Focus Topic: Artificial Intelligence and EchocardiographyClinical InvestigationsUnsupervised Time-Series Clustering of Left Atrial Strain for Cardiovascular Risk Assessment
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Study Participants
The Flemish Study on Environment, Genes and Health Outcomes (FLEMENGHO) received ethical approval from the ethics committee of the University of Leuven (S64406). This study is a family-based population resource on the genetic epidemiology of CV phenotypes for which we randomly recruited a population sample within northeastern Belgium, as described elsewhere (https://flemengho.eu/en/).10 From 2005 to 2015, we invited 1,851 former FLEMENGHO participants for examinations including
Cluster Analysis of LA Strain Time Series
The 929 FLEMENGHO participants (52.9% women) included 433 subjects with hypertension (46.6%), of whom 238 (55.0%) were receiving antihypertensive drug treatment. The mean age at baseline was 51.6 ± 16.0 years.
On the basis of the inertia index, the optimal number of AE/k-medoids-based clusters was five (Figure 2). Relying on the same inertia index, we optimized the hyperparameters as outlined in Supplemental Table 2. Figure 3 shows the individual LA strain curves within each cluster as well as
Discussion
In this large prospective general population study, we applied unsupervised ML methods to group LA strain curves into clinically meaningful clusters. We identified five distinct patterns of LA strain curves associated with different clinical and echocardiographic characteristics and CV risk profiles. Although the first three clusters did not show differences in the risk for developing cardiac events, clinical characteristics revealed important differences in age and sex distribution. For
Conclusion
The use of unsupervised learning approaches integrating LA strain curves in the temporal domain could provide clinically meaningful phenogroups associated with risk for developing adverse cardiac events. Such phenogrouping might facilitate the early detection of cardiac dysfunction and, therefore, further improve CV risk stratification. As such, the constructed LA clustering model may effectively stratify individuals into low-, intermediate-, and high-risk groups. This could pave the way for
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The Research Unit Hypertension and Cardiovascular Epidemiology received grants from Internal Funds KU Leuven (C24M/21/025) and the Research Foundation Flanders (1225021N, 1S07421N, and G0C5319N).
Conflicts of Interest: None.