Objective:To construct and validate a depression risk prediction model for middle-aged and elderly patients with diabetes.Methods:Data were extracted from the fifth wave (2020) of the China Health and Retirement Longitudinal Study (CHARLS). A total of 900 diabetic patients were identified, and after excluding those with missing data or invalid questionnaires, 769 patients were included in the analysis. Patients were randomly divided into a training set and a validation set in a 7∶3 ratio. Univariate analysis and logistic regression analysis were performed to screen the optimal predictors of depression in diabetic patients, and a nomogram model was developed. The predictive performance of the model was assessed by the area under the receiver operating characteristic curve ( AUC). Model calibration and accuracy were evaluated using bootstrap resampling, calibration plots, and the Hosmer-Lemeshow test. The clinical utility was further assessed by decision curve analysis (DCA) and clinical impact curves (CIC) . Results:Among the 769 patients, 366 (47.59%) had depression. Logistic regression analysis showed that place of residence, pain, difficulty in toileting, difficulty in bathing, sleep duration, physical exercise, life satisfaction, and children's satisfaction were independent predictors of depression in diabetic patients. A nomogram was constructed based on these variables, yielding an AUC of 0.775. At the optimal cutoff value of 0.557, the model demonstrated a sensitivity of 59.1% and a specificity of 84.8%, indicating good discriminative ability. The Hosmer-Lemeshow test showed (χ 2=15.821, P=0.105), suggesting good agreement between predicted and observed outcomes. In the validation set, the AUC was 0.778, with Hosmer-Lemeshow (χ 2=8.557, P=0.575). DCA and CIC indicated favorable clinical applicability of the model. Conclusions:The depression risk prediction model constructed in this study demonstrated good predictive performance. It can assist clinicians in early identification of high-risk individuals with diabetes and provide a theoretical basis for targeted interventions.

Objective:To construct and validate a depression risk prediction model for middle-aged and elderly patients with diabetes.Methods:Data were extracted from the fifth wave (2020) of the China Health and Retirement Longitudinal Study (CHARLS). A total of 900 diabetic patients were identified, and after excluding those with missing data or invalid questionnaires, 769 patients were included in the analysis. Patients were randomly divided into a training set and a validation set in a 7∶3 ratio. Univariate analysis and logistic regression analysis were performed to screen the optimal predictors of depression in diabetic patients, and a nomogram model was developed. The predictive performance of the model was assessed by the area under the receiver operating characteristic curve ( AUC). Model calibration and accuracy were evaluated using bootstrap resampling, calibration plots, and the Hosmer-Lemeshow test. The clinical utility was further assessed by decision curve analysis (DCA) and clinical impact curves (CIC) . Results:Among the 769 patients, 366 (47.59%) had depression. Logistic regression analysis showed that place of residence, pain, difficulty in toileting, difficulty in bathing, sleep duration, physical exercise, life satisfaction, and children's satisfaction were independent predictors of depression in diabetic patients. A nomogram was constructed based on these variables, yielding an AUC of 0.775. At the optimal cutoff value of 0.557, the model demonstrated a sensitivity of 59.1% and a specificity of 84.8%, indicating good discriminative ability. The Hosmer-Lemeshow test showed (χ 2=15.821, P=0.105), suggesting good agreement between predicted and observed outcomes. In the validation set, the AUC was 0.778, with Hosmer-Lemeshow (χ 2=8.557, P=0.575). DCA and CIC indicated favorable clinical applicability of the model. Conclusions:The depression risk prediction model constructed in this study demonstrated good predictive performance. It can assist clinicians in early identification of high-risk individuals with diabetes and provide a theoretical basis for targeted interventions.

Objective:To evaluate the item properties and applicability of the eHealth Literacy Scale in Web 3.0 context (eHLS-Web 3.0) among diabetic patients by applying classical test theory (CTT) and item response theory (IRT) to provide a new effective tool for assessing the level of eHealth literacy among diabetic patients.Methods:A cross-sectional study design was adopted and 230 diabetic patients from the inpatient endocrinology department of the Affiliated Yantai Yuhuangding Hospital of Qingdao University were selected by convenience sampling from November 2022 to May 2023 and were surveyed by using the self-general information questionnaire and eHLS-Web 3.0. The correlation coefficient method, Cronbach α coefficient, Guttman split-half reliability coefficient, and structural validity in CCT were used, and discriminant validity, convergent validity, and Samejima Graded Response model in IRT were used to evaluate the scale items. Results:The CTT results showed that Cronbach α coefficient for the eHLS-Web 3.0 was 0.973, the Guttman split-half reliability coefficient was 0.894, and the correlation coefficient between the items and the total score of the scale ranged from 0.708 to 0.845 (all P<0.01). Exploratory factor analysis extracted three common factors with a cumulative variance contribution rate of 76.75%, and the confirmatory factor analysis model had a good fit. The reliability of each dimension combination was >0.60, and the average variance extracted was >0.50. The square root of the average variance extracted from each dimension (0.817, 0.875, 0.845) was greater than the correlation coefficient between this dimension and other dimensions (0.742, 0.763, 0.715). The IRT results showed that the discrimination parameter of all the items ranged from 2.174 to 4.054, the difficulty coefficients increased with the increase of the level (- 2.471 - 2.187), and the average information content of all the items ranged from 1.217 to 2.609. Conclusions:The eHLS-Web 3.0 has good characteristics among diabetic patients as tested by CTT and IRT, and can be used as an effective tool to assess the eHealth literacy level of diabetic patients in China.

Objective:To explore the influencing factors and pathways of home-based exercise rehabilitation compliance in patients after percutaneous coronary intervention (PCI), so as to provide theoretical basis for formulating targeted intervention program.Methods:From July 2022 to February 2023, convenience sampling was used to select 220 patients who underwent PCI at Yantai Yuhuangding Hospital affiliated with Qingdao Medical College, Qingdao University as the study subject. A survey was conducted on patients using the General Information Questionnaires, Self-Efficacy for Exercise Scale, Illness Perception and Behavior Questionnaire in Patients with Coronary Heart Disease after PCI, Social Support Rating Scale, and Exercise Rehabilitation Compliance Follow-up Scale.Results:A total of 220 questionnaires were distributed, and 215 valid questionnaires were collected, with an effective response rate of 97.73% (215/220). The Exercise Rehabilitation Compliance Follow-up Scale in 215 patients after PCI was (11.35±3.85). The mediating effect of exercise self-efficacy between social support and exercise compliance was 0.150 [95% CI (0.055, 0.167), P<0.01], and the mediating effect of disease cognition between social support and exercise compliance after PCI was 0.065 [95% CI (0.016, 0.090), P<0.05]. The direct effect of social support on exercise compliance was 0.238 [95% CI (0.064, 0.278), P<0.01], and the indirect effect was 0.215 [95% CI (0.083, 0.233), P<0.05], and the mediating effect accounted for 47.46% of the total effect. Conclusions:Social support can directly or indirectly affect the compliance of patients with home-based exercise rehabilitation through the mediating effect of exercise self-efficacy and post PCI disease cognition. Medical and nursing staff can improve patient exercise self-efficacy and disease cognition by constructing intervention strategies based on social support, thereby enhancing home-based exercise rehabilitation compliance.

Objective:To explore the reasons for delayed medical treatment in diabetic foot patients using the health ecology model as a framework.Methods:From November of 2021 to March of 2022, descriptive phenomenological research methods was used in 20 patients with diabetic foot who delayed medical treatment were interviewed face-to-face semi-structured in depth, and the data analysis was carried out using the seven-step method of Colaizzi phenomenology to refine the theme.Results:According to the five levels of the health ecology model, the reasons for delayed medical treatment were summarized into 11 themes, and the reasons for the personal trait layer included foot ulcers are not detected promptly, and the perceptual decline of advanced patients. Behavioral characteristics included lack of knowledge of diabetic foot, negative medical psychology. Reasons for the interpersonal network included insufficient family support and lack of social support; reasons for living and working conditions included high economic pressure, low electronic health literacy, and inconvenient medical treatment; the reasons for the policy environment included uneven medical insurance reimbursement ratios and limited medical conditions.Conclusions:The reason for the delay in seeking medical treatment for diabetic foot patients is multi-layered and systematic, and medical staff need to improve the self-management ability of diabetic patients′ feet from the individual level, and also need to guide patients from the system level on how to obtain the best medical resources.

OBJECTIVE: To explore lung ultrasound radiomics features which related to extravascular lung water index (EVLWI), and to predict EVLWI in critically ill patients based on lung ultrasound radiomics combined with machine learning and validate its effectiveness. METHODS: A retrospective case-control study was conducted. The lung ultrasound videos and pulse indicated continuous cardiac output (PiCCO) monitoring results of critically ill patients admitted to the department of critical care medicine of the First Affiliated Hospital of Guangxi Medical University from November 2021 to October 2022 were collected, and randomly divided into training set and validation set at 8:2. The corresponding images from lung ultrasound videos were obtained to extract radiomics features. The EVLWI measured by PiCCO was regarded as the "gold standard", and the radiomics features of training set was filtered through statistical analysis and LASSO algorithm. Eight machine learning models were trained using filtered radiomics features including random forest (RF), extreme gradient boost (XGBoost), decision tree (DT), Naive Bayes (NB), multi-layer perceptron (MLP), K-nearest neighbor (KNN), support vector machine (SVM), and Logistic regression (LR). Receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive performance of models on EVLWI in the validation set. RESULTS: A total of 151 samples from 30 patients were enrolled (including 906 lung ultrasound videos and 151 PiCCO monitoring results), 120 in the training set, and 31 in the validation set. There were no statistically significant differences in main baseline data including gender, age, body mass index (BMI), mean arterial pressure (MAP), central venous pressure (CVP), heart rate (HR), cardiac index (CI), cardiac function index (CFI), stroke volume index (SVI), global end diastolic volume index (GEDVI), systemic vascular resistance index (SVRI), pulmonary vascular permeability index (PVPI) and EVLWI. The overall EVLWI range in 151 PiCCO monitoring results was 3.7-25.6 mL/kg. Layered analysis showed that both datasets had EVLWI in the 7-15 mL/kg interval, and there was no statistically significant difference in EVLWI distribution. Two radiomics features were selected by using LASSO algorithm, namely grayscale non-uniformity (weight was -0.006 464) and complexity (weight was -0.167 583), and they were used for modeling. ROC curve analysis showed that the MLP model had better predictive performance. The area under the ROC curve (AUC) of the prediction validation set EVLWI was higher than that of RF, XGBoost, DT, KNN, LR, SVM, NB models (0.682 vs. 0.658, 0.657, 0.614, 0.608, 0.596, 0.557, 0.472). CONCLUSIONS The gray level non-uniformity and complexity of lung ultrasound were the most correlated radiomics features with EVLWI monitored by PiCCO. The MLP model based on gray level non-uniformity and complexity of lung ultrasound can be used for semi-quantitative prediction of EVLWI in critically ill patients.
Humans ; Extravascular Lung Water/diagnostic imaging* ; Retrospective Studies ; Critical Illness ; Case-Control Studies ; Bayes Theorem ; China ; Lung/diagnostic imaging*

Objective:To systematically review the qualitative researches on the experience of exercise rehabilitation in discharged patients after lung cancer surgery, so as to provide a theoretical basis for the formulation of intervention strategies.Methods:Qualitative researches on the experience of exercise rehabilitation in discharged patients after lung cancer surgery was retrieved by computer in Chinese and English databases such as PubMed, Embase, CINAHL, Web of Science, APA PsycInfo, China National Knowledge Infrastructure, WanFang Data, and VIP. The retrieval time limit was from the establishment of the database to September 20, 2021. The quality of the article was evaluated using the quality evaluation criteria for qualitative research of the Joanna Briggs Institute Evidence-Based Health Care Center (2016) in Australia. The results were analyzed by pooled synthesis method.Results:A total of 8 articles were included, and 49 research results were extracted. The results were grouped into 8 new categories, which were further integrated into 4 integrated results, namely, obstacles for discharged patients after lung cancer surgery to participate in exercise rehabilitation, facilitators for discharged patients after lung cancer surgery to participate in exercise rehabilitation, benefits felt by the patients, patients' opinions and suggestions on exercise rehabilitation program design.Conclusions:The participation of exercise rehabilitation in discharged patients after lung cancer surgery is affected by many factors. When formulating exercise rehabilitation program, medical and nursing staff should formulate exercise rehabilitation program that meets the individual needs of patients according to the obstacles and facilitators of patients participating in exercise rehabilitation, combined with the patients' attitudes and opinions on the implementation of exercise rehabilitation.

PRECEDE-PROCEED model is a new research hotspot in the field of health education in recent years, which has been widely used in many education fields and has achieved good results. This article discussed in detail the theoretical framework, advantages and application status of PRECEDE-PROCEED model in the field of health education, so as to enhance the medical and nursing staff's understanding of this model, and provide a reference for applying this model to clinical work scientifically.

Objective:To explore the effect of intervention plan based on a shared decision-making model on the diabetes knowledge, self-efficacy, self-management behavior, and blood glucose level of patients with diabetic ketosis.Methods:From November 2019 to March 2020, a total of 212 patients with diabetic ketosis who were admitted to the Endocrinology Department of a ClassⅢGrade A hospital in Yantai were recruited by convenience sampling. Among them, 104 patients admitted from July to October 2019 were involved into the control group, and 108 patients for the experimental group. The control group was given regular health education, and the experimental group was given the education based on the shared decision-making model. The changes of diabetes knowledge, diabetes management self-efficacy, self-management behavior, fasting blood glucose (FBG) , 2-hour postprandial blood glucose (2hPG) , glycosylated hemoglobin value (HbA1c) in two groups were compared before intervention, 3 months and 6 months after intervention.Results:3 months and 6 months after the intervention, the scores of diabetes knowledge with 85 (89.3, 70.0) and 88 (71.3, 92) , diabetes self-efficacy with 77.5 (70.0, 88.0) and 79.2 (63.8, 92.4) , self-management behavior with [64.5 (55.8, 74.8) ] and [62.5 (53.9, 74.2) ]in the experimental group were significantly higher than those of the control group ( P<0.05) . The level of FBG, 2hPG, and HbA1c of the experimental group were lower than those of the control group, and the differences were statistically significant ( P<0.05) . There was no significant difference in the recurrent cases of ketosis between the two groups within 6 months ( P>0.05) . Conclusions:The self-management intervention based on the shared decision-making model as the starting point may improve the level of diabetes knowledge, diabetes management self-efficacy and self-management behavior and stabilize the level of blood glucose of patients with diabetic acidosis.

Objective:To translate Pediatric Quality of Life Inventory TM 3.2 Diabetes Module (PedsQL TM 3.2-DM) for children and adolescents into Chinese and analyze the reliability and validity of it. Methods:Following the principle of Brislin translation, the source scale was translated by 2 supervisor nurses with master's degrees and the scale was back translated by 2 nursing experts with overseas study experience. The semantic adjustment of the scale was carried out by pre-surveys. By the convenient sampling method, a total of 175 children and adolescents with diabetes who were treated in Department of Endocrinology and Department of Pediatrics in ClassⅢ Grade A hospitals in Yantai and Qingdao from March to August 2019 were investigated and 175 effective scales were finally included to analyze the reliability and validity of the revised scale in children and adolescents with diabetes.Results:The Chinese version of PedsQL TM 3.2-DM had 33 items in total, including 4 dimensions such as disease-related symptoms, treatment compliance, disease-caused worry and communication. The Cronbach's α coefficient of Chinese version of PedsQL TM 3.2-DM was 0.927 and that of 4 dimensions were respectively 0.885, 0.910, 0.828 and 0.798. After assessment by panel of experts, the content validity index of Chinese version of PedsQL TM 3.2-DM was 0.97 and the content validity index of every item was from 0.80 to 1.00. A total of 4 common factors were extracted by exploratory factor analysis and the cumulative variance contribution rate was 59.539%. In addition, the factor loading of each item on its common factor was over 0.4. Conclusions:The Chinese version of PedsQL TM 3.2-DM has good reliability and validity, which can be used as a simple tool for self-assessment of quality of life in children aged from 8 to 19 years with diabetes.