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10.1111/ajt.16117 http://scihub22266oqcxt.onion/10.1111/ajt.16117 C7300693!7300693 !32515544     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=32515544 &cmd=llinks): Failed to open stream: HTTP request failed! 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Identifying scenarios of benefit or harm from kidney transplantation during the COVID-19 pandemic: A stochastic simulation and machine learning study |pdf=|usr=}}{{32515544 }} gab.com Text Identifying scenarios of benefit or harm from kidney transplantation during the COVID-19 pandemic: A stochastic simulation and machine learning study JO: Am J Transplant http://www.kidney.de/pget.php?&tw=1&pmid=32515544 #FOAvip 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. Twit Text FOAVip Identifying scenarios of benefit or harm from kidney transplantation during the COVID-19 pandemic: A stochastic simulation and machine learning study ????Am J Transplant http://www.kidney.de/pget.php?&tw=1&pmid=32515544 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32515544 #FOAvip English Wikipedia <ref>{{Cite pmid|32515544 }}</ref> Identifying scenarios of benefit or harm from kidney transplantation during the COVID-19 pandemic: A stochastic simulation and machine learning study #MMPMID32515544 Massie AB ; Boyarsky BJ ; Werbel WA ; Bae S ; Chow EKH ; Avery RK ; Durand CM ; Desai N ; Brennan D ; Garonzik-Wang JM ; Segev DL Am J Transplant 2020[Nov]; 20 (11 ): 2997-3007 PMID32515544 show ga 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. |*Kidney Transplantation [MESH] |*Machine Learning [MESH] |*Pandemics [MESH] |*SARS-CoV-2 [MESH] |Adolescent [MESH] |Adult [MESH] |Aged [MESH] |COVID-19/*epidemiology [MESH] |Child [MESH] |Child, Preschool [MESH] |Female [MESH] |Humans [MESH] |Infant [MESH] |Infant, Newborn [MESH] |Kidney Failure, Chronic/*epidemiology/surgery [MESH] |Male [MESH] |Middle Aged [MESH] |Tissue Donors/*supply & distribution [MESH] |United States/epidemiology [MESH] |Waiting Lists/mortality [MESH]Identifying scenarios of benefit or harm from kidney transplantation d(epmc).pdf DeepDyve Pubget Overpricing