Abstract: Objective: Stroke patients with large core infarctions benefit from endovascular intervention, though only approximately 20% are functionally independent at 90 days. We studied prognostic factors for patients presenting with a large computed tomography perfusion (CTP) core.
Methods: Retrospective analysis from a health system stroke registry, including consecutive thrombectomy patients treated within 24 hours from August 2020-December 2022 with an anterior circulation large vessel occlusion and CTP core infarct ≥50 milliliters. Logistic regression was used to determine independent predictors of 90-day modified Rankin Scale (mRS) score 4-6. The prognostic ability of previously reported scales was also assessed.
Results: In 118 included patients, with mean age 64.3 ± 14.1 years, poor functional outcomes were present in 66 subjects (55.9%). The multivariable regression analysis demonstrated that higher presenting National Institutes of Health Stroke Scale (NIHSS) score (odds ratio [OR] 1.12, 95% confidence interval [CI] 1.02-1.23, p = 0.014), elevated glucose (OR 1.02, 95% CI 1.01-1.03, p = 0.002), absence of treatment with intravenous thrombolysis (OR 4.01, 95% CI 1.35-11.95, p = 0.013), and poor revascularization (OR 4.76, 95% CI 1.24-18.37, p = 0.023) were independently associated with primary outcome. The Charlotte Large artery occlusion Endovascular therapy Outcome Score (CLEOS) predicted 90-day mRS 4-6 (per 25-point increase, OR 1.22, 95% CI 1.10-1.34, p<0.001) and mRS 5-6 (per 25-point increase, OR 1.21, 95% CI 1.10-1.33, p<0.001). Nineteen of 20 (95%) patients with CLEOS ≥ 675 had 90-day mRS scores of 4-6, while 10 of 12 (83.3%) with CLEOS ≥ 725 had 90-day mRS scores of 5-6.
Conclusion: We report prognostic factors that can risk stratify thrombectomy patients with large CTP core infarctions.
Competing Interests: Dr. Rahul Karamchandani receives research support from Genentech. Dr. Joe Bernard declares stock/stock options from Viz.Ai and personal fees from Stryker, Terumo, and Rapid AI. Dr. Andrew Asimos is a consultant for Rapid AI. All other authors report no disclosures.
(Copyright: © 2024 Karamchandani et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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