|
|
Construction and Validation of a Nomogram Model for Predicting Symptomatic Intracranial Hemorrhage after Intravenous Thrombolysis in Acute Ischemic Stroke |
JIA Guangjie |
Department of Neurology, Qixian People's Hospital, Qixian Henan 475200 |
|
|
Abstract 【Objective】To construct and validate a nomogram model based on clinical data to predict symptomatic intracranial hemorrhage (sICH) following intravenous thrombolysis in patients with acute ischemic stroke (AIS).【Methods】 A total of 213 AIS patients who received intravenous thrombolysis were randomly divided into a training set and a validation set in a 7∶3 ratio using stratified sampling. Multivariate logistic regression analysis was used to identify independent predictors of sICH, which were then used to build a nomogram model. The model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).【Results】 The training set included 149 AIS patients, among whom 25 cases (16.78%) developed sICH. Multivariate logistic regression analysis identified age, baseline National Institutes of Health Stroke Scale (NIHSS) score, onset-to-treatment time (OTT), neutrophil-to-lymphocyte ratio (NLR), and hyperdense middle cerebral artery sign (HMCAS) as independent predictors of sICH (P<0.05). ROC curve analysis showed that the area under the curve (AUC) of the nomogram model was 0.972 in the training set and 0.940 in the validation set, indicating good discrimination. The calibration curve demonstrated good agreement between predicted and observed sICH risk (P=0.196). DCA indicated that the nomogram had good clinical utility.【Conclusion】 Age, NIHSS score, OTT, NLR, and HMCAS are independent predictors of sICH after intravenous thrombolysis in AIS patients. The developed nomogram model can effectively predict the risk of sICH.
|
Received: 14 February 2025
|
|
|
|
|
[1] 李知静,金香兰. 急性缺血性脑卒中氧治疗的研究进展[J].医学综述,2020,26(2):306-310.
[2] 徐小隔,李静雪,魏孟丽. 阿加曲班序贯疗法联合rt-PA溶栓治疗急性缺血性脑卒中患者的临床疗效[J].医学临床研究,2024,41(3):406-409.
[3] SUN J C, LAM C, CHRISTIE L, et al. Risk factors of hemorrhagic transformation in acute ischaemic stroke: A systematic review and meta-analysis[J].Front Neurol,2023,14:1079205.
[4] IANCU A, BULEU F, CHITA D S, et al. Early hemorrhagic transformation after reperfusion therapy in patients with acute ischemic stroke: Analysis of risk factors and predictors[J].Brain Sci,2023,13(5):840.
[5] 杨琼芳,舒彩敏,冯兰芳. 基于列线图模型构建慢性阻塞性肺病患者急性加重后脑卒中风险预测模型及模型验证[J].中国卫生统计,2024,41(4):582-585.
[6] 中华医学会神经病学分会,中华医学会神经病学分会脑血管病学组. 中国急性缺血性脑卒中诊治指南2018[J].中华神经科杂志,2018,51(9): 666-682.
[7] 蒋云秋,刘健,赵丹,等. rt-PA静脉溶栓联合介入治疗对急性缺血性脑卒中患者MRS评分、Barthel指数、脑部血流动力学的影响[J].临床和实验医学杂志,2023,22(15):1583-1586.
[8] 杭代,蔡志敏,刘才荣. 急性缺血性脑卒中患者并发深静脉血栓风险预测列线图模型构建与评价[J].脑与神经疾病杂志,2024,32(8):497-503.
[9] RHINER N, THUT M Z, THURNER P, et al. Impact of age on mechanical thrombectomy and clinical outcome in patients with acute ischemic stroke[J].J Stroke Cerebrovasc Dis,2023,32(9):107248.
[10] 吕华东,蓝瑞芳,陈强棠,等. 肝纤维化与急性缺血性脑卒中患者血管内治疗后发生sICH的相关性[J].广西医学,2024,46(7):1002-1006.
[11] 钱宇,陆新宇,李巧玉,等. 急性缺血性脑卒中患者血管内机械取栓术后发生症状性颅内出血的危险因素分析[J].山东医药,2020,60(23):79-81.
[12] 钟介石,刘松,朱榆红,等. 尿激酶溶栓后出血及早期神经功能恶化危险因素的多中心研究[J].中华脑血管病杂志(电子版),2021,15(3):163-169.
[13] 宁文君,虎玮兵. 急性缺血性脑卒中患者PLR、NLR与认知功能的关系[J].医学临床研究,2023,40(12):1854-1856.
[14] 郭洪权. 中国急性缺血性脑卒中患者静脉溶栓后临床结局预测模型的构建[D]. 广州:南方医科大学,2022.
[15] 杨洁. 急性缺血性脑卒中静脉溶栓后症状性颅内出血风险模型的研究[D]. 合肥:安徽医科大学,2022.
|
|
|
|