Predicting Target Lesion Failure following Percutaneous Coronary Intervention through Machine Learning Risk Assessment Models.

Mamas, Mamas A; Roffi, Marco; Fröbert, Ole; Chieffo, Alaide; Beneduce, Alessandro; Matetic, Andrija; Tonino, Pim A L; Paunovic, Dragica; Jacobs, Lotte; Debrus, Roxane; El Aissaoui, Jérémy; van Leeuwen, Frank; Kontopantelis, Evangelos

Abstract

Aims
Central to the practice of precision medicine in percutaneous coronary intervention (PCI) is a risk-stratification tool to predict outcomes following the procedure. This study is intended to assess machine learning (ML)-based risk models to predict clinically relevant outcomes in PCI and to support individualized clinical decision-making in this setting.

Methods and results
Five different ML models [gradient boosting classifier (GBC), linear discrimination analysis, Naïve Bayes, logistic regression, and K-nearest neighbours algorithm) for the prediction of 1-year target lesion failure (TLF) were trained on an extensive data set of 35 389 patients undergoing PCI and enrolled in the global, all-comers e-ULTIMASTER registry. The data set was split into a training (80%) and a test set (20%). Twenty-three patient and procedural characteristics were used as predictive variables. The models were compared for discrimination according to the area under the receiver operating characteristic curve (AUC) and for calibration. The GBC model showed the best discriminative ability with an AUC of 0.72 (95% confidence interval 0.69–0.75) for 1-year TLF on the test set. The discriminative ability of the GBC model for the components of TLF was highest for cardiac death with an AUC of 0.82, followed by target vessel myocardial infarction with an AUC of 0.75 and clinically driven target lesion revascularization with an AUC of 0.68. The calibration was fair until the highest risk deciles showed an underestimation of the risk.

Conclusion
Machine learning–derived predictive models provide a reasonably accurate prediction of 1-year TLF in patients undergoing PCI. A prospective evaluation of the predictive score is warranted.

Registration
Clinicaltrial.gov identifier is NCT02188355.

Citation

Mamas, M. A., Roffi, M., Fröbert, O., Chieffo, A., Beneduce, A., Matetic, A., …Kontopantelis, E. (in press). Predicting Target Lesion Failure following Percutaneous Coronary Intervention through Machine Learning Risk Assessment Models. European Heart Journal – Digital Health, 4(6), 433–443. https://doi.org/10.1093/ehjdh/ztad051

• Journal Article Type: Article
• Acceptance Date: Aug 31, 2023
• Online Publication Date: Aug 31, 2023
• Deposit Date: Sep 21, 2023
• Journal: European Heart Journal - Digital Health
• Print ISSN: 2634-3916
• Publisher: Oxford University Press
• Peer Reviewed: Peer Reviewed
• Volume: 4
• Issue: 6
• Pages: 433–443
• DOI: https://doi.org/10.1093/ehjdh/ztad051
• Keywords: General Earth and Planetary Sciences, General Engineering, General Environmental Science