Lesion Volume as a Predictor for Return to Work After Endovascular Therapy - a Four-year Prospective Cohort Study

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 define a cut-off value of lesion volume on RTW after endovascular treatment (EVT). Methods: We included patients <65 years undergoing EVT at Oslo University Hospital (OUS) between January 2017 and May 2019. Employment status was obtained at both baseline and a four-year follow-up. Stroke lesion volumes were segmented using magnetic resonance imaging (MRI) scans 24 hours post EVT. 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 cut-off. Machine learning (ML) regression models were employed 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 four-year follow-up with MRI 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), 50% were female. Median lesion volume was 18 ml (IQR 45-7). After four 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 [aOR], 95% CI, 1.02?1.11], P=0.02). A lesion volume cut-off value of 29 cm3 yielded a sensitivity of 0.91 and specificity of 0.65 for predicting RTW. Notably, the most influential feature in the ML model for predicting RTW was lesion volume. Conclusion: Lesion volume was the most robust predictor of RTW four years after EVT. Our findings suggest that a cut-off of 29 cm3 is suitable to distinguish between those with high and low chance of RTW.