ArticleBMC neurology2026
Development of a predictive model for hemorrhagic transformation following mechanical thrombectomy in patients with acute ischemic stroke.
Article in BMC neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Predictors and a Nomogram for Parenchymal Hematoma After Endovascular Thrombectomy in Large Core Ischemic Stroke.CNS neuroscience & therapeutics · 2026Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aimed to develop a predictive model for assessing the risk of hemorrhagic transformation (HT) following mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS).
methodsThis retrospective study included 143 patients who underwent MT for AIS between March 2021 and December 2023. Participants were stratified into two groups based on the presence of post-procedural HT: the HT group (n = 90) and the non-HT group (n = 53). Risk factors associated with the development of HT were examined using univariate and multivariate logistic regression analyses. A predictive model was subsequently constructed based on the independent risk factors identified. The discrimination, calibration, and clinical applicability of the nomogram model were evaluated using the receiver operating characteristic (ROC) curve with its area under the curve (AUC), calibration curve, and decision curve. Bootstrap resampling was performed 1,000 times to validate the model.
resultsThe incidence of HT following MT was 62.9%. Independent predictors of HT included the number of thrombectomy device passes, the time from admission to the initiation of surgery, total volume of preoperative low-density areas on CT or high-signal areas on MRI-DWI, and BMI (p < 0.05 for all). The AUC of the predictive model was 0.889. After 1,000 repeated re-samplings using the Bootstrap method, the AUC was 0.874 (95% CI: 0.801-0.936). The calibration curve and decision curve demonstrated that the model had good consistency and net benefit.
conclusionThe predictive model developed in this study demonstrated high accuracy in identifying patients with AIS at increased risk of HT after MT. This model may support early identification of high-risk individuals and inform the implementation of targeted preventive strategies.
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