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

BackgroundThere 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). MethodsWe 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. ResultsOf 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. ConclusionLesion 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.