Choosing Immune Suppression in Renal Transplantation by Efficacy and Morbidity2 (CISTEM2)

Summary/Abstract Kidney transplant (KT) survival faces challenges from increased patient complexity, use of non-standard donors, and longer cold ischemic times. Despite refinement in immunosuppression (IS) management over several decades, nearly all KT patients experience progressive loss of allograft function, leading to eventual graft failure

while still risking life-threatening IS-related complications. Development of evidence-based IS management practice to optimize long-term KT function has been limited by the need for detailed, longitudinal clinical data for large representative populations. Our previous NIDDK-supported R01 grant (Choosing IS regimens in kidney

Transplant by Efficacy and Morbidity; CISTEM), leveraged integrated transplant registry, Medicare claims and national pharmacy clearinghouse data to assess the impact of early IS regimen selection on key post-KT events: infections, malignancy, new onset diabetes, as well as traditional metrics (acute rejection rate, allograft survival

and patient death). We developed a free web-based interface to assist transplant professionals and patients in shared-decision making about IS choice at the time of KT. However, IS requires lifelong dynamic adjustments. We will enhance our prior work with the following Specific Aims in CISTEM2: 1) Recognizing the lack of

longitudinal and granular clinical observations and lab results, we will establish a novel CISTEM2 dataset, integrating: a) national transplant registry data granular clinical data from 12 transplant centers housed in the Greater Plains Collaborative (GPC,) a component of the Patient Centered Clinical Research Network

(PCORnet); b) administrative claims; and c) social determinants of health (SDOH) indicators for KT recipients. GPC utilizes the PCORnet common data model (CMD) for all clinical data, which can be securely linked to national transplant registry, administrative claims and SDOH indicators. We will expand from the multivariable

propensity and Cox proportional hazard models with time-varying covariates used in CISTEM, to more responsive and clinical meaningful endpoints such as percentage drop in KT function (via estimated glomerular filtration rate; eGFR) and the validation of computed phenotypes for key clinical events. 2) We will extend

CISTEM by developing longitudinal machine learning (ML) algorithms to dynamically identify IS strategies that optimize longer-term KT eGFR, reduce cost, and limit those IS comorbidities that post-transplant patient focus groups identify as contributors to diminished quality of life. 3) We will validate the predictive models refined in

Aim 1 and the ML models developed in Aim 2, using two additional PCORnet sites (12 more KT centers; Total N: 40,535 KTs). We will analyze PCORnet CDM from all centers using distributed learning models to refine the first dynamic, evidence based clinical decision tool for longitudinal IS management after KT. Balancing the risk

of acute rejection, patient and graft survival, and risk of IS-related complications after KT, based on highly granular, multicenter, longitudinal clinical data from real-world patient experience, will allow patients and physicians to dynamically optimize IS in a more personalized, patient focused, and cost-effective manner.