Development and validation of a prediction model for activities of daily living dysfunction among stroke survivors: insights from the CHARLS cohort

 I'd fire anyone doing prediction, biomarkers, prognistication or assessments. None of them do a damn thing getting survivors recovered! All they do is turn on anxiety and depression!

Development and validation of a prediction model for activities of daily living dysfunction among stroke survivors: insights from the CHARLS cohort

• Hongying Ren,
• Wen Wang,
• Li Tao,
• Xiaoshu Song,
• Bin Bai,
• Min Wang &
• Xiaoyan Li 

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Abstract

Background

Activities of daily living (ADL) dysfunction is prevalent in stroke survivors and places a significant burden on both patients and healthcare systems. Improved identification of individuals with ADL dysfunction may facilitate more targeted rehabilitation strategies.

Methods

The China Health and Retirement Longitudinal Study (CHARLS) provided the data. A training set (n = 906) and a validation set (n = 389) were randomly selected from a total of 1,295 stroke survivors. Least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic regression were used to select predictors and develop a prediction model, which was visualized using a nomogram. SHapley Additive exPlanations (SHAP) were applied for model interpretation. The area under the receiver operating characteristic curve (AUC), calibration analysis, and decision curve analysis (DCA) were used to evaluate the model’s performance.

Results

Ten predictors were identified, including CES-D scores, age, sleep time duration, drinking, lung disease, social contact, falls, hypertension, arthritis, and sex. SHAP analysis identified CES-D scores as the most influential predictors. The model demonstrated acceptable discriminative ability in both the training set (AUC: 0.76, 95% CI: 0.73–0.79) and validation set (AUC: 0.76, 95% CI: 0.72–0.81). Calibration was satisfactory in both the training and validation sets (Hosmer–Lemeshow test, P = 0.16 and P = 0.99, respectively). Positive clinical usefulness was suggested by DCA analysis.

Conclusions

The model demonstrated acceptable predictive performance and may assist in identifying individuals with prevalent ADL dysfunction. Further external validation is required before broader clinical application.