Abstract
Background: After successful reperfusion is achieved (extended Thrombolysis in Cerebral Infarction (eTICI) ≥2b50), decision on pursuing additional treatment strategies in order to achieve complete reperfusion (eTICI=2c/3), is multifactorial and depends on patient’s clinical and imaging characteristics. We have developed and validated a clinical decision tool to provide individualized predictions on achieving delayed reperfusion based on individual patient data. Methods: Single-center registry analysis for all consecutive patients admitted between 02/2015 - 12/2020. Primary variable of interest was perfusion imaging outcome in patients with incomplete reperfusion (eTICI 2a-2c), evaluated on the 24-hour follow-up imaging. This variable was dichotomized into delayed reperfusion, in case of non-observable perfusion deficit, and persistent perfusion deficit, in case of perfusion deficit captured on the final angiography imaging. Final model variable selection was performed via bootstrapped (n=200) stepwise backwards regression. Model was split into a training and testing set (80:20 ratio), with 10-fold cross validation resampling. Results: 372 patients (50.8% female, mean age 74) were included, with 228 (61.2%) of them having delayed reperfusion. Final model identified seven variables of importance including: age, sex, atrial fibrillation, Intervention-to-Follow-Up time, maneuver count, eTICI and collateral status. Model’s discriminative ability for predicting delayed reperfusion was adequate (AUC 0.83, 95% CI 0.74 -0.92), with an overall adjusted calibration (Brier score 0.17, 95% CI 0.15-0.18). Conclusions: Current model presents a tool that may aid clinical decision-making process in selection of patients for pursuing additional treatment strategies after incomplete reperfusion has been achieved. This is an important next step towards personalized treatment of stroke patients undergoing mechanical thrombectomy.
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