Beyond MELD Score: Association of Machine Learning-derived CT Body Composition with 90-Day Mortality Post Transjugular Intrahepatic Portosystemic Shunt Placement.

IF 2.8 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
Tarig Elhakim, Arian Mansur, Jordan Kondo, Omar Moustafa Fathy Omar, Khalid Ahmed, Azadeh Tabari, Allison Brea, Gabriel Ndakwah, Shams Iqbal, Andrew S Allegretti, Florian J Fintelmann, Eric Wehrenberg-Klee, Christopher Bridge, Dania Daye
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引用次数: 0

Abstract

Purpose: To determine the association of machine learning-derived CT body composition and 90-day mortality after transjugular intrahepatic portosystemic shunt (TIPS) and to assess its predictive performance as a complement to Model for End-Stage Liver Disease (MELD) score for mortality risk prediction.

Materials and methods: This retrospective multi-center cohort study included patients who underwent TIPS from 1995 to 2018 and had a contrast-enhanced CT abdomen within 9 months prior to TIPS and at least 90 days of post-procedural clinical follow-up. A machine learning algorithm extracted CT body composition metrics at L3 vertebral level including skeletal muscle area (SMA), skeletal muscle index (SMI), skeletal muscle density (SMD), subcutaneous fat area (SFA), subcutaneous fat index (SFI), visceral fat area (VFA), visceral fat index (VFI), and visceral-to-subcutaneous fat ratio (VSR). Independent t-tests, logistic regression models, and ROC curve analysis were utilized to assess the association of those metrics in predicting 90-day mortality.

Results: A total of 122 patients (58 ± 11.8, 68% male) were included. Patients who died within 90 days of TIPS had significantly higher MELD (18.9 vs. 11.9, p < 0.001) and lower SMA (123 vs. 144.5, p = 0.002), SMI (43.7 vs. 50.5, p = 0.03), SFA (122.4 vs. 190.8, p = 0.009), SFI (44.2 vs. 66.7, p = 0.04), VFA (105.5 vs. 171.2, p = 0.003), and VFI (35.7 vs. 57.5, p = 0.02) compared to those who survived past 90 days. There were no significant associations between 90-day mortality and BMI (26 vs. 27.1, p = 0.63), SMD (30.1 vs. 31.7, p = 0.44), or VSR (0.97 vs. 1.03, p = 0.66). Multivariable logistic regression showed that SMA (OR = 0.97, p < 0.01), SMI (OR = 0.94, p = 0.03), SFA (OR = 0.99, p = 0.01), and VFA (OR = 0.99, p = 0.02) remained significant predictors of 90-day mortality when adjusted for MELD score. ROC curve analysis demonstrated that including SMA, SFA, and VFA improves the predictive power of MELD score in predicting 90-day mortality after TIPS (AUC, 0.84; 95% CI: 0.77, 0.91; p = 0.02).

Conclusion: CT body composition is positively predictive of 90-day mortality after TIPS and improves the predictive performance of MELD score.

Level of evidence: Level 3, Retrospective multi-center cohort study.

超越 MELD 评分:经颈静脉肝内门体分流术后机器学习衍生 CT 身体成分与 90 天死亡率的关系。
目的:确定机器学习衍生的CT身体成分与经颈静脉肝内门体系统分流术(TIPS)后90天死亡率的关联,并评估其作为终末期肝病模型(MELD)评分的补充对死亡率风险预测的预测性能:这项回顾性多中心队列研究纳入了1995年至2018年期间接受TIPS治疗的患者,这些患者在接受TIPS治疗前9个月内接受过对比增强腹部CT检查,且接受过至少90天的术后临床随访。机器学习算法提取了L3椎体水平的CT身体成分指标,包括骨骼肌面积(SMA)、骨骼肌指数(SMI)、骨骼肌密度(SMD)、皮下脂肪面积(SFA)、皮下脂肪指数(SFI)、内脏脂肪面积(VFA)、内脏脂肪指数(VFI)和内脏与皮下脂肪比率(VSR)。利用独立 t 检验、逻辑回归模型和 ROC 曲线分析来评估这些指标在预测 90 天死亡率方面的相关性:共纳入 122 名患者(58 ± 11.8,68% 为男性)。在 TIPS 术后 90 天内死亡的患者的 MELD 值明显更高(18.9 vs. 11.9,P 结论:TIPS 术后 90 天内死亡的患者的 MELD 值明显更高:CT身体成分对TIPS术后90天死亡率有积极的预测作用,并提高了MELD评分的预测性能:3级,回顾性多中心队列研究。
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来源期刊
CiteScore
5.50
自引率
13.80%
发文量
306
审稿时长
3-8 weeks
期刊介绍: CardioVascular and Interventional Radiology (CVIR) is the official journal of the Cardiovascular and Interventional Radiological Society of Europe, and is also the official organ of a number of additional distinguished national and international interventional radiological societies. CVIR publishes double blinded peer-reviewed original research work including clinical and laboratory investigations, technical notes, case reports, works in progress, and letters to the editor, as well as review articles, pictorial essays, editorials, and special invited submissions in the field of vascular and interventional radiology. Beside the communication of the latest research results in this field, it is also the aim of CVIR to support continuous medical education. Articles that are accepted for publication are done so with the understanding that they, or their substantive contents, have not been and will not be submitted to any other publication.
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