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

 

Will your competent? doctor and hospital ensure research is completed to determine WHAT WILL CURE POST STROKE FATIGUE? Not manage or anything less than a FULL CURE!

This prediction crapola is COMPLETELY FUCKING USELESS!

Let's see how long everyone in stroke has been incompetent at this problem!

• post stroke fatigue (60 posts to January 2018)

At least half of all stroke survivors experience fatigue Known since March 2017

Or is it 70%? Known since March 2015.

Or is it 40%? Known since September 2017.

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

• X

  Xiaoqing Tao 1†

• S

  Shan Wang 2†

• L

  Li Pang 1

• Y

  Yiling Sun 1

• 

  Yifei Ji 1,2*

• 

  Yu Ren 1,3*

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

• 2. Department of Neurology, The Second Clinical Medical College of North Sichuan Medical College, Nanchong, Sichuan, China

Abstract

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: 

We conducted a retrospective cohort study of stroke patients hospitalized in the Department of Neurology at the First Affiliated Hospital of Chongqing Medical University were randomly split into training (n = 592) and internal validation (n = 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: 

A total of 846 stroke patients were enrolled and randomly split into training (n = 592), internal validation (n = 254) and external validation (n = 440) sets. Eight independent predictors of PSF were identified: brainstem, basal ganglia, and thalamic lesions, female sex, older age, modified Rankin Scale (mRS) score, white blood cell (WBC) count, and C-reactive protein (CRP) level (all p < 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.