External Validation of a Model for Persistent Perfusion Deficit in Patients With Incomplete Reperfusion After Thrombectomy: EXTEND-PROCEED

Abstract

Background and Objectives We recently developed a model (PROCEED) that predicts the occurrence of persistent perfusion deficit (PPD) at 24 hours in patients with incomplete angiographic reperfusion after thrombectomy. This study aims to externally validate the PROCEED model using prospectively acquired multicenter data. Methods Individual patient data for external validation were obtained from the Endovascular Therapy for Ischemic Stroke with Perfusion-Imaging Selection, Tenecteplase versus Alteplase Before Endovascular Therapy for Ischemic Stroke part 1 and 2 trials, and a prospective cohort of the Medical University of Graz. The model's primary outcome was the occurrence of PPD, defined as a focal, wedge-shaped perfusion delay on 24-hour follow-up perfusion imaging that corresponds to the capillary phase deficit on last angiographic series in patients with <Thrombolysis in Cerebral Infarction 3 reperfusion after thrombectomy. The model's performance was evaluated with discrimination, calibration accuracy, and clinical decision curves. Results We included 371 patients (38% with PPD). The externally validated model had good discrimination (C-statistic 0.81, 95% CI 0.77-0.86) and adequate calibration (intercept 0.25, 95% CI 0.21-0.29 and slope 0.98, 95% CI 0.90-1.12). Across a wide range of probability thresholds (i.e., depending on the physicians' preferences on how the model should be used), the model shows net benefit on clinical decision curves, informing physicians on the likelihood of PPD. If a physician's attitude toward false-positive and false-negative ratings is equal, the model would reduce 13 in 100 unnecessary interventions by correctly predicting complete delayed reperfusion, without missing a patient with PPD. Discussion The externally validated model had adequate predictive accuracy and discrimination. Depending on the acceptable threshold probability, the model accurately predicts persistent incomplete reperfusion and may advise physicians whether additional reperfusion attempts should be performed. Copyright � 2024 The Author(s).