Li Fan

Background: Elderly patients with coexisting coronary artery disease (CAD) and atrial fibrillation (AF) are at significantly increased risk of mortality. Accurate risk stratification is crucial for improving clinical management, yet a dedicated predictive tool for this specific population is lacking. The widely used CHA ₂ DS ₂ -VASc score demonstrates limited performance in predicting all-cause mortality in this complex comorbid group. Methods: A cohort of elderly inpatients (≥65 years) diagnosed with CAD and AF were retrospectively enrolled from the Department of Cardiology at the Chinese PLA General Hospital between January 2010 and December 2017. Baseline clinical data during hospitalization were collected, and all patients were followed up for all-cause mortality. Patients were randomly divided into a training set (70%) and a validation set (30%). Variable selection was performed using LASSO-Cox regression. Predictive models were established through a Cox proportional hazards model (via LASSO-Cox) to predict all-cause mortality risk. To assess model robustness and address potential overfitting, we performed 10-fold cross-validation and bootstrap resampling (500 replicates) with optimism-corrected C-statistics. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA), and compared against the CHA ₂ DS ₂ -VASc score. Results: A total of 1678 elderly patients with CAD and AF were randomly divided into a training set ( n = 1174) and a validation set ( n = 504). Through LASSO-Cox regression, 17 variables were identified as predictors associated with all-cause mortality. Two distinct models were developed: LASSO-Cox Model A included factors such as age, left ventricular ejection fraction, blood glucose, plasma fibrinogen, D-dimer, prothrombin time, NT-proBNP, hemoglobin, and hematocrit; LASSO-Cox Model B incorporated all variables from Model A plus acute myocardial infarction, history of myocardial infarction, renal insufficiency, heart failure, diabetes, stroke, chronic obstructive pulmonary disease, and malignant tumor. The analysis of Cox proportional hazards regression models showed that in the training sets, the Area Under Curves (AUC) of model A, model B and CHA ₂ DS ₂ -VASc scores for predicting 1-year all-cause death were 0.83, 0.85 and 0.66, respectively; the AUC for 5-year all-cause death were 0.74, 0.76 and 0.62, respectively. In the validation sets, the AUC of model A, model B and the CHA ₂ DS ₂ -VASc score for predicting 1-year all-cause mortality were 0.79, 0.78 and 0.57, respectively; the AUC for 5-year all-cause death were 0.74, 0.75 and 0.57, respectively. The 10-fold cross-validation yielded a mean AUC of 0.83 (SD: 0.04) for 1-year mortality, and bootstrap resampling produced an optimism-corrected C-statistic of 0.679 (95% CI: 0.651–0.708), confirming that the model maintains clinically meaningful discriminative ability after accounting for optimism. Model B demonstrated better calibration and provided greater net clinical benefit in DCA. Conclusion: The LASSO-Cox machine learning model demonstrates superior predictive performance for all cause mortality relative to the traditional CHA ₂ DS ₂ -VASc score in elderly patients with CAD and AF.

Frailty is a prevalent yet reversible condition in older adults. While exercise is a cornerstone intervention, the optimal protocols tailored to varying fu

Given the clear association between obstructive sleep apnea (OSA) and metabolic disorders, coupled with a limited understanding of sarcopenia in patients with OSA, this study aimed to investigate the relationship between triglyceride-glucose (TyG) index and sarcopenia and in an elderly population with OSA. Multiple hematological and sleep-breathing status were meticulously recorded in the cohorts. The SARC-F scale ≥ 4 was considered indicative of probable sarcopenia. The correlations between clinical indicators and the SARC-F score were analyzed. The area under the curve (AUC) was utilized to assess the predictive ability of TyG for sarcopenia and sarcopenic obesity. Logistic regression analysis and sensitivity stratification were employed to explore the influence of TyG. A total of 1,148 individuals were included, among whom the median age was 66 (62, 71). 46.3% (n = 531) were diagnosed with severe OSA, while 24.0% (n = 276) had probable sarcopenia. The SARC-F score exhibited positive correlations with TyG (r = 0.122, P < 0.01), but it was negatively correlated with mean peripheral oxygen saturation (mean SpO2, r = -0.157, P < 0.01). The AUC for assessing sarcopenia using TyG was 65.7% (95% confidence intervals (95%CI): 61.9–69.5%. Furthermore, the cutoff value for TyG was 8.855 (sensitivity = 67.4%, specificity = 62.8%). Logistic regression and stratified sensitivity analyses, adjusted for various influencing factors, collectively revealed that TyG was a risk-related predictor of probable sarcopenia (all odds ratio > 2.0, P < 0.05). The TyG index emerges as an independent predictor of sarcopenia in patients with OSA, shedding light on the complex interplay between nighttime hypoxia and muscle health.

Objective: To construct a novel model or a scoring system to predict hypertension comorbidity in older patients with obstructive sleep apnea syndrome (OSAS). Methods: A total of 1290 older patients with OSAS from six tertiary hospitals in China were enrolled. The sample was randomly divided into a modeling set (80%) and validation set (20%) using a bootstrap method. Binary logistic regression analysis was used to identify influencing factors. According to the regression coefficients, a vivid nomogram was drawn, and an intuitive score was determined. The model and score were evaluated for discrimination and calibration. The Z-test was utilized to compare the predictive ability between the model and scoring system. Results: In the multivariate analysis, age, body mass index (BMI), apnea–hypopnea index (AHI), total bilirubin (TB), high-density lipoprotein cholesterol (HDL-C), and fasting blood glucose (FBG) were significant predictors of hypertension. The area under the receiver operating characteristic curve of the model in the modeling and validation sets was 0.714 and 0.662, respectively. The scoring system had predictive ability equivalent to that of the model. Moreover, the calibration curve showed that the risk predicted by the model and the score was in good agreement with the actual hypertension risk. Conclusions: This accessible and practical correlation model and diagram can reliably identify older patients with OSAS at high risk of developing hypertension and facilitate solutions on modifying this risk most effectively.

To examine the correlation among body composition, sleep‐breathing indicators, and Framingham risk score (FRS) to identify and amplify nontraditional factors that influence the risk of CVD in males, A total of 195 male participants underwent examinations for body composition and sleep‐breathing monitoring. We compared the differences in individual factors across various FRS groups. We further conducted multiple linear regression analysis. A cutoff value of FRS ≥ 14 was utilized, and potential influencing factors were examined by logistic regression analysis. Statistical differences were observed in the levels of fasting blood glucose (FBG), CO₂, serum ferritin, hemoglobin (HB), and ECT/TBW among the FRS tripartite groups. However, no significant differences were found in AHI and MSpO₂. The multiple linear regression analysis revealed positive correlations between ECW/TBW and FBG with FRS (β = 0.324 and 0.324, p < 0.001), while HB and muscle/fat mass exhibited negative correlations with the score (β = −0.185 and − 0.169, p < 0.01). These five factors—ECW/TBW, FBG, HB, serum ferritin, and muscle/fat mass—collectively accounted for 28.6% of the variation in FRS. A higher ECW/TBW was significantly associated with FRS ≥ 14 (OR = 2.208, 95% CI: 1.503–3.244). Conversely, reduced levels of muscle/fat mass, HB, and basal metabolic rate (BMR) were significantly linked to moderate‐to‐high CVD risk (OR_ratio = 0.532, 95% CI: 0.284–0.996; OR_HB = 0.961, 95% CI: 0.932–0.991; OR_BMR = 0.997, 95% CI: 0.995–1.000). This study revealed correlations among ECW/TBW, HB, FBG, and muscle‐to‐fat mass ratio with the risk of CVD predicted using FRSs.

Background: The prevalence of chronic coronary heart diseases (CHDs) increases with age in the elderly, which represents one of the top-ranked causes of death and disease burden. Methods: This study aimed to investigate the management efficiency of telemedicine based on the remote multiparameter monitoring in elderly patients with CHD. A total of 1248 elderly patients diagnosed with CHD were enrolled. The subjects were randomly divided into two groups, wearable equipment-based telemedical management (WTM) group and traditional follow-up management (TFM) group. Face-to-face clinical interview at least once every 2 months was required in TFM group to collect the medical records. Patients in WTM group were provided with wearable equipment to complete remote monitoring, real-time alerts and health intervention via virtual consultations and remote medication recommendations. Results: The mean age of patients in WTM group and TFM group was 71.1 (68.0–82.0) years and 71.0 (68.0–81.0) years, respectively‏. After a 12-month management, patients in WTM group presented a lower occurrence of hospitalisation (HR 0.59, 95% CI=0.47 to 0.73, p<0.0001) and major adverse cardiac events (HR 0.60, 95% CI=0.44 to 0.82, p=0.0012) compared with patients in TFM group. Conclusion: The multiparameter telemedical management could help with the out-of-hospital management and reduce the incidence of rehospitalisation in elderly patients with CHD.

BACKGROUND While the impact of depression on cognition is well-documented, the relationship between feelings and cognition has received limited attention. AIM To explore the potential association between feelings and cognition with a two-sample Mendelian randomization (MR) analysis. METHODS Our analysis utilized genome-wide association data on various feelings (fed-up feelings, n = 453071; worrier/anxious feelings, n = 450765; guilty feelings, n = 450704; nervous feelings, n = 450700; sensitivity/hurt feelings, n = 449419; miserableness, n = 454982; loneliness/isolation, n = 455364; happiness, n = 152348) in the European population and their impact on cognitive functions (intelligence, n = 269867). Conducting a univariable MR (UVMR) analysis to assess the relationship between feelings and cognition. In this analysis, we applied the inverse variance weighting (IVW), weighted median, and MR Egger methods. Additionally, we performed sensitivity analysis (leave-one-out analysis), assessed heterogeneity (using MR-PRESSO and Cochran’s Q test), and conducted multiple validity test (employing MR-Egger regression). Subsequently, a multivariable MR (MVMR) analysis was employed to examine the impact of feelings on cognition. IVW served as the primary method in the multivariable analysis, complemented by median-based and MR-Egger methods. RESULTS In this study, UVMR indicated that sensitivity/hurt feelings may have a negative causal effect on cognition (OR = 0.63, 95%CI: 0.43-0.92, P = 0.017). After adjustment of other feelings using MVMR, a direct adverse causal effect on cognition was observed (ORMVMR = 0.39, 95%CI: 0.17-0.90, PMVMR = 0.027). While a potential increased risk of cognitive decline was observed for fed-up feelings in the UVMR analysis (ORUVMR = 0.64, 95%CI: 0.42-0.97, PUVMR = 0.037), this effect disappeared after adjusting for other feelings (ORMVMR = 1.42, 95%CI: 0.43-4.74, PMVMR = 0.569). These findings were generally consistent across MV-IVW, median-based, and MR-Egger analyses. MR-Egger regression revealed pleiotropy in the impact of worrier/anxious feelings on cognition, presenting a challenge in identifying the effect. Notably, this study did not demonstrate any significant impact of guilty feelings, nervous feelings, miserableness, or loneliness/isolation on cognition. Due to a limited number of instrumental variables for happiness, this study was unable to analyze the relationship between happiness and cognition. CONCLUSION This MR study finds that sensitivity/hurt feelings are associated with cognitive decline, while the link between worrier/anxious feelings and cognition remains inconclusive. Insufficient evidence supports direct associations between happiness, guilty feelings, nervous feelings, miserableness, loneliness/isolation, and cognition.