Affiliation:
1. Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, 80636 Munich, Germany
2. DZHK (German Center for Cardiovascular Research), Partner Site Munich Heart Alliance, 80802 Munich, Germany
3. Biomax, Robert-Koch-Str. 2, 82152 Planegg, Germany
4. Department of Cardiology, Klinikum Rechts der Isar, Technische Universität München, 81675 Munich, Germany
Abstract
Objective: Machine learning (ML) approaches have the potential to uncover regular patterns in multi-layered data. Here we applied self-organizing maps (SOMs) to detect such patterns with the aim to better predict in-stent restenosis (ISR) at surveillance angiography 6 to 8 months after percutaneous coronary intervention with stenting. Methods: In prospectively collected data from 10,004 patients receiving percutaneous coronary intervention (PCI) for 15,004 lesions, we applied SOMs to predict ISR angiographically 6–8 months after index procedure. SOM findings were compared with results of conventional uni- and multivariate analyses. The predictive value of both approaches was assessed after random splitting of patients into training and test sets (50:50). Results: Conventional multivariate analyses revealed 10, mostly known, predictors for restenosis after coronary stenting: balloon-to-vessel ratio, complex lesion morphology, diabetes mellitus, left main stenting, stent type (bare metal vs. first vs. second generation drug eluting stent), stent length, stenosis severity, vessel size reduction, and prior bypass surgery. The SOM approach identified all these and nine further predictors, including chronic vessel occlusion, lesion length, and prior PCI. Moreover, the SOM-based model performed well in predicting ISR (AUC under ROC: 0.728); however, there was no meaningful advantage in predicting ISR at surveillance angiography in comparison with the conventional multivariable model (0.726, p = 0.3). Conclusions: The agnostic SOM-based approach identified—without clinical knowledge—even more contributors to restenosis risk. In fact, SOMs applied to a large prospectively sampled cohort identified several novel predictors of restenosis after PCI. However, as compared with established covariates, ML technologies did not improve identification of patients at high risk for restenosis after PCI in a clinically relevant fashion.
Funder
German Federal Ministry of Economics and Energy
German Federal Ministry of Education and Research
British Heart Foundation (BHF)/German Centre of Cardiovascular Research (DZHK)-collaboration
Leducq Foundation for Cardiovascular Research
Bavarian State Ministry of Health and Care
Bayerisches Staatsministerium für Wissenschaft und Kunst
Cited by
5 articles.
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