A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study.
Arch Phys Med Rehabil
; 105(5): 921-929, 2024 May.
Article
em En
| MEDLINE
| ID: mdl-38242298
ABSTRACT
OBJECTIVE:
This study aimed to predict fatigue 18 months post-stroke by utilizing comprehensive data from the acute and sub-acute phases after stroke in a machine-learning set-up.DESIGN:
A prospective multicenter cohort-study with 18-month follow-up.SETTING:
Outpatient clinics at 3 university hospitals and 2 local hospitals.PARTICIPANTS:
474 participants with the diagnosis of acute stroke (mean ± SD age; 70.5 (11.3), 59% male; N=474).INTERVENTIONS:
Not applicable. MAIN OUTCOMEMEASURES:
The primary outcome, fatigue at 18 months, was assessed using the Fatigue Severity Scale (FSS-7). FSS-7≥5 was defined as fatigue. In total, 45 prediction variables were collected, at initial hospital-stay and 3-month post-stroke.RESULTS:
The best performing model, random forest, predicted 69% of all subjects with fatigue correctly with a sensitivity of 0.69 (95% CI 0.50, 0.86), a specificity of 0.74 (95% CI 0.66, 0.83), and an Area under the Receiver Operator Characteristic curve of 0.79 (95% CI 0.69, 0.87) in new unseen data. The proportion of subjects predicted to suffer from fatigue, who truly suffered from fatigue at 18-months was estimated to 0.41 (95% CI 0.26, 0.57). The proportion of subjects predicted to be free from fatigue who truly did not have fatigue at 18-months was estimated to 0.90 (95% CI 0.83, 0.96).CONCLUSIONS:
Our findings indicate that the model has satisfactory ability to predict fatigue in the chronic phase post-stroke and may be applicable in clinical settings.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Acidente Vascular Cerebral
/
Fadiga
/
Aprendizado de Máquina
Tipo de estudo:
Clinical_trials
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Aged
/
Aged80
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
Arch Phys Med Rehabil
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Noruega