A nomogram prediction model based on imaging markers of cerebral small vessel disease for short-term poor outcome after intravenous thrombolysis in patients with acute ischemic stroke
10.3760/cma.j.issn.1673-4165.2024.04.002
- VernacularTitle:基于脑小血管病影像学标志物的急性缺血性卒中患者静脉溶栓后短期转归不良的列线图预测模型
- Author:
Feng GAO
1
;
Huakun LIU
Author Information
1. 济宁市第一人民医院神经内科 272011
- Keywords:
Ischemic stroke;
Cerebral small vessel diseases;
Thrombolytic therapy;
Magnetic resonance imaging;
Treatment outcome;
Risk factors;
Nomograms
- From:
International Journal of Cerebrovascular Diseases
2024;32(4):247-253
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop a nomogram model for predicting short-term poor outcome after intravenous thrombolysis (IVT) in patients with acute ischemic stroke (AIS) based on imaging markers of cerebral small vessel disease (CSVD).Methods:Patients with AIS received intravenous thrombolysis treatment at Jining No. 1 People's Hospital from January 2021 to December 2023 were retrospectively included. MRI was used to evaluate imaging markers of CSVD, including lacunar infarction (LI), cerebral microbleeds (CMBs), white matter hyperintensities (WMHs), and enlarged perivascular spaces (EPVS). The outcome evaluation was performed at 90 days after onset using the modified Rankin Scale, and the score of >2 was defined as poor outcome. LASSO regression analysis was used to screen the variables most correlated with poor outcome after intravenous thrombolysis, and construct a nomogram for predicting poor outcome through a logistic regression model. The predictive ability of the nomogram was verified through the receiver operating characteristic curve, calibration chart, and decision curve analysis. Results:A total of 167 patients were included, of which 96 (57%) had good outcome and 71 (43%) had poor outcome. The variables with P<0.05 in univariate analysis were included in the LASSO regression model to screen for variables. Finally, left side infarction, atrial fibrillation, baseline systolic blood pressure, baseline National Institutes of Health Stroke Scale (NIHSS) score, high-density lipoprotein cholesterol, WMHs (1 point), CMBs (1 point), EPVS (1 point), LI (1 point), and overall CSVD load (2-4 points) were included in the multivariate logistic regression analysis. The results showed that atrial fibrillation (odds ratio [ OR] 6.75, 95% confidence interval [ CI] 1.49-41.40; P=0.022), baseline systolic blood pressure ( OR 1.01, 95% CI 1.00-1.04; P=0.049), baseline NIHSS score ( OR 1.47, 95% CI 1.23-1.80; P<0.001), WMHs ( OR 3.40, 95% CI 1.28-9.53; P=0.015), CMBs ( OR 3.24, 95% CI 1.12-9.90; P=0.032) and EPVS ( OR 2.89, 95% CI 1.05-8.23; P=0.041) were the independent risk factors for poor outcome. The nomogram model was developed using these variables. The receiver operating characteristic curve analysis showed that the area under the curve was 0.885 (95% CI 0.837-0.933; P<0.01), indicating that the model had good discrimination. The consistency between the predicted and actual values of the nomogram model was good. Conclusion:The nomogram model for predicting the probability of poor outcome developed from atrial fibrillation, baseline systolic blood pressure, baseline NIHSS score, WMHs, CMBs, and EPVS has good discrimination and calibration, and has certain clinical practicality.
