Acute kidney injury requiring renal replacement therapy in people with COVID-19 disease in Ontario, Canada: a prospective analysis of risk factors and outcomes
Bibliographic record
Abstract
BACKGROUND: Severely ill people with coronavirus disease 2019 (COVID-19) are at risk of acute kidney injury treated with renal replacement therapy (AKI-RRT). The understanding of the risk factors and outcomes for AKI-RRT is incomplete. METHODS: We prospectively collected data on the incidence, demographics, area of residence, time course, outcomes and associated risk factors for all COVID-19 AKI-RRT cases during the first two waves of the pandemic in Ontario, Canada. RESULTS: There were 271 people with AKI-RRT, representing 0.1% of all diagnosed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases. These included 10% of SARS-CoV-2 admissions to intensive care units (ICU). Median age was 65 years, with 11% <50 years, 76% were male, 47% non-White and 48% had diabetes. Overall, 59% resided in the quintile of Ontario neighborhoods with the greatest ethnocultural composition and 51% in the two lowest income quintile neighborhoods. Mortality was 58% at 30 days after RRT initiation, and 64% at 90 days. By 90 days, 20% of survivors remained RRT-dependent and 31% were still hospitalized. On multivariable analysis, people aged >70 years had higher mortality (odds ratio 2.4, 95% confidence interval 1.3, 4.6). Cases from the second versus the first COVID-19 wave were older, had more baseline comorbidity and were more likely to initiate RRT >2 weeks after SARS-CoV-2 diagnosis (34% versus 14%; P < 0.001). CONCLUSIONS: AKI-RRT is common in COVID-19 ICU admissions. Residency in areas with high ethnocultural composition and lower socioeconomic status are strong risk factors. Late-onset AKI-RRT was more common in the second wave. Mortality is high and 90-day survivors have persisting high morbidity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 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".