Incidence and Outcomes of Acute Kidney Injury in Patients Admitted to Hospital With COVID-19: A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The incidence of acute kidney injury (AKI) in patients with COVID-19 and its association with mortality and disease severity is understudied in the Canadian population. OBJECTIVE: To determine the incidence of AKI in a cohort of patients with COVID-19 admitted to medicine and intensive care unit (ICU) wards, its association with in-hospital mortality, and disease severity. Our aim was to stratify these outcomes by out-of-hospital AKI and in-hospital AKI. DESIGN: Retrospective cohort study from a registry of patients with COVID-19. SETTING: Three community and 3 academic hospitals. PATIENTS: A total of 815 patients admitted to hospital with COVID-19 between March 4, 2020, and April 23, 2021. MEASUREMENTS: Stage of AKI, ICU admission, mechanical ventilation, and in-hospital mortality. METHODS: We classified AKI by comparing highest to lowest recorded serum creatinine in hospital and staged AKI based on the Kidney Disease: Improving Global Outcomes (KDIGO) system. We calculated the unadjusted and adjusted odds ratio for the stage of AKI and the outcomes of ICU admission, mechanical ventilation, and in-hospital mortality. RESULTS: Of the 815 patients registered, 439 (53.9%) developed AKI, 253 (57.6%) presented with AKI, and 186 (42.4%) developed AKI in-hospital. The odds of ICU admission, mechanical ventilation, and death increased as the AKI stage worsened. Stage 3 AKI that occurred during hospitalization increased the odds of death (odds ratio [OR] = 7.87 [4.35, 14.23]). Stage 3 AKI that occurred prior to hospitalization carried an increased odds of death (OR = 5.28 [2.60, 10.73]). LIMITATIONS: Observational study with small sample size limits precision of estimates. Lack of nonhospitalized patients with COVID-19 and hospitalized patients without COVID-19 as controls limits causal inferences. CONCLUSIONS: Acute kidney injury, whether it occurs prior to or after hospitalization, is associated with a high risk of poor outcomes in patients with COVID-19. Routine assessment of kidney function in patients with COVID-19 may improve risk stratification. TRIAL REGISTRATION: The study was not registered on a publicly accessible registry because it did not involve any health care intervention on human participants.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".