Evaluation of hemolysis, lipemia, and icterus interference with common clinical immunoassays
Bibliographic record
Abstract
Abstract Objectives Hemolysis, icterus, and lipemia (HIL) are common sources of endogenous interference in clinical laboratory testing. Defining the threshold of interference for immunoassays enables appropriate reporting of their results when they are affected by HIL. Methods Pools of residual patient serum samples were spiked with a known amount of interferent to create samples with varying concentrations of hemolysate, bilirubin, and Intralipid that mimicked the effects of endogenous HIL. Samples were analysed on the Alinity i analyser (Abbott Diagnostics) for more than 25 immunoassays. The average recovery relative to the non-spiked sample was calculated for each interference level and was compared to a predefined allowable bias. Results C-peptide, estradiol, serum folate, free T4, homocysteine, insulin, and vitamin B12 were found to be affected by hemolysis, at hemoglobin concentrations between 0.3 to 20 g/L. Immunoassays for BNP, estradiol, free T3, and homocysteine were affected by icterus at conjugated bilirubin concentrations between 50 to 1,044 μmol/L. BNP, serum folate, and homocysteine were affected by Intralipid with measured triglyceride concentrations between 0.8 to 10 mmol/L. Lastly, serological immunoassays for HIV and hepatitis A, B and C were also affected by interferences. Conclusions Immunoassays are impacted by varying degrees of HIL interference. Some measurands, in the presence of interference, are affected in a manner not previously indicated. The data presented herein provide an independent evaluation of HIL thresholds and will be of aid to resource-limited clinical laboratories that are unable to internally verify endogenous interferences when implementing the Alinity i analyser.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".