Liver Transpl. 2021 Jun 12. doi: 10.1002/lt.26205. Online ahead of print.
ABSTRACT
BACKGROUND: Computed tomography (CT) derived body metrics such as skeletal muscle index (SMI), psoas muscle index (PMI), and subcutaneous fat index (ScFI) are measurable components of sarcopenia, frailty, and nutrition. While these body metrics are advocated in adults for predicting postoperative outcomes after liver transplantation (LT), little is known about the value in pediatric populations. This study assessed the relation between preoperative CT-based body metrics and postoperative short-term outcomes in pediatric LT recipients.
METHODS: Patients aged 0-18 years who underwent a primary LT were retrospectively included (N=101, median age 0.5 years, range 0.2-17.1). SMI, PMI, and ScFI were derived from preoperative axial CT slices. Postoperative outcomes and complications within 90 days were correlated with the CT-based body metrics. To classify postoperative infections, the Clavien-Dindo (CD) classification was used. Subgroup analyses were performed for age groups (<1, 1-10, and >10 years old). An optimal threshold for test performance was defined using Youden’s J-statistic and receiver operating characteristic curve as appropriate.
RESULTS: ScFI was significantly (P=0.001) correlated with moderate to severe postoperative infections (CD grade 3-5) in children <1 year old, with the optimal ScFI threshold being ≤27.1 cm2 /m2 (sensitivity 80.4% and specificity 77.8%). A weak negative correlation between SMI and the total duration of hospital stay (R=-0.33, P=0.010) and intensive care unit stay (R=-0.32, P 0.013) was observed in children <1 year old. No other associations between CT-based body metrics and postoperative outcomes were shown.
CONCLUSIONS: In children <1 year old with cirrhotic liver disease undergoing LT, pre-operative CT-based body metrics were correlated with moderate to severe postoperative infections (ScFI), and with longer duration of hospital and ICU stay (SMI), and thus can be considered important tools for pre-LT risk assessment.
PMID:34118133 | DOI:10.1002/lt.26205
