A multicenter clinical nomogram for predicting post-stroke fatigue: development and validation

Background and purpose: Post-stroke fatigue (PSF) is a common and disabling complication after stroke, yet its pathophysiological mechanisms remain unclear and reliable prediction tools are lacking. This study aimed to identify risk factors for PSF and develop a visualized nomogram for early prediction based on clinical and laboratory data. Methods: = 254) sets. An independent cohort of 440 patients from Nanchong Central Hospital was used as the external validation cohort. Fatigue was assessed at week 4 after admission using the Fatigue Severity Scale (FSS) and Fatigue Assessment Scale (FAS). Demographic, clinical, imaging, and laboratory data were collected. LASSO regression was used for variable selection, followed by multivariate logistic regression to construct a nomogram. Model performance was assessed using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA), with internal and external validation via bootstrapping. Results: < 0.05). The nomogram showed good discrimination (AUC: 0.870, 0.862, and 0.672 for training, internal, and external validation sets, respectively), calibration, and clinical utility. Conclusion: We developed a clinically applicable nomogram based on routinely available data for early prediction of PSF. The model demonstrated good accuracy and may aid in identifying high-risk patients to guide timely intervention.