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

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.<p>

<p>Design - A prospective multicenter cohort-study with 18-month follow-up.<p>

<p>Setting - Outpatient clinics at 3 university hospitals and 2 local hospitals.<p>

<p>Participants - 474 participants with the diagnosis of acute stroke (mean ± SD age; 70.5 (11.3), 59% male; N=474).<p>

<p>Interventions - Not applicable.<p>

<p>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.<p>

<p>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).<p>

<p>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.