The Center for Surgical & Transplant Applied Research (C-STAR) is a research group focused
on organ transplantation at NYU Langone. Below are some of the decision models we have developed.
For more information, please visit our website.
This model predicts recipient risk of graft loss after living donor kidney transplantation based on donor characteristics, on the same scale as the KDPI ...
This model is intended for low-risk adults considering living kidney donation in the United States. It provides an estimate of 15-year and lifetime incidence of end-stage renal disease...
When a patient with end stage renal disease (ESRD) on the waitlist for a kidney is offered an Infectious Risk Donor (IRD) kidney, they need to decide whether they will accept the IRD kidney and the associated infectious risk, or if they will decline it and continue to wait for the next available infectious-risk free kidney ...
Chow, E. K. H., Massie, A. B., Muzaale, A. D., Singer, A. L., Kucirka, L. M., Montgomery, R. A., ... & Segev, D. L. (2013). Identifying appropriate recipients for CDC infectious risk donor kidneys. American Journal of Transplantation, 13(5), 1227-1234.
This prediction model is intended for adults with ESRD on dialysis aged 65 and above; it provides the predicted probability of 3-year survival after kidney transplantation (KT). Patients with predicted 3-year post-KT survival in the top quintile are deemed "excellent" candidates ...
Grams, M. E., Kucirka, L. M., Hanrahan, C. F., Montgomery, R. A., Massie, A. B., & Segev, D. L. (2012). Candidacy for kidney transplantation of older adults. Journal of the American Geriatrics Society, 60(1), 1-7.
Most pediatric kidney transplant recipients live long enough to require retransplantation. The most beneficial timing for living donor transplantation in candidates with one living donor is not clear...
Van Arendonk, K. J., Chow, E. K., James, N. T., Orandi, B. J., Ellison, T. A., Smith, J. M., Colombani, P. M., & Segev, D. L. (2012). Choosing the Order of Deceased Donor and Living Donor Kidney Transplantation in Pediatric Recipients: A Markov Decision Process Model. Am J Transplant, 99(2):360-6.
Risk estimation is critical for appropriate informed consent and varies substantially across living kidney donors.
This model predicts the survival benefit of kidney transplantation based on the combination of the offered kidney's KDPI and the candidate's EPTS.
Bae S, Massie AB, Thomas AG, Bahn G, Luo X, Jackson KR, et al. Who Can Tolerate a Marginal Kidney? Predicting Survival After Deceased-Donor Kidney Transplantation by Donor-Recipient Combination. Am J Transplant. 2019 Feb;19(2):425-433.
This simulator models waitlist and post-transplant survival in the face of the COVID-19 pandemic, based on patient characteristics, COVID-19 case-fatality rates, and risk of disease acquisition.
Massie AB et al. Identifying scenarios of benefit or harm from kidney transplantation during the COVID-19 pandemic: a stochastic simulation and machine learning study. Am J Transplant. 2020 Nov;20(11):2997-3007.
This model predicts the development of a positive antibody response after 2-dose SARS-2-CoV mRNA vaccines in organ transplant recipients.
Alejo J et al. Predicting a Positive Antibody Response after 2-Dose SARS-CoV-2 mRNA Vaccine Series in Transplant Recipients: A Machine Learning Approach with External Validation. Transplantation. In Press.
This model predicts the development of a positive antibody response after 3-dose SARS-2-CoV vaccines in organ transplant recipients.
Alejo J et al. A Machine Learning Model for Predicting Antibody Response After Three Doses of Vaccine Against SARS-CoV-2 In Solid Organ Transplant Recipients. Under Review.