The Impact of COVID-19 on Patients With ADPKD
Bibliographic record
Abstract
PURPOSE OF REVIEW: Patients with autosomal dominant polycystic kidney disease (ADPKD) have kidney cysts and kidney enlargement decades before progressing to advanced chronic kidney disease (CKD), meaning patients live most of their adult life with a chronic medical condition. The coronavirus disease 2019 (COVID-19) pandemic has created common questions among patients with ADPKD. In this review, we discuss COVID-19 concerns centered around a patient with a common clinical vignette. SOURCES OF INFORMATION: We performed PubMed and Google scholar searches for English, peer-reviewed studies related to "COVID-19," "ADPKD," "CKD," "tolvaptan," "angiotensin-converting enzyme inhibitors" (ACEi), "angiotensin receptor blockers" (ARB), and "vaccination." We also evaluated transplant data provided by the Ontario Trillium Gift of Life Network. METHODS: Following an assessment of available literature, this narrative review addresses common questions of patients with ADPKD in the context of the COVID-19 pandemic. KEY FINDINGS: Data regarding the risk of developing COVID-19 and the risk of adverse COVID-19 outcomes in patients with ADPKD remain limited, but patients with ADPKD with impaired estimated glomerular filtration rate (eGFR), kidney transplants, or on dialysis are likely at similar increased risk as those with generally defined CKD. We provide strategies to improve virtual care, which is likely to persist after the pandemic. Current evidence suggests ACEi, ARB, and tolvaptan treatment should be continued unless contraindicated due to severe illness. When available, and in the absence of a severe allergy, vaccination is recommended for all patients with ADPKD. LIMITATIONS: This narrative review is limited by a paucity of high-quality data on COVID-19 outcomes in patients specifically with ADPKD. IMPLICATIONS: Patients with ADPKD who have developed advanced CKD, require dialysis, or who have received a kidney transplant are at elevated risk of COVID-19 complications.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".