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

Xiaoqing Tao¹, Shan Wang², Li Pang¹

¹Department of Neurology, Beijing Anzhen Nanchong Hospital of Capital Medical University & Nanchong Central Hospital, Nanchong, Sichuan, China.

Frontiers in Neurology

|April 29, 2026

Summary

This summary is machine-generated.

Post-stroke fatigue (PSF) prediction is improved by a new nomogram identifying key risk factors like lesion location and inflammation markers. This tool aids early identification of high-risk patients for timely intervention.

Keywords:

LASSO multicenter nomogram post-stroke fatigue predictors

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Computerized Adaptive Testing System of Functional Assessment of Stroke

Computerized Adaptive Testing System of Functional Assessment of Stroke

Published on: January 7, 2019

Area of Science:

Neurology
Medical Diagnostics
Biostatistics

Background:

Post-stroke fatigue (PSF) is a prevalent and debilitating complication following stroke.
The underlying pathophysiological mechanisms of PSF are not fully understood.
Reliable tools for predicting PSF are currently lacking.

Purpose of the Study:

To identify clinical and laboratory risk factors associated with PSF.
To develop and validate a predictive nomogram for early PSF detection.

Main Methods:

Retrospective cohort study involving 846 stroke patients.
Data included demographics, clinical information, imaging, and laboratory results.
LASSO and logistic regression models were used to construct and validate a nomogram.

Main Results:

Eight independent predictors for PSF were identified: specific lesion locations (brainstem, basal ganglia, thalamus), female sex, older age, mRS score, WBC count, and CRP level.
The developed nomogram demonstrated good predictive performance across training, internal, and external validation sets (AUCs ranging from 0.672 to 0.870).
The model showed good calibration and clinical utility.

Conclusions:

A clinically applicable nomogram for early PSF prediction was successfully developed.
The nomogram utilizes routinely available data, facilitating its integration into clinical practice.
This tool can assist in identifying high-risk individuals, enabling prompt therapeutic interventions.