raw CPET data could identify distinct CRF phenogroups and improve risk stratification. Methods In 1399 participants (mean age, 56.4 years; 37.7% women), who underwent maximal CPET by cycle ergometer, we collected baseline clinical characteristics and information on incident cardiovascular (CV) events (n=297) on average 4.3 years later. We employed dynamic time warping combined with k-medoids to identify phenogroups from raw CPET time-series. To evaluate the clinical significance of the derived phenogroups we compared clinical characteristics and incidence of CV events, while an external population cohort (n=171) was used to further validate the trained model. Results The optimal number of clusters was 5. We observed significant differences in age, use of medication, spirometry indexes and disease prevalence across all clusters. Cluster 5 was associated with the worst CV profile with higher use of antihypertensive medication and history of CV disease while cluster 1 represented the most favourable risk profile. After adjusting for traditional clinical covariables, clusters 4 (HR: 1.30; 95%CI: 1.02-1.65; p=0.033) and 5 (HR: 1.36; 95%CI: 1.08-1.72; p=0.0088) had significantly higher risk for incident CV events compared to clusters 1 and 2. Comparing clinical characteristics across clusters in the external validation cohort revealed similar patterns. Conclusion – Employing unsupervised machine learning on time series CPET recordings, we identified clinically meaningful CRF phenogroups. An integrative CRF profiling might facilitate CV risk stratification and consequently risk management.