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Objectives: We aimed to develop and validate a nomogram for the individualized prediction of the risk of post-stroke fatigue (PSF) after discharge. Materials and methods: Fatigue was measured using the Fatigue Assessment Scale. Multivariable logistic regression analysis was applied to build a prediction model incorporating the feature selected in the least absolute shrinkage and selection operator regression model. Discrimination, calibration, and clinical usefulness of the predictive model were assessed using the C-index, calibration plot, and decision curve analysis. Internal validation was conducted using bootstrapping validation. Finally, a web application was developed to facilitate the use of the nomogram. Results: We developed a nomogram based on 95 stroke patients. The predictors included in the nomogram were sex, pre-stroke sarcopenia, acute phase fatigue, dysphagia, and depression. The model displayed good discrimination, with a C-index of 0.801 (95% confidence interval: 0.700-0.902) and good calibration. A high C-index value of 0.762 could still be reached in the interval validation. Decision curve analysis showed that the risk of PSF after discharge was clinically useful when the intervention was decided at the PSF risk possibility threshold of 10% to 90%. Conclusion: This nomogram could be conveniently used to provide an individual, visual, and precise prediction of the risk probability of PSF after being discharged home. Thus, as an aid in decision-making, physicians and other healthcare professionals can use this predictive method to provide early intervention or a discharge plan for stroke patients during the hospitalization period.

Aim To analyse the interactions of associated factors with post stroke fatigue (PSF) after discharge home and determine the predictors of PSF and their impact on stroke survivors. Design A prospective observational study. Methods A total of 94 patients with acute stroke were recruited between May 2019 -July 2020. The main outcomes were fatigue, depression, insomnia, sarcopenia, and health-related quality of life (HRQOL) and were assessed at admission and 1 month after discharge. Fatigue was measured using the Fatigue Assessment Scale. Depression and Insomnia were assessed using the Hospital Anxiety and Depression Scale-Depression and Insomnia Severity Index, respectively. Sarcopenia was measured using the SARC-F questionnaire, and HRQOL was assessed using the Short Form-8. Results Acute phase PSF was an independent predictor of PSF after discharge home. Moreover the path analysis revealed that this effect is mediated through both the direct effect of acute-phase PSF on PSF after discharge home and through the indirect effect of interaction with pre-stroke SARC-F, acute phase depression, and acute phase insomnia, which remains a separate predictor of acute-phase PSF. In total, 17% of the survivors had persistent PSF. Persistent PSF was significantly associated with depression, insomnia, sarcopenia, and a lower quality of life scores. Conclusions Post-stroke fatigue may occur in the acute phase and persists after discharge, it will not only affect later depression, insomnia, and quality of life, but also sarcopenia. Impact Acute phase PSF was found to be an independent predictor of PSF after discharge home. In addition, the interaction with pre-stroke SARC-F, acute phase depression and insomnia had an indirect connection with PSF after discharge home, which remains a separate predictor of acute-phase PSF. Thus, early assessment and management of mental status, sleep problems, and sarcopenia during hospitalization might be an important step in post-stroke rehabilitatio