Estimated glomerular filtration rates are higher when creatinine‐based equations are compared with a cystatin C‐based equation in coronavirus disease 2019
Bibliographic record
Abstract
OBJECTIVES: Estimations of glomerular filtration rate (eGFR) are based on analyses of creatinine and cystatin C, respectively. Coronavirus disease 2019 (COVID-19) patients in the intensive care unit (ICU) often have acute kidney injury (AKI) and are at increased risk of drug-induced kidney injury. The aim of this study was to compare creatinine-based eGFR equations to cystatin C-based eGFR in ICU patients with COVID-19. METHODS: After informed consent, we included 370 adult ICU patients with COVID-19. Creatinine and cystatin C were analyzed at admission to the ICU as part of the routine care. Creatinine-based eGFR (ml/min) was calculated using the following equations, developed in chronological order; the Cockcroft-Gault (C-G), Modified Diet in Renal Disease (MDRD)1999, MDRD 2006, Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), and Lund-Malmö revised (LMR) equations, which were compared with eGFR calculated using the cystatin C-based Caucasian Asian Pediatric Adult (CAPA) equation. RESULTS: The median eGFR when determined by C-G was 99 ml/min and interquartile range (IQR: 67 ml/min). Corresponding estimations for MDRD1999 were 90 ml/min (IQR: 54); MDRD2006: 85 ml/min (IQR: 51); CKD-EPI: 91 ml/min (IQR: 47); and for LMR 83 ml/min (IQR: 41). eGFR was calculated using cystatin C and the CAPA equation value was 70 ml/min (IQR: 38). All differences between creatinine-based eGFR versus cystatin C-based eGFR were significant (p < .00001). CONCLUSIONS: Estimation of GFR based on various analyses of creatinine are higher when compared with a cystatin C-based equation. The C-G equation had the worst performance and should not be used in combination with modern creatinine analysis methods for determination of drug dosage in COVID-19 patients.
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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 | 0.006 | 0.017 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".