Maximizing matching, equity and survival in kidney transplantation using molecular HLA immunogenicity quantitation.

HLA matching improves long-term outcomes of kidney transplantation, yet implementation challenges persist, particularly within the African American (Black) patient demographic due to donor scarcity. Consequently, kidney survival rates among Black patients significantly lag behind those of other racial groups. A refined matching scheme holds promise for improving kidney survival, with prioritized matching for Black patients potentially bolstering rates of HLA-matched transplants. To facilitate quantity, quality and equity in kidney transplants, we propose two matching algorithms based on quantification of HLA immunogenicity using the hydrophobic mismatch score (HMS) for prospective transplants. We mined the national transplant patient database (SRTR) for a diverse group of donors and recipients with known racial backgrounds. Additionally, we use novel methods to infer survival assessment in the simulated transplants generated by our matching algorithms, in the absence of actual target outcomes, utilizing modified unsupervised clustering techniques. Our allocation algorithms demonstrated the ability to match 87.7% of Black and 86.1% of White recipients under the HLA immunogenicity threshold of 10. Notably, at the lowest HMS threshold of 0, 4.4% of Black and 12.1% of White recipients were matched, a marked increase from the 1.8% and 6.6% matched under the prevailing allocation scheme. Furthermore, our allocation algorithms yielded similar or improved survival rates, as illustrated by Kaplan-Meier (KM) curves, and enhanced survival prediction accuracy, evidenced by C-indices and Integrated Brier Scores.

Differences in Responses of Immunosuppressed Kidney Transplant Patients to Moderna mRNA-1273 versus Pfizer-BioNTech.

Immunosuppressed kidney transplant (KT) recipients produce a weaker response to COVID-19 vaccination than immunocompetent individuals. We tested antiviral IgG response in 99 KT recipients and 66 healthy volunteers who were vaccinated with mRNA-1273 Moderna or BNT162b2 Pfizer-BioNTech vaccines. A subgroup of participants had their peripheral blood leukocytes (PBLs) evaluated for the frequency of T helper 1 (Th1) cells producing IL-2, IFN-γ and/or TNF-α, and IL-10-producing T-regulatory 1 (Tr) cells. Among KT recipients, 45.8% had anti-SARS-CoV-2 IgG compared to 74.1% of healthy volunteers (p = 0.009); also, anti-viral IgG levels were lower in recipients than in volunteers (p = 0.001). In terms of non-responders (≤2000 U/mL IgG), Moderna's group had 10.8% and Pfizer-BioNTech's group had 34.3% of non-responders at 6 months (p = 0.023); similarly, 15.7% and 31.3% were non-responders in Moderna and Pfizer-BioNTech groups at 12 months, respectively (p = 0.067). There were no non-responders among controls. Healthy volunteers had higher Th1 levels than KT recipients, while Moderna produced a higher Th1 response than Pfizer-BioNTech. In contrast, the Pfizer-BioNTech vaccine induced a higher Tr1 response than the Moderna vaccine (p < 0.05); overall, IgG levels correlated with Th1(fTTNF-α)/Tr1(fTIL-10) ratios. We propose that the higher number of non-responders in the Pfizer-BioNTech group than the Moderna group was caused by a more potent activity of regulatory Tr1 cells in KT recipients vaccinated with the Pfizer-BioNTech vaccine.

Empirical Comparison of Continuous and Discrete-time Representations for Survival Prediction.

Survival prediction aims to predict the time of occurrence of a particular event of interest, such as the time until a patient dies. The main challenge in survival prediction is the presence of incomplete observations due to censoring. The classical formulation for survival prediction treats the survival time as a continuous outcome, which leads to a censored regression problem. Recent work has reformulated the survival prediction problem by discretizing time into a finite number of bins and then applying multi-task binary classification. While the discrete-time formulation is convenient and potentially requires less assumptions than the continuous-time approach, it also loses information by discretizing time. In this paper, we empirically investigate continuous and discrete-time representations for survival prediction to try to quantify the trade-offs between the two formulations. We find that discretizing time does not necessarily decrease prediction accuracy. Furthermore, discrete-time models can result in even more accurate predictors than continuous-time models, but the number of time bins used for discretization has a significant effect on accuracy and should thus be tuned as a hyperparameter rather than specified for convenience.