Search Results (3,118)

This review analyses and logically structures modern intelligent sensor technologies in the context of animal husbandry, feed production, and veterinary medicine. The main research discussed in the article focuses on machine learning based on modern neural network models, computer vision, and sensor systems that are transforming the methods for assessing the health, behavior, and nutrition of farm animals. The first part examines modern approaches to quality control and optimization of mineral and vitamin premixes, including visual inspection using visual sensors and neural networks. Key roles are played by precise dosing, component stability (minerals, vitamins), and the transition to more bioefficient organic forms of micronutrients to reduce environmental impact. Improvements in feed and premix production are analyzed, including automation, energy management, and the use of machine learning for non-destructive quality control, defect detection, mixing homogeneity assessment, and vitamin stability prediction. The second part analyzes methods for animal location and behavior detection. This article presents computer vision-based systems, including modifications of YOLO, for automatically tracking and classifying key behavioral patterns (lying down, standing, feeding, and aggression) in cattle and pigs, even in crowded conditions. It also discusses the use of ultra-wideband (UWB) systems and accelerometers combined with machine learning for high-precision positioning and detection of specific behavioral anomalies, such as lameness and playfulness. The third section focuses on the application of machine learning in veterinary diagnostics, including the automated interpretation of medical images (X-ray, ultrasound, and MRI) as sensor data streams for the diagnosis of cardiovascular, oncological, and orthopedic diseases in farm and small animals. Furthermore, the article examines the use of machine learning models for proactive disease diagnosis in farm animals and poultry based on multimodal data and image analysis. Considerable attention is given to methods and tools for radiometric diagnosis of animal diseases at an early stage using microwave sensors, as well as laser therapy and surgery in veterinary medicine. The review concludes that the integration of intelligent systems enables a transition to data-driven livestock management, significantly improving animal welfare and, consequently, the efficiency and sustainability of agricultural production. Full article

Myxomatous mitral valve disease (MMVD) is the most common acquired cardiac disease in small-breed dogs and shows particularly high prevalence and early onset in Cavalier King Charles Spaniels (CKCS). Although MMVD is considered a complex, polygenic disease, the clinical relevance of individual genetic variants remains incompletely understood. The angiotensin-converting enzyme (ACE) gene variant rs850683722 has previously been associated with altered ACE activity and differences in renin–angiotensin–aldosterone system-related responses in dogs with MMVD. The aim of this study was to determine the prevalence of rs850683722 in a Polish population of CKCS dogs and to assess whether this variant is associated with the clinical course of MMVD. A total of 105 CKCS dogs were included in the study. All dogs underwent standardized cardiovascular evaluation, including echocardiography, electrocardiography, and systolic blood pressure measurement. MMVD diagnosis and staging were performed according to current ACVIM consensus criteria. Genotyping of the rs850683722 variant was performed using Sanger sequencing for 95 dogs, while next-generation sequencing data was obtained for 10 dogs. Genotype distribution, allele frequencies, conformity with the Hardy–Weinberg equilibrium (HWE), sex-related differences, and associations between genotype and age at progression to selected MMVD stages or the primary clinical endpoint were assessed statistically. The most frequent genotype was AA, detected in fifty-nine dogs, followed by GG in thirty-seven dogs and AG in nine dogs. When dogs carrying at least one A allele were considered variant-positive, the overall prevalence of the variant-positive genotype was 64.8%. The calculated allele frequencies were 0.605 for the A allele and 0.395 for the G allele. The observed genotype distribution deviated markedly from the Hardy–Weinberg equilibrium, mainly because of a pronounced deficit of heterozygous dogs. No significant association was detected between genotype and sex. Genotype was also not significantly associated with age at progression to stage B2 or stage C. A statistically significant difference in age of death was demonstrated by genotype, but this difference was not reflected in the survival analysis. The rs850683722 variant was highly prevalent in the studied Polish CKCS population, with a frequency comparable to previously reported data for this breed. Despite its documented biological association with ACE activity and RAAS-related responses, the variant was not significantly associated with the clinical progression of MMVD in this cohort. These findings suggest that rs850683722 alone seems unlikely to be a reliable marker for predicting the severity or rate of MMVD progression in Polish CKCS dogs. Further studies including larger cohorts, longer follow-up, pedigree information, and the direct assessment of RAAS activity may help clarify whether this variant has stage-dependent or treatment-related clinical relevance. Full article

Background: Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with over 75% of deaths occurring in low- and middle-income countries, where conventional risk models often demonstrate poor calibration and limited generalizability. Objective: This study aimed to develop an interpretable, pharmacotherapy-informed machine learning model for cardiovascular risk prediction using national electronic health record (EHR) data from Jordan. Methods: A retrospective cohort study was conducted using approximately 600,000 individuals from the national Hakeem EHR system (2018–2022). Demographic, clinical, blood pressure, laboratory, and medication data were integrated to construct three datasets reflecting varying levels of feature completeness. Multiple machine learning models were benchmarked, followed by optimization, hybrid modeling, and probability calibration. Model interpretability was assessed using SHAP analysis. Results: The national cohort demonstrated a high cardiometabolic burden, with prevalence of hypertension (50.2%), hyperlipidemia (54.9%), and diabetes (47.9%). Antihypertensive and lipid-lowering therapies were more frequently used among CVD patients (56.9% and 49.6%, respectively). Treatment patterns were dominated by amlodipine (19.9%) and atorvastatin (74.4%). The final calibrated seed-bagged gradient boosting model achieved robust performance (ROC-AUC 0.844; PR-AUC 0.813) with consistent generalization across datasets. Key predictors included antihyperlipidemic therapy, systolic blood pressure variability, age, and sex. Conclusions: This study presents JoRisk, a calibrated and interpretable machine learning framework that integrates pharmacotherapy and clinical data for short-term cardiovascular risk prediction. The model demonstrates strong performance using routinely available EHR variables and offers a scalable decision-support tool for risk stratification in resource-constrained healthcare systems. Full article

Background/Objectives: Cardiopulmonary exercise testing (CPET) provides an integrated assessment of cardiovascular, respiratory, and metabolic responses during exercise. Although CPET is increasingly used in older adults for preoperative physiological evaluation and assessment of exercise limitation, evidence regarding its safety and clinical feasibility in elderly patients with mixed cardiopulmonary comorbidities remains limited. Methods: In this retrospective observational study, we evaluated 235 consecutive patients who underwent CPET at a tertiary referral center. Patients were categorized into two groups according to age: ≥65 years and <65 years. Clinical characteristics, pulmonary function parameters, CPET findings, feasibility outcomes, and adverse events during testing were analyzed. Results: A total of 235 patients were included, with a mean age of 62.3 ± 12.8 years. Among them, 112 (47.6%) patients were aged ≥65 years and 35 (14.8%) were aged ≥75 years. Comorbidities were present in 170 patients, with hypertension being the most common. The leading indication for CPET was preoperative evaluation prior to thoracic surgery. Most elderly patients successfully completed CPET and provided clinically interpretable physiological data. In the ≥65 years group, CPET was terminated prematurely in 10 patients due to syncope, severe dyspnea, bronchospasm, chest pain, or arrhythmia. In the ≥65 years group, exercise-induced desaturation occurred in 24 patients; the lowest recorded oxygen saturation was 84%, and no desaturation episode required premature termination of the test. No major complications, deaths, myocardial infarctions, or cardiac arrests were observed during CPET or within the subsequent three days. No statistically significant differences in adverse event rates were observed between the age groups. Univariate logistic regression analysis demonstrated that lower FEV1 % predicted and lower FEV1/FVC % predicted ratio were associated with clinically significant adverse events in elderly patients [OR (95% CI): 0.96 (0.94–0.99), p = 0.02, OR (95% CI): 0.90 (0.84–0.96), p = 0.001, respectively]. Conclusions: CPET was feasible in the majority of elderly patients with cardiopulmonary comorbidities, with most individuals successfully completing testing and providing clinically interpretable physiological data. No major complications were observed in this cohort. These findings suggest that, when performed under appropriate supervision and careful patient selection, CPET may represent a practical tool for functional assessment and preoperative physiological evaluation in older adults. Larger prospective multicenter studies are warranted to further define its safety and feasibility in this population. Full article

Background/Objectives: Childhood overweight and obesity represent a major global public health challenge, with increasing prevalence and significant long-term metabolic, cardiovascular, and psychosocial consequences. Standard pediatric weight-management strategies based on lifestyle modification often achieve modest and variable results, highlighting the need for more personalized and scalable approaches. Artificial intelligence (AI) has emerged as a promising tool to enhance prevention, early risk stratification, and management of pediatric overweight and obesity. Methods: This narrative review was conducted through a structured search of PubMed, Scopus, and Web of Science for English-language studies published up to January 2026. The main search terms included “artificial intelligence”, “machine learning”, and “deep learning”, combined with “child”, “adolescent”, “pediatric”, “childhood obesity”, “pediatric overweight”, “body mass index”, “weight management”, “nutrition”, “diet”, “physical activity”, “lifestyle”, and “behavior change”. After title/abstract and full-text screening according to predefined eligibility criteria, the included studies were qualitatively synthesized and grouped by main application domains. The initial database search identified 412 records. After removal of 96 duplicates, 316 records were screened by title and abstract. Full-text assessment was subsequently performed for 175 potentially eligible articles. Following this evaluation, 51 studies met the eligibility criteria and were retained from the database search. Additional relevant articles were identified through manual screening of reference lists and related reviews, resulting in the final set of studies included in the narrative synthesis. Results: The review identified five main domains of AI application in pediatric weight management: risk assessment and prediction, dietary assessment and nutritional support, physical activity and lifestyle monitoring, behavioral and psychological support, and clinical decision support. Across the included literature, AI-based approaches were most frequently applied to predictive modeling using longitudinal BMI or growth trajectories, birth characteristics, parental BMI, sleep duration, physical activity, sedentary behavior, and family or socioeconomic factors. However, the evidence base was largely composed of observational and predictive-modeling studies, whereas interventional studies, real-world implementation studies, and long-term pediatric weight-outcome data remained limited. Conclusions: This narrative review indicates that AI has potential as a complementary tool within multidisciplinary, family-centered pediatric weight-management pathways, particularly for early risk stratification, personalized monitoring, and behavioral support. However, the findings also highlight that current evidence remains mainly exploratory and predictive rather than interventional. Further longitudinal, real-world, and ethically grounded research is required to confirm effectiveness, safety, clinical usefulness, and equitable implementation in pediatric populations. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly transforming cardiology through advanced analytical tools capable of identifying complex patterns across cardiovascular imaging, electrophysiology, and clinical datasets. Machine learning (ML) and deep learning (DL) algorithms are being integrated into echocardiography, cardiac computed tomography (CT), cardiac magnetic resonance imaging (MRI), and electrocardiography (ECG), enabling earlier diagnosis and more personalized cardiovascular care. This narrative review summarizes current clinical and organizational applications of AI in cardiology and discusses emerging concepts related to explainable and trustworthy AI. Methods: A narrative review was conducted according to SANRA recommendations using the PubMed, MEDLINE, Web of Science, and Scopus databases, including peer-reviewed publications from 2015 to 2026 addressing clinical, organizational, and ethical applications of AI in cardiology, with particular emphasis on cardiovascular imaging, electrocardiography, heart failure, digital health, and explainable AI frameworks. Results: Substantial evidence demonstrates that AI-based tools can achieve expert-level performance in cardiovascular imaging interpretation, automated electrocardiographic analysis, and clinical risk prediction. Across multiple cardiovascular settings, AI has been associated with improved diagnostic accuracy, enhanced workflow efficiency, and earlier detection of cardiovascular disease. Predictive models support risk stratification in heart failure and ischemic heart disease, while chatbots and digital health platforms may facilitate patient engagement, remote monitoring, and continuity of care. Despite these advances, important challenges remain, including algorithmic bias, limited transparency, insufficient external validation, data heterogeneity, and barriers to routine clinical implementation. Emerging explainable AI approaches may improve model interpretability, clinician confidence, and the safe adoption of AI-driven decision support systems. Conclusions: Artificial intelligence is rapidly evolving from a research-oriented technology into a clinically relevant component of cardiovascular care. Current evidence indicates that AI can enhance diagnostic performance, improve risk prediction, streamline clinical workflows, and facilitate more personalized management across multiple cardiovascular domains. However, the successful translation of AI into routine practice will depend on robust external validation, transparent decision-making mechanisms, regulatory oversight, and clinician acceptance. The development of explainable and trustworthy AI frameworks represents a critical step toward the safe, ethical, and sustainable integration of AI into modern cardiology. Full article

This study presents a hybrid artificial intelligence system for latent cardiovascular risk stratification based on publicly available clinical and laboratory data. The proposed system integrates data preprocessing, auxiliary target modeling, latent phenotyping using UMAP and Gaussian mixture models, fuzzy logic-based risk integration, and multilevel predictive modeling. The key contribution of the system is the construction of a proxy target reflecting latent risk progression by combining phenotypic structure, probabilistic indicators, and mortality-related anchor points. Experimental evaluation was conducted on the NHANES dataset. The final analytical cohort included 78,822 adult participants, and the modeling set was divided into training, validation, and test subgroups using a stratified 70/15/15 design. The proposed PhaseFuzzy Hybrid model achieved an accuracy of 0.8390, a balanced accuracy of 0.7302, an F1-score of 0.5225, an MCC of 0.4203, an ROC-AUC of 0.8489, a PR-AUC of 0.5014, and a best LogLoss value of 0.4290 on the test set. The latent phenotyping step also demonstrated acceptable internal validity with a silhouette coefficient of 0.4138 and a confidence of 0.8800. The results demonstrate that the proposed framework identifies hidden cardiometabolic risk factors and provides an interpretable, scalable, and calibration-aware framework for latent cardiometabolic risk stratification and population-level screening. Full article

Human digital twins (HDTs) are patient-specific computational models that combine medical imaging, physiological measurements and predictive algorithms. They are moving from an exciting concept to a realistic clinical opportunity. The key question is no longer whether HDTs can be built. The key question is which methods are mature enough to support clinical decisions and what is still missing for routine use. This systematic review maps the methodological landscape of HDTs and highlights practical bottlenecks that limit clinical translation. A PRISMA 2020 guided search of PubMed, Scopus, IEEE Xplore, and the Cochrane Library, covering publications from 2016 to 2026, identified 151 eligible studies. Bibliometric mapping and thematic synthesis were used to characterize research clusters, computational paradigms, and collaboration patterns. Three dominant application streams were identified: cardiovascular HDTs for hemodynamic simulation and procedural planning, musculoskeletal HDTs for biomechanics-driven orthopedic innovation, and neurological HDTs integrating neuroimaging with computational neuroscience. Across domains, the strongest technical trend is the rise in hybrid pipelines that combine physics-based simulation, including finite element and computational fluid dynamics models, with machine learning for segmentation, parameter identification, reduced-order modeling, and faster inference. However, reporting of verification, validation, uncertainty quantification, and explicit context of use remains uneven and prospective clinical evidence is still limited. Overall, the literature shows rapid progress toward clinically credible HDTs, while highlighting the need for scalable computation, standardized credibility pipelines, and workflow-integrated platforms to support safe and reproducible clinical adoption. Full article

Background: Stroke remains a leading cause of mortality, disability, and health-system burden in Korea’s rapidly aging population. We aimed to describe national stroke incidence trends from 2011 to 2023, characterize ecological associations between stroke incidence and health-screening indicators, and generate model-based projections through 2028 to support health-system planning. Methods: This retrospective ecological longitudinal analysis used three publicly available aggregate national data sources: (1) NHIS annual aggregate statistics on crude and age-standardized stroke incidence, stroke case counts, first-onset vs. recurrent stroke, and case-fatality rates (2011–2023); (2) regional standardized health-awareness survey rates for stroke symptoms, myocardial infarction symptoms, blood pressure, and blood glucose (2017–2025); and (3) national cancer-screening outcome tallies for breast and cervical cancer (2010–2024). All analyses used pre-aggregated annual summary data; individual-level NHIS records were not used. Annual trends were modeled with ordinary least-squares linear regression (n = 13 annual observations). Pearson correlations were computed only for overlapping observation windows. Model-based projections are presented with 95% prediction intervals and are explicitly distinguished from observed NHIS values. This study is purely descriptive and ecological; no causal inference is made. Results: Crude stroke incidence increased from 199.2 to 221.1 per 100,000 (2011–2023; slope +2.32/year, R² = 0.83), whereas age-standardized incidence declined from 158.3 to 113.2 per 100,000 (slope −3.41/year, R² = 0.96), a pattern consistent with demographic aging as a contributing factor to growing absolute burden, though formal age-decomposition analysis would be required to confirm this inference. Total cases increased from 99,837 to 113,098; the 30-day case-fatality rate declined from 8.5% to 7.5%. Ecological correlations showed that blood glucose awareness was strongly negatively correlated with age-standardized incidence (r = −0.944, p = 0.001, n = 7), though these are ecological associations and must not be interpreted as individual-level causal relationships. Model-based projections estimate crude incidence near 230.7 (95%PI 219.2–242.2) and age-standardized incidence near 103.2 (95%PI 95.7–110.8) per 100,000 by 2026. Conclusions: Concurrent increase in crude burden and decline in age-standardized incidence reflects demographic aging as the primary driver of Korea’s stroke burden. Projections support integrated cardiovascular prevention, public health education, and age-sensitive service planning. All projections are short-horizon statistical extrapolations intended for policy scenario planning only and must not be interpreted as observed future NHIS outcomes. Full article

Background/Objectives: Parathyroid hormone (PTH) has emerged as a novel biomarker in heart failure (HF), reflecting neurohormonal, renal, and metabolic dysregulation within the cardiorenal–mineral axis. However, its independent prognostic value and incremental contribution remain unclear when evaluated through formal nested structures Therefore, this study aimed to evaluate the association between PTH and all-cause mortality in patients with heart failure with reduced ejection fraction (HFrEF) and to determine whether PTH provides additional prognostic information beyond NT-proBNP. Methods: In this retrospective cohort study, 1594 patients with HFrEF (LVEF ≤ 40%) were analyzed. Serum PTH and NT-proBNP levels were log-transformed and evaluated as predictors of all-cause mortality. Patients were stratified according to PTH levels, and survival analysis was performed. Incremental model fit was evaluated using nested likelihood ratio tests. Stratified multivariable Cox models and formal interaction tests were executed across predefined clinical strata (age, renal function, and heart failure etiology). Results: During a median follow-up of 36 months, 525 deaths occurred. Elevated PTH levels were associated with worse survival outcomes. In multivariable Cox regression analysis, both LnPTH (HR: 1.233, p = 0.0147) and LnNT-proBNP (HR: 1.374, p < 0.0001) were independent predictors of mortality. Combined elevation of PTH and NT-proBNP identified patients at the highest risk. The addition of LnPTH to the baseline model significantly improved global model fit (χ² = 4.242, p = 0.0394). Importantly, the prognostic value of LnPTH was significantly modified by age (P_interaction = 0.026) and renal function (P_interaction = 0.038), demonstrating independent predictive power specifically in patients aged < 65 years (HR: 1.402) and those with eGFR ≥ 60 mL/min/1.73 m² (HR: 1.454), but not in older or advanced renal impairment strata. Conclusions: PTH is independently associated with mortality in patients with HFrEF and provides incremental prognostic value beyond NT-proBNP by optimizing global model fit. These findings support its role as a complementary biomarker within a multimarker strategy for improved risk stratification of the cumulative metabolic and cardiovascular burden. Full article

Clonal hematopoiesis of indeterminate potential (CHIP) and the gut microbiota-derived metabolite trimethylamine N-oxide (TMAO) are both linked to NLRP3-mediated cardiovascular inflammation, but their interaction has not previously been explored. This work proposes the CHIDT axis (clonal hematopoiesis–dysbiosis–TMAO), a feed-forward mechanism in which TET2 loss-of-function CHIP- and TMAO-generating Gram-negative gut dysbiosis mutually enhance cardiovascular risk. The model proceeds in three nodes. CHIP-associated intestinal immune dysregulation promotes luminal expansion of Gammaproteobacteria, which produce both trimethylamine via CntA/CntB-mediated L-carnitine oxidation and ADP-heptose as an obligate LPS biosynthetic intermediate. TMAO amplifies NLRP3 inflammasome activation through the SIRT3 → SOD2 → mtROS pathway. The evidence base of the CHIDT model is strongest for TET2-CHIP; the proposed extension to DNMT3A-CHIP rests on indirect, associative data and requires dedicated experimental confirmation before it can be considered established. TXNIP cascade, with predicted disproportionate potency in macrophages epigenetically primed by TET2 haploinsufficiency. High concentrations of TMAO have also been shown to suppress TET2 expression in endothelial cells through CYTB promoter hypermethylation, inducing NLRP3–GSDMD-dependent pyroptosis, although it remains unclear whether physiological TMAO levels can trigger this effect. Concurrently, ADP-heptose activates the ALPK1–TIFA–NF-κB pathway in bone marrow progenitors, favoring the expansion of mutant hematopoietic stem and progenitor cells. The model identifies three potential therapeutic strategies: NLRP3 inhibition, microbial TMA lyase inhibition, and microbiome-targeted reduction in Gram-negative bacteria. None has been tested in CHIP carriers stratified by plasma TMAO. Further studies in preclinical models and human cohorts integrating CHIP genotyping and TMAO quantification are needed to validate the CHIDT axis as a target for precision cardiovascular prevention. Full article

The development of atrial fibrillation involves the synergistic effects of electrical remodeling, structural remodeling and neural remodeling, among which remodeling of the autonomic nervous system (ANS) plays a pivotal role in disease initiation and progression. Heart rate variability (HRV), as an important tool for assessing autonomic function, has been widely applied in cardiovascular research. In particular, ultra-short-term heart rate variability (usHRV) analysis has demonstrated significant value in the early prediction of atrial fibrillation. Full article

Background and Objectives: Transcatheter aortic valve implantation (TAVI) is the standard therapy for patients with severe aortic stenosis at intermediate or high surgical risk. Mitral annular calcification (MAC) is frequently observed in this population and has been linked to adverse cardiovascular outcomes. This study evaluated the association between MAC and TAVI-related complications and mortality, and identified predictors of all-cause mortality and permanent pacemaker implantation (PPI) following TAVI. Materials and Methods: Patients undergoing self-expanding TAVI between January 2010 and June 2020 were retrospectively analyzed. Outcomes included TAVI-related complications, in-hospital and long-term mortality, and predictors of all-cause mortality and PPI. Results: A total of 245 patients (98 men [40%], mean age 76.3 ± 8.3 years) were included. Mean left ventricular ejection fraction was 54.8 ± 11.4%, and aortic valve area was 0.74 ± 0.14 cm². MAC was present in 148 patients (60.4%). Pericardial effusion (26.4% vs. 12.4%, p = 0.013) and acute kidney injury (21.6% vs. 7.2%, p = 0.005) were significantly more frequent in patients with MAC. PPI was required in 42 patients (17.8%). In-hospital mortality occurred in 14 patients (5.7%), and all-cause mortality was observed in 89 patients (36.3%) during a median follow-up of 23.1 months (IQR, 11.6–44.3). MAC extension into the left ventricular outflow tract was the only independent predictor of PPI (OR: 3.32, p = 0.002). Independent predictors of all-cause mortality included use of renin–angiotensin–aldosterone system blockers (HR: 0.54, p = 0.012), hemoglobin level (HR: 0.79, p = 0.006), severe MAC (HR: 1.94, p = 0.024), and post-TAVI atrial fibrillation (HR: 2.39, p = 0.002). Conclusions: MAC is common in TAVI patients and is associated with increased procedural complications, including higher rates of pericardial effusion and acute kidney injury. Greater MAC severity independently predicts higher all-cause mortality. In addition, MAC extension into the left ventricular outflow tract is an independent predictor of PPI following self-expanding TAVI, emphasizing the importance of comprehensive pre-procedural imaging. Full article

Background: Patients undergoing open-heart surgery (OHS) are at risk of postoperative morbidity and mortality. Physical performance has been increasingly recognized as an important factor influencing postoperative outcomes. Therefore, the study aimed to investigate the associations and predictive value of physical performance on postoperative complications and duration of hospital stay. Methods: A prospective cohort study was conducted in 116 patients who were admitted to OHS. Preoperative assessment of physical performance, i.e., Short Physical Performance Battery (SPPB), Five Times Sit to Stand Test (5STS), gait speed (5 m walk test: 5MWT), Timed Up and Go (TUG), and handgrip strength. Duration of hospital stay and incidence of post-operative complications were recorded. Differences between participants with and without postoperative complications were analyzed using independent samples t-tests for continuous variables and chi-square tests for categorical variables. The associations between physical performance and postoperative outcomes were assessed using Spearman’s rank correlation coefficient. Hierarchical regression analysis was conducted to determine the predictive contribution of physical performance. Results: A total of 116 participants were submitted for OHS in two medical school hospitals; however, 108 individuals completed the pre-operative physical performance. The most common procedures were coronary artery bypass grafting and valve surgery. Fifty-one participants (47.22%) experienced postoperative complications, including five deaths, corresponding to 4.63% mortality. For the length of hospital stay analysis, five participants who died postoperatively were excluded, resulting in a final sample of 103 participants. Physical performance was significantly associated with the length of hospital stay (p < 0.05). Hierarchical regression analysis showed that the final prediction model explained 13.4% of the variance in length of hospital stay, with SPPB independently contributing an additional 6.0% to the model, followed by 5STS, 5MWT, handgrip strength, and TUG, which accounted for an additional 5.1%, 4.6%, 4.4%, and 3.7%, respectively. Conclusions: Preoperative physical performance was associated with length of hospital stay. While each measure explained a relatively small proportion of the variance in hospital stay, these assessments offer a simple, non-invasive, and clinically feasible approach to evaluating functional reserve before surgery. These findings highlight the importance of incorporating functional assessment into perioperative care to support risk stratification and guide rehabilitation strategies. Full article

Background: The triglyceride–glucose (TyG) index has emerged as a simple surrogate marker of insulin resistance and metabolic disruption. In the context of ST-elevation myocardial infarction (STEMI), such disturbances have been associated with adverse cardiovascular outcomes, more complex angiographic profiles, and microvascular complications. However, data on the association between TyG and intracoronary thrombus burden (TB) in STEMI remain limited. Methods: In this prospective observational study, we included consecutive STEMI patients treated with primary percutaneous coronary intervention (pPCI). The TyG index was calculated using the following formula: ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. TB was graded according to the modified thrombolysis in myocardial infarction (mTIMI) thrombus classification score after restoration of antegrade flow with a wire or small balloon when the culprit vessel was initially totally occluded. Patients were categorized as low-TB (LTB; mTIMI grades 1–3) and high-TB (HTB; mTIMI grade 4). The primary outcome was HTB; secondary outcomes were distal embolization and no-reflow. Associations between TyG and outcomes were assessed using univariable and multivariable logistic regression, restricted cubic spline analysis, and receiver operating characteristic (ROC) curves to evaluate incremental predictive value. Results: A total of 309 patients were analyzed. The TyG index was significantly higher in the HTB group compared with the LTB group (9.12 ± 0.62 vs. 8.92 ± 0.64, p = 0.004). In a stepwise multivariable model, TyG remained independently associated with HTB (adjusted odds ratio = 1.61; 95% confidence interval: 1.11–2.37; p = 0.014). Adding TyG to a baseline clinical model only numerically improved discrimination for HTB, as reflected by a small increase in ROC area under the curve. Restricted cubic spline analysis demonstrated a monotonic rise in the probability of HTB with higher TyG values. Higher TyG also showed non-significant trends toward increased odds of distal embolization and no-reflow. Conclusions: The TyG index was independently associated with HTB in STEMI patients undergoing pPCI and may serve as an accessible adjunctive marker for incremental risk stratification beyond conventional clinical and angiographic factors. Full article