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Development and Internal Validation of a Nomogram to Predict Post-Stroke Fatigue After Discharge

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Title: Development and Internal Validation of a Nomogram to Predict Post-Stroke Fatigue After Discharge
Authors: Su, Ya Browse this author
Yuki, Michiko Browse this author →KAKEN DB
Hirayama, Kengo Browse this author →KAKEN DB
Otsuki, Mika Browse this author →KAKEN DB
Keywords: Nomogram
Sarcopenia, Dysphagia
Issue Date: Feb-2021
Publisher: Elsevier
Journal Title: Journal of stroke & cerebrovascular diseases
Volume: 30
Issue: 2
Start Page: 105484
Publisher DOI: 10.1016/j.jstrokecerebrovasdis.2020.105484
Abstract: 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.
Type: article
Appears in Collections:保健科学院・保健科学研究院 (Graduate School of Health Sciences / Faculty of Health Sciences) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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