ArticleMayo Clinic proceedings. Digital health2023
Topological Data Analysis Identified Prognostically-Distinct Phenotypes in Transcatheter Edge-to-Edge Repair Patients.
Article in Mayo Clinic proceedings. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Clinical Applications of Artificial Intelligence in Structural Heart Disease.Journal of the Society for Cardiovascular Angiography & Interventions · 2026Review
- Topological Data Analysis in the Assessment of Coronary Atherosclerosis: A Comprehensive Narrative Review.Mayo Clinic proceedings. Digital health · 2025Review
- Deciphering Popliteal Artery Aneurysm Patient Diversity: Insights From a Cluster Analysis of the POPART Registry.Journal of the American Heart Association · 2024Article
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Authors and funding
21 authors.
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Abstract
Objective: To identify prognostically distinct phenotype clusters in transcatheter edge-to-edge repair (TEER) patients based on topological data analysis (TDA), which was never used to assess the heterogeneous TEER population. Patients and Methods: Patients who underwent TEER (June 2014-September 2020) at Mayo Clinic sites were identified from the institutional database. Thirteen variables were used for TDA. The topological network graph was created using the Python Scikit-TDA Kepler-Mapper package (v. 2.0.1), and clustering was performed at the graph level with Louvain's modularity method. Kaplan-Meier survival analysis was used to assess the all-cause mortality of each cluster. Result: A total of 389 consecutive patients were included in the final analysis, and 2 major clusters were identified. The mean age was 80.3 ± 8.7 years; 256 (65.8%) were male. Fifty-five (14.5%) patients died during the mean follow-up duration (185 days). Kaplan-Meier analysis showed significant survival differences among the 2 clusters (hazard ratio, 2.70; 95% CI, 1.50-4.87; Conclusion: TDA can identify prognostically distinct clusters in TEER patients based on mitral valve morphology and clinical risk factors. This simple model can facilitate risk stratification for TEER patients regarding procedural success and survival performance.
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