Development of a nomogram to predict hemorrhage transformation after mechanical thrombectomy in patients with acute ischemic stroke caused by large vessel occlusion in the anterior circulation

Abstract

Abstract Background: Hemorrhage transformation (HT) is one of the serious complications after mechanical thrombectomy (MT) in patients with acute ischemic stroke (AIS) and is associated with poor prognosis. The aim of this study was to develop a nomogram to predict the risk of post-MT HT in AIS patients. Methods: AIS patients treated with MT between January 2020 and June 2022 were enrolled in this study. The enrolled patients were randomly divided into training and validation cohorts, in a 2:1 ratio. Lasso regression and machine learning algorithms were used for feature selection. Multivariate logistic analysis was applied to identify the optimal predictors. A nomogram was developed to predict the risk of post-MT HT. Performance of the nomogram was determined by its discrimination, calibration, and clinical usefulness. Results:A total of 205 patients were enrolled in the study, with 145 in the training cohort and 60 in the validation cohort. The common risk factors revealed by Lasso regression and random forest algorithm were blood glucose on admission, CRP, NIHSS score, ASPECTS and CRP-to-albumin ratio. Multivariate logistic analysis showed that blood glucose on admission (Odds Ratio (OR)=5.61, 95%CI:1.86-20.83, P=0.004) and CRP (OR=73.52, 95%CI: 25.06-276.77, P<0.001) were independent predictors of post-MT HT. The nomogram was developed based on blood glucose on admission and CRP. Moreover, the proposed nomogram showed good discriminative ability with an area under the curve of 0.924 and 0.846 in the training and validation cohort, respectively. The calibration plot showed good concordance between nomogram prediction and actual observation. Decision curve analysis indicated that the nomogram had favorable clinical application benefits. Conclusion: This study proposed a nomogram based on CRP and blood glucose on admission to predict the risk of post-MT HT in anterior AIS patients. The nomogram showed reliable predictive performance and can help clinicians identify patients at high risk of HT.