Serum Creatinine in Patients with Advanced Liver Disease Is of Limited Value for Identification of Moderate Renal Dysfunction: Are the Equations for Estimating Renal Function Better?
Bibliographic record
Abstract
BACKGROUND: The Cockcroft-Gault formula (CGF) is used to estimate the glomerular filtration rate (GFR) based on serum creatinine (Cr) levels, age and sex. A new formula developed by the Modification of Diet in Renal Disease (MDRD) Study Group, based on the patient's Cr levels, age, sex, race and serum urea nitrogen and serum albumin levels, has shown to be more accurate. However, the best formula to identify patients with advanced liver disease (ALD) and moderate renal dysfunction (GFR 60 mL/min/1.73 m2 or less) is not known. The aim of the present study was to compare calculations of GFR, using published formulas (excluding those requiring urine collections) with standard radionuclide measurement of GFR in patients with ALD. METHODS: Fifty-seven consecutive subjects (40% women) with a mean age of 50 years (range 16 to 67 years) underwent 99m-technetium-diethylenetriamine pentaacetic acid (99mTc-DTPA) (single injection) radionuclide measurement of GFR. To calculate GFR, three formulas were used: the reciprocal of Cr multiplied by 100 (100/Cr), the CGF and the MDRD formulas. Pearson's correlation coefficient (r) and Bland-Altman analyses of agreement were used to analyze the association between 99mTc-DTPA clearance and the three equations for GFR. RESULTS: The mean 99mTc-DTPA clearance was 83 mL/min/1.73 m2 (range 28 mL/min/1.73 m2 to 173 mL/min/1.73 m2). Mean calculated GFRs by 100/Cr, the CGF and the MDRD formula were 106 mL/min/1.73 m2, 98 mL/min/1.73 m2 and 86 mL/min/1.73 m2, respectively. Regression analysis showed good correlation between radionuclide GFR and calculated GFR with r(100/Cr)=0.74, r(CGF)=0.80, r(MDRD)=0.87, all at P > or = 0.0001. The MDRD formula provided the least bias. The Bland-Altman plot showed best agreement between GFR calculated by the MDRD formula and 99mTc-DTPA clearance, with only 3 mL/min/1.73 m2 overestimation. There was higher variability between radionuclide GFR and calculated GFR by the CGF and by 100/Cr. Although there was no difference in precision, GFR calculated by the MDRD formula had the best overall accuracy. The sensitivity and specificity for detection of moderate renal dysfunction by the MDRD formulas were 73% and 87%, respectively. CONCLUSIONS: Among the Cr-based GFR formulas, the MDRD formula showed a larger proportion of agreement with radionuclide GFR in patients with ALD. In clinical practice, the MDRD is the best formula for detection of moderate renal dysfunction among those with ALD.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".