A Predictive Model for Functional Outcome in Patients with Acute Ischemic Stroke Undergoing Endovascular Thrombectomy |
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| Affiliation: | 1. Dept. of Neurosurgery, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY USA;2. Dept. of Neurology, Dept. of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx NY USA;1. Department of Neurology, The John Paul II Hospital, Krakow, Poland;2. 2nd Department of Neurology, Institute of Psychiatry and Neurology, Warsaw, Poland;3. KCRI, Krakow, Poland;4. Institute of Cardiology, Jagiellonian University Medical College, Krakow, Poland, The John Paul II Hospital, Krakow, Poland;1. Division of Neurology, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan;2. Division of Diabetes and Metabolic Diseases, Department of Medicine, Nihon University School of Medicine, Tokyo, Japan;3. Department of Neurological Surgery, Nihon University School of Medicine, Tokyo, Japan;1. Department of Neurosurgery, Zhujiang Hospital, Southern Medical University, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Guangzhou, 510282, China;2. Department of Cerebrovascular Surgery, The Third Affiliated Hospital of Sun Yat-Sen University, No 600 Tianhe Road, Guangzhou, 510630, Guangdong, China;3. Department of Neurosurgery, Shenzhen Baoan People''s Hospital, Southern Medical University, Shenzhen 518101, China;4. Southern Medical University, Guangzhou 510282, China;1. Department of Surgery, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, United States;2. Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, United States;3. Department of Neurology, SUNY Downstate Health Sciences University, 450 Clarkson Avenue, MSC 1213, Brooklyn, NY 11203, United States;4. Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, United States;5. Department of Neurology, A.T. Still University, Mesa, AZ; Midwestern University, Glendale, AZ Honor Health Neurocritical care and Stroke Services, Phoenix, AZ, United States;6. The Stroke and Cognition Institute, The Rambam Health Care Campus, Haifa, Israel;7. Department of Neurology, Maine Medical Center, Portland, ME, United States;8. Departments of Neurology and Emergency Medicine, Stroke Center, SUNY Downstate Health Sciences University at Brooklyn, Brooklyn, NY, United States;9. Department of Neurology, Kings County Hospital Center, Brooklyn, NY, United States;10. Jaffe Stroke Center, Maimonides Medical Center, Brooklyn, NY, United States;1. Department of Neurology, University of Pecs, Medical School, Pecs, Hungary;2. Department of Anaesthesiology and Intensive Care, University of Pecs, Medical School, Pecs, Hungary;3. Department of Immunology and Biotechnology, University of Pecs, Medical School, Pecs, Hungary;4. Salisbury NHS Foundation Trust, Salisbury, United Kingdom;5. Department of Radiology, University of Örebro, Örebro, Sweeden;6. Department of Neurosurgery, University of Pecs, Medical School, Pecs, Hungary |
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| Abstract: | ![]()
IntroductionEndovascular 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.MethodsThis 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.Results326 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.ConclusionOur 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. |
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