Background Predictive obesity indices are often based on the body mass index (BMI). Although BMI is widely used, it does not provide a direct measure of obesity. We aimed to utilize multiple machine learning-driven metabolic frameworks to investigate the long-term risk of major adverse cardiovascular and cerebrovascular events (MACCEs) in individuals with hypertension and obstructive sleep apnea (OSA).Methods This study included 708 patients with hypertension and OSA between January 2017 and December 2021. The measurements of height, weight, neck circumference (NC), waist circumference (WC), neck-circumference-to-height ratio (NHtR), and waist-to-height ratio (WHtR) were collected to calculate the triglyceride-glucose (TyG)-BMI, as well as TyG-NC, TyG-WC, TyG-NHtR, and TyG-WHtR indices.Results All patients were allocated to the training cohort (n = 446) and independent validation cohort (n = 262). The Boruta plot presented for identifying key predictors is as follows: male sex, age, TyG, TyG-BMI, HbA1c, FPG, triglyceride, creatinine, fibrinogen and AHI. We constructed nine machine learning models-XGBoost, Light Gradient Boosting Machine, Random Forest, Decision Tree, Gradient Boosting, Multi-Layer Perceptron, Support Vector Machine, K-Nearest Neighbors, and Gaussian Naive Bayes-to predict MACCEs. The XGBoost model was selected due to its superior performance evidenced by an AUC of 0.898 (95% CI: 0.822-0.973) and net clinical benefit. SHAP analysis further clarified variable contributions to MACCE risk.Conclusion This study employed various machine-learning techniques and multidimensional data assessment, allowing for enhanced prediction of metabolic results and supporting the timely detection of high-risk patients with OSA and hypertension in need of focused preventive measures.Clinical Trial Registration https://www.chictr.org.cn/bin/project/edit?pid=206415, identifier ChiCTR2300075727.

Large language models (LLMs) show considerable potential for atrial fibrillation (AF) management, yet current clinical applications frequently remain suboptimal due to accuracy limitations. To address these limitations, this study developed PULSE (Potentiated User-friendly LLM-driven Search Engine), a novel knowledge-enhanced, domain-aware LLM agent specifically designed to improve AF patient self-management across the entire care continuum. The proposed framework integrates multimodal inputs, meticulously curated clinical knowledge bases, optimized prompt engineering, and retrieval-augmented generation within an agent-based architecture. Performance was rigorously evaluated against four leading base LLMs using response quality (clinical accuracy, content integrity, practical utility, and patient safety) and readability (clarity, conciseness, and empathy). Comprehensive clinical validation was subsequently conducted through blinded expert assessment of 75 real-world AF-related patient queries. The results demonstrated that PULSE improved clinical accuracy, content integrity, utility, and safety (P < 0.05) across all tested models. Furthermore, it substantially enhanced empathy and clarity while maintaining comparable conciseness. Overall, PULSE improves both the factual accuracy and readability of patient-facing medical outputs, highlighting the immense clinical potential of agent-driven LLM systems to advance chronic disease self-management and improve long-term patient outcomes.

BackgroundPatients with concurrent hypertension, heart failure with preserved ejection fraction (HFpEF), and unstable angina pectoris (UAP) represent a high-risk population with heterogeneous long-term cardiovascular outcomes. However, practical tools for individualized risk stratification in this clinically complex population remain limited. This study aimed to develop and validate a machine learning-assisted nomogram for predicting long-term major adverse cardiovascular events (MACEs) in patients with concomitant hypertension, HFpEF, and UAP.MethodsThis multicenter retrospective cohort study included 1,669 patients with hypertension, HFpEF, and UAP from Six medical centers in China. Patients were assigned according to treating hospital to a development cohort, a validation cohort, and an independent test cohort. The primary endpoint was MACEs, defined as cardiac death, unplanned coronary revascularization, or rehospitalization for acute heart failure. Candidate predictors were selected using least absolute shrinkage and selection operator regression, the Boruta algorithm, and random forest feature importance analysis. Variables consistently identified across these methods were entered into a multivariable Cox proportional hazards model, and a Cox-based nomogram was constructed to estimate 2-, 3-, and 4-year MACE risk. Model performance was assessed using receiver operating characteristic curves, time-dependent area under the curve, calibration curves, decision curve analysis, and Kaplan-Meier survival analysis.ResultsDuring a median follow-up of 48 months, 254 patients experienced MACEs. Five predictors were retained in the final model: diabetes mellitus, previous myocardial infarction, systemic immune-inflammation index, triglyceride, and N-terminal pro-B-type natriuretic peptide. In the development cohort, the nomogram yielded area under the curve values of 0.785, 0.787, and 0.760 for predicting 2-, 3-, and 4-year MACEs, respectively. The corresponding values were 0.674, 0.697, and 0.682 in the validation cohort, and 0.685, 0.711, and 0.718 in the test cohort. Calibration curves showed acceptable agreement between predicted and observed risks, and decision curve analysis suggested potential net benefit across clinically relevant threshold probabilities. Patients classified as high risk according to the nomogram had a significantly higher incidence of MACEs than those classified as low risk across all cohorts.ConclusionA machine learning-assisted, Cox-based nomogram incorporating five routinely available clinical and laboratory variables provided acceptable discrimination in the development cohort and modest-to-acceptable performance in geographically distinct validation and test cohorts. This model may support individualized long-term risk stratification in patients with concurrent hypertension, HFpEF, and UAP, although prospective validation and clinical impact studies are warranted before routine implementation.

The effects of various prompt engineering on Large Language Models (LLMs) performance in hypertension decision-making are not yet fully understood. We evaluate the impact of different prompt engineering on LLM performance in hypertension treatment decision-making. We conducted a two-stage validation study using 300 de-identified simulated hypertension cases based on real-world clinical scenarios. ChatGPT-4.1 with Guidance-Self-Consistency achieved optimal performance (91.3% accuracy), nearing expert-level competency, while zero-shot prompting yielded worst results (62.7% with DeepSeek-V3). Optimal LLM assistance consistently enhanced physicians’ average accuracy across all levels (community hospital: 73.4% to 82.5%; county hospital: 84.0% to 87.9%; teaching hospital: 91.5% to 92.0%) and reduced inappropriate regimen rates. The worst LLM configurations decreased physician performance below baseline, increasing inappropriate regimen rates from 26.6% to 35.2% across all levels. Effectively designed prompt strategies enable LLMs to provide reliable hypertension treatment recommendations, thereby supporting physicians’ clinical decisions. This study has been trial-registered (ChiCTR2500099307, March 21, 2025).

Large Language Models (LLMs) demonstrate considerable potential in enhancing the retrieval of health information. However, the hallucinatory they produce poses a security challenge. This study aimed to improve the accuracy and reliability of LLMs in hypertension education through the integration of integrating Retrieval-Augmented Generation (RAG) technology. We constructed a hypertension supplement knowledge base, and subsequently integrated it into a RAG technology, resulting in the development of the HEART (Hypertension Enhancing Answer Retrieval Tool) framework. A set of 50 commonly asked questions related to hypertension was used to evaluate the performance of four base LLMs—ChatGPT-4o, Claude-3.5, Gemini-2.5, and Llama-3.3—as well as their corresponding HEART-enhanced versions. Clinical experts assessed each response in terms of accuracy, completeness, consistency, robustness, security, and overall quality. The integration with the HEART framework led to a significant improvement in the performance of all four LLMs across five key evaluation dimensions: accuracy, completeness, consistency, security, and robustness (all P < 0.05). The average overall quality scores for all models increased significantly: from 3.57 (SD 0.72) to 4.20 (SD 0.41) for Llama-3.3, from 3.92 (SD 0.70) to 4.38 (SD 0.42) for Claude-3.5, from 3.91 (SD 0.73) to 4.32 (SD 0.39) for ChatGPT-4o, and from 4.03 (SD 0.69) to 4.38 (SD 0.41) for Gemini-2.5 (all P < 0.001). This study highlights the importance of combining high-quality, domain-specific medical data with advanced artificial intelligence techniques to enhance accuracy and reduce misinformation in healthcare applications.

Despite significant advances in guideline-directed medical therapy (GDMT), statin-treated patients with non-ST-elevation acute coronary syndrome (NSTE-ACS), heart failure with preserved ejection fraction (HFpEF), and type 2 diabetes mellitus remain at substantial residual risk of major adverse cardiac and cerebrovascular events (MACCEs). Within the framework of predictive, preventive, and personalized medicine (3PM/PPPM), this study aimed to develop and externally validate a data-driven survival model for individualized long-term risk stratification to support the transition from reactive management to proactive and targeted prevention. In this multicenter retrospective cohort study, 1,206 NSTE-ACS patients with HFpEF and type 2 diabetes who underwent successful percutaneous coronary intervention (PCI) were enrolled from six Chinese tertiary hospitals and geographically divided into a training cohort (n = 763) and an independent validation cohort (n = 443). Sixty clinical, laboratory, and echocardiographic variables were screened using wrapper-based feature selection across five machine learning algorithms with stratified 5-fold cross-validation. Thirty-one survival models spanning four methodological categories were developed and compared. The primary endpoint was MACCEs, defined as a composite of recurrent acute coronary syndrome, stroke, and hospitalization for heart failure. Seven consensus biomarkers were identified: age, albumin, creatinine, monocyte-to-lymphocyte ratio, non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and non-high-density lipoprotein cholesterol (non-HDL-C). Among 31 survival models, Xgboost.Cox demonstrated the best overall performance, achieving a mean Uno’s C-index of 0.879 across cohorts (training, 0.947; validation, 0.810). Time-dependent SurvSHAP(t) (survival Shapley additive explanations) was employed to provide instance-specific interpretability. An interactive web-based calculator implementing the Xgboost.Cox model was developed to facilitate clinical translation and individualized risk assessment. This 3PM/PPPM-guided framework enables early identification of high-risk phenotypes among NSTE-ACS patients with HFpEF and type 2 diabetes before irreversible clinical deterioration occurs. We recommend: (1) implementing multidimensional baseline profiling including inflammatory, metabolic, and cardiac functional bio-marker panels for the statin-treated patients with NSTE-ACS to facilitate risk stratification at discharge; (2) integrating the web-based XGBoost-Cox calculator into clinical workflows to generate individualized risk estimates and support shared decision-making; and (3) tailoring follow-up intensity and preventive strategies to predicted risk, with closer monitoring and more intensive GDMT optimization considered for high-risk patients, while ensuring guideline-recommended therapy for all eligible individuals.

BackgroundDiabetic Cardiac Autonomic Neuropathy (DCAN), a critical yet frequently underdiagnosed microvascular complication, is associated with increased mortality. Standard cardiovascular autonomic reflex tests (CARTs) are complex and time-consuming, hindering their widespread use in routine screening in clinical settings. This study aimed to develop and validate a predictive nomogram for DCAN in patients with diabetes using readily available clinical variables.MethodsWe retrospectively analyzed the clinical data of 453 patients with type 1 or type 2 diabetes hospitalized at Shenzhen People’s Hospital between February 2022 and December 2025. The dataset was randomly divided into training (70%) and validation (30%) cohorts. Key predictors were identified using a rigorous selection strategy that combined univariate analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate logistic regression. Four candidate prediction models (Logistic Regression (LR), Random Forest, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) were constructed and evaluated for discrimination, calibration, and clinical utility. The optimal model was visualized as a nomogram and interactive web calculator.ResultsThe prevalence of DCAN in the study population was 45.0% (204/453). The following seven independent predictors were identified: a history of diabetic retinopathy (DR) or diabetic kidney disease (DKD), diabetes duration, age, heart rate (HR), fasting plasma glucose (FPG), and HbA1c. Among the algorithms tested, the LR model exhibited the most balanced performance in the validation cohort (area under the curve (AUC) = 0.838) with the highest sensitivity (77.0%) and was thus selected as the optimal prediction tool. Consequently, the LR model was transformed into a predictive nomogram. This nomogram demonstrated good calibration and potential clinical utility for individualized risk assessment.ConclusionWe successfully developed and validated a high-sensitivity prediction model for DCAN applicable to type 1 and type 2 diabetes. The developed visual nomogram and interactive web-based tool are cost-effective and user-friendly instruments that can facilitate early risk assessment and personalized clinical management.

The long-term prognosis of patients with non-ST-elevation acute coronary syndrome (NSTE–ACS) with concomitant mild-to-moderate aortic regurgitation (AR) remains poorly understood in contemporary cardiovascular practice. Consequently, we aimed to develop and externally validate a novel prognostic model for predicting Major Adverse Cardiovascular Events (MACE) in NSTE–ACS patients. This multicenter retrospective cohort study involved 719 patients with a confirmed diagnosis of NSTE–ACS complicated with mild-to-moderate AR across four tertiary cardiovascular centers between January 2018 and December 2020. The primary endpoint was MACE occurrence during follow-up. Based on institutional enrollment, the subjects were stratified into two groups: training (n = 466) and independent external validation (n = 253) cohorts. The predictive model was developed using four independent predictors [diabetes mellitus (DM), neutrophil-to-lymphocyte ratio (NLR), N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels, and number of coronary arteries with ≥ 50

BackgroundDigital education for outpatient patients with atrial fibrillation (AF) has gradually increased. However, research on digital education for patients undergoing atrial fibrillation catheter ablation (AFCA) is limited. ObjectiveThis study aimed to develop a novel digital animation-based multistage education system and evaluate its quality-of-life benefits for patients with AFCA. MethodsThis randomized controlled clinical trial included 208 patients with AF who underwent catheter ablation in the Department of Cardiology at Renmin Hospital of Wuhan University between January 2022 and August 2023. The patients were randomly assigned to the digital animation intervention (n=104) and standard treatment (n=104) groups. The primary outcome was the difference in the quality of life of patients with atrial fibrillation (AF-QoL-18) scores at 3 months. Secondary outcomes included differences in scores on the 5-item Medication Adherence Report Scale (MARS-5), Self-rating Anxiety Scale (SAS), and Self-Rating Depression Scale (SDS) at 3 months. ResultsIn the digital animation intervention group, the AF-QoL-18 score increased from 38.02 (SD 6.52) to 47.77 (SD 5.74), the MARS-5 score increased from 17.04 (SD 3.03) to 20.13 (SD 2.12), the SAS score decreased from 52.82 (SD 8.08) to 45.39 (SD 6.13), and the SDS score decreased from 54.12 (SD 6.13) to 45.47 (SD 5.94), 3 months post discharge from the hospital. In the conventional treatment group, the AF-QoL-18 score increased from 36.97 (SD 7.00) to 45.31 (SD 5.71), the MARS-5 score increased from 17.14 (SD 3.01) to 18.47 (SD 2.79), the SAS score decreased from 51.83 (SD 7.74) to 47.31 (SD 5.87), and the SDS score decreased from 52.78 (SD 5.21) to 45.37 (SD 6.18). The mean difference in AF-QoL-18 score change between the 2 groups was 1.41 (95% CI 2.42-0.40, P=.006) at 3 months. The mean difference in MARS-5 score change was 1.76 (95% CI 2.42-1.10, P<.001). The mean difference in SAS score was –2.91 (95% CI –3.88 to –1.95, P<.001). Additionally, the mean difference in SDS score was –1.23 (95% CI –0.02 to –2.44, P=.047). ConclusionsOur study introduces a novel digital animation educational approach that provides multidimensional, easily understandable, and multistage education for patients with AF undergoing catheter ablation. This educational model effectively improves postoperative anxiety, depression, medication adherence, and quality of life in patients at 3 months post discharge. Trial RegistrationChinese Clinical Trial Registry ChiCTR2400081673; https://www.chictr.org.cn/showproj.html?proj=201059

Background:Cardiac rehabilitation (CR) interventions for patients with coronary heart disease are increasingly adopted. However, research on the integration of digital health technologies into CR for patients with unstable angina (UA) undergoing percutaneous coronary intervention (PCI) remains limited. Objective:This study assessed the effectiveness of a multidimensional digital CR program for patients with UA undergoing PCI. Methods:This prospective study enrolled 164 patients with UA who underwent PCI between April and June 2022. Patients were assigned to either the usual care group (April-May 2022) or the multidimensional digital CR intervention group (May-June 2022). The intervention group received rehabilitation through a nurse-led, multidisciplinary team, using a customized digital CR program. This program encompassed 7 key rehabilitation components: exercise, medication management, nutritional guidance, psychological support, sleep management, health education, and smoking cessation assistance. The usual care group received standardized treatment and routine nursing care. To minimize selection bias, propensity score matching was applied between the 2 groups. The primary outcomes included changes in the 6-minute walk test (6MWT), 12-item Short Form Health Survey (SF-12) scores, and frailty phenotype scores at 3 months. Secondary outcomes assessed differences in gait speed, 30-second chair stand test (30-s CST), grip strength, waist circumference (WC), BMI, and lipid profiles at 3 months. Results:A total of 136 patients were included in the final analysis. At 3 months, the intervention group demonstrated significant improvements in frailty status compared to the control group. The proportion of prefrail patients decreased from 100% (68 patients) to 75% (51 patients), while nonfrail patients increased from 0% to 25% (17 patients; P<.001). Regarding physical fitness, the intervention group exhibited improvements in 6MWT: from 347.06 (SD 32.43) to 375.22 (SD 29.71) m (P<.001); gait speed: from 0.87 (SD 0.17) to 1.05 (SD 0.14) m/s (P<.001); and 30-s CST: from 10.0 (SD 1.89) to 12.71 (SD 1.97; P<.001). Grip strength, BMI, and WC improved significantly in the intervention group. Grip strength increased from 16.64 (SD 6.57) to 20.74 (SD 5.37; P<.001). BMI decreased from 25.74 (SD 3.05) to 23.88 (SD 2.14; P<.001), and WC decreased from 94.98 (SD 7.87) to 89.91 (SD 7.50) cm (P<.001). The intervention group achieved greater improvements in lipid profiles, with significant reductions in total cholesterol (P<.001), triglycerides (P<.001), and low-density lipoprotein cholesterol (P<.001), while high-density lipoprotein cholesterol remained stable (P=.45). Conclusions:This study demonstrates that a novel multidimensional digital CR program is acceptable and effective in improving functional status and health-related quality of life in patients with UA undergoing PCI within a short timeframe.

Within the mHealth framework, systematic research that collects and analyzes patient data to establish comprehensive digital health archives for hypertensive patients, and leverages large language models (LLMs) to assist clinicians in health management and Blood Pressure (BP) control remains limited. In this study, our aims to describe the design, development and usability evaluation process of a management platform (Hyper-DREAM) for hypertension. Our multidisciplinary team employed an iterative design approach over the course of a year to develop the Hyper-DREAM platform. This platform's primary functionalities encompass multimodal data collection (personal hypertensive digital phenotype archive), multimodal interventions (BP measurement, medication assistance, behavior modification, and hypertension education) and multimodal interactions (clinician-patient engagement and BP Coach component). In August 2024, the mHealth App Usability Questionnaire (MAUQ) was conducted involving 51 hypertensive patients recruited from three distinct centers. In parallel, six clinicians engaged in management activities and contributed feedback via the Doctor’s Software Satisfaction Questionnaire (DSSQ). Concurrently, a real-world comparative experiment was conducted to evaluate the usability of the BP Coach, ChatGPT-4o Mini, ChatGPT-4o and clinicians. The comparative experiment demonstrated that the BP Coach achieved significantly higher scores in utility (mean scores 4.05, SD 0.87) and completeness (mean scores 4.12, SD 0.78) when compared to ChatGPT-4o Mini, ChatGPT-4o, and clinicians. In terms of clarity, the BP Coach was slightly lower than clinicians (mean scores 4.03, SD 0.88). In addition, the BP Coach exhibited lower performance in conciseness (mean scores 3.00, SD 0.96). Clinicians reported a marked improvement in work efficiency (2.67 vs. 4.17, P < .001) and experienced faster and more effective patient interactions (3.0 vs. 4.17, P = .004). Furthermore, the Hyper-DREAM platform significantly decreased work intensity (2.5 vs. 3.5, P = .01) and minimized disruptions to daily routines (2.33 vs. 3.55, P = .004). The Hyper-DREAM platform demonstrated significantly greater overall satisfaction compared to the WeChat-based standard management (3.33 vs. 4.17, P = .01). Additionally, clinicians exhibited a markedly higher willingness to integrate the Hyper-DREAM platform into clinical practice (2.67 vs. 4.17, P < .001). Furthermore, patient management time decreased from 11.5 min (SD 1.87) with Wechat-based standard management to 7.5 min (SD 1.84, P = .01) with Hyper-DREAM. Hypertensive patients reported high satisfaction with the Hyper-DREAM platform, including ease of use (mean scores 1.60, SD 0.69), system information arrangement (mean scores 1.69, SD 0.71), and usefulness (mean scores 1.57, SD 0.58). In conclusion, our study presents Hyper-DREAM, a novel artificial intelligence-driven platform for hypertension management, designed to alleviate clinician workload and exhibiting significant promise for clinical application. The Hyper-DREAM platform is distinguished by its user-friendliness, high satisfaction rates, utility, and effective organization of information. Furthermore, the BP Coach component underscores the potential of LLMs in advancing mHealth approaches to hypertension management.

Background:Heart failure with preserved ejection fraction (HFpEF) and unstable angina (UA) often coexist in clinical practice, constituting a high-risk cardiovascular phenotype with a markedly increased incidence of major adverse cardiovascular events (MACEs). The identification of high-risk patients within this population is crucial for reducing complications, improving outcomes, and guiding clinical decision-making. Objective:This study aimed to develop and externally validate predictive models based on machine learning algorithms to estimate the risk of MACEs in patients with coexisting UA and HFpEF, and to construct an online risk calculator to support individualized prevention strategies. Methods:This multicenter cohort study included 4459 patients with both HFpEF and UA admitted to 7 hospitals across eastern, central, and western China between January 1, 2015, and December 31, 2021. Patients were divided into the derivation cohort (n=2923) and external validation cohort (n=1536) based on geographic regions. Clinical, laboratory, and imaging data were extracted from electronic medical records. Key predictors were identified using a hybrid feature selection method combining least absolute shrinkage and selection operator and Boruta algorithms. A total of 33 survival models were developed, including a variety of machine learning algorithms and survival analysis models. The model with the best concordance index (C-index) performance was deployed as a web-based risk calculator. Additionally, we assessed other performance indicators of the best-performing model, including the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, recall, F1-score, Brier scores, calibration curves, and decision curve analysis. Results:Using a combination of the least absolute shrinkage and selection operator regression and the Boruta algorithm, 7 key predictors were identified: diabetes mellitus, blood platelet count, triglyceride, systemic inflammatory response index, triglyceride-glucose-BMI, N-terminal pro-brain natriuretic peptide, and atherogenic index of plasma. The surv.xgboost.cox model was used to predict MACEs in patients with UA and HFpEF due to its superior C-index. The model demonstrated the following performance metrics in the external validation cohort: a C-index of 0.788; cumulative/dynamic area under the curve of 0.81; and area under the curve values at 20, 30, and 40 months of 0.809 (95% CI 0.745-0.873), 0.784 (95% CI 0.745-0.824), and 0.807 (95% CI 0.776-0.838), respectively. The model exhibited satisfactory calibration and clinical utility in predicting 40-month MACEs. Model interpretability was enhanced using Shapley Additive Explanations for survival analysis to provide global and individual explanations. Furthermore, we converted the surv.xgboost.cox-based model into a publicly available tool for predicting 40-month MACEs, providing estimated probabilities based on the predictive indicators entered. Conclusions:We developed a surv.xgboost.cox-based predictive model for MACEs in patients with the dual phenotype of HFpEF and UA. We implemented this model as a web-based calculator to facilitate clinical application.

This study aimed to investigate the association between dietary copper intake and muscle mass. Low muscle mass was defined according to the Foundation for the National Institutes of Health (FNIH) consensus. Among 9693 adults (female 50.3

Atrial fibrillation (AF) presents a considerable challenge in patients with Type 2 diabetes and obstructive sleep apnea syndrome (OSAS), as metabolic disturbance plays a role in the pathophysiological mechanisms that underlie arrhythmias. This study aimed to resolve this issue by developing a predictive nomogram using a machine learning algorithm, integrating a comprehensive range of clinical variables, including demographic data, laboratory findings, and sleep monitoring information. This multicenter cohort study included patients with Type 2 diabetes who were scheduled for sleep monitoring for OSAS between January 2018 and December 2020. A predictive nomogram was developed using random forests and Cox regression analysis. We utilized data from multiple hospitals to construct a development cohort comprising 417 participants and an independent validation cohort consisting of 245 participants. The nomogram was developed using four clinical variables: age, apnea-hypopnea index, triglyceride-glucose (TyG) index, and TyG-body mass index (BMI). The areas under the curve values, derived from 500 bootstrap samples, were 0.862 (95

Myocardial fibrosis is a prevalent pathological hallmark of a diverse range of chronic and acute cardiovascular disorders. However, the relevant literature currently provides limited evidence regarding the determinants of myocardial fibrosis severity in patients with new-onset ST-elevation myocardial infarction (STEMI) following successful emergent percutaneous coronary intervention (PCI) utilizing contrast-enhanced cardiac magnetic resonance imaging (CE-CMR). We prospectively enrolled a cohort of 78 patients who presented with new-onset ST-segment elevation myocardial infarction and who underwent successful emergent PCI within 12 h from the onset of symptoms. Late gadolinium-enhanced LGE (LGE) was quantified via CE-CMR, and patients were categorized into two groups on the basis of the median LGE value. The median LGE was 16

BACKGROUND:Doxorubicin (DOX) is a powerful chemotherapeutic drug with limited clinical effectiveness due to the risk of cardiotoxicity and glycometabolic disorders at high dosages. Epalrestat (EPS), a potent AKR1B1 inhibitor, has been extensively documented for its remarkable efficacy in treating glycometabolic disorders. We aimed to investigate the protective effects of EPS against DOX-induced cardiotoxicity and elucidate the underlying mechanisms. METHODS:C57BL/6 mice (24) were divided into the saline-treated control, EPS-treated, DOX-treated, and DOX plus EPS-treated groups. Cardiotoxicity was induced via intraperitoneal DOX administration (cumulative dose: 20 mg/kg), and EPS (100 mg/kg/day) was administered for four weeks. RESULTS:DOX treatment increased creatine kinase isoenzyme-MB, lactate dehydrogenase, and brain natriuretic peptide levels, indicating cardiotoxicity. EPS treatment notably ameliorated DOX-induced adverse effects in a dose-dependent manner, with decreased apoptosis-related protein expression, reduced reactive oxygen species (ROS) levels, and preserved mitochondrial membrane potential. In protein expression analyses, EPS suppressed proapoptotic gene expression (Bax and caspase-3) while upregulating antiapoptotic gene expression (Bcl-2). Additionally, EPS reduced ROS levels, suppressed proinflammatory cytokine production, and preserved mitochondrial integrity by inhibiting nuclear factor kappa B (NF-κB) expression. Furthermore, EPS increased serum antioxidant enzyme levels, highlighting its potential as an antioxidative agent. The protective effect of EPS on mitochondrial integrity was visually confirmed by transmission electron microscopy. Transcriptomic analysis identified key biological processes and signaling pathways implicated in DOX-induced cardiotoxicity, emphasizing the significant contributions of the advanced glycation end product-receptor for advanced glycation end products (AGE-RAGE) signaling pathway and endocrine resistance. CONCLUSION:EPS is a promising pharmacological agent for protection against DOX-induced cardiotoxicity due to its antioxidative and antiapoptotic effects and its ability to inhibit AGE/RAGE/NF-κB signaling.

BackgroundCurrently, there is a paucity of literature addressing personalized risk stratification using multimodal data in patients with symptomatic aortic stenosis and heart failure with preserved ejection fraction (HFpEF) following transcatheter aortic valve replacement (TAVR). ObjectiveThis study aimed to enhance the performance of risk assessment models in this patient population by developing a predictive model for adverse outcomes using various machine learning (ML) techniques. MethodsThis multicenter cohort study included 326 patients diagnosed with severe AS and HFpEF who underwent TAVR between January 2017 and December 2023. Patients were allocated to training (n=195) and independent validation (n=131) sets based on hospital affiliation. A dual-phase feature selection process, combining least absolute shrinkage and selection operator logistic regression and the Boruta algorithm, was used to identify relevant variables from the multimodal dataset. A total of 5 ML model-decision trees, K-nearest neighbors, random forest, support vector machine, and extreme gradient boosting were used to construct a visualization and explainable predictive framework to elucidate model decision-making processes. ResultsThe primary features identified included age, N-terminal pro-brain natriuretic peptide, fasting blood glucose, triglyceride/high-density lipoprotein cholesterol ratio, triglyceride glucose index, triglyceride glucose-BMI index, atherogenic index of plasma index, and Apolipoprotein B. Among the 5 models, the support vector machine demonstrated the best predictive performance for major adverse cardiovascular and cerebrovascular events in patients with severe AS and HFpEF following TAVR, achieving an area under the curve of 0.756 (95% CI 0.631-0.881) in the independent validation set. The model exhibited good calibration and robust predictive power in both training and validation sets and demonstrated the highest net benefit in decision curve analysis compared to other models. To extract significant variables influencing the algorithm and ensure model appropriateness, we interpreted cohort and personalized model predictions using Shapley Additive Explanations values. ConclusionsOur ML-based multimodal model, incorporating 8 readily accessible predictors, demonstrated robust predictive capability for 12 months of major adverse cardiovascular and cerebrovascular events risk. This model can be used to identify high-risk individuals with AS and HFpEF following TAVR, potentially aiding in risk stratification and personalized treatment strategies.