Machine learning improves the accuracy of graft weight prediction in living donor liver transplantation.

Precise graft weight (GW) estimation is essential for planning living donor liver transplantation to select grafts of adequate size for the recipient. This study aimed to investigate whether a machine-learning model can improve the accuracy of GW estimation. Data from 872 consecutive living donors of a left lateral sector, left lobe, or right lobe to adults or children for living-related liver transplantation were collected from January 2011 to December 2019. Supervised machine-learning models were trained (80% of observations) to predict GW using the following information: donor's age, sex, height, weight, and body mass index; graft type (left, right, or left lateral lobe); computed tomography estimated graft volume and total liver volume. Model performance was measured in a random independent set (20% of observations) and in an external validation cohort using the mean absolute error (MAE) and the mean absolute percentage error and compared with methods currently available for GW estimation. The best-performing machine-learning model showed an MAE value of 50 ± 62 g in predicting GW, with a mean error of 10.3%. These errors were significantly lower than those observed with alternative methods. In addition, 62% of predictions had errors <10%, whereas errors >15% were observed in only 18.4% of the cases compared with the 34.6% of the predictions obtained with the best alternative method ( p < 0.001). The machine-learning model is made available as a web application ( http://graftweight.shinyapps.io/prediction ). Machine learning can improve the precision of GW estimation compared with currently available methods by reducing the frequency of significant errors. The coupling of anthropometric variables to the preoperatively estimated graft volume seems necessary to improve the accuracy of GW estimation.

Large for size in pediatrics liver transplant using left lateral segment grafts: A single center experience.

BACKGROUND: There are still controversies in using the large left lateral segment in pediatrics LT, with the possibility of the problem of LFS grafts, and the use of monosegmental or reduced liver grafts in small infants. This study aimed to evaluate our experience with LFSG in pediatrics LT. METHODS: A cohort retrospective analysis was conducted including pediatric recipients who underwent LT between January 2011 and October 2019. We compared recipients with GRWR ≥ 4% (LFS) vs GRWR < 4% as an average for size grafts. RESULTS: There were 331 pediatric LT, 74 patients with GRWR ≥ 4%, and 257 patients with GRWR < 4%. In the group of LFS grafts, temporary abdominal closure by silicon patch was done in 39 patients (52.7%), 2 patients (2.7%) had postoperative HAT, 3 patients (4.1%) early PVT, 1 patient (1.3%) bile leak, and 3 patients (4.1%) had wound infection, with no significant difference in these complications between the 2 groups. In patients with LFS- grafts, the 1-, 3-, 5-, and 7-year patients survival rates were 94.6%, 91.7%, 91.7%, and 91.7%, respectively, while the survival rates in patients of the other group were 96.1%, 92.6%, 91.9%, and 91.9%, respectively, with no significant difference (p = .85). CONCLUSION: Using LFS graft by left lateral segment in pediatric LT with potential delayed abdominal closure is a safe and feasible option with good outcomes and unnecessary need for graft reduction if performed by an experienced multidisciplinary team.