|
| 急性缺血性脑卒中患者行血管内治疗后严重预后不良的预测模型建立及验证 |
| Development and validation of prediction model for severe disability or death after endovascular treatment for acute ischemic stroke patients |
| |
| DOI：10.12089/jca.2024.11.002 |
| 中文关键词: 急性缺血性脑卒中 血管内治疗 严重预后不良 危险因素 预测模型 |
| 英文关键词: Acute ischemic stroke Endovascular treatment Severe disability or death Risk factors Prediction model |
| 基金项目:国家重点研发计划（2016YFC1301500）；北京市医管局扬帆计划（ZYLX201708）；北京市医管局登峰计划人才团队（DFL20180502）；首都医学发展基金（CFH2024-2046） |
|
| 摘要点击次数: 460 |
| 全文下载次数: 82 |
| 中文摘要: |
目的： 探讨急性缺血性脑卒中患者行血管内治疗后严重预后不良（SDD）的相关危险因素，建立并验证SDD的列线图风险预测模型。
方法： 回顾性分析2017年11月至2019年3月在急性缺血性脑卒中血管内治疗关键技术及急救流程改进（ANGEL-ACT）登记研究数据库中的患者1 677例，男1 111例，女566例，年龄≥18岁。根据是否发生SDD（术后90 d mRS 5～6分）为将患者分为两组：SDD组（n=478）和非SDD组（n=1 199）。采用多因素分析、LASSO 回归及RF-RFE方法筛选急性缺血性脑卒中患者SDD的危险因素，建立列线图模型并进行性能检测及内部验证。
结果： 训练集中有380例（28.1%）患者发生SDD，验证集中有98例（30.2%）患者发生SDD。综合三种变量筛选方法，最终选择10个SDD的危险因素纳入模型，分别为年龄、入院NIHSS评分、是否成功再通、入院血糖浓度、血红蛋白浓度、血细胞比容、发病到穿刺时间、入院收缩压、ASPECT评分和有无与治疗相关的严重不良事件。模型1包含治疗前7个变量，模型2包含治疗前和治疗后共10个变量。训练集中模型1的曲线下面积（AUC）为0.705（95%CI 0.674～0.736），模型2的AUC为0.731（95%CI 0.701～0.760）。两个模型的校准斜率均为1.000，具有良好的校准度，决策曲线分析显示两个模型具有良好的临床适用度。
结论： 年龄、入院NIHSS评分、是否成功再通、入院血糖浓度、血红蛋白浓度、红细胞压积、发病到穿刺时间、入院收缩压、ASPECT 评分和有无与治疗相关的严重不良事件是急性缺血性脑卒中患者发生SDD的危险因素，基于以上因素构建的两种风险预测模型可分别在血管内治疗前和治疗后使用，以较好地预测SDD的发生。 |
| 英文摘要: |
Objective: To develop and validate a prediction model for severe disability or death (SDD) in acute ischemic stroke (AIS) patients who underwent endovascular treatment (EVT).
Methods: Based on the dataset of ANGEL-ACT study who received EVT for AIS between november 2017 and march 2019, a retrospective analysis was performed on 1 677 patients, including 1 111 males and 566 females, aged ≥ 18 years. Patients were divided into two groups according to whether SDD occurred (mRS 5-6 scores 90 days after surgery): SDD group (n = 478) and non-SDD group (n = 1 199). Risk factors that might influence SDD after EVT in AIS patients were screened and analyzed by multifactorial analysis, LASSO regression, and RF-RFE methods. A nomogram was developed after evaluating the model performance and the execution of internal validation.
Results: SDD occurred in 380 (28.1%) patients in the development cohort and 98 (30.2%) patients in the validation cohort. Combining the three variable screening methods, 10 risk factors were selected for inclusion in the final model: age, NIHSS score, whether successful recanalization, glucose level, hemoglobin, hematocrit, onset to puncture time, systolic blood pressure, ASPECT score, and whether have treatment-related serious adverse events. A two-stage model means that model 1 contains pre-treatment variables (7 in total) and model 2 contains pre-treatment and post-treatment variables (10 in total). The area under the curve (AUC) of model 1 in the development cohort was 0.705 (95% CI 0.674-0.736) and 0.731 (95% CI 0.701-0.760) in model 2. Both models had good calibration with aslope of 1.000, and the decision curve analysis showed good clinical applicability. The results of the validation cohort were similar to those of the development cohort.
Conclusion: Age, admission NIHSS score, whether successful recanalization, admission glucose level, hemoglobin content, erythrocyte pressure volume, onset to puncture time, admission systolic blood pressure, ASPECT score, and whether have treatment-related serious adverse events are risk factors for SDD in patients with acute ischemic stroke. The two prediction models based on the above factors were used before and after endovascular treatment to predict SDD occurrence better. |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
|
|
|
