Laboratory Testing for Hit Antibodies: How Much Class Do We Need?.
Bibliographic record
Abstract
Abstract HIT is usually caused by platelet-activating antibodies of IgG class that bind to neoepitopes on platelet factor 4 (PF4) bound to heparin or certain other polyanions. Commercial enzyme-immunoassays (EIAs), however, measure PF4/polyanion-reactive antibodies of IgA and IgM class, in addition to IgG class antibodies. This raises the question: Does the detection of these IgA and IgM class antibodies improve HIT assay operating characteristics (perhaps because IgA and IgM are a marker for pathogenic HIT-IgG and/or for clinical HIT) or worsen operating characteristics (perhaps via detection of numerous non-HIT sera containing non-pathogenic IgA and/or IgM class antibodies)? We performed systematic serologic studies of 362 stored sera from a clinical trial of heparin therapy (Arch Intern Med2003; 163: 2518) in which 12 patients developed HIT by clinical criteria (>50% platelet count fall) and 350 patients did not develop HIT. Assays used included: serotonin release assay (SRA); in-house PF4/heparin-EIA that individually detects IgG (EIA-G), IgA (EIA-A), and IgM (EIA-M) class antibodies; and commercial EIA from GTI (EIA-GTI). The 12 patients with clinical HIT tested strongly positive in the SRA, EIA-G, and EIA-GTI (Table 1). Positive tests among the 350 non-HIT patients (to calculate test specificity) were seen in 12 (SRA), 28 (EIA-G), and 69 (EIA-GTI) patients. Table 1. Serologic features of 12 patients with clinical HIT. Serotonin Release In-house EIA-IgG Commercial EIA IQR=interquartile (25%, 75%) range; data are percent serotonin release using washed platelets and absorbance units (OD405) using EIA-G and EIA-GTI. Sensitivity (true-POS) 12/12 (100%) 12/12 (100%) 12/12 (100%) Median (IQR) POS result 98.5% (90.0, 99.5) 1.669 (1.223, 2.056) 1.802 (1.355, 2.344) Specificity (true-NEG) 338/350 (96.6%) 322/350 (92.0%) 281/350 (80.3%) In contrast, the EIA-A and EIA-M assays were positive in less than half of the HIT patients. Further, the magnitude of the IgA and IgM anti-PF4/heparin immune response did not differ between the 12 HIT patients, and the 69 non-HIT patients who had any PF4/polyanion immune response, as defined by a positive EIA-GTI without HIT (Table 2). Table 2. Comparison of the HIT and non-HIT Immune Response for IgA and IgM. IgA Positive IgA: Median (IQR) IgM Positive IgM: Median (IQR) * Non-HIT immune response defined as positive EIA-GTI but no clinical HIT. HIT (n=12) 5/12 (41.7%) 0.400 (0.210, 1.421) 3/12 (25.0%) 0.340 (0.269, 0.522) Non-HIT immune response (n=69)* 25/69 (36.2%) 0.316 (0.208, 0.870) 18/69 (26.1%) 0.322 (0.193, 0.475) P value 0.75 0.58 1.00 0.25 Similar observations were made when we compared the 24 SRA-positive patients (including the 12 HIT patients) against the 57 SRA-negative, EIA-GTI positive patients. CONCLUSION: Detection of PF4/polyanion-reactive IgA and IgM class antibodies worsens the operating characteristics of HIT assays through the detection of numerous non-pathogenic antibodies, without any offsetting advantages in the detection of pathogenic HIT antibodies. Optimal diagnostic laboratory testing for HIT antibodies should include a platelet activation assay and an EIA that detects only IgG class antibodies reactive against PF4/heparin (or PF4/polyanion).
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".