Long-Term Effect of COVID-19 Infection on Kidney Function Among COVID-19 Patients Followed in a Post-COVID-19 Recovery Clinic in British Columbia, Canada
Bibliographic record
Abstract
Background: Recent research suggests that COVID-19 is associated with acute kidney dysfunction. Effect of COVID-19 infection on downstream kidney function is unknown. We investigated this using the BC Interdisciplinary COVID-19 Care Network data. Methods: This retrospective cohort study analyzed a 2,212 COVID-19 patient cohort, aged ≥18 years, referred to the Post COVID Recovery Clinic (PCRC) in BC, Canada between July 9, 2020 & April 21, 2022. COVID-19 diagnosis date was the index date. Patients with history of kidney transplantation or dialysis before index date were excluded. Patients who deceased within 3 months of cohort entry were excluded. eGFR values were retrieved from the Provincial Laboratory Information System. We examined change in eGFR at 3-, 6-, 12-months after COVID-19 infection among the same study individuals using linear mixed model. Subgroup analysis included comparison between hospitalized vs. non-hospitalized, & diabetics vs. non-diabetics. Results: Analytic cohort included 457 patients (median age 59 years, 50% male) for whom eGFR was recorded at 3-, 6-, 12-months from index date. Prevalence of reduced eGFR (≤59ml/min/1.73m2) was 16%, 16%, 17% at 3-, 6- and 12- months post-index date, respectively. Median (IQR) eGFR at baseline was 90 (73, 102) that was reduced to 85 (70, 101) at 6-months & remained stable or <previous value at 12 months postindex date, 86 (69, 101). Results from linear mixed model indicated a 0.23 ml/min decrease in eGFR in each month after COVID-19 infection (intercept 85.51, slope -0.23, p-value=0.0003). In subgroup analyses, similar trends of decreasing eGFR over time were observed among diabetic (n=188, intercept 83.08, slope -0.42, p-value=0.0001) & nondiabetic patients (n=269, intercept 87.33, slope -0.12, p-value=0.13). Interestingly, eGFR appeared to improve over time in non-hospitalized patients (n=133, intercept 88.34, slope 0.24, p-value=0.03) compared to a decreasing trend among hospitalized patients (n=324, intercept 83.94, slope -0.41, p-value=<0.001). Conclusions: One in 6 COVID-19 patients who were referred to PCRC had reduced eGFR. COVID-19 was associated with a statistically significant decrease in eGFR, particularly in diabetic & hospitalized patients that warrants ongoing monitoring following COVID-19 infection.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".