The value evaluation of Nomogram prediction model based on CTA imaging features for selecting treatment methods for isolated superior mesenteric artery dissection

基于CTA影像特征的列线图预测模型在孤立性肠系膜上动脉夹层治疗方法选择中的价值评价

阅读:1

Abstract

OBJECTIVE: To evaluate value of Nomogram prediction model based on CTA imaging features for selecting treatment methods for isolated superior mesenteric artery dissection (ISMAD). METHODS: Symptomatic ISMAD patients were randomly divided into a training set and a validation set in a 7:3 ratio. In the training set, relevant risk factors for conservative treatment failure in ISMAD patients were analyzed, and a Nomogram prediction model for treatment outcome of ISMAD was constructed with risk factors. The predictive value of the model was evaluated. RESULTS: Low true lumen residual ratio (TLRR), long dissection length, and large arterial angle (superior mesenteric artery [SMA]/abdominal aorta [AA]) were identified as independent high-risk factors for conservative treatment failure (P < 0.05). The receiver operating characteristic curve (ROC) results showed that the area under curve (AUC) of Nomogram prediction model was 0.826 (95% CI: 0.740-0.912), indicating good discrimination. The Hosmer-Lemeshow goodness-of-fit test showed good consistency between the predicted curve and the ideal curve of the Nomogram prediction model. The decision curve analysis (DCA) analysis results showed that when probability threshold for the occurrence of conservative treatment failure predicted was 0.05-0.98, patients could obtain more net benefits. Similar results were obtained for the predictive value in the validation set. CONCLUSION: Low TLRR, long dissection length, and large arterial angle (SMA/AA) are independent high-risk factors for conservative treatment failure in ISMAD. The Nomogram model constructed with independent high-risk factors has good clinical effectiveness in predicting the failure.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。