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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".