Premium
Identifying scenarios of benefit or harm from kidney transplantation during the COVID‐19 pandemic: A stochastic simulation and machine learning study
Author(s) -
Massie Allan B.,
Boyarsky Brian J.,
Werbel William A.,
Bae Sunjae,
Chow Eric K. H.,
Avery Robin K.,
Durand Christine M.,
Desai Niraj,
Brennan Daniel,
GaronzikWang Jacqueline M.,
Segev Dorry L.
Publication year - 2020
Publication title -
american journal of transplantation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.89
H-Index - 188
eISSN - 1600-6143
pISSN - 1600-6135
DOI - 10.1111/ajt.16117
Subject(s) - pandemic , medicine , calculator , harm , context (archaeology) , case fatality rate , covid-19 , intensive care medicine , disease , infectious disease (medical specialty) , population , computer science , environmental health , psychology , geography , social psychology , archaeology , operating system
Clinical decision-making in kidney transplant (KT) during the coronavirus disease 2019 (COVID-19) pandemic is understandably a conundrum: both candidates and recipients may face increased acquisition risks and case fatality rates (CFRs). Given our poor understanding of these risks, many centers have paused or reduced KT activity, yet data to inform such decisions are lacking. To quantify the benefit/harm of KT in this context, we conducted a simulation study of immediate-KT vs delay-until-after-pandemic for different patient phenotypes under a variety of potential COVID-19 scenarios. A calculator was implemented (http://www.transplantmodels.com/covid_sim), and machine learning approaches were used to evaluate the important aspects of our modeling. Characteristics of the pandemic (acquisition risk, CFR) and length of delay (length of pandemic, waitlist priority when modeling deceased donor KT) had greatest influence on benefit/harm. In most scenarios of COVID-19 dynamics and patient characteristics, immediate KT provided survival benefit; KT only began showing evidence of harm in scenarios where CFRs were substantially higher for KT recipients (eg, ≥50% fatality) than for waitlist registrants. Our simulations suggest that KT could be beneficial in many centers if local resources allow, and our calculator can help identify patients who would benefit most. Furthermore, as the pandemic evolves, our calculator can update these predictions.