Search Results (2,566)

Background/Objectives: Individualized treatment selection for head and neck cancer requires survival models that use routine clinical variables while accounting for censored time-to-event outcomes. This study developed an interpretable Cox proportional hazards baseline and a DeepSurv framework to estimate mortality risk and explore treatment-specific predictions among radiotherapy-based options. Methods: Clinical data were obtained from the RADCURE collection in The Cancer Imaging Archive. After preprocessing, stage harmonization, exclusion of sparse treatment categories, missing-data assessment, one-hot encoding, and standardization, 3266 patients were analyzed. Cox regression and DeepSurv were evaluated using a held-out 80%/20% split, paired bootstrap confidence intervals for C-index differences, and five-fold cross-validation. For treatment recommendation, treatment modality was hypothetically varied across radiotherapy alone, chemoradiotherapy, and radiotherapy plus epidermal growth factor receptor inhibitor while other covariates were held fixed. Results: On the held-out test set, Cox achieved a C-index of 0.684 and DeepSurv achieved a C-index of 0.695. The absolute difference was 0.011, with a paired bootstrap 95% CI of −0.010 to 0.030, indicating no statistically significant improvement. Five-fold cross-validation showed mean C-index values of 0.688 for Cox and 0.707 for DeepSurv. Cox regression identified older age and advanced tumor stage as higher-risk factors, whereas former and non-smoking status were associated with lower hazard than current smoking. Conclusions: DeepSurv provided only a modest numerical gain over the Cox baseline. The recommendation framework illustrates how survival models can generate treatment-specific risk estimates, but these outputs should be interpreted as decision-support signals rather than causal treatment effects. External validation and prospective evaluation are needed before clinical use. Full article

Background/Objectives: Multimorbidity is increasingly recognized as a major contributor to mortality worldwide, yet its underlying patterns and prognostic implications remain poorly understood in the United States. This study identified distinct multimorbidity patterns and examined their association with all-cause mortality in a nationally representative sample of U.S. adults. Methods: We conducted a retrospective cohort study using data from the 2004 National Health Interview Survey linked to the National Death Index through 2019 (n = 28,598). Latent class analysis identified unobserved multimorbidity classes based on patterns of co-occurring physician-diagnosed chronic conditions, and Cox proportional hazards models were fitted to estimate mortality risk while accounting for complex survey design. Six distinct multimorbidity classes were identified, reflecting cardiometabolic, respiratory, cardiovascular, and inflammatory disease profiles. Results: Compared with the Low Multimorbidity group, all other classes were associated with increased mortality risk. In fully adjusted analyses, the Severe Cardiopulmonary–Metabolic (HR 3.71, 95% CI 2.99–4.60) and Advanced Cardiovascular (HR 2.81, 95% CI 2.52–3.14) classes showed the highest risks. Intermediate risks were observed in the Cardiometabolic–Arthritis (HR 2.45, 95% CI 2.13–2.81) and Respiratory–Musculoskeletal (HR 1.39, 95% CI 1.22–1.58) classes, while the Inflammatory Pain–Airway class showed a more modest increase. Subgroup analyses suggested stronger relative effects among younger adults and women in the most severe classes. Conclusions: The study findings highlight the heterogeneous nature of multimorbidity and suggest that specific disease clusters carry substantially different mortality risks. Recognizing these patterns may improve risk stratification and support more targeted, patient-centered care strategies. Full article

Background: Anatomical staging is often insufficient for predicting outcomes in stage I-IIIA non-small cell lung cancer (NSCLC) patients after curative resection due to inherent prognostic variability. In this study, we evaluated the prognostic value of the C-reactive protein–albumin–lymphocyte–monocyte (CALM) index as an integrative biomarker for overall survival (OS). Methods: This retrospective study included 533 patients with stage I-IIIA NSCLC. We assessed the association between the CALM index and OS using multivariable Cox proportional hazards models, adjusted for established clinicopathological factors. We employed LASSO-penalized regression to optimize variable selection and ensure model stability. Comprehensive performance metrics were calculated, including Harrell’s C-index, integrated area under the curve (iAUC), integrated discrimination improvement (IDI), and decision-curve analysis (DCA). Results: The CALM index was identified as a stable, independent predictor of OS across both conventional and LASSO-penalized models. The final multivariable model incorporated the CALM index alongside age, pathological stage, American Society of Anesthesiologists status, pleural invasion, and the modified Shine–Lal index. Incorporation of the CALM index significantly enhanced model discrimination and risk reclassification at 3 and 5 years, while consistently increasing net clinical benefit across a wide range of threshold probabilities. Conclusions: The CALM index is a biologically plausible, robust, and readily accessible prognostic biomarker that provides incremental prognostic information beyond established factors in patients with resected stage I-IIIA NSCLC. Its integration into postoperative assessment offers a framework for refined prognostic stratification and more individualized clinical decision-making. Full article

Background: Recurrence risk in completely resected stage II–III cutaneous melanoma remains incompletely defined by conventional staging alone. This study evaluated whether the baseline vitamin B12 (VB12)/C-reactive protein (CRP) index (BCI), a simple biomarker reflecting metabolic and inflammatory status, is associated with recurrence-free survival (RFS). Methods: This retrospective study included 136 patients. RFS was analyzed using Kaplan–Meier curves and Cox proportional hazards models. Receiver operating characteristic (ROC) analysis was used to determine the optimal BCI cut-off for recurrence, and a median-based cut-off was additionally examined as a sensitivity analysis. The natural logarithms (ln) of CRP, VB12, and BCI were standardized and compared in adjusted Cox models. Results: During a median follow-up of 62.0 months, recurrence occurred in 49 patients (36.0%). ROC analysis yielded an area under the curve of 0.66 (95% confidence interval [CI]: 0.57–0.76; p = 0.002), and the optimal BCI cut-off was 3386, with 65% sensitivity and 67% specificity. Patients with high BCI had significantly shorter median RFS than those with low BCI (63.5 months vs. not reached; p < 0.001). In multivariable analysis using the ROC-derived cut-off, higher Breslow thickness (hazard ratio [HR]: 1.10, 95% CI: 1.03–1.17; p = 0.002), stage III disease (HR: 2.37, 95% CI: 1.06–5.30; p = 0.035), and high BCI (HR: 2.39, 95% CI: 1.30–4.38; p = 0.005) remained independently associated with shorter RFS. The association remained significant using the median BCI cut-off (HR: 2.22, 95% CI: 1.21–4.07; p = 0.010). In head-to-head adjusted models, standardized ln(CRP) (HR per 1-standard-deviation [SD] increase: 1.57, 95% CI: 1.17–2.11; p = 0.002) and ln(BCI) (HR per 1-SD increase: 1.54, 95% CI: 1.16–2.04; p = 0.003) were associated with shorter RFS, whereas ln(VB12) was not (HR per 1-SD increase: 1.01, 95% CI: 0.80–1.28; p = 0.894). The CRP- and BCI-based models showed similar model fit. Conclusions: Higher baseline BCI was associated with shorter RFS after adjustment for clinicopathological factors in patients with resected stage II–III cutaneous melanoma. However, its prognostic performance was comparable to that of CRP alone, and its incremental value over CRP was not established. These findings are exploratory and require external validation before clinical application. Full article

Background: Immune checkpoint inhibitors (ICIs) have significantly improved outcomes in metastatic non-small cell lung cancer (NSCLC), yet only a subset of patients derives durable benefit, suggesting that host and tumor-related factors may modify treatment efficacy. The renin–angiotensin system has been implicated in regulation of the tumor microenvironment, and angiotensin-converting enzyme inhibitors (ACEIs) have therefore been proposed as potential modulators of immunotherapy response. Material and methods: We conducted a retrospective observational cohort study including patients with advanced metastatic NSCLC treated in the first-line setting with treatment-based immunotherapy, with or without chemotherapy, between January 2017 and September 2025. Chronic ACEI exposure was defined as continuous use for at least two years prior to initiation of immunotherapy. Progression-free survival (PFS) and overall survival (OS) were analyzed using Kaplan–Meier estimates and compared using the log-rank test, and multivariable Cox proportional hazards models were adjusted. Results: Among 446 eligible patients, 71 (16%) received ACEIs and 375 (84%) did not. The median age of the cohort was 67.5 years, and 70% were male. Adenocarcinoma was the predominant histology (67.7%), and most patients received chemo–immunotherapy (81.6%), while 18.4% received immunotherapy alone. PD-L1 expression ≥ 1% was present in 59.2% of patients. In the overall cohort, median PFS and OS were 12 and 15 months, respectively. Median OS was 17 months in the ACEI group compared with 14 months in the non-ACEI group (log-rank p < 0.047), while median PFS was 14 months versus 11 months, respectively (p = 0.066). The survival advantage was more pronounced for OS than for PFS and remained consistent after adjustment for clinical characteristics including age, sex, ECOG performance status, smoking status, histology, treatment regimen, and PD-L1 expression. Conclusions: These findings suggest that chronic ACE inhibitor use may be associated with improved outcomes in metastatic NSCLC patients treated with immune checkpoint inhibitors. Full article

Introduction: Patients with rheumatoid arthritis (RA) have higher risk for pneumonia, interstitial lung disease (ILD) and pulmonary fibrosis (PF). However, the association between an ICD-10-coded pneumonia event (CPE) and the incidence of ILD or PF in the RA population remains unclear. Methods: We conducted a retrospective cohort study using the TriNetX database. Patients with ICD-10 for RA aged 50 or older who had a CPE within one year of RA diagnosis (CPE cohort, n = 4553) were matched 1:1 by propensity score for key factors, including demographics, comorbidities (i.e., COPD), and medication use (DMARDs, corticosteroids) to RA patients without a CPE (Control cohort, n = 4553). Cox proportional hazard models assessed the incidence of a composite ILD outcome, PF, and secondary complications over a 4-year follow-up after the index event defined as 1-year after RA diagnosis for both cohorts. Results: The CPE cohort showed an increased risk for all outcomes. Patients with CPE had a 2.48-fold increased risk for PF (HR = 2.48; 95% CI, 1.78–3.45; p < 0.01) and a 2.87-fold increased risk for the composite ILD outcome (HR = 2.87; 95% CI, 2.18–3.80; p < 0.01). The risk of rheumatoid lung disease was 4.15 times higher (HR = 4.15; 95% CI, 2.30–7.50; p < 0.01). Furthermore, the CPE group had a higher risk for all-cause mortality (HR = 1.82; 95% CI, 1.58–2.09; p < 0.01). Conclusions: The CPE within one year of RA diagnosis is associated with an increase in subsequent ILD-coded outcomes. While this retrospective design cannot establish causality, an unspecified pneumonia code in early RA may represent an early clinical manifestation of unrecognized ILD, serving as a high-risk marker that warrants pulmonary surveillance. Full article

Background and Objectives: Lung adenocarcinoma (LUAD) is a clinically heterogeneous malignancy, and reliable prognostic biomarkers are still needed. Kinesin family members 4A and 4B (KIF4A and KIF4B) are mitosis-related motor proteins with potential functional overlap as paralogs. However, their coordinated prognostic significance in LUAD has not been systematically investigated. Materials and Methods: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) were analyzed to evaluate the expression patterns, clinicopathologic associations, and prognostic significance of KIF4A and KIF4B in LUAD. Correlation analysis, Kaplan–Meier survival analysis, and Cox proportional hazards regression analyses were performed. In addition, a combined paralog score and four-group expression model were evaluated. An independent LUAD cohort from the Gene Expression Omnibus (GEO; GSE81089) was analyzed for external validation. Results: KIF4A and KIF4B expression showed a strong positive correlation (r = 0.767, p < 0.001), and both genes were positively correlated with EGFR, KRAS, BRAF, MKI67, and PCNA expression. High KIF4A expression was significantly associated with age, sex, smoking status, pathologic stage, N stage, and T stage, whereas high KIF4B expression was significantly associated with age and pathologic stage. Kaplan–Meier analysis demonstrated that high expression of both KIF4A and KIF4B was associated with poorer overall survival. In Cox regression analyses, elevated expression of both genes remained significantly associated with unfavorable overall survival in univariable and multivariable models. However, when both genes were simultaneously included in the same Cox model, their individual prognostic effects were attenuated, suggesting substantial overlap in prognostic information. By contrast, the combined paralog score remained independently associated with poor overall survival. In four-group analysis, only patients with concurrent high expression of both KIF4A and KIF4B showed significantly worse overall survival compared with the low/low group. External validation demonstrated generally consistent survival patterns, although the prognostic associations were attenuated after multivariable adjustment. Conclusions: KIF4A and KIF4B expression showed substantial prognostic overlap in LUAD, and their concurrent high expression was associated with poorer overall survival. These findings suggest that KIF4A/KIF4B co-expression may serve as a candidate prognostic indicator that warrants validation in independent clinical cohorts and functional studies. Full article

Objectives: Assessment of the impact on survival of endometrial carcinoma according to the 2009 FIGO (International Federation of Gynecology and Obstetrics) classification and the new FIGO 2023 classification highlighting the worse prognosis of the aggressive subtypes. Methods: This multicenter retrospective study included 1181 patients with endometrial cancer. Comprehensive clinical, pathological and treatment-related variables were collected. Primary outcomes included overall survival assessed through five-year follow-ups. Statistical analysis included comparative tests, Kaplan–Meier survival estimation, Cox proportional hazards models and ROC curves analysis to review prognostic accuracy. Results: Aggressive endometrial carcinoma (n = 353) showed significant worse overall survival compared with non-aggressive cases (35.7 versus 60 months). A novel classification based on FIGO 2023 was developed, integrating histological aggressiveness into a different stage and combining early non-aggressive stages in only one stage. While FIGO 2009 and 2023 classifications showed prognostic value, the new model improved risk stratification, clearly distinguishing high-risk groups. Multivariate analysis identified aggressive subtype, stage, age, diabetes, myometrial invasion and lymphovascular invasion as independent predictors. Conclusions: Aggressive histological subtype in endometrial cancer should carry greater prognostic weight in terms of survival and clinical management. Our findings support a potential shift in the current paradigm for these relatively rare but high-risk cases. Full article

Background/Objectives: Metabolic-obesity phenotypes are dynamic, yet little is known about how their longitudinal transitions affect cardiovascular disease (CVD) risk. We aimed to characterize these transitions and examine their associations with incident CVD in middle-aged and older Chinese adults. Methods: We included 4516 participants from the China Health and Retirement Longitudinal Study (CHARLS) who were free of CVD at the follow-up baseline (wave 3). Metabolic-obesity phenotypes (metabolically healthy non-obese [MHNO], metabolically unhealthy non-obese [MUNO], metabolically healthy obese [MHO], and metabolically unhealthy obese [MUO]) were assessed in 2011 and 2015, and six trajectory patterns were defined. Incident CVD (heart disease or stroke) was ascertained from 2015 to 2020. Cox proportional hazards models estimated associations between trajectory groups and incident CVD, and Kaplan–Meier curves compared cumulative incidence across groups. Results: Stable MHNO accounted for 35.96% of the cohort, whereas the stable non-MHNO group accounted for 28.06%. After multivariable adjustment, the small obesity recovery group (n = 89) showed an elevated CVD risk estimate compared with the stable MHNO (HR 2.32, 95% CI 1.33–4.05), while stable non-MHNO group were associated with higher CVD risk (HR 1.72, 95% CI 1.36–2.18). Incident obesity showed elevated but non-significant risk (HR 1.71, 95% CI 0.94–3.10), and metabolic decline showed borderline significance (HR 1.36, 95% CI 1.00–1.85). Sex-stratified analyses showed heterogeneous risk patterns. Conclusions: Metabolic-obesity phenotypes are dynamic, but unfavorable phenotypes often persist. The obesity recovery group, defined using BMI, may represent a heterogeneous group and does not necessarily indicate true cardiometabolic recovery; the observed association should not be interpreted as evidence that weight loss itself increases CVD risk. These findings highlight the importance of jointly considering longitudinal changes in metabolic health and obesity when examining their associations with incident CVD. Full article

Background: Heart failure with preserved ejection fraction (HFpEF) accounts for over half of all heart failure (HF) cases and remains diagnostically challenging due to nonspecific symptoms and a lack of accessible noninvasive screening tools, leading to widespread underdiagnosis and delayed treatment. The electrocardiogram (ECG) is a low-cost, widely available tool that reflects myocardial electrical remodeling. We previously developed and externally validated an ECG-based artificial intelligence (ECG-AI) model capable of classifying ECGs into four categories: reduced ejection fraction (rEF), mid-range ejection fraction (mEF), HFpEF, and controls. In this study, we evaluate the ability of this ECG-AI model to predict future HFpEF diagnosis using real-world data from a large integrated health system. Methods: We applied the validated ECG-AI model to an independent cohort of 7713 patients from Wake Forest Baptist Health (WFBH), using one ECG per patient recorded before the clinical diagnosis of HFpEF. Model discrimination was evaluated across multiple prediction windows from six months up to ten years before diagnosis using the area under the receiver operating characteristic curve (AUC), with comparisons performed using DeLong’s test. Performance was also compared head-to-head with the H2FPEF score, a validated clinical score for HFpEF, in the subset of patients with complete data for score calculation. ECG-AI outputs were stratified into quartiles and evaluated using Kaplan–Meier survival analysis and multivariable Cox proportional hazards regression adjusted for demographics and major comorbidities. Results: Of the 7713 patients, 283 (3.7%) were subsequently diagnosed with HFpEF. The ECG-AI model achieved an AUC of 0.79 (95% CI, 0.72–0.86) using ECGs recorded within six months before diagnosis. Discrimination remained stable across prediction windows extending up to ten years before diagnosis (AUC range, 0.78–0.80; all DeLong p > 0.01 vs. the six-month window). Compared with the H2FPEF score, ECG-AI demonstrated superior discrimination, improving the AUC by 0.06–0.07 across the evaluated prediction windows. Patients in the highest ECG-AI risk quartile had an unadjusted hazard ratio (HR) of 11.18 (95% CI, 7.27–17.21; C-index, 0.75) and an adjusted HR of 6.60 (95% CI, 4.24–10.28; C-index, 0.82). Adding clinical covariates to ECG-AI improved the C-index by 0.07 and 0.09 for the five- and ten-year prediction windows, respectively. Conclusions: An independently validated ECG-AI model predicted incident HFpEF up to ten years before clinical diagnosis and outperformed the H2FPEF score using ECG alone, indicating that ECG-derived signatures precede clinical recognition of the syndrome. ECG-AI shows promise as a prognostic, risk-stratification tool to prioritize further evaluation; prospective validation and single-lead assessment are needed before screening or wearable deployment. Full article

Background and Objectives: Risk stratification is an important tool for guiding clinical decision-making in patients with non-ST-segment elevation myocardial infarction (NSTEMI), yet mortality remains considerable despite contemporary therapeutic advances. Endothelial Activation and Stress Index (EASIX), calculated using lactate dehydrogenase, serum creatinine, and platelet count, is a readily available composite prognostic index that has demonstrated prognostic value across various cardiovascular settings. However, its role in NSTEMI has not been fully established. We therefore evaluated the association between EASIX and 1-year mortality and examined whether it provides incremental prognostic information beyond the GRACE risk score. Materials and Methods: We retrospectively evaluated 624 consecutive patients with NSTEMI who underwent invasive coronary angiography. EASIX was calculated from laboratory parameters obtained at admission. The prognostic significance of EASIX was evaluated using Cox proportional hazards models, Kaplan–Meier survival analysis, restricted cubic spline modelling, and incremental performance metrics. Results: During 1-year follow-up, 75 patients (12.0%) died. Admission EASIX values were higher among non-survivors than survivors (p < 0.001). Mortality increased progressively across EASIX tertiles (p < 0.001). In multivariable analyses, log₂(EASIX) remained independently associated with mortality both in the clinical model (HR 1.381, p = 0.002) and after adjustment for the GRACE score (HR 1.315, p = 0.005). Restricted cubic spline analyses supported a graded relationship between EASIX and mortality risk. Addition of EASIX to the GRACE score improved discrimination (ΔAUC = 0.015, p = 0.032) and risk reclassification (continuous NRI = 0.385, p = 0.020). Conclusions: Admission EASIX emerged as an independent predictor of 1-year mortality among patients with NSTEMI. Furthermore, it provides incremental prognostic information beyond the GRACE risk score and may represent a simple, inexpensive, and readily available tool for risk stratification in contemporary NSTEMI practice. Full article

Background: Surgical sepsis complicated by acute kidney injury (AKI) is associated with high mortality, and early risk stratification remains challenging. The prognostic nutritional index (PNI), derived from serum albumin and lymphocyte count, reflects nutritional and immune status, but its prognostic value in surgical sepsis with AKI has not been well defined. Methods: In this retrospective cohort study based on the Medical Information Mart for Intensive Care IV (MIMIC-IV) (version 3.1) database, we included adult patients admitted to a surgical or surgery-related intensive care unit (ICU) between 2008 and 2022 who met the Sepsis-3 criteria and developed AKI according to KDIGO. The primary endpoint was 90-day all-cause mortality. We used multivariable Cox proportional hazards models, restricted cubic splines, Kaplan–Meier analysis, and predefined subgroup analyses to examine the association between PNI at ICU admission and 90-day mortality. Results: A total of 1483 patients were included, with a 90-day mortality rate of 30.2%. Non-survivors had a lower median PNI than survivors (35 vs. 36, p < 0.001). After sequential adjustment for demographics, comorbidities, illness severity, and major interventions, each 1-point increase in PNI was associated with a 1.5% reduction in 90-day mortality (hazard ratio 0.985, 95% confidence interval 0.974–0.997, p = 0.012). Restricted cubic spline analysis showed an approximately linear inverse relationship between PNI and mortality risk (p for overall association = 0.046; p for nonlinearity = 0.534). Using an optimal cut-off of 29.51, patients with low PNI had significantly lower 90-day survival than those with high PNI (log-rank p < 0.0001), and subgroup analyses demonstrated generally consistent protective associations across age, sex, illness severity, and key treatments. In propensity score-matched analysis, the association was attenuated and no longer significant (HR = 1.174, 95% CI: 0.901–1.529, p = 0.234), although the direction of effect remained consistent. Conclusions: Lower PNI at ICU admission was associated with higher 90-day mortality in multivariable-adjusted models and may serve as a simple, routinely available adjunctive marker for early risk stratification in surgical sepsis patients with AKI. However, given the non-significant finding in propensity score-matched analysis, its independent prognostic value remains uncertain, and further prospective studies are needed to validate its clinical utility. Full article

Background/Objectives: Type 2 diabetes mellitus (DM) and carotid atherosclerosis are established risk factors for cardiovascular disease (CVD). However, their relative and combined prognostic significance in individuals without known CVD remains uncertain. This study aimed to evaluate the independent and combined associations of carotid plaques and DM with cardiovascular events and mortality in a high-cardiometabolic-risk primary prevention cohort. Methods: This prospective cohort study followed 6138 participants (3571 men [57%] and 2567 women [43%]; median age 53 years, IQR 48–58) enrolled in the Lithuanian High Cardiovascular Risk (LitHiR) primary prevention program from 2006 to 2023. All individuals underwent clinical assessment and carotid ultrasonography. Participants were stratified by DM status and the presence of carotid plaques. Associations with cardiovascular events, major adverse cardiovascular events (MACE, including non-fatal ischaemic stroke, non-fatal myocardial infarction, and cardiovascular death), and all-cause mortality were evaluated using Cox proportional hazards models. Results: During follow-up (median 6.5 years, IQR 4.5–9.0), 954 participants (16%) experienced composite cardiovascular events (MACE, including non-fatal ischaemic stroke, non-fatal myocardial infarction, and cardiovascular death). Bilateral carotid plaques were more common in individuals with cardiovascular events (23.3% vs. 19.4%; p = 0.007). Individuals with both DM and carotid plaques had the highest risk of MACE (HR = 1.696; 95% CI: 1.346–2.136; p < 0.001), myocardial infarction (MI; HR = 4.089; 95% CI: 1.590–10.516; p = 0.003), and all-cause mortality (HR = 1.802; p = 0.015). The presence of carotid plaques without DM was independently associated with increased risk of MACE (HR = 1.234; 95% CI: 1.062–1.433; p = 0.006) and MI (HR = 2.492; 95% CI: 1.176–5.278; p = 0.017), but not all-cause mortality (HR = 0.986; 95% CI: 0.689–1.413; p = 0.941). In contrast, DM without carotid plaques was not significantly associated with any outcome examined. Conclusions: In this high-cardiometabolic-risk primary prevention cohort, carotid plaques were independently and more strongly associated with cardiovascular outcomes than diabetes alone. Individuals with both carotid plaques and diabetes showed the highest observed risk of adverse cardiovascular events and all-cause mortality. These findings are associational and apply to a high-risk metabolic syndrome cohort; extrapolation to the general population requires further study. The results support the prognostic value of carotid plaque assessment as an adjunctive tool in cardiovascular risk evaluation. Full article

Background: Transcatheter Edge-to-Edge Repair (TEER) is a therapeutic option established for older patients with heart failure and concomitant mitral regurgitation. Frailty is associated with prognosis after transcatheter valve interventions. However, despite clinical potential, evidence for gait speed as a simplified prognostic marker in patients undergoing TEER remains insufficient. Objectives: This study aimed to investigate the association between preoperative gait speed and mid- to long-term mortality after TEER. Methods: We conducted a single-center retrospective cohort study of 97 patients (mean age: 78.9 ± 8.7 years; 56.7% male) who survived the first 7 days after TEER and had available preoperative gait speed data. Preoperative gait speed was assessed, and clinical data were obtained from medical records. Cox regression analysis was performed to elucidate the association between preoperative gait speed and mortality. Results: In Cox proportional hazards models, higher gait speed (per 0.1 m/s increase) was associated with lower mortality after adjustment for the Society of Thoracic Surgeons risk score and handgrip strength (hazard ratio: 0.81; 95% confidence interval: 0.68–0.97; p = 0.02). In time-dependent receiver operating characteristic curve analysis, gait speed showed moderate discriminative ability for mortality, with area under the curve values of 0.749 at 1 year and 0.710 at 2 years. A gait speed of 0.8 m/s was used as an exploratory threshold for survival stratification, and patients with gait speed < 0.8 m/s had lower survival than those with gait speed ≥ 0.8 m/s (log-rank p < 0.01). Conclusions: Lower preoperative gait speed was associated with higher mid- to long-term mortality after TEER. Preoperative gait speed may provide clinically useful information for exploratory risk stratification, although the cutoff-based findings require external validation. Full article

Background/Objectives: Multiple sclerosis (MS) is a chronic immune-mediated neurological disorder for which few modifiable risk factors are established. Vitamin C, due to its antioxidant and neuroprotective properties, has been hypothesized to reduce MS risk, but epidemiological evidence remains inconsistent. Methods: We conducted a prospective cohort study using UK Biobank data, including 486,908 adults aged 37–70 years free of neurological disease. Regular vitamin C supplementation was self-reported at recruitment. Incident MS cases were identified during a median follow-up of 13.5 years. Propensity score weighting was used to balance a wide range of demographic, lifestyle, and health-related confounders. Weighted Cox proportional hazards models were used to estimate hazard ratios (HRs). Effect modification by polygenic risk score (PRS) for MS was assessed, and multiple sensitivity analyses were performed. Results: During follow-up, 452 participants were diagnosed with MS. Vitamin C supplementation was reported by 8.8% of participants (missingness = 1.5%) and was associated with a lower risk of incident MS (HR = 0.51, [95%CI: 0.32; 0.82], p = 0.004). The estimate was consistent across multiple sensitivity analyses. The association varied according to genetic susceptibility, with significant nonlinear multiplicative interaction (p = 0.004) and additive interaction (p < 0.001). Specifically, significant associations were observed only among those with average or high MS-PRS. Conclusions: Vitamin C supplementation was associated with a lower risk of incident MS, with the association varying across levels of genetic susceptibility. Although residual confounding cannot be excluded, sensitivity analyses did not identify similar associations for overall supplement use or multivitamin use, providing some reassurance against a generalized healthy-user effect. Replication in independent cohorts is warranted. Full article