Xianwen Li

Purpose: This retrospective study aimed to identify factors associated with postoperative cognitive dysfunction (POCD) in patients undergoing hepatectomy, with particular attention to liver disease-related characteristics and perioperative variables. A secondary aim was to develop a clinically applicable nomogram for individualized risk estimation in this population. Patients and Methods: A retrospective cohort study was conducted in 314 consecutive patients who underwent hepatectomy at Nanjing Drum Tower Hospital Affiliated to Nanjing University Medical School between January 2023 and December 2024. Patients were included if they had complete clinical data and underwent preoperative and postoperative cognitive assessment. Exclusion criteria included preoperative cognitive impairment (Montreal Cognitive Assessment [MoCA] score < 26), preexisting neurological or psychiatric disorders, and in-hospital death within 72 h after surgery. POCD was defined as a decline of ≥3 points in the MoCA score from baseline to postoperative day 5. Clinical, surgical, nutritional, and perioperative variables were analyzed, and a nomogram was constructed based on the final multivariable logistic regression model. Results: The overall incidence of POCD was 27.4% (86/314). The final multivariable model included sarcopenia, preoperative hemoglobin < 120 g/L, Child–Pugh classification, alcohol consumption, operative duration, and pain score on postoperative day 1. The nomogram incorporating these variables showed good discriminative ability, with an area under the curve of 0.87 (95% CI: 0.83–0.92). Conclusions: In this retrospective cohort of patients undergoing hepatectomy, several perioperative clinical factors were associated with POCD. The proposed nomogram may serve as a practical tool for perioperative risk estimation and support more individualized management in higher-risk patients.

Aim: Cardiac rehabilitation (CR) is an effective intervention for improving outcomes in patients with coronary heart disease (CHD). However, the actual participation rate is unsatisfactory and exhibits significant gender disparities. This study aimed to investigate gender‐specific determinants of the decision to participate in CR among patients with CHD, together with underlying causes. Design: Cross‐sectional study. Methods: A cross‐sectional survey of 264 patients with CHD from 3 Chinese tertiary hospitals between February 2024 and February 2025. Data were collected using questionnaires based on the Information Need in Cardiac Rehabilitation scale, the Chinese version of Cardiac Rehabilitation Barriers Scale, the Family APGAR index questionnaire, and the International Physical Activity Questionnaire‐Short Form. Results: There were 158 men aged 66 (SD = 13.3) and 106 women aged 66 (SD = 11.0). 55.1% of men CHD patients decided to participate in CR, significantly higher than in women patients (34.0%; χ ² = 11.351, p = 0.001). Logistic regression analysis for men indicated that the facilitators of the decision to participate in CR were the level of family functioning and ≥ 2 comorbidities. The barriers included emergency/safety information needs and functional status. For women, the facilitators were retirement, family functioning level, being overweight/obesity, ≥ 2 comorbidities, and work/vocational/social factors. The barriers included medication information needs, logistical factors, and functional status. Fairlie decomposition revealed gender differences primarily driven by work/vocational/social factors (contribution: 71.19%), functional status (50.50%), and retirement (−39.16%) (all p < 0.05). Impact: This study highlights the necessity of gender‐specific interventions during the decision‐making phase for CR. Healthcare professionals should tailor CR strategies to address women's social role barriers and men's emergency risk concerns, while enhancing family functioning and targeting support for functional status and work‐related factors. Reporting Method: STROBE checklist, cross‐sectional. Patient or Public Contribution: Three tertiary hospitals assisted in participant recruitment.

Background/Objectives: This study aimed to explore the association between internet use and executive function among older adults and the mediating role of social participation and loneliness in internet use and executive function. Methods: A cross-sectional study was conducted among 439 community-dwelling older adults (≥60 years) in Nanjing, China, from September to December 2022. Participants were selected using simple random sampling and assessed with four standardized instruments: the Internet Use Questionnaire, the Social Participation Capacity Assessment, the six-item UCLA Loneliness Scale (ULS-6), and the Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A). Data were analyzed with the SPSS 21.0 software for descriptive statistics and correlation analysis and the AMOS 23.0 software for structural equation modeling to test the chain mediation effects. Model fit was evaluated using Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), and Weighted Root Mean Square (WRMR), with bootstrap resampling for indirect effect estimation. Results: The results showed that internet use was positively correlated with loneliness (r = 0.203, p < 0.01), social participation impairment (r = 0.193, p < 0.01), and executive function (r = 0.420, p < 0.01). Structural equation modeling showed that greater internet use was significantly associated with poorer executive function (β = 0.306, p < 0.01). These associations were partially explained by pathways involving social participation and loneliness through three indirect pathways: internet use via social participation (indirect effect = 0.087, 18.3% of the total effect); internet use via loneliness (indirect effect = 0.049, 10.3%); and internet use via social participation and then loneliness in sequence (indirect effect = 0.035, 7.1%). Conclusions: In community-dwelling older adults, more frequent internet use was associated with greater executive function impairment through mechanisms involving reduced social participation and increased loneliness. Therefore, there is a need to limit excessive internet use while promoting social participation and reducing isolation, which can have the greatest benefits for executive functioning in older adults.

Background : Acute ischemic stroke is a major cause of cognitive dysfunction. Early identification of post‐stroke cognitive impairment (PSCI) is crucial for improving patient prognosis. While there has been extensive research on prognostic models for acute ischemic stroke, the selection of predictive factors remains heavily reliant on neuroimaging parameters. This study aims to create and compare the eXtreme gradient boosting (XGBoost) and logistic regression (LR) models based on serum biomarkers for predicting the risk of PSCI following acute ischemic stroke. Methods : The study enrolled 261 adult patients with acute ischemic stroke within 7 days of onset. Their baseline characteristics, serum markers, and scores anthe National Institutes of Health Stroke Scale (NIHSS) and the Montreal Cognitive Assessment (MoCA) were collected. Cognitive function assessment was completed 3 months (±2 weeks) after stroke, with PSCI diagnosis based on a MoCA score < 26. Patients were randomly assigned to the training dataset ( n = 183) and testing dataset ( n = 78) in a ratio of 7:3. Significant features for predicting the risk of PSCI were selected via LassoCV in R. The accuracy, F1 score, Cohen's kappa, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were measured to assess the accuracy of the XGBoost and LR prediction models. Finally, the performance of the optimal prediction model was evaluated by SHapley additive exPlanations (SHAP) beeswarm and force plots. Results : The incidence of PSCI and other baseline characteristics were comparable between the training and testing datasets (all P > 0.05). Vascular endothelial cadherin (VE‐Cad), NIHSS score, age, drink history, C‐reactive protein (CRP), and education years were features associated with the risk of PSCI. The XGBoost model was superior in accuracy, F1 score and sensitivity in predicting the risk of PSCI than the LR model. Beeswarm and force plots displayed the excellent ability of the XGBoost model in predicting the risk of PSCI in patients with acute ischemic stroke. Conclusion : Based on serum biomarkers, the XGBoost model can accurately predict the risk of PSCI in patients with acute ischemic stroke, with superior performance than the LR model, and may serve as a reliable tool for early identification to improve the diagnosis.From 261 acute ischemic stroke patients (training n = 183, testing n = 78), we collected demographic data, cognitive assessments, and serum indicators. LassoCV identified sensitive predictors including VE‐Cad, NIHSS score, CRP, age, drinking history, and education years. The XGBoost model demonstrated superior performance over LR in predicting PSCI risk. SHAP analysis revealed how these variables influenced model predictions. Based on serum biomarkers, the XGBoost model accurately predicts PSCI risk and may serve as a reliable tool for early identification to improve diagnosis.

Aim: This study aimed to investigate the mediating effect of self‐efficacy and perceived stress on the relationship between depression and subjective memory complaints (SMC) among patients with depression. Methods: A cross‐sectional survey was conducted with Chinese patients experiencing SMC. The assessment of SMC, depression, self‐efficacy and perceived stress was performed using the Subjective Cognitive Decline Questionnaire‐9, Hamilton Depression Rating Scale‐17, General Self‐efficacy Scale and the Chinese version of the Perceived Stress Scale. Pearson correlation analysis was utilised to examine the correlations among these variables. AMOS 26.0 was employed to construct a chain mediation model, and the mediating effects of this model were tested using the bootstrap method. Results: A total of 372 patients with depression voluntarily participated in the questionnaires during their leisure time, and 343 valid questionnaires were recovered. The analysis indicated that SMC significantly affected the depression of patients ( β = 0.211, critical ratio = 3.915, p < 0.001). Lower self‐efficacy and higher perceived stress were associated with increased depression. Specifically, self‐efficacy and perceived stress continuously mediated the relationship between SMC and depression, with an overall mediating effect value of 0.209. After including self‐efficacy and perceived stress, the total effect of SMC on depression was found to be 0.420. Conclusions: This study highlights the collective contributions of SMC, self‐efficacy and perceived stress to depression. Self‐efficacy and perceived stress continuously mediate the association between depression and SMC, indicating that depressed patients with SMC exhibit lower self‐efficacy; additionally, reduced self‐efficacy may exacerbate perceived stress, ultimately leading to elevated levels of depression. Implications for Practice: These findings underscore the importance of developing self‐efficacy and stress coping strategies. Enhancing self‐efficacy and reducing perceived stress can alleviate SMC in patients with depression and improve overall cognitive function. Regular monitoring and intervention for self‐efficacy and perceived stress in patients with SMC are crucial for assessing and screening subclinical depression patients, initiating early interventions to alleviate depression and promoting mental health.

Intelligent eldercare has positive implications for maintaining social networks and improving the quality of life. The study aimed to examine spatio-temporal evolution characteristics of intelligent eldercare development in China, and provide recommendations for sustainable development. Enterprise information was available from the TianYanCha website, and spatial analysis was employed to analyse the spatio-temporal evolution characteristics. We obtained policy documents from official websites and the PKULAW Database, and established a three-dimensional analytical framework to investigate structural characteristics of the policy system. A total of 4689 enterprises and 81 policy documents were included. The number of newly added enterprises per year showed an increasing trend. The kernel density analysis revealed that the distribution of enterprises has evolved from the core-area agglomeration structure to a multi-core distribution spreading to the surrounding areas. The distribution direction of enterprises was in the “Northeast-Southwest” direction. Hotspot areas and high-high cluster areas showed a trend of decreasing first and then increasing, and were concentrated in the central and eastern regions overall, indicating that there was a dense distribution of intelligent eldercare enterprises in these areas. Policy analysis indicated a disproportionate internal structure. The items with the largest proportion across each dimension are supply-side policy instruments (50.63%), product development and service supply (27.85%), and government departments (31.30%). The development of intelligent eldercare enterprises showed an upward trend from 1982 to 2024, but the spatial distribution displayed regional imbalances. Intelligent eldercare enterprises were densely distributed in the central and eastern regions overall. Subsequent policies should balance the allocation of policy instruments and policy objectives, encourage the construction of cross-regional cooperation, and facilitate the sustained development of the industry. The online version contains supplementary material available at 10.1186/s12913-026-14310-x.

Cognitive dysfunction (CD) is a frequent but often underrecognized clinical feature in systemic lupus erythematosus (SLE) patients. It markedly impairs their health-related quality of life. Investigating the risk associated with the onset of CD and establishing a prediction model are crucial for early detection of CD. This allows for timely intervention, potentially delaying or reversing the progression of CD. This study aimed to establish a predictive model for CD in SLE patients and evaluate its predictive efficacy. This study enrolled adult patients with SLE who underwent inpatient management at the Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University. Variables for analysis included demographic characteristics, physiological and psychological status, medication details, and laboratory examination. Key predictors were selected using the least absolute shrinkage and selection operator (LASSO) approach. A range of machine learning (ML) classification models were developed and evaluated to determine the best-performing model. The prediction accuracy of the best-performing model was evaluated using calibration curve analysis, and the clinical applicability of the model was further evaluated by decision curve analysis. Additionally, Shapley Additive exPlanations (SHAP) were applied to realize personalized risk assessment and enhance model interpretability. The study included 283 eligible patients, divided into a training set of 199 and a test set of 84. The eXtreme Gradient Boosting (XGBoost) model emerged as the optimal model. In the training set, the area under the curve (AUC) (95% confidence interval, CI) was 1.000 (0.953, 1.000), and in the test set, it was 0.774 (0.763, 0.831). The robustness of the XGBoost model was further substantiated by repeated cross-validation on the training set, with a mean AUC of 0.750. Regarding the F1 value in the test set, Logistic Regression had the highest value (F1 = 71.58%), followed by XGBoost (F1 = 65.85%) and Decision Tree and Random Forest (F1 = 65.82%, 60%, respectively). The congruence of the XGBoost model's predictions with actual findings was corroborated by the calibration curve. Furthermore, decision curve analysis affirmed the clinical value of the model in predicting CD. The prediction model of the present study provides clinicians with a tool to identify SLE patients at high risk of cognitive dysfunction. Its application could support the early initiation of preventive interventions in this vulnerable population. Key Points • The study integrated LASSO regression for feature selection and multiple ML algorithms, with XGBoost emerging as the optimal predictive model for CD in SLE. • SHAP interpretation was applied to provide individualized risk predictions, enhancing clinical utility for patient-specific management. • The developed ML-based tool aids clinicians in early identification of high-risk SLE patients, enabling timely interventions to potentially mitigate or reverse CD progression.