Clinical Impact of Postrecanalization Hemorrhagic Transformation and Its Prediction Using Baseline Noncontrast CT.
Authors
Affiliations (24)
Affiliations (24)
- Department of Neurology, Seoul Hospital, Ewha Womans University College of Medicine, Republic of Korea (H.-J.H.).
- Artificial Intelligence Research Center, JLK, Inc, Seoul, Republic of Korea (H.-J.H., W.-S.R., Myungjae Lee).
- Department of Radiology (L.S.), Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
- Department of Neurology, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul, Republic of Korea (K.K.).
- Department of Neurology, School of Medicine, Daejeon Eulji Medical Center, Eulji University, Republic of Korea (J.G.K., S.J.L.).
- Department of Neurology, Dong-A University Hospital, Busan, Republic of Korea (J.-K.C.).
- Department of Neurology, Seoul Medical Center, Republic of Korea (T.H.P.).
- Department of Neurology, Soonchunhyang University Seoul Hospital, Soonchunhyang University College of Medicine, Republic of Korea (J.-Y.L., K.L.).
- Department of Neurology, Yeungnam University Medical Center, Daegu, Republic of Korea (D.H.K., J.L.).
- Department of Neurology, Inje University Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Republic of Korea (H.-K.P., K.-S.H.).
- Department of Neurology, Hallym University Sacred Heart Hospital, Anyang, Republic of Korea (Minwoo Lee, M.S.O., K.-H.Y.).
- Department of Neurology, Dongguk University Ilsan Hospital, Goyang, Republic of Korea (D.-S.G., D.-E.K.).
- Department of Neurology, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Republic of Korea (H.K., J.-T.K.).
- Department of Neurology, Jeju National University Hospital, Republic of Korea (J.-G.K., J.C.C.).
- Department of Neurology (W.-J.K., J.H.K.), Ulsan University Hospital, University of Ulsan College of Medicine, Republic of Korea.
- Department of Radiology (Y.C.W.), Ulsan University Hospital, University of Ulsan College of Medicine, Republic of Korea.
- Chungbuk National University Hospital, Chungbuk National University College of Medicine, Cheongju, Republic of Korea (K.S.Y., D.-I.S.).
- Department of Neurology, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, Daegu, Republic of Korea (J.-H.H., S.-I.S.).
- Department of Neurology, Hallym University Chuncheon Sacred Heart Hospital, Republic of Korea (S.-H.L., C.K.).
- Department of Neurology, Chung-Ang University Hospital, Seoul, Republic of Korea (H.B.J., K.-Y.P.).
- Department of Neurology, Korea University Guro Hospital, Seoul (C.K.K.).
- Department of Neurology (J.K., J.Y.K., H.-J.B., B.J.K.), Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea.
- Department of Neurology, College of Medicine, Seoul National University, Republic of Korea (H.-J.B.).
- Department of Neurology, University of New South Wales South Western Sydney Clinical School, Liverpool, Australia (L.L., M.P.).
Abstract
Hemorrhagic transformation (HT) after recanalization therapy remains a critical concern in acute ischemic stroke management. While severe hemorrhages clearly worsen outcomes, the prognostic impact of mild HT and its optimal prediction methods remain uncertain. In this study, we aimed to evaluate the clinical significance of all HT subtypes and develop automated HT prediction models based on noncontrast computed tomography (CT) images. We analyzed 2211 patients receiving intravenous thrombolysis, endovascular thrombectomy, or both from the multicenter Clinical Research Collaboration for Stroke in Korea Imaging repository (2022-2024). HT was classified on follow-up imaging of magnetic resonance or CT. Baseline ischemic lesion volume and Alberta Stroke Program Early CT Score-based net water uptake were quantified on baseline noncontrast CT. Multivariable regression was used to assess the association of HT and 90-day modified Rankin Scale scores. The performance of HT prediction models was compared, the automated imaging model versus established scores (hemorrhage after thrombolysis score and SEDAN), using the area under the curve. HT occurred in 41.2% of patients (hemorrhagic infarction [HI] 1: 13.8% and HI2: 16.8%, parenchymal hematoma [PH] 1: 6.5% and PH2: 4.1%). All HT subtypes independently predicted worse functional outcomes with stepwise increasing odds ratios: HI1 (1.77 [95% CI, 1.40-2.22]), HI2 (2.83 [95% CI, 2.27-3.53]), PH1 (4.65 [95% CI, 3.41-6.36]), and PH2 (14.76 [95% CI, 9.61-22.90]). This association persisted across treatment modalities and vascular territories. For PH prediction, the automated imaging model (noncontrast CT imaging markers combined with clinical variables) achieved superior performance (area under the curve, 0.77 [95% CI, 0.73-0.80]) compared with hemorrhage after thrombolysis score (0.71 [95% CI, 0.68-0.75]) and SEDAN (0.72 [95% CI, 0.69-0.76]) scores (both <i>P</i><0.01 for area under the curve comparison). Even mild HI was independently associated with poor functional outcomes after reperfusion therapy. Automated noncontrast CT-derived biomarkers provide superior HT risk prediction compared with conventional scores, offering a practical tool for individualized stroke management in the reperfusion era.