Introduction: Effective prediction of in-stent restenosis and revascularization after coronary stent implantation and interventions targeting risk factors that may lead to these events are crucial for their prevention and management.
Methods: Based on a C5.0 decision tree approach, data from 2,326 patients from two centers were included. We comprehensively analyzed 34 risk factors that may affect in-stent restenosis and revascularization after stent implantation and conducted predictions and risk factor analyses for in-stent restenosis and revascularization following coronary stent implantation.
Results: The accuracy of predicting in-stent restenosis following coronary stent implantation with a median follow-up period of 30 months was as follows: area under the curve (AUC) in the training set, 0.996; AUC in the internal validation set, 0.988; and AUC in the external validation set, 0.889, with an f1 value of 0.95, a sensitivity of 99.16%, and a specificity of 91.72%. Additionally, the accuracy of revascularization prediction was as follows: AUC in the training set, 0.984; AUC in the internal validation set, 0.956; and AUC in the external validation set, 0.876, with an f1 value of 0.84, a sensitivity of 96.43%, and a specificity of 25%. We also conducted a risk factor analysis.
Conclusion: We successfully constructed a predictive and risk factor analysis model for in-stent restenosis and revascularization following coronary stent implantation. This model may be helpful for clinical decision-making.
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