TY - JOUR
T1 - Machine Learning Identifies New Predictors on Restenosis Risk after Coronary Artery Stenting in 10,004 Patients with Surveillance Angiography
AU - Güldener, Ulrich
AU - Kessler, Thorsten
AU - von Scheidt, Moritz
AU - Hawe, Johann S.
AU - Gerhard, Beatrix
AU - Maier, Dieter
AU - Lachmann, Mark
AU - Laugwitz, Karl Ludwig
AU - Cassese, Salvatore
AU - Schömig, Albert W.
AU - Kastrati, Adnan
AU - Schunkert, Heribert
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/4
Y1 - 2023/4
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - coronary artery disease
KW - machine learning
KW - percutaneous coronary intervention
KW - prediction
KW - restenosis
UR - http://www.scopus.com/inward/record.url?scp=85154609224&partnerID=8YFLogxK
U2 - 10.3390/jcm12082941
DO - 10.3390/jcm12082941
M3 - Article
AN - SCOPUS:85154609224
SN - 2077-0383
VL - 12
JO - Journal of Clinical Medicine
JF - Journal of Clinical Medicine
IS - 8
M1 - 2941
ER -