An Evaluation of Commonly Used Surrogate Baseline Creatinine Values to Classify AKI During Acute Infection
Bibliographic record
Abstract
INTRODUCTION: Classification of acute kidney injury (AKI) requires a premorbid baseline creatinine, often unavailable in studies in acute infection. METHODS: We evaluated commonly used surrogate and imputed baseline creatinine values against a "reference" creatinine measured during follow-up in an adult clinical trial cohort. Known AKI incidence (Kidney Disease: Improving Global Outcomes [KDIGO] criteria) was compared with AKI incidence classified by (1) back-calculation using the Modification of Diet in Renal Disease (MDRD) equation with and without a Chinese ethnicity correction coefficient; (2) back-calculation using the Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) equation; (3) assigning glomerular filtration rate (GFR) from age and sex-standardized reference tables; and (4) lowest measured creatinine during admission. Back-calculated distributions were performed using GFRs of 75 and 100 ml/min. RESULTS: All equations using an assumed GFR of 75 ml/min underestimated AKI incidence by more than 50%. Back-calculation with CKD-EPI and GFR of 100 ml/min most accurately predicted AKI but misclassified all AKI stages and had low levels of agreement with true AKI diagnoses. Back-calculation using MDRD and assumed GFR of 100 ml/min, age and sex-reference GFR values adjusted for good health, and lowest creatinine during admission performed similarly, best predicting AKI incidence (area under the receiver operating characteristic curves [AUC ROCs] of 0.85, 0.87, and 0.85, respectively). MDRD back-calculation using a cohort mean GFR showed low total error (22%) and an AUC ROC of 0.85. CONCLUSION: Current methods for estimating baseline creatinine are large sources of potential error in acute infection studies. Preferred alternatives include MDRD equation back-calculation with a population mean GFR, age- and sex-specific GFR values corrected for "good health," or lowest measured creatinine. Studies using surrogate baseline creatinine values should report specific methodology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".