Nomogram prediction model for the risk of intracranial hemorrhagic transformation after intravenous thrombolysis in patients with acute ischemic stroke

Abstract

Abstract Background: Hemorrhagic transformation (HT) after intravenous thrombolysis (IVT) leads to poor clinical prognosis, and a reliable predictive system is needed to identify the risk of hemorrhagic transformation after IVT. Methods: Retrospective collection of patients with acute cerebral infarction treated with intravenous thrombolysis in our hospital from 2018 to 2022. 197 patients were included in the research study. Multivariate logistic regression analysis was used to screen the factors in the predictive nomogram. The performance of nomogram was assessed on the basis of area under the curve (AUC-ROC) of subjects' work characteristics, calibration plots and decision curve analysis (DCA). Results: A total of 197 patients were recruited, of whom 24 (12.1%) developed HT. In multivariate logistic regression model National Institute of Health Stroke Scale (NIHSS) (OR, 1.362; 95% CI, 1.161 −1.652; P = 0.001), N-terminal pro-brain natriuretic peptide (NT-pro BNP) (OR, 1.012; 95% CI, 1.004 −1.020; P = 0.003), neutrophil to lymphocyte ratio (NLR) (OR, 3.430; 95% CI, 2.082 −6.262; P < 0.001), systolic blood pressure (SBP) (OR, 1.039; 95% CI, 1.009 −1.075; P = 0.016) were the independent predictors of HT which were used to generate nomogram. The nomogram showed good discrimination due to AUC-ROC values. Calibration plot showed good calibration. DCA showed that nomogram is clinically useful. Conclusion: Nomograms consisting of NIHSS, NT-pro BNP, NLR, SBP scores predict the risk of HT in AIS patients treated with IVT.