Development and validation of a multivariable prediction model in pediatric liver transplant patients for predicting intensive care unit length of stay

Abstract

Background: Liver transplantation is the life-saving treatment for many end-stage pediatric liver diseases. The perioperative course, including surgical and anesthetic factors, have an important influence on the trajectory of this high-risk population. Given the complexity and variability of the immediate postoperative course, there would be utility in identifying risk factors that allow prediction of adverse outcomes and intensive care unit trajectories. Aims: The aim of this study was to develop and validate a risk prediction model of prolonged intensive care unit length of stay in the pediatric liver transplant population. Methods: This is a retrospective analysis of consecutive pediatric isolated liver transplant recipients at a single institution between April 1, 2013 and April 30, 2020. All patients under the age of 18 years receiving a liver transplant were included in the study (n = 186). The primary outcome was intensive care unit length of stay greater than 7 days. Results: Recipient and donor characteristics were used to develop a multivariable logistic regression model. A total of 186 patients were included in the study. Using multivariable logistic regression, we found that age < 12 months (odds ratio 4.02, 95% confidence interval 1.20–13.51, p =.024), metabolic or cholestatic disease (odds ratio 2.66, 95% confidence interval 1.01–7.07, p =.049), 30-day pretransplant hospital admission (odds ratio 8.59, 95% confidence interval 2.27–32.54, p =.002), intraoperative red blood cells transfusion >40 mL/kg (odds ratio 3.32, 95% confidence interval 1.12–9.81, p =.030), posttransplant return to the operating room (odds ratio 11.45, 95% confidence interval 3.04–43.16, p =.004), and major postoperative respiratory event (odds ratio 32.14, 95% confidence interval 3.00–343.90, p