Investigating Interferences of a Whole-Blood Point-of-Care Creatinine Analyzer: Comparison to Plasma Enzymatic and Definitive Creatinine Methods in an Acute-Care Setting
Bibliographic record
Abstract
BACKGROUND: Although measurement of whole-blood creatinine at the point of care offers rapid assessment of renal function, agreement of point-of-care (POC) results with central laboratory methods continues to be a concern. We assessed the influence of several potential interferents on POC whole-blood creatinine measurements. METHODS: We compared POC creatinine (Nova StatSensor) measurements with plasma enzymatic (Roche Modular) and isotope dilution mass spectrometry (IDMS) assays in 119 hospital inpatients. We assessed assay interference by hematocrit, pH, pO(2), total and direct bilirubin, creatine, prescribed drugs, diagnosis, red blood cell water fraction, and plasma water fraction. RESULTS: CVs for POC creatinine were 1.5- to 6-fold greater than those for plasma methods, in part due to meter-to-meter variation. Regressioncomparison of POC creatinine to IDMS results gave a standard error (S(y|x)) of 0.61 mg/dL (54 μmol/L), whereas regression of plasma enzymatic creatinine to IDMS was S(y|x) 0.16 mg/dL (14 μmol/L). By univariate analysis, bilirubin, creatine, drugs, pO(2), pH,plasma water fraction, and hematocrit were not found to contribute to method differences. However, multivariate analysis revealed that IDMS creatinine, red blood cell and plasma water fractions, and hematocrit explained 91.8% of variance in POC creatinine results. CONCLUSIONS: These data suggest that whole-blood POC creatinine measurements should be used with caution. Negative interferences observed with these measurements could erroneously suggest adequate renal function near the decision threshold, particularly if estimated glomerular filtration rate is determined. Disparity between whole-blood and plasma matrices partially explains the discordance between whole-blood and plasma creatinine methods.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".