Neural network-based prediction of atrial fibrillation at discharge following cardiac surgery

Exploratory Modeling of Postoperative Atrial Fibrillation After Cardiac Surgery with Cardiopulmonary Bypass Using Inflammatory Biomarkers and Clinical-Surgical Factors

Background/Objectives: Postoperative atrial fibrillation (POAF) is a common complication after cardiac surgery with cardiopulmonary bypass (CPB), increasing morbidity and prolonging hospitalization. This study aimed to develop and validate an exploratory prediction model that integrates perioperative inflammatory biomarkers with clinical and surgical variables to identify patients at risk of early POAF. Methods: A prospective exploratory cohort of 89 patients undergoing coronary artery bypass grafting (CABG; n = 36), valve surgery (n = 40), or CABG–valve surgery (n = 13) was evaluated. Clinical, surgical, and proinflammatory serum biomarkers (IL-6, IL-8, IL-10, and CRP) were recorded preoperatively (T1) and at 24 h (T2) and 48 h (T3) postoperatively. Multiple-comparison adjustments were made using the Benjamini–Hochberg false discovery rate. Predictor selection was based on bootstrap-derived stability using LASSO-penalized logistic regression, and the final model was estimated using Firth’s bias-reduced logistic regression. Results: POAF incidence was 8.3% in CABG, in contrast to 22.5% and 30.8% in valve and CABG-valve surgeries, respectively. After multiple-comparison corrections, only IL-6 at T2 postoperatively was significantly higher in patients who subsequently developed POAF. Bootstrap-based stability selection retained T2 postoperative IL-10 and magnesium concentrations in the final model, which achieved an apparent AUC of 0.776 and a bootstrap optimism-corrected AUC of 0.728, with acceptable calibration (Brier score = 0.103), negligible multicollinearity (VIF = 1.04), and a negative predictive value of 95.5% at the optimal Youden threshold. Conclusions: Our findings support an exploratory prediction model with moderate discrimination for POAF after cardiac surgery with CPB, providing a methodological foundation for future multicenter validation studies.

Machine learning for prediction of newly diagnosed atrial fibrillation after emergency percutaneous coronary intervention during hospitalization in patients with acute ST-segment elevation myocardial infarction: a multi-center prospective study

The present study developed and compared eight machine learning models for the prediction of in-hospital NDAF among acute patients with STEMI treated with emergency PCI and found the Gradient Boosting model achieved optimal predictive performance.

Dynamic Risk Assessment for the Development of Persistent Atrial Fibrillation Using Statistical and Machine Learning Approaches

CIED‐detected AF burden is strongly associated with progression to persistent AF, and ML‐based analysis of 6‐month device data enables accurate, point‐in‐time risk stratification to support earlier and more targeted clinical management.

Multimodal Machine Learning Model Predicting Postoperative Delirium Based on Heart Rate Variability: A Prospective Observational Study.

BACKGROUND Postoperative delirium is a common and serious complication after general anesthesia; its accurate prediction remains a substantial challenge in perioperative medicine. Existing models primarily rely on clinical variables and may have limited predictive accuracy. This study aimed to evaluate the added value of heart rate variability parameters in predicting postoperative delirium and construct an interpretable multimodal predictive model. METHODS In this prospective observational study, 1418 patients undergoing general anesthesia were included. Seventy-three features, including electrocardiogram abnormalities and heart rate variability time-, frequency-, and nonlinear-domain indicators, were extracted from electrocardiogram data. Postoperative delirium was assessed using the Chinese version of the 3-Minute Diagnostic Interview for Delirium within 3 days postoperatively. Feature selection was conducted by combining least absolute shrinkage and selection operator (LASSO) regression, the Boruta algorithm, and random forests, and 10 machine learning models were developed. Model performance was evaluated through receiver operating characteristic curves and decision curve analysis, with interpretability assessed via Shapley additive explanations. Clinical prediction tools were derived from key features. We used an external validation set to further evaluate the generalization ability of the models. RESULTS Postoperative delirium occurred in 255 (18%) patients. Seventeen key predictors were identified in total. The combined clinical-electrocardiogram-heart rate variability model demonstrated the highest predictive performance (area under the curve = 0.728), outperforming clinical-only (area under the curve = 0.673) and electrocardiogram-only models (area under the curve = 0.679). Logistic regression showed the highest discrimination. In the external validation set, the model maintained robust performance with an area under the curve value of 0.836. Shapley additive explanations highlighted seven core predictors: atrial or ventricular arrhythmia, operative time, ST-segment abnormalities, age, American Society of Anesthesiologists classification, heart rate variability entropy, and overall electrocardiogram abnormalities. A nomogram and online platform enabled personalized risk assessment. CONCLUSIONS Our results indicate that integrating heart rate variability with clinical and electrocardiogram features significantly enhances the personalized predictive efficacy of postoperative delirium.

Autonomics, More Than Fibrosis, Predict Postoperative Atrial Fibrillation

Multiple machine learning models for predicting major adverse cardiovascular events in dialysis with clinical and echocardiographic parameters: a retrospective cohort study

Abstract Background Patients undergoing dialysis are at an elevated risk of cardiovascular events. This study aimed to develop machine learning (ML) prediction models to identify risk factors for major adverse cardiovascular events (MACE) in dialysis patients. Materials and Methods This retrospective study included 203 patients undergoing dialysis with a median age of 45.0 years and 64.0% male. The participants were divided into training and test sets in a 7:3 ratio. LASSO regression selected characteristic variables from patients’general information, laboratory tests, and echocardiographic parameters (including global longitudinal strain [GLS]). Eight ML models were constructed,and SHAP analysis evaluated feature importance. Results The incidence of MACE (including myocardial infarction, unstable angina, heart failure, and cardiovascular death) in dialysis patients was 38.92%. The average follow-up period was 18 months. LASSO regression identified eight feature variables. Among the ML models, AdaBoost demonstrated superior performance, with an AUC of 0.883 (95% CI: 0.830–0.937), accuracy of 0.804, sensitivity of 0.864 and specificity of 0.762 in the training set, and an AUC of 0.809 (95% CI: 0.706–0.912), accuracy of 0.750, sensitivity of 0.90 and specificity of 0.675 in the test set. The SHAP analysis identified N-terminal pro-brain natriuretic peptide (NT-proBNP) level, estimated glomerular filtration rate (eGFR), GLS and age as the four most important features for predicting MACE in patients undergoing dialysis (mean absolute SHAP values: 0.199, 0.176, 0.096 and 0.091, respectively). Conclusion Elevated NT-proBNP, advanced age, reduced eGFR and impaired GLS were independently associated with an increased risk of MACE in patients undergoing dialysis.