Influence of Baseline Kidney Function on Patient and Kidney Outcomes in Patients with COVID-19: A Multi-National Observational Study
Bibliographic record
Abstract
Background/Objectives: Acute kidney injury (AKI) is a common complication of coronavirus disease-19 (COVID-19), but the impact of baseline kidney function and care processes on outcomes is not well understood. We hypothesized that baseline kidney health status may influence courses and outcomes of AKI. Methods: This is a multinational, multicenter, retrospective cohort study. We included hospitalized adult COVID-19 patients with kidney disease (AKI, end-stage kidney disease (ESKD), chronic kidney disease (CKD), or kidney transplant (KT) recipients) from 1 January 2020 to 31 March 2022, across 52 centers in 23 countries. Patients with no prior kidney function information were classified as acute kidney disease (AKD) if estimated glomerular filtration rate (eGFR) at admission was <60 mL/min/1.73 m2 and as no known kidney disease (NKD) if eGFR was ≥60 mL/min/1.73 m2. We defined combined outcome as death or non-kidney recovery at hospital discharge. Multivariable binary regression models were applied. Results: Among 4158 patients, 882 had ESKD, and 3038 developed AKI. AKI patients were categorized as NKD (31.8%), AKD (38.6%), CKD (23.3%), and KT recipients (3.3%). NKD patients had higher AKI severity and more intensive care unit care needs. In the multivariable analyses, the risk of the combined outcome was higher in AKD (OR 1.459 [1.061, 2.005]) or CKD (OR 1.705 [1.206, 2.410]) patients, although the risk of in-hospital mortality was similar to NKD. Among the survivors at hospital discharge, the risk of partial or non-recovery was higher in CKD (OR 5.445 [3.864, 7.672]) or KT recipients (OR 4.208 [2.383, 7.429]) compared to NKD. These findings were consistent across income categories. Conclusions: Among AKI patients with COVID-19, nearly two-thirds had underlying kidney dysfunction, with 55% identified as having baseline AKD, which had higher risk of death or non-kidney recovery at discharge compared to NKD.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".