The Effect of Paraproteins and Rheumatoid Factor on Four Commercial Immunoassays for Vancomycin
Bibliographic record
Abstract
BACKGROUND: Paraproteins, immunoglobulins (Igs), which are elevated in various autoimmune disorders, are known to interfere with various laboratory immunoassays, including vancomycin (VANC). Rheumatoid factor (RF), a known immunoassay interferant, may cause falsely elevated results. OBJECTIVES: The aims of this study were to (1) evaluate the effect of 3 paraproteins (IgA, IgG, and IgM) on 4 commercial VANC immunoassays [fluorescence polarization immunoassay; enzyme multiplied immunoassay; 2 particle-enhanced turbidimetric inhibition immunoassays]; (2) determine the concentration at which the effect is obtained, and (3) examine the influence of RF on the VANC methods. METHOD: Serum and plasma pools from patients prescribed VANC and a spiked VANC pool (20 mg/L) were each mixed 1:1 with individual patient specimens containing IgA (6-63 g/L), IgG (6-54 g/L), IgM (3-30 g/L) (n = 4 for each Ig), and a patient RF pool (196 IU/L). The mixtures (n = 39) were split and distributed for VANC analysis. RESULTS: IgA and IgG in serum and plasma did not affect any of the VANC immunoassays. RF added to plasma specimens did not interfere, but in serum, elevated VAN results were observed. IgM did not affect the fluorescence polarization immunoassay and enzyme multiplied immunoassay methods but did attenuate VANC concentrations by both particle-enhanced turbidimetric inhibition immunoassays (Siemens, Beckman Coulter), with a more pronounced effect on the latter, producing concentrations >20% lower than expected in the patient serum and spiked plasma pools. The effect was progressively negative at effective IgM concentrations of 10 and 15 mg/L. CONCLUSIONS: This phenomenon is a major analytical and clinical issue that must be communicated to health care professionals caring for patients receiving VANC, so optimal therapy is achieved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".