Patel, Shubh K

Introduction Angina with no obstructive coronary artery disease (ANOCA) affects millions and is frequently under-recognised because diagnostic pathways and risk tools predominantly target obstructive coronary artery disease (CAD). This protocol describes shared methods for two machine-learning (ML) studies: (1) differentiating ANOCA from stable angina with obstructive CAD and (2) predicting long-term mortality among patients with ANOCA and obstructive CAD. Methods and analysis We will develop and cross-site validate ML classification models using a multicentre retrospective cohort drawn from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease registry and institutional datasets from the University of Ottawa Heart Institute and the University Health Network. Eligible participants are adults (≥18 years) undergoing initial cardiac catheterisation for chest pain/anginal equivalents since 1995, excluding prior revascularisation, major structural heart disease and predefined non-anginal indications. Outcomes are (1) ANOCA (0% to <50% stenosis) versus obstructive CAD (≥50% stenosis) and (2) 1, 3 and 5-year mortality, modelled separately for ANOCA and obstructive CAD. Model development will use nested cross-validation with stratified k-fold inner-loop tuning and leave-one-site-out cross-validation for repeated external validation. Candidate predictors will be harmonised across sites, filtered for missingness and refined using expert/directed acyclic graph-guided selection plus Boruta and Least Absolute Shrinkage and Selection Operator. Preprocessing includes appropriate encoding, missing-data imputation (multivariate imputation by chained equations) and feature scaling. Algorithms will include elastic-net logistic regression, random forest, LightGBM and multilayer perceptron models; hyperparameters will be optimised via Bayesian optimisation. Performance and threshold tuning will be reported. Explainability and subgroup fairness will be assessed using SHapley Additive exPlanations. Final models will be deployed as a web-based clinical risk calculator. Ethics and dissemination Ethics approval has been obtained from the University of Calgary and the University Health Network (#24-5916). Analyses will use deidentified data in secure environments; only aggregate results will be reported. Findings will be disseminated via peer-reviewed publications, conferences and a web-based calculator.

Machine-learning (ML) models have the potential to transform health care by enabling more personalized and data-driven clinical decision making. However, their successful implementation in clinical practice requires careful consideration of factors beyond predictive accuracy. We provide an overview of essential considerations for developing clinically applicable ML models, including methods for assessing and improving calibration, selecting appropriate decision thresholds, enhancing model explainability, identifying and mitigating bias, as well as methods for robust validation. We also discuss strategies for improving accessibility to ML models and performing real-world testing. Highlights This tutorial provides clinicians with a comprehensive guide to implementing machine-learning classification models in clinical practice. Key areas covered include model calibration, threshold selection, explainability, bias mitigation, validation, and real-world testing, all of which are essential for the clinical deployment of machine-learning models. Following these guidance can help clinicians bridge the gap between machine-learning model development and real-world application and enhance patient care outcomes.

ABSTRACT Ventricular arrhythmias (VA), including ventricular tachycardia and fibrillation, are critical cardiac conditions that are often managed by catheter ablation among those unresponsive to pharmacologic therapy. The choice of anesthesia and sedation regimens for VA ablations may impact arrhythmia inducibility and hemodynamic stability, which can affect procedural success and complication rates. This systematic review and meta-analysis aimed to compare the efficacy and safety of sedation versus general anesthesia (GA) among patients undergoing VA ablation. The review was prospectively registered on PROSPERO (CRD42023441553). Database searches were conducted across five major databases from inception to March 9, 2024 to identify randomized trials or observational studies including adult patients undergoing ablations for VA. Screening and data extraction were completed in duplicate. Risk-of-bias assessments were conducted using ROBINS-I as all included studies were observational, and the quality of evidence was evaluated using the GRADE framework. Six observational studies (N = 16,435) were included. No significant differences were found between sedation and GA for total procedure time (MD: −14.16 minutes; 95%CI: −38.61 to 10.29 minutes), arrhythmia non-inducibility (RR: 0.73; 95% CI: 0.33–1.58), acute ablation success (RR: 1.06; 95% CI: 0.65–1.71), or procedural complications (RR: 0.72; 95% CI: 0.28–1.85). However, sedation was associated with significantly lower intraprocedural hemodynamic instability (RR: 0.28; 95% CI: 0.12–0.70). These findings indicate that while sedation and GA have comparable outcomes, sedation may be associated with less hemodynamic instability during VA ablation. However, more high-quality studies are needed to confirm these results.