Beyond MELD Score: Association of Sarcopenia with 90-Day Mortality Post Transjugular Intrahepatic Portosystemic Shunt (TIPS) Placement

Tuesday, March 7, 2023

3:09 PM – 3:18 PM

Location: Phoenix Convention Center, 226ABC

Presenting Author(s)

Tarig Elhakim, MD

Medicine Instructor & Research Associate
Penn (Medicine Instructor) & MGH (Research Associate)

Disclosure(s): No financial relationships to disclose

Author/Co-author(s)

AM

Arian Mansur, BA (he/him/his)

Medical Student
Harvard Medical School
jK

jordan Kondo, BS

Medical student
Harvard Medical School
cs

corey suraci, MBS

Medical Student
Geisinger Commonwealth School of Medicine
oo

omar moustafa omar, BS

Medical Student
University of conneticticut
CB

Christopher Bridge, PhD

Director of Machine Learning
Massachusetts General Hospital
Florian J. Fintelmann, MD (he/him/his)

Associate Professor
Massachusetts General Hospital; Harvard Medical School
EW

Eric Wehrenberg-Klee, MD

Faculty
Massachusetts General Hospital
DD

Dania Daye, MD, PhD

Clinical Professor
Massachusetts General Hospital

Disclosure(s):

Tarig Elhakim, MD: No financial relationships to disclose

Arian Mansur, BA: No financial relationships to disclose

Dania Daye, MD, PhD: medtronic: Consultant (Ongoing)

Purpose: To determine the association of skeletal muscle CT body composition parameters derived from a fully automated machine learning algorithm with 90-day mortality after TIPS and to assess its predictive performance as a complement to MELD score for mortality risk prediction.

Materials and Methods: In this retrospective study, a total of 122 patients met the inclusion criteria for enrollment: 1) Have undergone TIPS placement between 2005-2018; 2) have an abdominal CT with contrast in a 6 - 9 months prior to intervention 3) have a clinical follow-up available for 90 days following the procedure. Clinical information was collected from the electronic health record. A fully automated machine learning algorithm was utilized to extract skeletal muscle-based body composition metrics [skeletal muscle area (SMA), skeletal muscle index (SMI), and skeletal muscle radiodensity (SMD)] at the L3 vertebral body level. Independent t-tests, univariate and multivariate logistic regression models, and ROC curve analysis were utilized to assess the association and performance of CT body composition metrics in predicting 90-day mortality.

Results: Of the 122 patients that met the inclusion criteria, 39 (32%) were female. The mean age was 58.2. 29 (23.8%) patients died within 90 days after TIPS. There were no significant associations between 90-day mortality and age (P = 0.36) or sex (P = 0.90). Patients who died within 90 days of TIPS had a significantly higher MELD scores (mean [SD], 18.9 [9.9] vs. 11.9 [6.3]; P < 0.0001), lower SMA (mean [SD], 123.0 [37.9] vs. 144.5 [30.8] cm2; P = 0.002), and lower SMI (mean [SD], 43.8 [14.4] vs. 50.5 [10.5] cm2/m2; P = 0.03) than those who survived past 90 days. There was no significant difference in SMD between those who died vs those who survived within 90 days of TIPS (mean [SD], 30.1 [9.2] vs. 31.8 [10.2]; P = 0.43). Multivariable logistic regression models showed that both SMA (OR=0.9, P = 0.003) and SMI (OR=0.9, P = 0.03) remained significant predictors of 90-day mortality when adjusting for MELD score. ROC curve analysis demonstrated that adding SMA improves the predictive power of MELD in predicting 90-day mortality after TIPS (AUC [95% CI]: 0.82 [0.75-0.90] versus 0.76 [0.66-0.86]).

Conclusion: Skeletal muscle-based CT body composition metrics are independently predictive of 90-day mortality after TIPS placement and improves the predictive performance independent of the MELD score. Further studies are needed to assess the generalizability of our findings and the potential role of skeletal muscle-based CT body composition metrics in assessing 90-day mortality after TIPS placement.