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 ...
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 ...
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...
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.
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.
This model predicts the development of a positive antibody response after 2-dose SARS-2-CoV mRNA vaccines in organ transplant recipients.
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.