Purpose: To develop and compare multiple machine learning (ML) algorithms with traditional logistic regression for predicting in-hospital cardiac arrest (IHCA) using comprehensive electronic health record data, with the goal of improving early risk stratification beyond conventional early-warning scores and providing potential integration into hospital early warning systems for timely clinical intervention. Patients and Methods: We performed a retrospective case-control study at a large tertiary medical center, including 800 IHCA cases and 3,464 controls. Candidate predictors comprised demographics, comorbidities, vital signs, and laboratory measurements. Five models-logistic regression, decision tree, random forest, XGBoost, and multivariate adaptive regression splines (MARS)-were trained and validated. Performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score. Results: XGBoost yielded strong discrimination and the highest accuracy (AUC 0.909; accuracy 0.883), while random forest showed comparable discrimination (AUC 0.910) with slightly lower accuracy (0.876). Logistic regression performed robustly but lower than ML models (AUC 0.895; accuracy 0.876). ML models consistently identified clinically meaningful predictors-including blood urea nitrogen, heart rate, and pre-existing heart failure-offering insights beyond traditional regression. Conclusion: Integrating ML approaches with conventional regression enhances IHCA risk prediction by capturing non-linear relationships and interactions while retaining the interpretability of regression. These approaches could strengthen hospital early-warning systems, enabling earlier detection and intervention, and ultimately improving patient outcomes.

Purpose: Frequent premature ventricular complexes are increasingly recognized as a contributor to ventricular dysfunction and progression to heart failure. Current clinical guidelines endorse radiofrequency catheter ablation as an effective treatment for symptomatic or drug-refractory premature ventricular complexes. However, nationwide long-term real-world outcomes, particularly those related to post-ablation medication de-escalation, remain insufficiently characterized. Patients and Methods: Using Taiwan’s National Health Insurance Research Database, we conducted a nationwide retrospective cohort study of adults who underwent first-time radiofrequency catheter ablation for premature ventricular complexes between 2002 and 2018, with follow-up extending to December 31, 2021, allowing for up to three years of observation. Clinical outcomes included all-cause mortality and heart failure rehospitalization. Antiarrhythmic drug utilization was assessed through both prescription prevalence and defined daily dose, evaluated at baseline, 90 days, and 1 year post-ablation. Subgroup analyses were stratified by baseline heart failure status. Results: A total of 4195 patients (mean age 53± 15 years; 49% women) were included, of whom 7% had heart failure. Baseline heart failure independently predicted mortality (hazard ratio 3.99, 95% confidence interval 2.71– 5.88) and heart failure rehospitalization (hazard ratio 7.29, 95% confidence interval 5.49– 9.66). Radiofrequency catheter ablation was associated with substantial reductions in antiarrhythmic drug use, declining from 58% before the procedure to 31% at 1 year. Medication analysis confirmed dose de-escalation, particularly for mexiletine, propafenone, and amiodarone. However, patients with heart failure exhibited smaller reductions in beta-blockers and Class III agents. Conclusion: This nationwide cohort offers real-world insight into radiofrequency catheter ablation outcomes for frequent premature ventricular complexes, highlighting divergent prognoses between patients with and without heart failure. Radiofrequency catheter ablation was associated with meaningful reductions in antiarrhythmic medication use, underscoring its potential therapeutic and economic benefits. These findings support radiofrequency catheter ablation as a component of long-term management strategies, while future prospective studies are needed to refine candidate selection and timing of intervention.

Introduction: Long-term heart failure and mortality after catheter ablation for premature ventricular contraction (PVC) remain underexplored. Methods: We retrospectively analyzed 4195 adults who underwent PVC ablation in a nationwide claims database. To address class imbalance, we used synthetic minority over-sampling technique (SMOTE) and random over-sampling examples (ROSE). Five supervised algorithms were compared: logistic regression, decision tree, random forest, XGBoost, and LightGBM. Discrimination was assessed by stratified five-fold cross-validation using the area under the receiver operating characteristic curve (ROC AUC). Because rare events can bias ROC, we also examined precision–recall (PR) curves. Results: For predicting three-year heart failure, LightGBM with ROSE achieved the highest ROC AUC at 0.822. For three-year mortality, logistic regression with ROSE and LightGBM with ROSE showed balanced performance with ROC AUCs of 0.886 and 0.882. Pairwise DeLong tests indicated that these leading models formed a high-performing cluster without significant differences in ROC AUC. Age, prior heart failure, malignancy, and end-stage renal disease were the most influential predictors by model explainability analysis. Discussion: Addressing class imbalance and benchmarking modern learners against a transparent logistic baseline yielded robust, clinically interpretable risk stratification after PVC ablation. These models are suitable for integration into electronic health record dashboards, with external validation and local threshold optimization as next steps.

Type 2 diabetes mellitus is a significant health concern among elderly individuals in Taiwan, and liver dysfunction, particularly nonalcoholic fatty liver disease, is prevalent in this population. Gamma-glutamyl transferase (γ-GT), a key enzyme involved in glutathione metabolism, has been linked to metabolic disorders, including insulin resistance (IR) and diabetes. However, its association with insulin secretion phases (first-phase insulin secretion, FPIS; second-phase insulin secretion, SPIS) and glucose effectiveness (GE) remains unclear. This study aimed to investigate these relationships in elderly nonobese Chinese individuals. A total of 10,933 participants (5082 men and 5851 women) aged ≥ 65 years were enrolled. Participants were classified based on metabolic syndrome (MetS) status and γ-GT quartiles. Blood samples were analyzed for γ-GT, fasting plasma glucose, insulin resistance, and lipid profiles. The study used established equations to calculate IR, FPIS, SPIS, and GE. Pearson correlation analysis and statistical models were applied to assess the associations. 768 men and 794 women had MetS. Those with MetS had higher IR, FPIS, SPIS, γ-GT levels, and lower GE. Higher γ-GT levels were significantly associated with MetS components and increased IR, FPIS, SPIS, and decreased GE. GE had the strongest negative correlation (r = -0.198 for men, −0.158 for women), followed by positive correlations with IR (r = 0.183 for men, 0.132 for women), SPIS (r = 0.099 for men, 0.060 for women), and FPIS (r = 0.028 for men, 0.048 for women). γ-GT was positively associated with IR, FPIS, and SPIS but negatively correlated with GE in elderly individuals. Among the 4 diabetes factors, GE exhibited the strongest correlation with γ-GT, followed by IR, SPIS, and FPIS. These findings highlight the need for further research into the role of γ-GT in glucose metabolism and potential clinical implications for elderly nonobese Chinese populations.

The prevalence of type 2 diabetes (T2D) has been increasing dramatically in recent decades, and 47.5% of T2D patients will die of cardiovascular disease. Thallium-201 myocardial perfusion scan (MPS) is a precise and non-invasive method to detect coronary artery disease (CAD). Most previous studies used traditional logistic regression (LGR) to evaluate the risks for abnormal CAD. Rapidly developing machine learning (Mach-L) techniques could potentially outperform LGR in capturing non-linear relationships. To aims were: (1) Compare the accuracy of Mach-L methods and LGR; and (2) Found the most important factors for abnormal TMPS. 556 T2D were enrolled in the study (287 men and 269 women). Demographic and biochemistry data were used as independent variables and the sum of stressed score derived from MPS scan was the dependent variable. Subjects with a MPS score ≥ 9 were defined as abnormal. In addition to traditional LGR, classification and regression tree (CART), random forest, Naïve Bayes, and eXtreme gradient boosting were also applied. Sensitivity, specificity, accuracy and area under the receiver operation curve were used to evaluate the respective accuracy of LGR and Mach-L methods. Except for CART, the other Mach-L methods outperformed LGR, with gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking emerging as the most important factors to predict abnormal MPS. Four Mach-L methods are found to outperform LGR in predicting abnormal TMPS in Chinese T2D, with the most important risk factors being gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking.

BACKGROUND In-hospital cardiac arrest (IHCA) remains a catastrophic event with persistently low survival, even amid advances in resuscitation and critical care. Conventional early warning scores provide only limited predictive accuracy, often failing to identify patients at highest risk. To overcome these limitations, we evaluated a multimodal prediction framework that integrates traditional logistic regression with advanced machine learning (ML) approaches to optimize risk stratification. OBJECTIVE This study aimed to develop and compare the performance of multiple machine learning algorithms against traditional logistic regression for predicting IHCA using a comprehensive set of electronic health record (EHR) data. METHODS We conducted a retrospective case–control study including 800 IHCA cases and 3,464 matched controls from a large tertiary medical center. Candidate predictors comprised demographics, comorbidities, laboratory values, and vital signs. Five models—logistic regression, decision tree, random forest, XGBoost, and multivariate adaptive regression splines (MARS)—were trained and validated. Model performance was assessed using accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve. RESULTS Among all models, XGBoost demonstrated the best predictive performance (AUC 0.909; accuracy 0.883), followed by random forest (AUC 0.910; accuracy 0.876). Logistic regression achieved robust but comparatively lower performance (AUC 0.895; accuracy 0.876). Importantly, ML models highlighted clinically relevant predictors—such as blood urea nitrogen, heart rate, and presence of heart failure—providing novel insights beyond traditional regression. CONCLUSIONS Integrating ML-based approaches with conventional regression substantially improved IHCA risk prediction. While logistic regression offers transparency and interpretability, ML models capture complex, non-linear interactions that enhance accuracy. This multimodal framework holds potential to strengthen hospital early warning systems, enabling earlier detection, timely intervention, and ultimately improved patient outcomes. CLINICALTRIAL The study protocol was approved by the Institutional Review Board of National Taiwan University Hospital (IRB No. 201807063RINC). Due to the retrospective nature of this study, which involved the analysis of pre-existing data, trial registration was not required.