Topological Data Analysis Identified Prognostically-Distinct Phenotypes in Transcatheter Edge-to-Edge Repair Patients
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 who underwent TEER (June 2014-September 2020) at Mayo Clinic sites were ide...
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Veröffentlicht in: | Mayo Clinic Proceedings. Digital health 2023-09, Vol.1 (3), p.381-392 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | 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 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.
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; P=.0005). Cluster 1 (n=227) had moderate or increased residual mitral regurgitation (cluster 0 vs cluster 1: 19.4% vs 41.9%; P |
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ISSN: | 2949-7612 2949-7612 |
DOI: | 10.1016/j.mcpdig.2023.07.002 |