While survival rates following neonatal surgery for congenital heart disease (CHD) have improved over the years, neurodevelopmental delays are still highly prevalent in these patients. After correcting for the CHD subtype, the severity of developmental impairment is dependent on multiple factors, including intraoperative brain injury, which is more frequent and more severe in those undergoing aortic arch repair with deep hypothermic circulatory arrest (DHCA). It is proposed that brain injury may be reduced if cooling is stopped at the point of electrocerebral inactivity (ECI) on electroencephalogram (EEG), but there is limited evidence to support this as few centres perform perioperative EEG routinely. This study aims to assess the feasibility of EEG monitoring during neonatal aortic arch repair and investigate the relationship between temperature and EEG to inform the design of a future clinical trial.

Single-centre prospective observational cohort study in a UK specialist children’s hospital, aiming to recruit 74 neonates (≤4 weeks corrected age) undergoing aortic arch repair with DHCA. EEG will be acquired at least 1–3 hours before surgery, and brain activity will be monitored continuously until 24 hours following admission to intensive care. Demographic, clinical, surgical and outcome variables will be collected. Feasibility will be measured by the number of patients recruited, data collection procedures, technically successful EEG recordings and adverse events. The main outcomes are the temperature at which ECI is achieved and its duration, EEG patterns at key perioperative steps and neurodevelopmental outcomes at 24 months postsurgery.

The study was approved by the Yorkshire and The Humber Sheffield National Health Service Research Ethics Committee (20/YH/0192) on 18 June 2020. Written informed consent will be obtained from the participant’s parent/guardian prior to surgery. Findings will be disseminated to the academic community through peer-reviewed publications and presentations at conferences. Parents/guardians will be informed of the results through a newsletter in conjunction with local charities.

To develop and validate an interpretable machine learning (ML)-based frailty risk prediction model that combines real-time health data with validated scale assessments for enhanced decision-making and targeted health management in integrated medical and older adult care institutions (IMOACIs) in central China.

Mixed-methods, cross-sectional study.

13 IMOACIs across seven cities in Hunan province, central China, from 8 to 16 July 2022.

Five healthcare experts and two data scientists participated in the requirements analysis stage. A total of 586 older adults were included in the assessment data collection stage, and 15 participants (10 healthcare professionals and five data scientists) were involved in the model evaluation stage.

A collaborative requirements analysis involving healthcare professionals and data scientists guided the design of an interpretable frailty risk prediction model. Five machine learning models were developed and evaluated: logistic regression, support vector machines (SVM), random forest, extreme gradient boosting (XGBoost) and a multimodel ensemble approach. Hyperparameter optimisation was performed using stratified fivefold cross-validation with grid search, incorporating class-weighted loss functions to address class imbalance and model-specific regularisation techniques to maximise performance while preventing overfitting. To enhance interpretability, the model incorporated Shapley Additive Explanations. The final model was integrated into a user-facing platform and validated using cross-sectional standardised assessment data collected from 13 IMOACIs. A mixed-methods evaluation approach combined quantitative performance metrics with qualitative user experience assessments.

The dataset (n=586) was randomly split into training (n=468) and validation (n=118) sets (4:1 ratio). Among models, XGBoost demonstrated superior performance, achieving an accuracy of 0.89 and an area under the receiver operating characteristic curve (AUC) of 0.89 on the training set. On the validation set, the XGBoost model achieved a precision of 0.76, recall of 0.72, F1 score of 0.74, accuracy of 0.83 and AUC of 0.80, outperforming other models. User experience surveys yielded high mean ratings for satisfaction (4.20/5), perceived accuracy (4.20/5), interpretability (4.30/5) and application value (4.10/5). Qualitative analysis of user feedback identified six key themes: practical and application value, performance and data analysis, interpretability and comprehensibility, impact and integration into practice, limitations and areas for improvement, and future development and innovation prospects, highlighting the model’s strong potential for practical implementation.

This novel, interpretable ML-based frailty risk prediction model can enhance decision-making in the care of older adults by providing transparent predictions and identifying crucial factors associated with frailty. It establishes a foundation for targeted management and broader ML applications in healthcare systems, such as IMOACIs, particularly in developing regions.

This study investigated the effects of work cessation on cognitive function among older adults in rural China. Given that cognitive disorders affect 6.04% of individuals aged 60 and above—with higher prevalence in rural areas—understanding this relationship is critical.

A cross-sectional study was employed, using data from the 2020 wave of the China Health and Retirement Longitudinal Study (CHARLS). Regression analysis assessed the impact of work cessation on cognitive function and the moderating effects of social activities, health behaviours and internet use.

Data were collected from 150 districts, 450 villages, and urban community units in China.

The study included 6,318 participants, with 4,045 currently employed and 2,273 no longer working.

Cognitive function was evaluated using measures of mathematical computation, temporal and image cognition, and situational memory was tested through 20 memory-related questions. Explanatory variables included work cessation status, while moderating variables encompassed social activities, health behaviours (smoking and alcohol consumption) and internet use.

Work cessation has a negative impact on cognitive function, particularly situational memory and overall cognitive ability. Stopping work was associated with a decrease in cognitive functioning by 0.796 SD (p

Work cessation significantly reduced cognitive function in rural Chinese older adults. Leisure activities can mitigate this decline, but they often lack quality and diversity. Health behaviour improvements show heterogeneity, and internet use mitigates cognitive decline despite urban–rural digital gaps. To protect rural older adults’ cognitive function, policies promoting flexible employment, enhanced recreational infrastructure, health outreach and bridging digital divides are proposed.