Caixia Xie

Background: With accelerating population aging, sarcopenia has become increasingly common in the elderly. Because the triglyceride-glucose index is correlated with sarcopenia, this study aims to investigate the association between the triglyceride-glucose index and sarcopenia. Methods: A systematic search of PubMed, Web of Science, Embase, and Cochrane Library databases was conducted for relevant studies published up to June 15, 2025. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using a random-effects model in STATA 15.0. Results: Analysis of 14 studies ( n = 82,798) revealed a significant association between the triglyceride- glucose index (TyG index) and sarcopenia (OR = 2.15, 95% CI [1.60–2.89]). A significant correlation was also observed in the TyG quartile groups: Q2 (OR = 1.38, 95% CI [1.08–1.75]), Q3 (OR = 1.64, 95% CI [1.21–2.20]), and Q4 (OR = 1.75, 95% CI [1.21–2.55]). The TyG-BMI index demonstrated a stronger association, with the strength of this association increasing significantly as the quartile level rose Q2 (OR = 2.51, 95% CI [1.55–4.05]); Q3 (OR = 5.01, 95% CI [1.89–13.25]); Q4 (OR = 9.08, 95% CI [2.91–28.37]). Subgroup analyses revealed that the Asian Working Group for Sarcopenia 2019 (AWGS 2019) diagnostic criteria (OR = 5.28, 95% CI [2.27–12.3]), middle-aged individuals (OR = 1.87, 95% CI [1.46–2.42]), individuals with comorbidities (OR = 1.94, 95% CI [1.54–2.44]), and different TyG measurement methods (OR = 2.14, 95% CI [1.24–3.71]) were also significantly associated with sarcopenia. Conclusions: This meta-analysis confirms a significant association between the TyG index and sarcopenia. The prevalence of sarcopenia increases with the elevation of the TyG index. This highlights the potential value of the TyG index as a key indicator for sarcopenia screening and early intervention. Trial registration:CRD420251067364, June 25, 2025.

Purpose: Systematic evaluation of the effectiveness of virtual reality technology in intervening in mental health problems of healthcare workers (PROSPERO: CRD42024558009). Methods: PubMed, The Cochrane Library, Embase, Web of Science, CNKI, China Wanfang Database, and China Biology Medicine disk were systematically searched from inception to May 21, 2025. We included experimental and quasi-experimental studies that examined the effects of VR interventions on mental health outcomes (stress, anxiety, burnout, or fatigue) in healthcare workers. Studies were excluded if they were not published in English or Chinese, if full texts or required data were unavailable, or if they were duplicate publications. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Methodological quality was assessed using the Effective Public Health Practice Project (EPHPP) tool, and meta-analyses were performed using RevMan 5.4 software. Results: 2,133 articles were identified through database searching. Ten studies published between 2021 and 2023, with 493 participants, were included in the review. Of these, there were three randomized cross-over trials, and seven were one-group pretest-posttest trials. The findings showed that both the stress and anxiety scores before and after the VR intervention were statistically significant [SMD = −0.64, 95% CI (−1.17, −0.11), p = 0.02], and [SMD = −0.51, 95% CI (−0.76, −0.27), p < 0.0001]. Since a meta-analysis of the burnout, fatigue, and satisfaction levels was not possible, a descriptive analysis was carried out. Exhaustion and burnout may be lessened following the intervention. The majority of the medical team expressed satisfaction with VR’s ability to alleviate mental health issues. Three studies received an EPHPP rating of ‘strong’, five were ‘moderate’, and two were ‘weak’. Conclusion: Existing evidence suggests that virtual reality technology is effective in improving stress and anxiety in healthcare workers, but more high-quality studies are needed to further validate whether it can improve burnout and fatigue.

Frequent healthcare reforms have led to change fatigue among nurses; however, the heterogeneous characterization of change fatigue has not been adequately

Introduction: This scoping review aims to summarise studies evaluating gastric contents in surgical patients using preoperative ultrasound, discuss current challenges, and propose future directions. Methods: The system retrieved data from 8 databases, including PubMed, with a search period spanning from the inception to May 11, 2025. The scoping review follows the JBI scoping review framework and the PRISMA-ScR reporting guidelines. Results: A total of 44 studies were included. The research involved various perioperative populations and surgical procedures, with a “full stomach” rate ranging from 0% to 57.7%. Ultrasound operators included anesthesiologists, sonographers, and medical consultants. Assessment methods comprised qualitative, quantitative, or combined approaches. Evaluation metrics included gastric contents nature, Perlas 3-point grading system, gastric antral cross-sectional area (CSA), and gastric volume. Conclusion: Preoperative gastric ultrasound can provide information about gastric contents, thereby aiding in the identification of aspiration risk and enabling the adoption of appropriate anaesthesia strategies. However, existing studies still face limitations, including restricted study populations and sample sizes, variations in operator training, a lack of consensus on an evaluation metric threshold value, and diverse gastric volume calculation models. Future studies should expand their scale, enhance training for clinicians in gastric ultrasound, and standardise assessment positions, threshold values, and selection criteria for calculation models. Furthermore, we believe that integrating artificial intelligence with gastric ultrasound can overcome the limitations of manual measurement, offering a direction for precise and convenient preoperative assessment of gastric contents.

Background: Early detection of malnutrition in critically ill patients is crucial for timely intervention and improved clinical outcomes. However, identifying individuals at risk remains challenging due to the complexity and variability of patient conditions. This study aimed to develop and externally validate machine learning models for predicting malnutrition within 24 h of intensive care unit (ICU) admission, culminating in a web-based malnutrition prediction tool for clinical decision support. Methods: A total of 1006 critically ill adult patients (aged ≥ 18 years) were included in the model development group, and 300 adult patients comprised the external validation group. The development data were partitioned into training (80%) and testing (20%) sets. Hyperparameters were optimized via 5-fold cross-validation on the training set, eliminating the need for a separate validation set while ensuring internal validation. External validation was performed on an independent group to assess generalizability. Predictors were selected using random forest recursive feature elimination; seven machine learning models—Extreme Gradient Boosting (XGBoost), random forest, decision tree, support vector machine (SVM), Gaussian naive Bayes, k-nearest neighbor (k-NN), and logistic regression—were trained and evaluated for accuracy, precision, recall, F1 score, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision-Recall Curve (AUC-PR). Model interpretability was analyzed using SHapley Additive exPlanations (SHAP) to quantify feature contributions. Results: In the development phase, among 1006 patients, 34.0% had moderate malnutrition and 17.9% severe malnutrition. The XGBoost model achieved superior predictive accuracy with an accuracy of 0.90 (95% CI = 0.86–0.94), precision of 0.92 (95% CI = 0.88–0.95), recall of 0.92 (95% CI = 0.89–0.95), F1 score of 0.92 (95% CI = 0.89–0.95), AUC-ROC of 0.98 (95% CI = 0.96–0.99), and AUC-PR of 0.97 (95% CI = 0.95–0.99) on the testing set. External validation confirmed robust performance with an accuracy of 0.75 (95% CI: 0.70–0.79), precision of 0.79 (95% CI: 0.75–0.83), recall of 0.75 (95% CI: 0.70–0.79), F1 score of 0.74 (95% CI: 0.69–0.78), AUC-ROC of 0.88 (95% CI: 0.86–0.91), and AUC-PR of 0.77 (95% CI: 0.73–0.80). Conclusions: Machine learning models, particularly XGBoost, demonstrated promising performance in early malnutrition prediction in ICU settings. The resultant web-based tool offers valuable resource for clinical decision support. Trial registration: Chinese Clinical Trial Registry ChiCTR2200058286 (https://www.chictr.org.cn/bin/project/edit? pid=248690). Registered 4th April 2022. Prospectively registered.

Burnout is prevalent among nursing staff, especially in the operating room, which is a high-intensity, high-pressure and fast-paced work environment. As the prevalence of nurse burnout increases, reducing the burnout of operating room nurses helps to improve the physical and mental health of nurses. Thus, stabilising the nursing team can also guarantee the quality of medical care, which in turn improves patient satisfaction. In this study, we conducted a meta-analysis based on the Pearson correlation coefficient to quantitatively analyse the factors related to burnout among operating room nurses and to provide a reliable basis for preventing and intervening in burnout among operating room nurses. CNKI, Wanfang Database, Wipro Database, China Biomedical Database, PubMed, Web of Science, Embase, CINAHL and Cochrane Library Database were searched, and the keywords ‘operating room’, ‘nurses’, ‘burnout’ and the corresponding English terms were used for the search. The time limit for the search was set from the creation of the databases to April 2024. Two researchers with evidence-based knowledge conducted the search, independently screened the literature according to the inclusion and exclusion criteria, extracted the information and assessed the quality of the included literature using the quality assessment criteria for observational studies recommended by the Agency for Healthcare Quality and Research, and performed a meta-analysis of the literature using the r value as an indicator of the outcome in RevMan 5.4 software. A total of 25 papers were included, with a sample size of 6,061 cases. The quality of the literature was moderate. The meta-analysis showed that job stress (r = 0.56, 95% confidence interval [CI]: 0.42–0.68, I2 = 86%), work–family conflict (r = 0.52, 95% CI: 0.36–0.64, I2 = 82%), willingness to leave (r = 0.42, 95% CI: 0.34–0.49, I2 = 0%) and hidden absenteeism (r = 0.49, 95% CI: 0.41–0.57, I2 = 55%) were positively correlated with burnout (p < 0.01), job immersion (r = − 0.39, 95% CI: − 0.40 to − 0.32, I2 = 0%), social support (r = − 0.46, 95%: − 0.58 to − 0.33, I2 = 74%), psychological capital (r = − 0.53, 95% CI: − 0.60 to − 0.45, I2 = 72%) and well-being (r = − 0.54, 95% CI: − 0.73 to − 0.27, I2 = 88%) were negatively correlated (p < 0.01). This study shows that burnout of operating room nurses is related to various factors, such as job stress, social support, work–family conflict, psychological capital and well-being. Therefore, it is suggested that hospital management should alleviate the burnout of operating room nurses from the multi-dimensional aspects of improving the work environment, enhancing social support and improving personal psychological capital and well-being to effectively alleviate burnout. (PROSPERO) International prospective register of systematic reviews: CRD42024547524.

Occupational fatigue in operating room nurses may influence their attentional control. However, few previous studies have explored the correlation between occupational fatigue and attentional control in operating room nurses. To better understand operating room nurses’ occupational fatigue and its relationship with attentional control, this study aimed to identify the latent profiles and their factors that influence operating room nurses’ occupational fatigue as well as differences in attentional control across each latent profile. A cross-sectional study was conducted from May 2024 to July 2024, and a total of 386 operating room nurses were recruited from 6 hospitals in Chengdu. The participants completed the Occupational Fatigue Scale and the Attentional Control Scale. Latent profile analysis (LPA) was employed to identify latent profiles of occupational fatigue among operating room nurses. The predictors of occupational fatigue in different latent profiles were assessed via multinomial logistic regression analysis. One-way ANOVA and the Kruskal–Wallis test were used to compare the scores on the attentional control scale for each latent profile of nurses’ occupational fatigue. This study identified three latent profiles of operating room nurses’. occupational fatigue: the “low-fatigue/high-recovery group” (n = 80, 21.2%), the “high-fatigue/low-recovery group” (n = 113,29.3%) and the “moderate-fatigue/mod-erate-recovery group” (n = 193, 49.4%). The results of the multinomial logistic regression analysis suggested that age, work experience, educational level and monthly income were predictors of operating room nurses’ occupational fatigue. There were significant differences in attentional control among the different pr-ofiles of occupational fatigue (P < 0.001). The scores for attentional focus were. Significantly different across each profile (P < 0.001), whereas the scores for at-tentional shift were not different across profiles (P = 0.342). Operating room nurses’ occupational fatigue can be divided into three latent profiles. Reducing chronic and acute fatigue while enhancing intershift recovery can improve nurses’ attentional control and improve the overall service quality of the hospital. Nursing managers should identify operating room nurses who are at risk and implement targeted interventions to reduce occupational fatigue. This study does not involve clinical trials or interventional procedures and therefore does not meet the criteria for clinical trial registration.

Background: Regional citrate anticoagulation‐continuous renal replacement therapy (RCA‐CRRT) has a wide range of applications in clinical practice, but unplanned downtime due to RCA‐CRRT circuit coagulation is as high as 15.75%–66.70%. Aim: To build a nomogram model for predicting the lifespan circuits of RCA‐CRRT. Study design: A prospective observational cohort study was conducted in Sichuan, China. The collected clinical data from 404 RCA‐CRRT sessions involving 135 patients were utilized. The patients' basic information, laboratory indicators and RCA‐CRRT parameters were used as independent variables, and the survival status and survival time of RCA‐CRRT circuits were used as dependent variables. A Cox multivariate analysis was performed to build the nomogram model for predicting the lifespan of RCA‐CRRT circuits. The model was validated internally and externally. Results: The median lifespan of RCA‐CRRT circuits was 28.0 (12.0–46.5) h, and the unplanned downtime rate was 23.76%. In the Cox multivariate analysis, venous pressure, haemoglobin, Sequential Organ Failure Assessment (SOFA), lactate, and blood transfusion were identified as statistically significant predictive factors for the lifespan of RCA‐CRRT circuits ( p < .05). Subsequently, a nomogram model for predicting the lifespan of RCA‐CRRT circuits was developed. The AUC values for internal and external validation within the 12–72‐h timeframe ranged from 0.648 to 0.816 and 0.613 to 0.956, respectively. Both the calibration curve and clinical decision curve demonstrated the model's good performance. Conclusion: The nomogram model developed in this study demonstrates its efficacy in accurately predicting the lifespan circuits of RCA‐CRRT. Relevance to Clinical Practice: Clinical nurses can use the prediction model to assess the lifespan of RCA‐CRRT circuits, so as to formulate a personalized RCA‐CRRT treatment plan for patients, thus reducing the unplanned downtime of RCA‐CRRT.