Prediction of neurologic outcome after out-of-hospital cardiac arrest: An interpretable approach with machine learning.

Out-of-hospital cardiac arrest (OHCA) is a critical condition with low survival rates. In patients with a return of spontaneous circulation, brain injury is a leading cause of death. In this study, we propose an interpretable machine learning approach for predicting neurologic outcome after OHCA, using information available at the time of hospital admission. METHODS: The study population were 55 615 OHCA cases registered in the Swedish Cardiopulmonary Resuscitation Registry between 2010 and 2020. The dataset was split to training and validation sets (for model development) and test set (for evaluation of the final model). We used an XGBoost algorithm with stratified, repeated 10-fold cross-validation along with Optuna framework for hyperparameters tuning. The final model was trained on 10 features selected based on the importance scores and evaluated on the test set in terms of discrimination, calibration and bias-variance tradeoff. We used SHapley Additive exPlanations to address the 'black-box' model and align with eXplainable artificial intelligence. RESULTS: The final model achieved: area under the receiver operating characteristic value 0.964 (95% confidence interval (CI) [0.960-0.968]), sensitivity 0.606 (95% CI [0.573-0.634]), specificity 0.975 (95% CI [0.972-0.978]), positive predictive value (PPV) 0.664 (95% CI [0.625-0.696]), negative predictive value (NPV) 0.969 (95% CI [0.966-0.972]), macro F1 0.803 (95% CI [0.788-0.816]), and showed a very good calibration. SHAP features with the highest impact on the model's output were:'ROSC on arrival to hospital', 'Initial rhythm asystole' and 'Conscious on arrival to hospital'. CONCLUSIONS: The XGBoost machine learning model with 10 features available at the time of hospital admission showed good performance for predicting neurologic outcome after OHCA, with no apparent signs of overfitting.

Coronary angiography following transcatheter aortic valve replacement: Insights from the SWEDEHEART registry.

BACKGROUND: Transcatheter aortic valve replacement (TAVR) is the most common treatment in patients with symptomatic severe aortic stenosis (AS). As concomitant coronary artery disease is common in AS patients, access to the coronary arteries following TAVR is of increasing importance. OBJECTIVES: This study evaluated the incidence and risk factors for unplanned coronary angiography following TAVR and, using fluoroscopic time as a surrogate, analyzed the complexity of coronary artery cannulation. METHODS: All patients who underwent TAVR in Sweden between 2008 and 2022 were identified using the SWEDEHEART registry. The cumulative incidence of coronary angiography after TAVR was analyzed with mortality as a competing risk. Angiography and PCI complexity were analyzed using fluoroscopic time and compared across different transcatheter heart valve designs. RESULTS: Out of 9806 patients, 566 subsequently required coronary angiography. The incidence was highest for three-vessel and/or left main disease. Younger age, the extent of prior coronary artery disease, and peripheral vascular disease were associated with an increased risk of coronary angiography. Fluoroscopy time was increased in TAVR patients compared to the control group with the longest fluoroscopy times observed in cases involving supra-annular and self-expanding valves. CONCLUSIONS: The incidence of coronary angiography following TAVR is still low. Younger patients and patients with concomitant coronary artery disease have a higher risk. Procedural time is longer in patients with a previous THV replacement. As TAVR is emerging as the first-line treatment in patients with longer life expectancy, facilitating coronary access is an important factor when considering which THV device to implant.