AKI Diagnostic Accuracy and Implications of AKI Baseline Creatinine (ABC) vs. Other Baseline Creatinine Estimating Equations
Bibliographic record
Abstract
Background: Acute kidney injury (AKI) definitions rely on a known baseline creatinine (Crb), which is missing in up to 75% of hospitalized children. A new method (ABC equation) for estimating Crb was derived from children without kidney disease. We aim to externally validate the ABC method in an international cohort and assess how different Crb estimating equations alter AKI epidemiology. Methods: AWARE is a prospective international study of 4984 critically ill children (age 0-25 years) from 32 PICUs. The validation of the ABC equations uses a subset of this cohort (n=2451) with a known Crb which serves as the gold standard, using statistical measures of accuracy and precision. The entire cohort is used for assessing changes in AKI epidemiology for different Crb estimating equations (3 ABC equations and 4 common eGFR equations). Univariate statistics determine how different Crb equations impact the incidence of AKI and its association with key clinical outcomes including 28-day mortality. Results: The simplified ABC equation (requiring only age) performed similarly to existing Crb equations (e.g., new Schwartz). When an admission hospital creatinine value was available, the ABC equations outperformed all existing equations up to 19% in accuracy and 32% in precision. AKI incidence varied from 2-10% depending on Crb definition. Adverse clinical outcomes were rare: 28-day mortality (n=169) was 3.4% and ICU length of stay>=14 days (n=147) was 2.9%. Compared to previous Crb equations, ABC equations consistently perform better (or similar) to predict poor clinical outcomes. For example, relative risk (RR) of AKI using the ABC equation for 28-day mortality was 4.5 (95% CI 2.8-7.2); this was 5-29% higher RR than AKI defined by other Crb equations. Conclusions: ABC equations outperform existing Crb estimating equations. This international cohort confirms earlier findings that ABC equations are improved methods for estimating Crb values. The data suggest ABC equations performed similarly, or perhaps better, in predicting select poor clinical outcomes.
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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 | 0.036 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".