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A model predicts bailout stenting risk in patients undergoing PCI with drug-coated balloons, suggesting a practical risk assessment tool.
Background/Objectives: Bail-out stenting remains a procedural challenge for percutaneous coronary intervention (PCI) performed with drug-coated balloons (DCBs). No dedicated bedside tool is currently available to predict this event. We aimed to develop and internally validate a bedside Bail-Out Risk Score. Methods: We analyzed patients treated with DCBs between 2021 and 2025. Predictors of bailout stenting were identified through univariate analysis, and variables with p < 0.10 were entered into a multivariable logistic regression model. Regression coefficients were then transformed into integer points using the Sullivan method. Model performance was evaluated by AUC-ROC, calibration, and bootstrap internal validation (B = 1000). Results: A total of 352 patients (399 de novo lesions) were treated with DCB-only PCI. Bail-out stenting occurred in 14.5% of lesions (58/399). Independent predictors of bail-out stenting were prior CABG (OR 4.29, p = 0.002), proximal lesion location (OR 2.99, p = 0.003), and diffuse disease (OR 2.18, p = 0.018). Prior PCI (OR 0.44, p = 0.009) and lipid-lowering therapy (OR 0.42, p = 0.029) were protective, while LAD involvement showed a non-significant trend (OR 1.57, p = 0.137). The model demonstrated moderate discrimination (AUC = 0.734; optimism-corrected AUC = 0.704) and excellent calibration (intercept = 0.000, slope = 1.000). The final score (range –4 to +8) stratified lesions into low (≤–1), intermediate (0–3), and high (≥3) risk groups, with progressively higher predicted probabilities (≤9%, 13–37%, and ≥49%). Conclusions: The Bail-Out Risk Score provides a practical and reliable bedside tool to estimate procedural risk during stentless PCI.
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Iossa et al. (2026) studied this question.
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