To explore the genetic causal association between six sleep characteristics and constipation using Mendelian randomization analysis.

This study selected the genome-wide association study data of six sleep characteristics from the OpenGWAS database of the Medical Research Council Integrative Epidemiology Unit at University of Bristol and the genome-wide association study data of constipation from the catalog database of Genome Wide Association Study.The inverse variance weighted method(IVW) was adopted as the main analysis method to evaluate the causal relationship between the six sleep characteristics and constipation.MR-Egger and weighted median method(WM) were used as supplementary evidence for the IVW results.At the same time,heterogeneity and horizontal pleiotropy tests were conducted to ensure the stability of the results.The instrumental variables of the positive results were annotated,and enrichment analysis of related genes was performed to predict their potential mechanisms.

The IVW method results showed that insomnia increased the risk of constipation(P=0.002),and sleep duration also affected constipation(P=0.026).Sensitivity analysis indicated that the results were stable.Gene enrichment analysis revealed that insomnia may affect the occurrence of constipation through the immune system pathway,while sleep duration may exert its effect through mechanisms such as regulating the intestinal-brain neural function and catecholamine secretion.

Insomnia is a risk factor for constipation.Ensuring adequate sleep has guiding significance for the prevention and treatment of constipation.

To construct the social frailty risk prediction model for elderly people with chronic diseases in communities,and to explore the predictive value of intrinsic capacity for social frailty among them.

A total of 785 elderly patients with chronic diseases in the communities,who were surveyed in Lu'nan district of Tangshan city from July 2024 to May 2025,were selected as research subjects.LASSO regression was used to screen the variables.Logistic regression analysis was conducted to analyze the influencing factors.A column chart was drawn,and a baseline model without intrinsic ability variables and a prediction model including intrinsic capacity variables were constructed.

The detection rate of social frailty among community-dwelling elderly with chronic diseases was 23.69%.The results of Logistic regression analysis showed that age,educational level,duration of chronic disease,level of e-health literacy,satisfaction with community public services,family function,and intrinsic capacity were the influencing factors of social frailty in community-dwelling elderly with chronic diseases(all P<0.05).Two models were established,one without the intrinsic ability variable and the other with the intrinsic ability variable.The results showed that the prediction model had significantly improved reclassification ability and discriminative ability compared to the baseline model.The area under the ROC curve of the prediction model training set and validation set was 0.828 and 0.856 respectively.The DCA curve indicated that the model had good clinical practicability.

The social frailty risk prediction model for elderly people with chronic diseases in the communities had certain predictive efficacy.It could be used to identify the risk of social frailty in elderly people with chronic diseases in the communities at an early stage.It could provide basis for taking timely preventive and intervention measures.

To analyze the influencing factors of the duration of stress hyperglycemia in patients with acute pancreatitis,and to construct its predictive model.

By using the convenience sampling method,a total of 206 patients with acute pancreatitis admitted to Suqian Hospital of Jiangsu Provincial People's Hospital from March 2022 to November 2024 were selected as the research subjects.The clinical data of the patients were collected.They were divided into the stress hyperglycemia≥48 hours group and the stress hyperglycemia<48 hours group according to the duration.Univariate and multivariate Logistic regression analysis were used to explore the influencing factors of the duration of stress hyperglycemia in patients.A risk prediction model for stress hyperglycemia of patients ≥48 hours was constructed based on the decision tree algorithm.The predictive efficacy of the Logistic regression model and the decision tree model was evaluated by using the receiver operating characteristic curve(ROC).

Among 206 patients with acute pancreatitis,41 cases(19.90%) had stress hyperglycemia for ≥48 hours.There were statistically significant differences in hypertension,admission blood glucose level,Acute Physiology and Chronic Health EvaluationⅡ(APACHEⅡ) scores,systemic inflammatory response syndrome(SIRS),pain,and nutritional risk between the stress hyperglycemia ≥48 hours group and the stress hyperglycemia <48 hours group(P<0.05).Multivariate Logistic regression analysis showed that the patients' admission blood glucose level,APACHEⅡ scores,SIRS,pain,and nutritional risk were the influencing factors of stress hyperglycemia ≥48 hours in patients with acute pancreatitis(P<0.05).The decision tree model screened out four influencing factors(pain,blood glucose level at admission,APACHEⅡ scores,and nutritional risk),with a prediction accuracy rate of 86.4%.The correlation between pain and stress hyperglycemia ≥48 hours in patients with acute pancreatitis was relatively stronger.When the pain score was >2 points,the admission blood glucose level was >13.07 mmol/L.The APACHEⅡ score was >7.The risk probability of stress hyperglycemia ≥48 hours in patients with acute pancreatitis was the highest(88.2%).The area under curve(AUC) of the Logistic regression model was 0.824,with a sensitivity of 82.9% and a specificity of 68.5%.The AUC predicted by the decision tree model was 0.828,with a sensitivity of 78.0% and a specificity of 77.6%.There was no statistically significant difference in terms of the predictive efficacy between the two models(Z=0.075,P=0.940).

The duration of stress hyperglycemia in patients with acute pancreatitis was influenced by multiple factors.Both the decision tree model and the Logistic regression model have good predictive value.However,the decision tree model demonstrates the interactive influence of various factors.Therefore,the two models could be used in combination to promote the targeted and specific natures of nursing intervention strategies from different levels.

To explore the independent influencing factors of delirium in elderly inpatients in internal medicine department using the Mayo Delirium Prediction Model(MDP) and Delirium Risk Tool(DELIKT),and to analyze the predictive performance of the two models.

The study subjects were elderly inpatients in the internal medicine department of a tertiary grade A general hospital in Beijing city from January to December 2024.The occurrence of delirium in patients was predicted using MDP and DELIKT.The performance of the model was evaluated using the area under the receiver operating characteristic curve(AUC) and diagnostic indicators.Univariate analysis and Logistic regression analysis were conducted on 22 predictive factors to determine the independent influencing factors related to delirium.

The AUC values for external validation of MDP and DELIKT were 0.955 and 0.869 respectively,with sensitivities of 92.50% and 77.50% respectively,and specificities of 93.50% and 86.30% respectively.The results of Logistic analysis showed that history of delirium,CrAS scores,indwelling urinary catheter,age,and fall risk score were independent influencing factors for delirium in elderly inpatients in internal medicine department(P<0.05).

Both MDP and DELIKT could be used for predicting the risk of delirium in elderly inpatients in internal medicine department.In the future,these two models could be integrated to develop a delirium prediction tool for elderly inpatients in internal medicine department in China.

To explore the potential categories and influencing factors of symptom cluster trajectories in breast cancer patients undergoing chemotherapy.

A convenience sampling method was adopted to select 225 breast cancer inpatients who underwent their first chemotherapy at a tertiary grade A hospital in a certain city from July to December 2024 as the survey subjects.General information questionnaires,Memorial Symptom Assessment Scale,Sense of Coherence Scale,and Female Self-Advocacy in Cancer Survivorship were used to conduct the survey on 1st day before chemotherapy,7th day after the first chemotherapy,7th day after the second chemotherapy,7th day after the third chemotherapy,and 7th day after the fourth chemotherapy.Data analysis was performed using the latent class growth model.

There were two potential categories in the symptom cluster trajectories of breast cancer patients undergoing chemotherapy,namely the high-level significant increase group(20.4%) and the low-level slow increase group(79.6%).Logistic regression analysis showed that occupation,place of residence,presence of comorbidities,self-advocacy ability,and sense of coherence were the influencing factors for the symptom cluster trajectory categories of breast cancer patients undergoing chemotherapy(P<0.05).

The symptom clusters of breast cancer patients undergoing chemotherapy could be divided into two different trajectories.There is also group heterogeneity.Nursing staff should provide personalized support care for patients belonging to different trajectory categories.

To construct the risk prediction model for pediatric nurses' effort-reward imbalance based on interpretable machine learning,and to compare the predictive performance of different models.To explain the results of the optimal model using SHAP interpretation.

Using the convenience sampling method,a total of 414 pediatric nurses from 6 hospitals in Anhui province,Shanxi province,Jiangxi province and Hunan province in June 2025 were selected as the research subjects.They were randomly divided into training set and validation set at a ratio of 7∶3.The Chinese Nurse Stressor Scale and the Effort-Reward Imbalance Questionnaire scale were used for investigation.LASSO regression was employed to screen the characteristic variables and identify the important predictors.The important predictors were incorporated into the machine learning model to construct three risk prediction models for pediatric nurses' effort-reward imbalance:Logistic regression model,Extreme Gradient Boosting model,and Random Forest model.The areas under the receiver operating characteristic curves(AUC),accuracy,sensitivity,and F1 score of the models were compared to evaluate the predictive performance of the models and select the optimal model.The SHAP explanation was used to interpret the optimal model.

LASSO regression identified three important factors:the number of night shifts per month,workload and time allocation,and educational background.The AUC values of the three prediction models(Logistic regression model,Extreme Gradient Boosting model,and Random Forest model) were 0.725,0.890,and 0.903 respectively,with accuracies of 0.673,0.794,and 0.801,sensitivities of 0.421,0.731,and 0.813,and F1 scores of 0.547,0.773,and 0.798.The SHAP explanation results showed that the importance ranking of the influencing factors were the number of night shifts per month,workload and time allocation,and educational background.

The risk prediction model for pediatric nurses' effort-reward imbalance based on interpretable machine learning constructed by Random Forest has better performance than Logistic regression and Extreme Gradient Boosting model.Personalized predictions should be made based on the number of night shifts each nurse takes per month,workload and time allocation,and educational backgroun.It provides a reference for the early identification of the imbalance between effort and reward for the nurses and the formulation of personalized intervention measures.

To explore the occurrence rate of cognitive impairment among older adults in China and its influencing factors based on the health ecological model,and to construct the identification model to provide data support and theoretical basis for the early and accurate identification of cognitive impairment in older adults.

A total of 4 204 older adults aged 60 and above from the 2018 data of the China health and retirement longitudinal study(CHARLS) were selected as the research subjects.They were randomly divided into training set and validation set at a ratio of 7∶3.LASSO regression was used to screen variables.Cognitive impairment identification model was constructed based on Logistic regression.The performance of the model was evaluated using the area under the receiver operating characteristic curve(AUC),sensitivity,and specificity.

The incidence of cognitive impairment among older adults aged 60 and above in China was 19.60%.The results of the Logistic regression analysis showed that personal traits(IADL),interpersonal networks(being married,frequency of meeting with children,social participation level),work and life conditions(retirement,heating facilities of housing),and policy environment(endowment insurance,medical insurance) were protective factors of cognitive impairment in older adults(P<0.05),and personal traits(age,disability,depression),behavioral characteristics(smoking),and policy environment(central region,western region) were risk factors of cognitive impairment in older adults(P<0.05).The AUC of the training set and validation set of the elderly cognitive impairment identification model constructed were 0.81 and 0.78, respectively.

The incidence of cognitive impairment among older adults was relatively high.The influencing factors included five aspects:personal traits,behavioral characteristics,interpersonal networks,working and living conditions,and policy environment.The constructed cognitive impairment identification model for the elderly was helpful for achieving early and accurate identification of cognitive impairment among people aged 60 and above.