Yuan Yuan, Jiang Shandong, Li Jingbo, Zhang Jing, Ding Jingjing, Liu Sainan, Wang Jingyi, Zhang Yanyan, Li Jianru, Chen Gao
Department of Nursing, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
J Neurointerv Surg. 2025 Apr 8. doi: 10.1136/jnis-2025-023036.
Studies have been conducted to explore the potential predictive indicators of unfavorable outcomes in patients with acute ischemic stroke (AIS) caused by large vessel occlusion (LVO). However, few studies have proposed a comprehensive predictive model combined with clinical baseline data and ancillary examination before surgery.
In a retrospective study, we collected data on 823 patients with AIS-LVO who had undergone endovascular therapy (EVT); 562 patients who achieved successful revascularization with complete clinical and prognostic information were incorporated into the study. Those patients with a 90-day modified Rankin Scale (mRS) score of 0-2 were defined as having a favorable outcome, while a score of 3-6 represented an unfavorable outcome or futile reperfusion. To build up a predictive model, we applied multivariate logistic regression stepwise backward selection to decide which factors are supposed to be the components of the predictive model. Final model validity was testified by the variance inflation factor test and the Hosmer-Lemeshow (HL) goodness of fit test. The ultimate efficacy was supported by an area under the curve (AUC) value in both training groups and validation groups.
562 patients were enrolled in our study and divided into the training group and verification group in a ratio of 7:3. Factors of baseline data with P<0.1 in univariate logistic regression analysis were enrolled as the potential risk variables to conduct stepwise backward selection. The model was constructed by eight variables; higher mRS score (adjusted OR (aOR) 93.64, 95% CI 12.05 to 727.82, P<0.01), age >80 years (aOR 91.11, 95% CI 1.36 to 6116.36, P<0.05), National Institutes of Health Stroke Scale (NIHSS) >14 (aOR 0.15, 95% CI 0.02 to 0.99, P<0.05), operation history (aOR 8.13, 95% CI 1.32 to 50.20, P<0.05), creatinine (aOR 1.10, 95% CI 1.04 to 1.17, P<0.01), and neutrophil count (aOR 1.07, 95% CI 1.01 to 1.13, P<0.05) were associated with poor outcomes.
We established an estimation model for invalid reperfusion in AIS-LVO patients and constructed the nomogram for individualized predictions. The AUC of the training group and validation group were both 0.96, with excellent HL and decision curve analysis, presenting excellent clinical prediction efficiency and application potential.
已有研究探索了大血管闭塞（LVO）所致急性缺血性卒中（AIS）患者不良预后的潜在预测指标。然而，很少有研究提出结合临床基线数据和术前辅助检查的综合预测模型。
在一项回顾性研究中，我们收集了823例行血管内治疗（EVT）的AIS-LVO患者的数据；纳入了562例实现成功再灌注且具有完整临床和预后信息的患者。将90天改良Rankin量表（mRS）评分为0-2分的患者定义为预后良好，而评分为3-6分则代表预后不良或再灌注无效。为建立预测模型，我们应用多因素逻辑回归逐步向后选择法来确定哪些因素应作为预测模型的组成部分。通过方差膨胀因子检验和Hosmer-Lemeshow（HL）拟合优度检验验证最终模型的有效性。训练组和验证组的曲线下面积（AUC）值支持最终疗效。
562例患者纳入本研究，并按7:3的比例分为训练组和验证组。单因素逻辑回归分析中P<0.1的基线数据因素被纳入作为潜在风险变量进行逐步向后选择。该模型由八个变量构建而成；较高的mRS评分（调整后比值比（aOR）93.64，95%置信区间12.05至727.82，P<0.01）、年龄>80岁（aOR 91.11，95%置信区间1.36至6116.36，P<0.05）、美国国立卫生研究院卒中量表（NIHSS）>14分（aOR 0.15，95%置信区间0.02至0.99，P<0.05）、手术史（aOR 8.13，95%置信区间1.32至50.20，P<0.05）、肌酐（aOR 1.10，95%置信区间1.04至1.17，P<0.01）以及中性粒细胞计数（aOR 1.07，95%置信区间1.01至1.13，P<0.05）与不良预后相关。
我们建立了AIS-LVO患者再灌注无效的评估模型，并构建了用于个体化预测的列线图。训练组和验证组的AUC均为0.96，HL和决策曲线分析良好，具有出色的临床预测效率和应用潜力。
