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Prediction of delayed reperfusion in patients with incomplete reperfusion following thrombectomy
Adnan Mujanovic1, Robin Brigger1, Christoph C Kurmann1,2
1Department of Diagnostic and Interventional Neuroradiology, University Hospital of Bern, University of Bern, Bern, Switzerland.
Background:
The clinical course of patients with incomplete reperfusion after thrombectomy, defined as an expanded Thrombolysis in Cerebral Infarction (eTICI) score of 2a-2c, is heterogeneous. Patients showing delayed reperfusion (DR) have good clinical outcomes, almost comparable to patients with ad-hoc TICI3 reperfusion. We aimed to develop and internally validate a model that predicts DR occurrence in order to inform physicians about the likelihood of a benign natural disease progression.
Patients And Methods:
Single-center registry analysis including all consecutive, study-eligible patients admitted between 02/2015 and 12/2021. Preliminary variable selection for the prediction of DR was performed using bootstrapped stepwise backward logistic regression. Interval validation was performed with bootstrapping and the final model was developed using a random forests classification algorithm. Model performance metrics are reported with discrimination, calibration, and clinical decision curves. Primary outcome was concordance statistics as a measure of goodness of fit for the occurrence of DR.
Results:
A total of 477 patients (48.8% female, mean age 74 years) were included, of whom 279 (58.5%) showed DR on 24 follow-up. The model's discriminative ability for predicting DR was adequate (C-statistics 0.79 [95% CI: 0.72-0.85]). Variables with strongest association with DR were: atrial fibrillation (aOR 2.06 [95% CI: 1.23-3.49]), Intervention-To-Follow-Up time (aOR 1.06 [95% CI: 1.03-1.10]), eTICI score (aOR 3.49 [95% CI: 2.64-4.73]), and collateral status (aOR 1.33 [95% CI: 1.06-1.68]). At a risk threshold of R = 30%, use of the prediction model could potentially reduce the number of additional attempts in one out of four patients who will have spontaneous DR, without missing any patients who do not show spontaneous DR on follow-up.
Conclusions:
The model presented here shows fair predictive accuracy for estimating chances of DR after incomplete thrombectomy. This may inform treating physicians on the chances of a favorable natural disease progression if no further reperfusion attempts are made.
