Constructing an Intent-to-Treat Score Index to Predict Survival Outcomes in Pediatric Liver Transplant Recipients.

BACKGROUND: Waitlist and posttransplant outcomes have been widely reported for pediatric liver transplantation. Yet, analyzing these metrics individually fails to provide a holistic perspective for patients and their families. Intent-to-treat (ITT) analysis fills this gap by studying the associations between waitlist outcomes, organ availability, and posttransplant outcomes. Our study aimed to construct a predictive index utilizing ITT analysis for pediatric liver transplant recipients (Pedi-ITT). METHODS: We performed a retrospective analysis utilizing de-identified data provided by the United Network for Organ Sharing (UNOS) from March 1, 2002, to December 31, 2021. We analyzed data for 12 926 pediatric recipients (age <18). We conducted a univariate and multivariable logistic regression to find the significant predictive factors affecting ITT survival. A scoring index was constructed to stratify outcome risk on the basis of the significant factors identified by regression analysis. RESULTS: Multivariable analysis found the following factors to be significantly associated with death on the waitlist or after transplant: gender, diagnosis, UNOS region, ascites, diabetes mellitus, age at the time of listing, serum sodium at the time of listing, total bilirubin at the time of listing, serum creatinine at the time of listing, INR at the time of listing, history of ventilator use, and history of re-transplantation. Using receiver operator characteristic analysis, the Pedi-ITT index had a c-statistic of 0.79 (95% confidence interval [CI]: 0.76-0.82). The c-statistics of the Model for End-Stage Liver Disease/Pediatric for End-Stage Liver Disease and pediatric version of the Survival Outcomes Following Liver Transplantation score indices were 0.74 (CI: 0.71-0.76) and 0.69 (CI: 0.66-0.72), respectively. CONCLUSIONS: The Pedi-ITT index provides an additional prognostic model with moderate predictive power to assess outcomes associated with pediatric liver transplantation. Further analysis should focus on increasing the predictive power of the index.

High Clinical Exome Sequencing Diagnostic Rates and Novel Phenotypic Expansions for Nonisolated Microphthalmia, Anophthalmia, and Coloboma.

Purpose: A molecular diagnosis is only made in a subset of individuals with nonisolated microphthalmia, anophthalmia, and coloboma (MAC). This may be due to underutilization of clinical (whole) exome sequencing (cES) and an incomplete understanding of the genes that cause MAC. The purpose of this study is to determine the efficacy of cES in cases of nonisolated MAC and to identify new MAC phenotypic expansions. Methods: We determined the efficacy of cES in 189 individuals with nonisolated MAC. We then used cES data, a validated machine learning algorithm, and previously published expression data, case reports, and animal models to determine which candidate genes were most likely to contribute to the development of MAC. Results: We found the efficacy of cES in nonisolated MAC to be between 32.3% (61/189) and 48.1% (91/189). Most genes affected in our cohort were not among genes currently screened in clinically available ophthalmologic gene panels. A subset of the genes implicated in our cohort had not been clearly associated with MAC. Our analyses revealed sufficient evidence to support low-penetrance MAC phenotypic expansions involving nine of these human disease genes. Conclusions: We conclude that cES is an effective means of identifying a molecular diagnosis in individuals with nonisolated MAC and may identify putatively damaging variants that would be missed if only a clinically available ophthalmologic gene panel was obtained. Our data also suggest that deleterious variants in BRCA2, BRIP1, KAT6A, KAT6B, NSF, RAC1, SMARCA4, SMC1A, and TUBA1A can contribute to the development of MAC.