目的:本研究旨在验证iScore,ASTRAL评分,龙得分,和THRIVE评分用于评估大血管闭塞-急性缺血性卒中(AIS-LVO),并建立AIS-LVO患者的预测模型,具有更好的性能,以指导临床实践。
方法:我们回顾性纳入439例AIS-LVO患者,并收集所有患者的基线数据。iScore的外部验证,ASTRAL评分,龙得分,并进行了THRIVE评分。通过单变量分析比较各组之间的所有变量,结果以OR和95%CI表示。多变量logistic分析中包括P<0.25的自变量。AIS-LVO患者预后的危险因素差异有统计学意义(P<0.05)。采用受试者工作特征(ROC)曲线分析和决策曲线分析(DCA)来评估我们模型的预测价值。
结果:我们的外部验证导致曲线下的iScore(AUC)为0.8475,ASTRALAUC为0.8324,DRAGONAUC为0.8196,THRIVEAUC为0.8039。在我们的研究中,多变量Cox回归显示8个独立预测因子。我们使用列线图来可视化数据分析的结果。训练队列的AUC为0.8855(95%CI,0.8487-0.9222),在验证队列中为0.8992(95%CI,0.8496-0。9488)。
结论:在这项研究中,我们验证了上述评分在预测AIS-LVO患者的预后方面具有良好的疗效。我们开发的列线图能够更准确地预测AIS-LVO的预后,并可能有助于个性化的临床决策和治疗,以供将来的临床工作使用。
OBJECTIVE: This study aimed to validate the iScore, ASTRAL score, DRAGON score, and
THRIVE score for assessing large vessel occlusion-acute ischemic stroke (AIS-LVO) and establish a predictive model for AIS-LVO patients that has better performance to guide clinical practice.
METHODS: We retrospectively included 439 patients with AIS-LVO and collected baseline data from all of them. External validation of the iScore, ASTRAL score, DRAGON score, and
THRIVE score was performed. All variables were compared between groups via univariate analysis, and the results are expressed as ORs and 95 % CIs. Independent variables with P < 0.25 were included in the multivariate logistic analysis, and statistically significant differences (P < 0.05) were identified as risk factors for prognosis in AIS-LVO patients. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive value of our model.
RESULTS: Our external validation resulted in an iScore under the curve (AUC) of 0.8475, an ASTRAL AUC of 0.8324, a DRAGON AUC of 0.8196, and a
THRIVE AUC of 0.8039. In our research, multivariate Cox regression revealed 8 independent predictors. We used a nomogram to visualize the results of the data analysis. The AUC for the training cohort was 0.8855 (95 % CI, 0.8487-0.9222), and that in the validation cohort was 0.8992 (95 % CI, 0.8496-0. 9488).
CONCLUSIONS: In this study, we verified that the above scores have excellent efficacy in predicting the prognosis of AIS-LVO patients. The nomogram we developed was able to predict the prognosis of AIS-LVO more accurately and may contribute to personalized clinical decision-making and treatment for future clinical work.