A Predictive Model for Functional Outcome in Patients with Acute Ischemic Stroke Undergoing Endovascular Thrombectomy

Introduction: Endovascular thrombectomy (EVT) is a well-established treatment of acute ischemic stroke. Variability in outcomes among thrombectomy patients results in a need for patient centered approaches to recovery. Identifying key factors that are associated with outcomes can help prognosticate and direct resources for continued improvement post-treatment. Thus, we developed a comprehensive predictive model of short-term outcomes post-thrombectomy. Methods: This is a retrospective chart review of adult patients who underwent EVT at our institution over the last four years. Primary outcome was dichotomized 90-day mRS (mRS 0–2 v mRS 3–6). Bivariate analyses were conducted, followed by logistic regression modelling via a backward-elimination approach to identify the best fit predictive model. Results: 326 thrombectomies were performed; 230 cases were included in the model. In the final predictive model, adjusting for age, gender, race, diabetes, and presenting NIHSS, pre-admission mRS = 0–2 (OR 18.1; 95% 3.44–95.48; p < 0.001) was the strongest predictor of a good outcome at 90-days. Other independent predictors of good outcomes included being a non-smoker (OR 5.4; 95% CI 1.53–19.00; p = 0.01) and having a post-thrombectomy NIHSS<10 (OR 9.7; 95% CI 3.90–24.27; p < 0.001). A decompressive hemicraniectomy (DHC) was predictive of a poor outcome at 90-days (OR 0.07; 95% CI 0.01–0.72; p = 0.03). This model had a Sensitivity of 79%, a Specificity of 89% and an AUC=0.89. Conclusion: Our model identified low pre-admission mRS score, low post-thrombectomy NIHSS, non-smoker status and not requiring a DHC as predictors of good functional outcomes at 90-days. Future works include developing a prognostic scoring system.