Metabolic Acidosis is Associated With Acute Kidney Injury in Patients With CKD
Bibliographic record
Abstract
Introduction: Metabolic acidosis in patients with chronic kidney disease (CKD) results from a loss of kidney function. It has been associated with CKD progression, all-cause mortality, and other adverse outcomes. We aimed to determine whether metabolic acidosis is associated with a higher risk of acute kidney injury (AKI). Methods: This was a retrospective cohort study. Using electronic health records and administrative data, we enrolled 2 North American cohorts of patients with CKD Stages G3-G5 as follows: (i) 136,067 patients in the US electronic medical record (EMR) based cohort; and (ii) 34,957 patients in the Manitoba claims-based cohort. The primary exposure was metabolic acidosis (serum bicarbonate between 12 mEq/l and <22 mEq/l). The primary outcome was the development of AKI (defined using ICD-9 and 10 codes at hospital admission or a laboratory-based definition based on Kidney Disease: Improving Global Outcomes guidelines). We applied Cox proportional hazards regression models adjusting for relevant demographic and clinical characteristics. Results: In both cohorts, metabolic acidosis was associated with AKI: hazard ratio (HR) 1.57 (95% confidence interval [CI] 1.52-1.61) in the US EMR cohort, and HR 1.65 (95% CI 1.58-1.73) in the Manitoba claims cohort. The association was consistent when serum bicarbonate was treated as a continuous variable, and in multiple subgroups, and sensitivity analyses including those adjusting for albuminuria. Conclusion: Metabolic acidosis is associated with a higher risk of AKI in patients with CKD. AKI should be considered as an outcome in studies of treatments for patients with metabolic acidosis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".