Kidney Transplantation in Times of Covid-19: Decision Analysis in the Canadian Context
Bibliographic record
Abstract
Background:The coronavirus disease 2019 (COVID-19) pandemic impacted transplant programs across Canada.Objective:We evaluated the implications of delays in transplantation among Canadian end-stage kidney disease (ESKD) patients to allow pretransplant vaccination.Design:We used a Markov microsimulation model and ESKD patient perspective to study the effectiveness (quality-adjusted life years [QALY]) of living (LD) or deceased donor (DD) kidney transplantation followed by 2-dose SARS-CoV-2 vaccine versus delay in LD (“Delay LD”) or refusal of DD offer (“Delay DD”) to receive 2-dose SARS-CoV-2 vaccine pretransplant.Setting:Canadian dialysis and transplant centers.Patients:We simulated a 10 000-waitlisted ESKD patient cohort, which was predictively modeled for a lifetime horizon in monthly cycles.Measurements:Inputs on patient and graft survival estimates by patient, LD or DD characteristics, were extracted from the Treatment of End-Stage Organ Failure in Canada, Canadian Organ Replacement Register, 2009 to 2018. In addition, a literature review provided inputs on quality of life, SARS-CoV-2 transmissibility, new variants of concern, mortality risk, and antibody responses to 2-dose SARS-CoV-2 mRNA vaccines.Methods:We conducted base case, scenario, and sensitivity analyses to illustrate the impact of patient, donor, vaccine, and pandemic characteristics on the preferred strategy.Results:In the average waitlisted Canadian patient, receiving 2-dose SARS-CoV-2 vaccine post-transplant provided an effectiveness of 22.32 (95% confidence interval: 22.00-22.7) for LD and 19.34 (19.02-19.67) QALYs for DD. Delaying transplants for 6 months to allow 2-dose SARS-CoV-2 vaccine before LD and DD transplant yielded effectiveness of 22.83 (21.51-23.14) and 20.65 (20.33-20.96) QALYs, respectively. Scenario analysis suggested a benefit to short delays in DD transplants to receive 2-dose SARS-CoV-2 vaccine in waitlisted patients ≥55 years. Two-way sensitivity analysis suggested decreased effectiveness of the strategy prioritizing 2-dose SARS-CoV-2 vaccine prior to DD transplant the longer the delay and the higher the Kidney Donor Risk Index of the eventual DD transplant. When assessing the impact of SARS-CoV-2 variants of concern (infection rates ≥10-fold and associated mortality ≥3-fold vs base case), we found short delays to allow 2-dose SARS-CoV-2 vaccine administration pretransplant to be preferable.Limitations:Risks associated with nosocomial exposure of LDs were not considered. There was uncertainty regarding input parameters related to SARS-CoV-2 infection, new variants, and COVID-19 severity in ESKD patients. Given rollout of population-level SARS-CoV-2 vaccination, we assumed a linear decrease in infection rates over 1 year. Proportions of patients mounting an antibody response to 2-dose SARS-CoV-2 mRNA vaccines were considered in lieu of data on vaccine efficacy in dialysis and following transplantation. Non-age-stratified annual mortality rates were used for waitlisted candidates.Conclusions:Our analyses suggest that short delays allowing pretransplant vaccination offered comparable to greater effectiveness than pursuing transplantation without delay, proposing transplant candidates should be prioritized to receive at least 2 doses of SARS-CoV-2 vaccine. Our scenario and sensitivity analyses suggest that caution must be exercised when declining DD offers in patients offered low risk DD and who are likely to incur significant delays in access to transplantation. While population-level herd immunity may decrease infection risk in transplant patients, more data are required on vaccine efficacy against SARS-CoV-2 and variants of concern in ESKD, and how efficacy may be modified by a third vaccine dose, maintenance immunosuppression and timing of induction and rejection therapies.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Markov decision-analytic model of delaying kidney transplant for COVID vaccination; uses a modeling method to answer a clinical question in Canada, and the Canadian research system is not the object.
The decision analysis concerns kidney-transplant timing during COVID-19, not research practice.
Clinical decision model of transplant timing versus COVID vaccination for Canadian patients, not research practice.
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".