Prediction of delayed reperfusion in patients with incomplete reperfusion following thrombectomy.

Mujanovic, Adnan; Brigger, Robin; Kurmann, Christoph C; Ng, Felix; Branca, Mattia; Dobrocky, Tomas; Meinel, Thomas R; Windecker, Daniel; Almiri, William; Grunder, Lorenz; Beyeler, Morin; Seiffge, David J; Pilgram-Pastor, Sara; Arnold, Marcel; Piechowiak, Eike I; Campbell, Bruce; Gralla, Jan; Fischer, Urs; Kaesmacher, Johannes (2023). Prediction of delayed reperfusion in patients with incomplete reperfusion following thrombectomy. European stroke journal, 8(2), pp. 456-466. Sage 10.1177/23969873231164274

Mujanovic_EurStrokeJ_2023_AAM.pdf - Accepted Version
Available under License Publisher holds Copyright.

Download (298kB) | Preview
[img] Text
23969873231164274.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (970kB) | Request a copy


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.


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.


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.


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.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology
04 Faculty of Medicine > Pre-clinic Human Medicine > Department of Clinical Research (DCR)
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology

UniBE Contributor:

Kurmann, Christoph Carmelino, Branca, Mattia, Dobrocky, Tomas, Meinel, Thomas Raphael, Almiri, William, Grunder, Lorenz Nicolas, Beyeler, Morin, Seiffge, David Julian, Pilgram-Pastor, Sara Magdalena, Arnold, Marcel, Piechowiak, Eike Immo, Gralla, Jan, Fischer, Urs Martin, Kaesmacher, Johannes


600 Technology > 610 Medicine & health








Pubmed Import

Date Deposited:

26 May 2023 15:22

Last Modified:

20 Feb 2024 14:15

Publisher DOI:


PubMed ID:


Uncontrolled Keywords:

Perfusion Imaging decision curves delayed reperfusion incomplete reperfusion prediction model




Actions (login required)

Edit item Edit item
Provide Feedback