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使用整合数据的心脏手术后主动脉风险预测模型

Aortic Risks Prediction Models after Cardiac Surgeries Using Integrated Data.

作者信息

Lenivtceva Iuliia, Panfilov Dmitri, Kopanitsa Georgy, Kozlov Boris

机构信息

National Center for Cognitive Research, ITMO University, 49 Kronverskiy Prospect, 197101 Saint-Petersburg, Russia.

Cardiology Research Institute, Tomsk National Research Medical Center of the Russian Academy of Science, 634012 Tomsk, Russia.

出版信息

J Pers Med. 2022 Apr 15;12(4):637. doi: 10.3390/jpm12040637.

Abstract

The complications of thoracic aortic disease include aortic dissection and aneurysm. The risks are frequently compounded by many cardiovascular comorbidities, which makes the process of clinical decision making complicated. The purpose of this study is to develop risk predictive models for patients after thoracic aneurysm surgeries, using integrated data from different medical institutions. Seven risk features were formulated for prediction. The CatBoost classifier performed best and provided an ROC AUC of 0.94-0.98 and an F-score of 0.95-0.98. The obtained results are widely in line with the current literature. The obtained findings provide additional support for clinical decision making, guiding a patient care team prior to surgical treatment, and promoting a safe postoperative period.

摘要

胸主动脉疾病的并发症包括主动脉夹层和动脉瘤。这些风险常常因多种心血管合并症而加剧,这使得临床决策过程变得复杂。本研究的目的是利用来自不同医疗机构的综合数据,为胸主动脉瘤手术后的患者开发风险预测模型。制定了七个风险特征用于预测。CatBoost分类器表现最佳,ROC曲线下面积为0.94 - 0.98,F分数为0.95 - 0.98。所得结果与当前文献广泛一致。所得发现为临床决策提供了额外支持,在手术治疗前指导患者护理团队,并促进术后安全恢复。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81f7/9024528/5cf74d3d4fe0/jpm-12-00637-g001.jpg

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