RESUMEN
Background: In patients with suspected obstructive coronary artery disease (CAD), evaluation using a pre-test probability model is the key element for diagnosis; however, its accuracy is controversial. This study aimed to develop machine learning (ML) models using clinically relevant biomarkers to predict the presence of stable obstructive CAD and to compare ML models with an established pre-test probability of CAD models. Methods: Eight machine learning models for prediction of obstructive CAD were trained on a cohort of 1,312 patients [randomly split into the training (80%) and internal validation sets (20%)]. Twelve clinical and blood biomarker features assessed on admission were used to inform the models. We compared the best-performing ML model and established the pre-test probability of CAD (updated Diamond-Forrester and CAD consortium) models. Results: The CatBoost algorithm model showed the best performance (area under the receiver operating characteristics, AUROC, 0.796, and 95% confidence interval, CI, 0.740-0.853; Matthews correlation coefficient, MCC, 0.448) compared to the seven other algorithms. The CatBoost algorithm model improved risk prediction compared with the CAD consortium clinical model (AUROC 0.727; 95% CI 0.664-0.789; MCC 0.313). The accuracy of the ML model was 74.6%. Age, sex, hypertension, high-sensitivity cardiac troponin T, hemoglobin A1c, triglyceride, and high-density lipoprotein cholesterol levels contributed most to obstructive CAD prediction. Conclusion: The ML models using clinically relevant biomarkers provided high accuracy for stable obstructive CAD prediction. In real-world practice, employing such an approach could improve discrimination of patients with suspected obstructive CAD and help select appropriate non-invasive testing for ischemia.
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Background: The clinical implication of new-onset left bundle branch block (LBBB) after transcatheter aortic valve replacement (TAVR) remains controversial. We investigated the impact of new-onset persistent LBBB on reverse cardiac remodeling and clinical outcomes after TAVR. Methods: Among 478 patients who had undergone TAVR for symptomatic severe aortic stenosis from 2011 to 2021, we analyzed 364 patients after excluding patients with pre-existing intraventricular conduction disturbance or a pacing rhythm before or during the indexed hospitalization for TAVR. Echocardiographic variables of cardiac remodeling at baseline and 1 year after TAVR were comprehensively analyzed. The primary outcome was a composite of cardiovascular death and hospitalization for heart failure. Secondary outcomes were all-cause death and individual components of the primary outcome. Result: New-onset persistent LBBB occurred in 41 (11.3%) patients after TAVR. The no LBBB group showed a significant increase in the left ventricular (LV) ejection fraction and decreases in LV dimensions, the left atrial volume index, and LV mass index 1 year after TAVR (all p < 0.001). However, the new LBBB group showed no significant changes in these parameters. During a median follow-up of 18.1 months, the new LBBB group experienced a higher incidence of primary outcomes [hazard ratio (HR): 5.03; 95% confidence interval (CI): 2.60-9.73; p < 0.001] and all-cause death (HR: 2.80; 95% CI: 1.38-5.69; p = 0.003). The data were similar after multivariable regression analysis. Conclusion: New-onset persistent LBBB after TAVR is associated with insufficient reverse cardiac remodeling and increased adverse clinical events.