Construction and validation of a nomogram model to predict symptomatic intracranial hemorrhage after intravenous thrombolysis in elderly population with severe white matter lesions

Background and Objective: Elderly people are at high prevalence of atherosclerotic cerebral infarction. Cerebral white matter lesions (WMLs) increase the risk of bleeding after intravenous thrombolysis (IVT) although they may also require the IVT. The aim of this study is to develop a clinical nomogram model for post-IVT symptomatic intracranial hemorrhage (sICH), with the aim to prevent sICH in elderly patients with severe WMLs when IVT is being considered. Methods: This is a large single-center retrospective analysis study of elderly patients with severe WMLs receiving IVT from January 2018 to December 2022. Univariate and multi-factor logistic regression analysis were used to construct nomogram model, and a series of validations were performed on the model. Results: More than 2,000 patients with IVT were screened for inclusion in this study after cranial magnetic resonance imaging evaluation. Out of these, 163 elderly patients had cerebral WMLs, and 25 had sICH. In univariate analysis, history of hypertension (p=0.037), hyperlipidemia (p<0.001), NIHSS score before IVT (p<0.001), low-density lipoprotein levels (p=0.016), cholesterol levels (p=0.020), platelet count (p=0.006), systolic blood pressure (p<0.001), diastolic blood pressure (p<0.001) were significantly associated with sICH. In a multifactorial analysis, the NIHSS score before IVT (OR 42.056 CI 7.308-242.012, p<0.001), and diastolic blood pressure (OR 1.050 CI 1.002-1.100, p=0.040) were found to be significantly associated with sICH after IVT. The four most significant risk factors from logistic regression are subsequently fitted to create a predictive model. The accuracy was verified using calibration curves, decision curves, and clinical impact curves, and the model was considered to have strong stability. Conclusions: The NHISS score before IVT and diastolic blood pressure are independent risk factors for sICH after IVT in elderly patients with severe WMLs. The models are highly accurate and can be applied clinically to provide a reliable predictive basis for IVT in elderly patients with severe WMLs.