Donor BMI and Post–living Donor Liver Transplantation Outcomes: A Preliminary Report

Background. Living liver donor obesity has been considered a relative contraindication to living donation given the association with hepatic steatosis and potential for poor donor and recipient outcomes. We investigated the association between donor body mass index (BMI) and donor and recipient posttransplant outcomes. Methods. We studied 66 living donors and their recipients who underwent living donor liver transplant at our center between 2013 and 2020. BMI was divided into 3 categories (<25, 25–29.9, and ≥30 kg/m2). Magnetic resonance imaging–derived proton density fat fraction was used to quantify steatosis. Donor outcomes included length of stay (LOS), emergency department visits within 90 d, hospital readmissions within 90 d, and complication severity. Recipient outcomes included LOS and in-hospital mortality. The Student t test was used to compare normally distributed variables, and Kruskal-Wallis tests were used for nonparametric data. Results. There was no difference in donor or recipient characteristics based on donor BMI. There was no significant difference in mean magnetic resonance imaging fat percentage among the 3 groups. Additionally, there was no difference in donor LOS (P = 0.058), emergency department visits (P = 0.64), and hospital readmissions (P = 0.66) across BMI category. Donor complications occurred in 30 patients. There was no difference in postdonation complications across BMI category (P = 0.19); however, there was a difference in wound complications, with the highest rate being seen in the highest BMI group (0% versus 16% versus 37%; P = 0.041). Finally, there was no difference in recipient LOS (P = 0.83) and recipient in-hospital mortality (P = 0.29) across BMI category. Conclusions. Selecting donors with BMI ≥30 kg/m2 can result in successful living donor liver transplantation; however, they are at risk for perioperative wound complications. Donor counseling and perioperative strategies to mitigate wound-related issues should be used when considering obese living donors

Outcome analysis of patients with acute pancreatitis by using an artificial neural network.

RATIONALE AND OBJECTIVES: The authors performed this study to evaluate the ability of an artificial neural network (ANN) that uses radiologic and laboratory data to predict the outcome in patients with acute pancreatitis. MATERIALS AND METHODS: An ANN was constructed with data from 92 patients with acute pancreatitis who underwent computed tomography (CT). Input nodes included clinical, laboratory, and CT data. The ANN was trained and tested by using a round-robin technique, and the performance of the ANN was compared with that of linear discriminant analysis and Ranson and Balthazar grading systems by using receiver operating characteristic analysis. The length of hospital stay was used as an outcome measure. RESULTS: Hospital stay ranged from 0 to 45 days, with a mean of 8.4 days. The hospital stay was shorter than the mean for 62 patients and longer than the mean for 30. The 23 input features were reduced by using stepwise linear discriminant analysis, and an ANN was developed with the six most statistically significant parameters (blood pressure, extent of inflammation, fluid aspiration, serum creatinine level, serum calcium level, and the presence of concurrent severe illness). With these features, the ANN successfully predicted whether the patient would exceed the mean length of stay (Az = 0.83 +/- 0.05). Although the Az performance of the ANN was statistically significantly better than that of the Ranson (Az = 0.68 +/- 0.06, P \u3c .02) and Balthazar (Az = 0.62 +/- 0.06, P \u3c .003) grades, it was not significantly better than that of linear discriminant analysis (Az = 0.82 +/- 0.05, P = .53). CONCLUSION: An ANN may be useful for predicting outcome in patients with acute pancreatitis