Is COVID-19 Infection Associated With the Progression of Kidney Disease? Findings From a Population Based Observational Study From British Columbia, Canada
Bibliographic record
Abstract
Background: Recent research suggests that COVID-19 infection is associated with acute kidney injury (AKI). Together the inflammation caused by the virus in the kidneys and the episodes of AKIs are risk factors for progression of kidney diseases. We investigated the risk of progression to kidney failure among chronic kidney disease (CKD) patients from BC, Canada who were infected with COVID-19. Methods: In this retrospective cohort study, we analyzed a cohort of 22,188 nondialysis CKD patients aged ≥18 years, with no prior history of ESKD and COVID-19 infection before the cohort entry date between January 27, 2020 & December 15, 2021. The cohort was derived from Patient Records and Outcome Management Information System (PROMIS), a population based integrated registry database for CKD patients under the nephrologist care in BC. Incident COVID-19 cases were iteratively matched without replacement to non-COVID-19 controls (1:3 ratio) based on age, sex, region of residency, diabetes status, eGFR and urine ACR, CKD vintage and COVID-19 vaccination status as of COVID-19 diagnosis date. The primary outcome was a composite of initiation of maintenance dialysis defined by dialysis performed for ≥4 weeks, a sustained decline in eGFR defined by ≥40% decline from baseline that sustained over ≥4 weeks or incident kidney transplantation. Estimated HR and 95% CI using Fine and Gray subdistribution hazard model to account for death as a competing risk. Results: The analytic data included 1,708 patients, 475 (28%) COVID-19 cases and 1,233 (72%) non-COVID-19 controls. Median age was 71 years, 53% was male. Median follow-up was 8.3 months, 70 (4.10%) patients progressed to kidney failure. Among the non-dialysis CKD patients, the risk of developing kidney failure in COVID-19 infected cases was 24% higher compared to matched, non-COVID-19 infected controls. The HR (95% CI) was 1.24 (0.75, 2.06) (p-value: 0.39). Conclusions: COVID-19 infection in non-dialysis CKD patients appeared to be associated with higher risk of progression to kidney failure. Although not statistically significant, the substantial increase in risk estimate warrants close monitoring of kidney function among CKD patients after COVID-19 infection. Funding: Government Support - Non-U.S.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".