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. 2025 May 30:16:1529724.
doi: 10.3389/fneur.2025.1529724. eCollection 2025.

Development and validation of a risk prediction model for activities of daily living dysfunction in stroke survivors

Fangbo Lin  1 , Nan Liu  1
Affiliations

Development and validation of a risk prediction model for activities of daily living dysfunction in stroke survivors

Fangbo Lin et al. Front Neurol. .

Abstract

Objective: Stroke is a leading cause of disability worldwide, imposing a significant burden on patients, families, and society. To create and verify a prediction model for activities of daily living (ADL) dysfunction in stroke survivors, pinpoint key predictors, and analyze the traits of those at risk.

Methods: Data from the China Health and Retirement Longitudinal Study wave 5 was used in this cross-sectional study. 1,131 stroke survivors were included and split into training and testing sets. The least absolute shrinkage and selection operator regression and multivariate logistic regression were applied for model development. Model performance was evaluated using the area under the receiver operating characteristic curve(AUC), calibration plots, and decision curve analysis. SHapley Additive exPlanations values were calculated to understand predictor importance.

Results: Six variables (age, the 10-item Center for Epidemiologic Studies Depression Scale score, memory disorder, self-rated health, pain count, and heavy physical activity) were identified as significant predictors. The model showed good discriminatory power (training set AUC = 0.804, testing set AUC = 0.779), accurate calibration, and clinical utility.

Conclusion: A prediction model for ADL dysfunction in stroke survivors was successfully developed and validated. It can help in formulating personalized medical plans, potentially enhancing stroke survivors' ADL ability and quality of life.

Keywords: ADL dysfunction; LASSO regression; nomogram; prediction model; stroke survivors.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flow chart of the study.
Figure 2
Figure 2
The LASSO plot.
Figure 3
Figure 3
Nomogram to predict the probability of ADL dysfunction in stroke survivors.
Figure 4
Figure 4
Assessment of the predictive accuracy of the nomogram: (A) ROC for the training set; (B) ROC for the testing set. Assessment of the predictive accuracy of the nomogram: (C) Calibration plot for the training set; (D) Calibration plot for the testing set. DCA curves of the nomogram: (E) The training set; (F) The testing set.
Figure 5
Figure 5
(A) Global importance plot; (B) Swarm plot; (C) Waterfall plots; (D) Force plots.
See this image and copyright information in PMC

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