Abstract
BACKGROUND: There are no known objective biomarkers for predicting return to work (RTW) in ischemic stroke survivors. This study aims to explore the predictive utility and defines a cutoff value of lesion volume on RTW after endovascular treatment. METHODS: We included patients aged <65 years undergoing endovascular treatment at Oslo University Hospital between January 2017 and May 2019. Employment status was obtained at both baseline and a 4-year follow-up. Stroke lesion volumes were segmented using magnetic resonance imaging scans 24 hours after endovascular treatment. Logistic regression models were conducted to assess the impact of lesion volume on RTW status at follow-up, adjusted for patients' characteristics, stroke-related factors, and treatment. We calculated the receiver operating characteristic curve to determine the optimal lesion volume cutoff. Machine learning regression models were used to assess the predictive abilities of baseline clinical and imaging variables for RTW. RESULTS: Of the 109 individuals treated, 81 (74%) were employed at baseline. Among these, 60 completed 4-year follow-up with magnetic resonance imaging available for stroke lesion segmentation and were included in the analyses. Mean age at stroke onset was 51.8 years (range, 23.5-64.9 years), and 50% were female. Median lesion volume was 18 mL (interquartile ranges, 45-47 mL). After 4 years, 34 (57%) had successfully RTW. The odds for not RTW increased by 5% for every 1 mL increase in lesion volume (adjusted odds ratio, 1.05 [95% CI, 1.02-1.11]; P = 0.02). A lesion volume cutoff value of 29 cm(3) yielded a sensitivity of 0.91 and specificity of 0.65 for predicting RTW. Notably, the most influential feature in the machine learning model for predicting RTW was lesion volume. CONCLUSION: Lesion volume was the most robust predictor of RTW 4 years after endovascular treatment. Our findings suggest that a cutoff of 29 cm(3) is suitable to distinguish between those with high and low chance of RTW.