Faculty Bibliography

ABO type B and O kidney transplant candidates have increased difficulty identifying a compatible donor for living donor kidney transplantation (LDKT) and are harder to match in kidney paired donation registries. A2-incompatible (A2i) LDKT increases access to LDKT for these patients. To better inform living donor selection, we evaluated the association between A2i LDKT and patient and graft survival.

In the United States, a small proportion of potential deceased organ donor referrals lead to donation and recovery. Understanding variation in the processes involved between organ procurement organizations (OPOs) may help increase deceased donation and reduce the organ shortage. We studied 103 923 referrals from 10 OPOs from 2018 to 2019, of which 14.4% led to approach for authorization, 8.2% led to authorization, 5.1% led to organ recovery, and 4.8% led to transplantation. First-person authorization (FPA) was associated with threefold higher odds of donation (OR = _2.83 3.02 _3.22 , p < .001). Female referrals had 11% lower odds of approach; when approached, Black and Hispanic referrals had 46% and 35% lower odds of authorization, respectively (all p < .001). There was substantial OPO-level variation in rates of approach, authorization, and organ recovery, which persisted after adjusting for age, sex, race, and FPA status. An OPO's relative rate of approach correlated strongly with its relative rate of donation among all referrals (ρ = 0.43). Correlation between an individual OPO's authorization rate among approached families, and overall rate of donation, was negative, suggesting that high authorization rates may be the result of selective approach practices. Therefore, approaching a higher proportion of families for authorization may lead to higher donation rates.

BACKGROUND:Solid organ transplant recipients (SOTRs) are less likely to mount an antibody response to SARS-CoV-2 mRNA vaccines. Understanding risk factors for impaired vaccine response can guide strategies for antibody testing and additional vaccine dose recommendations. METHODS:Using a nationwide observational cohort of 1031 SOTRs, we created a machine learning model to explore, identify, rank, and quantify the association of 19 clinical factors with antibody responses to 2 doses of SARS-CoV-2 mRNA vaccines. External validation of the model was performed using a cohort of 512 SOTRs at Houston Methodist Hospital. RESULTS:Mycophenolate mofetil use, a shorter time since transplant, and older age were the strongest predictors of a negative antibody response, collectively contributing to 76% of the model's prediction performance. Other clinical factors, including transplanted organ, vaccine type (mRNA-1273 versus BNT162b2), sex, race, and other immunosuppressants, showed comparatively weaker associations with an antibody response. This model showed moderate prediction performance, with an area under the receiver operating characteristic curve of 0.79 in our cohort and 0.67 in the external validation cohort. An online calculator based on our prediction model is available at http://transplantmodels.com/covidvaccine/. CONCLUSIONS:Our machine learning model helps understand which transplant patients need closer follow-up and additional doses of vaccine to achieve protective immunity. The online calculator based on this model can be incorporated into transplant providers' practice to facilitate patient-centric, precision risk stratification and inform vaccination strategies among SOTRs.