A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study

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 Outcome Measures: 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. (c) 2024 by the American Congress of Rehabilitation Medicine. Published by Elsevier Inc. This is an open access article under the CC BY -NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)