Long-term effect of COVID-19 infection on kidney function among COVID-19 patients followed in post-COVID-19 recovery clinics in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: We investigated the effect of Post-Acute COVID Syndrome or "long-COVID" on kidney function among patients followed in post-COVID recovery clinics (PCRC) in British Columbia, Canada. METHODS: Long-COVID patients referred to PCRC between July 2020 to April 2022, aged ≥18 years who had an estimated glomerular filtration rate (eGFR) value recorded at 3 months from the coronavirus disease 2019 (COVID-19) diagnosis (index) date were included. Those requiring renal replacement therapy prior to index date were excluded. Primary outcome was change in eGFR and urine albumin-creatinine ratio (UACR) after COVID-19 infection. The proportion of patients in each of the six eGFR categories (<30, 30-44, 45-59, 60-89, 90-120 and >120 mL/min/1.73 m2) and three UACR categories (<3, 3-30 and >30 mg/mmol) in all of the study time points were calculated. Linear mixed model was used to investigate change in eGFR over time. RESULTS: The study sample included 2212 long-COVID patients. Median age was 56 years, 51% were male. Half (∼47%-50%) of the study sample had normal eGFR (≥90 mL/min/1.73 m2) from COVID-19 diagnosis to 12 months post-COVID and <5% of patients had an eGFR <30 mL/min/1.73 m2. There was an estimated 2.96 mL/min/1.73 m2 decrease in eGFR within 1 year after COVID-19 infection that was equivalent to 3.39% reduction from the baseline. Decline in eGFR was highest in patients hospitalized for COVID-19 (6.72%) followed by diabetic patients (6.15%). More than 40% of patients were at risk of CKD. CONCLUSIONS: People with long-COVID experienced a substantial decline in eGFR within 1 year from the infection date. The prevalence of proteinuria appeared to be high. Close monitoring of kidney function is prudent among patients with persistent COVID-19 symptoms.
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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 | 0.003 |
| 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 | 0.001 | 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".