Elhakim Tarig, Mansur Arian, Kondo Jordan, Omar Omar Moustafa Fathy, Ahmed Khalid, Tabari Azadeh, Brea Allison, Ndakwah Gabriel, Iqbal Shams, Allegretti Andrew S, Fintelmann Florian J, Wehrenberg-Klee Eric, Bridge Christopher, Daye Dania
Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Massachusetts General Hospital, Boston, MA, USA.
Cardiovasc Intervent Radiol. 2025 Feb;48(2):221-230. doi: 10.1007/s00270-024-03886-8. Epub 2024 Oct 29.
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.
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.
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).
CT body composition is positively predictive of 90-day mortality after TIPS and improves the predictive performance of MELD score.
Level 3, Retrospective multi-center cohort study.
确定经颈静脉肝内门体分流术（TIPS）后机器学习衍生的CT身体成分与90天死亡率之间的关联，并评估其作为终末期肝病模型（MELD）评分对死亡率风险预测的补充的预测性能。
这项回顾性多中心队列研究纳入了1995年至2018年接受TIPS治疗且在TIPS术前9个月内进行过腹部增强CT检查以及术后至少有90天临床随访的患者。一种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）以及内脏与皮下脂肪比（VSR）。采用独立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天死亡率中的关联。
共纳入122例患者（58±11.8岁，68%为男性）。在TIPS术后90天内死亡的患者，其MELD评分显著更高（18.9对11.9，p<0.001），而SMA（123对144.5，p=0.002）、SMI（43.7对50.5，p=0.03）、SFA（122.4对190.8，p=0.009）、SFI（44.2对66.7，p=0.04）、VFA（105.5对171.2，p=0.003）和VFI（35.7对57.5，p=0.02）则显著更低。90天死亡率与BMI（26对27.1，p=0.63）、SMD（30.1对31.7，p=0.44）或VSR（0.97对1.03，p=0.66）之间无显著关联。多变量逻辑回归显示，在根据MELD评分进行调整后，SMA（OR=0.97，p<0.01）、SMI（OR=0.94，p=0.03）、SFA（OR=0.99，p=0.01）和VFA（OR=0.99，p=0.02）仍然是90天死亡率的显著预测因素。ROC曲线分析表明，纳入SMA、SFA和VFA可提高MELD评分对TIPS术后90天死亡率的预测能力（AUC为0.84；95%CI：0.77，0.91；p=0.02）。
CT身体成分对TIPS术后90天死亡率具有正向预测作用，并可提高MELD评分的预测性能。
3级，回顾性多中心队列研究。
