Management and Outcome of COVID-19 Infection Using Nirmatrelvir/Ritonavir in Kidney Transplant Patients
Bibliographic record
Abstract
BACKGROUND: Nirmatrelvir/ritonavir has been shown to reduce the risk of coronavirus disease 2019 (COVID-19)-related complications in patients at high risk for severe COVID-19. However, clinical experience of nirmatrelvir/ritonavir in the transplant recipient population is scattered due to the complex management of drug-drug interactions with calcineurin inhibitors. We describe the clinical experience with nirmatrelvir/ritonavir at The Ottawa Hospital kidney transplant program. METHODS: Patients who received nirmatrelvir/ritonavir between April and June 2022 were included and followed up to 30 days after completion of treatment. Tacrolimus was withheld for 24 hours and resumed 72 hours after the last dose of nirmatrelvir/ritonavir (on day 8) on the basis of the drug level the day before. The first 30 patients had their dose adjusted according to drug levels performed twice in the first week and as needed thereafter. Subsequently, a simplified algorithm with less frequent calcineurin inhibitor-level monitoring was implemented. Outcomes, including tacrolimus-level changes, serum creatinine and AKI (defined as serum creatinine increase by 30%), and clinical outcomes were described globally and compared between algorithms. RESULTS: Fifty-one patients received nirmatrelvir/ritonavir. Tacrolimus levels drawn at the first time point, 7 days after withholding of calcineurin inhibitor, and 2 days after discontinuing nirmatrelvir/ritonavir were within the therapeutic target in 17/44 (39%), subtherapeutic in 21/44 (48%), and supratherapeutic in 6/44 (14%). Two weeks after, 55% were within the therapeutic range, 23% were below, and 23% were above it. The standard and simplified algorithms provided similar tacrolimus level (median 5.2 [4.0-6.2] µg/L versus 4.8 [4.3-5.7] µg/L, P = 0.70). There were no acute rejections or other complications. CONCLUSIONS: Withholding tacrolimus starting the day before initiation of nirmatrelvir/ritonavir with resumption 3 days after completion of therapy resulted in a low incidence of supratherapeutic levels but a short period of subtherapeutic levels for many patients. AKI was infrequent. The data are limited by the small sample size and short follow-up. PODCAST: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2023_07_10_CJN0000000000000186.mp3.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".