Acute kidney injury among critically ill patients with pandemic H1N1 influenza A in Canada: cohort study
Bibliographic record
Abstract
BACKGROUND: Canada's pandemic H1N1 influenza A (pH1N1) outbreak led to a high burden of critical illness. Our objective was to describe the incidence of AKI (acute kidney injury) in these patients and risk factors for AKI, renal replacement therapy (RRT), and mortality. METHODS: From a prospective cohort of critically ill adults with confirmed or probable pH1N1 (16 April 2009-12 April 2010), we abstracted data on demographics, co-morbidities, acute physiology, AKI (defined by RIFLE criteria for Injury or Failure), treatments in the intensive care unit, and clinical outcomes. Univariable and multivariable logistic regression analyses were used to evaluate the associations between clinical characteristics and the outcomes of AKI, RRT, and hospital mortality. RESULTS: We included 562 patients with pH1N1-related critical illness (479 [85.2%] confirmed, 83 [14.8%] probable]: mean age 48.0 years, 53.4% female, and 13.3% aboriginal. Common co-morbidities included obesity, diabetes, and chronic obstructive pulmonary disease. AKI occurred in 60.9%, with RIFLE categories of Injury (23.0%) and Failure (37.9%). Independent predictors of AKI included obesity (OR 2.94; 95%CI, 1.75-4.91), chronic kidney disease (OR 4.50; 95%CI, 1.46-13.82), APACHE II score (OR per 1-unit increase 1.06; 95%CI, 1.03-1.09), and P(a)O2/F(i)O2 ratio (OR per 10-unit increase 0.98; 95%CI, 0.95-1.00). Of patients with AKI, 24.9% (85/342) received RRT and 25.8% (85/329) died. Independent predictors of RRT were obesity (OR 2.25; 95% CI, 1.14-4.44), day 1 mechanical ventilation (OR 4.09; 95% CI, 1.21-13.84), APACHE II score (OR per 1-unit increase 1.07; 95% CI, 1.03-1.12), and day 1 creatinine (OR per 10 μmol/L increase, 1.06; 95%CI, 1.03-1.10). Development of AKI was not independently associated with hospital mortality. CONCLUSION: The incidence of AKI and RRT utilization were high among Canadian patients with critical illness due to pH1N1.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".