A predictive model for social participation of middle-aged and older adult stroke survivors: the China Health and Retirement Longitudinal Study.

This study aims to develop and validate a prediction model for evaluating the social participation in the community middle-aged and older adult stroke survivors. The predictive model is based on data from the China Health and Retirement Longitudinal Study (CHARLS), which focused on individuals aged 45 years or older. The study utilized subjects from the CHARLS 2015 and 2018 wave, eighteen factors including socio-demographic variables, behavioral and health status, mental health parameters, were analyzed in this study. To ensure the reliability of the model, the study cohort was randomly split into a training set (70%) and a validation set (30%). The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to identify the most effective predictors of the model through a 10-fold cross-validation. The logistic regression model was employed to investigate the factors associated with social participation in stroke patients. A nomogram was constructed to develop a prediction model. Calibration curves were used to assess the accuracy of the nomogram model. The model's performance was evaluated using the area under the curve (AUC) and decision curve analysis (DCA). A total of 1,239 subjects with stroke from the CHARLS database collected in 2013 and 2015 wave were eligible in the final analysis. Out of these, 539 (43.5%) subjects had social participation. The model considered nineteen factors, the LASSO regression selected eleven factors, including age, gender, residence type, education level, pension, insurance, financial dependence, physical function (PF), self-reported healthy,cognition and satisfaction in the prediction model. These factors were used to construct the nomogram model, which showed a certain extent good concordance and accuracy. The AUC values of training and internal validation sets were 0.669 (95%CI 0.631-0.707) and 0.635 (95% CI 0.573-0.698), respectively. Hosmer-Lemeshow test values were p = 0.588 and p = 0.563. Calibration curves showed agreement between the nomogram model and actual observations. ROC and DCA indicated that the nomogram had predictive performance. The nomogram constructed in this study can be used to evaluate the probability of social participation in middle-aged individuals and identify those who may have low social participation after experiencing a stroke.

Liu Y ，Li T ，Ding L ，Cai Z ，Nie S ... -  《Frontiers in Public Health》

Development and validation of a risk prediction model for older adults with social isolation in China.

Older adults are vulnerable to social isolation due to declining physical and cognitive function, decreased interpersonal interactions, and reduced outdoor activities after retirement. This study aimed to develop and validate a predictive model to assess the risk of social isolation among older adults in China. Using data from the 2011 China Health and Retirement Longitudinal Study (CHARLS). The study cohort was randomly divided into training and validation groups in a 70:30 ratio. We used least absolute shrinkage and selection operator (LASSO) regression analysis with tenfold cross-validation to identify optimal predictive factors and examined the correlates of social isolation using logistic regression. A nomogram was constructed for the predictive model, and its accuracy was assessed using calibration curves. The predictive performance of the model was assessed using area under the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). From the 2011 CHARLS database, 4,747 older adults were included in the final analysis, of whom 1,654 (34.8%) experienced social isolation. Multifactorial logistic regression identified educational level, marital status, gender, physical activity, physical self -maintenance ability, and number of children as predictive factors for social isolation. The predictive model achieved an AUC of 0.739 (95%CI = 0.722-0.756) in the training set and 0.708 (95%CI = 0.681-0.735) in the validation set. The Hosmer-Lemeshow test yielded P values of 0.111 and 0.324, respectively (both P > 0.05), indicating significant agreement between the nomogram and observed outcomes. The nomogram showed excellent predictive ability according to ROC and DCA. The predictive model developed to assess the risk of social isolation in the Chinese older adults shows promising utility for early screening and intervention by clinical healthcare professionals.

Li Q ，Cen W ，Yang T ，Tao S ... -  《-》

Development and validation of a risk prediction model for frailty in patients with diabetes.

Frailty is the third most common complication of diabetes after macrovascular and microvascular complications. The aim of this study was to develop a validated risk prediction model for frailty in patients with diabetes. The research used data from the China Health and Retirement Longitudinal Study (CHARLS), a dataset representative of the Chinese population. Twenty-five indicators, including socio-demographic variables, behavioral factors, health status, and mental health parameters, were analyzed in this study. The study cohort was randomly divided into a training set and a validation set at a ratio of 70 to 30%. LASSO regression analysis was used to screen the variables for the best predictors of the model based on a 10-fold cross-validation. The logistic regression model was applied to explore the associated factors of frailty in patients with diabetes. A nomogram was constructed to develop the prediction model. Calibration curves were applied to evaluate the accuracy of the nomogram model. The area under the receiver operating characteristic curve and decision curve analysis were conducted to assess predictive performance. One thousand four hundred thirty-six patients with diabetes from the CHARLS database collected in 2013 (n = 793) and 2015 (n = 643) were included in the final analysis. A total of 145 (10.9%) had frailty symptoms. Multivariate logistic regression analysis showed that marital status, activities of daily living, waist circumference, cognitive function, grip strength, social activity, and depression as predictors of frailty in people with diabetes. These factors were used to construct the nomogram model, which showed good concordance and accuracy. The AUC values of the predictive model and the internal validation set were 0.912 (95%CI 0.887-0.937) and 0.881 (95% CI 0.829-0.934). Hosmer-Lemeshow test values were P = 0.824 and P = 0.608 (both > 0.05). Calibration curves showed significant agreement between the nomogram model and actual observations. ROC and DCA indicated that the nomogram had a good predictive performance. Comprehensive nomogram constructed in this study was a promising and convenient tool to evaluate the risk of frailty in patients with diabetes, and contributed clinicians to screening the high-risk population.

Bu F ，Deng XH ，Zhan NN ，Cheng H ，Wang ZL ，Tang L ，Zhao Y ，Lyu QY ... -  《BMC Geriatrics》

Development and Validation of a Predictive Model for Early Identification of Cognitive Impairment Risk in Community-Based Hypertensive Patients.

Li Y ，Xin J ，Fang S ，Wang F ，Jin Y ，Wang L ... -  《-》

Development and validation of a nomogram to predict the depressive symptoms among older adults: A national survey in China.

Rong J ，Zhang N ，Wang Y ，Cheng P ，Zhao D ... -  《-》