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• About 20% of patients with minor stroke suffer from long-term fatigue. • About 40% of those identified at risk will suffer from long-term fatigue. • About 90% of those identified not at risk will not suffer from long-term fatigue. 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. A prospective multicenter cohort-study with 18-month follow-up. Outpatient clinics at 3 university hospitals and 2 local hospitals. 474 participants with the diagnosis of acute stroke (mean ± SD age; 70.5 (11.3), 59% male; N=474). Not applicable. 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. 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). 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. [ABSTRACT FROM AUTHOR]

Advanced age is associated with post-stroke cognitive decline. Machine learning based on brain scans can be used to estimate brain age of patients, and the corresponding difference from chronological age, the brain age gap (BAG), has been investigated in a range of clinical conditions, yet not thoroughly in post-stroke neurocognitive disorder (NCD). We aimed to investigate the association between BAG and post-stroke NCD over time. Lower BAG (younger appearing brain compared to chronological age) was found associated with lower risk of post-stroke NCD up to 36 months after stroke, even among those showing no evidence of impairments 3 months after hospital admission. For patients with no NCD at baseline, survival analysis suggested that higher baseline BAG was associated with higher risk of post-stroke NCD at 18 and 36 months. In conclusion, a younger appearing brain is associated with a lower risk of post-stroke NCD. [ABSTRACT FROM AUTHOR]