Health Advocate for Children After Liver Transplant

After the perioperative period, it is an objective outcome closely linked with poor adherence/challenges with self-management. Investigators found strong associations between material economic hardship and TCMR episodes of TCMR increase risks of graft failure/death post-transplant and early TCMR increases risk of late TCMR and vulnerability to infections. All analyses will be intention-to-treat. Investigators will use Kaplan-Meier plots to descriptively compare survival distributions of the primary outcome (TCMR) by trial arm. We will compare time-to-TCMR between treatment arms using Cox regression. For participants recruited in years 1 & 2 of the study period, we will collect 2 years of follow-up data. For participants recruited in year 3, we will apply administrative censoring after 1 year of follow up data.

An MLVI >2.0 predicted late TCMR, even after accounting for other causes of tacrolimus fluctuations. In the first 6 months after transplant, tacrolimus levels can fluctuate based on physiological changes associated with transplant. After 6 months, fluctuations are most likely related to poor adherence. Investigators will measure the incidence of MLVI >2.0 between 6 months - 1 year, and 1year and 2 years. If a patient has an episode of TCMR within this time, investigators will only collect tacrolimus levels preceding this event. Investigators will calculate MLVI based on available levels for 6 months - 1 year, and for 1year - 2 years. Investigators will use generalized linear mixed effects models for dichotomous outcomes to analyze MLVI >2.0. Models will also include random effects for each patient, and fixed effects that include trial arm, time since randomization and site. If differences in groups are observed, interaction terms may be introduced to assess heterogeneity in trends.

Parent-reported social needs will be assessed using a SIREN-informed patient-reported outcome measure. Scores will be calculated at baseline and 90 days. Change scores will be compared between treatment arms using a t-test, and the primary analysis will use linear regression (ANCOVA) with the baseline score as a covariate to estimate the mean difference in change between groups.

Investigators will use generalized linear mixed effects models for dichotomous outcomes to analyze liver inflammation and overimmunosuppression. Models will include random effects for each patient, and fixed effects for trial arm, time since randomization (allowing investigators to assess trends), and site.

Parents will report whether social services were received in the 90-day period following enrollment. Investigators will summarize utilization using counts and percentages and compare across centers using chi-square tests.

Receipt of social services will be recorded at baseline and 90 days. The change in utilization will be compared between treatment arms using t-tests and modeled using ANCOVA with baseline utilization as a covariate.

Baseline social needs scores will be summarized using medians and quartiles for continuous measures.

Follow-up social needs scores at 90 days will be summarized and compared across centers using Kruskal-Wallis tests.

Reach: Differences in EHR-abstracted sociodemographic characteristics and center differences (enrolled vs. declined) Adoption: Differences in adoption by clinician type Implementation: Identify factors from enrollment associated with quantitative implementation measures Maintenance: Investigators will analyze whether attrition rates differ by type

Investigators will collect interviews from both treatment arms at baseline and the 90-day timepoint. Patients in the treatment arm will have one additional qualitative interview (45-day timepoint.) The interviews will be recorded and transcribed. Codebooks will be developed for the caregiver, health advocate and clinician informant interviews using COM-B components to identify barriers and facilitators to adoption, implementation and maintenance. Investigators will iteratively refine and update the codebooks during data collection, allowing for open coding to occur to capture inductive codes. Each transcript will be coded by two coders.