A comparison of observed versus estimated baseline creatinine for determination of RIFLE class in patients with acute kidney injury
Bibliographic record
Abstract
BACKGROUND: The RIFLE classification scheme for acute kidney injury (AKI) is based on relative changes in serum creatinine (SCr) and on urine output. The SCr criteria, therefore, require a pre-morbid baseline value. When unknown, current recommendations are to estimate a baseline SCr by the MDRD equation. However, the MDRD approach assumes a glomerular filtration rate of approximately 75 mL/min/1.73 m(2). This method has not been validated. METHODS: Data from the Beginning and Ending Supportive Therapy for the Kidney (BEST Kidney) study, a prospective observational study from 54 ICUs in 23 countries of critically ill patients with severe AKI, were analysed. The RIFLE class was determined by using observed (o) pre-morbid and estimated (e) baseline SCr values. Agreement was evaluated by correlation coefficients and Bland-Altman plots. Sensitivity analysis by chronic kidney disease (CKD) status was performed. RESULTS: Seventy-six percent of patients (n = 1327) had a pre-morbid baseline SCr, and 1314 had complete data for evaluation. Forty-six percent had CKD. The median (IQR) values were 97 micromol/L (79-150) for oSCr and 88 micromol/L (71-97) for eSCr. The oSCr and eSCr determined at ICU admission and at study enrolment showed only a modest correlation (r = 0.49, r = 0.39). At ICU admission and study enrolment, eSCr misclassified 18.8% and 11.7% of patients as having AKI compared with oSCr. Exclusion of CKD patients improved the correlation between oSCr and eSCr at ICU admission and study enrolment (r = 0.90, r = 0.84) resulting in 6.6% and 4.0% being misclassified, respectively. CONCLUSIONS: While limited, estimating baseline SCr by the MDRD equation when pre-morbid SCr is unavailable would appear to perform reasonably well for determining the RIFLE categories only if and when pre-morbid GFR was near normal. However, in patients with suspected CKD, the use of MDRD to estimate baseline SCr overestimates the incidence of AKI and should not likely be used. Improved methods to estimate baseline SCr are needed.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".