An Algorithm for “Indeterminate” Test Results in the Platelet Serotonin Release Assay for Heparin-Induced Thrombocytopenia (HIT).
Bibliographic record
Abstract
Abstract HIT is a prothrombotic complication of heparin caused by antibodies that recognize complexes of platelet factor 4 (PF4) bound to heparin or certain other polyanions. These antibodies produce thrombocytopenia by activating platelets via their Fc receptors. A “functional” (platelet activation) assay that utilizes washed platelets, known as the 14C-serotonin release assay (SRA), has the highest reported sensitivity-specificity tradeoff for detecting clinically-significant antibodies that recognize PF4/heparin complexes (“HIT antibodies”). Platelet activation (% serotonin release) induced by patient (or control) serum is assessed under several different reaction conditions, including absence of heparin, therapeutic concentrations of unfractionated heparin (UFH, 0.1 to 0.3 U/mL) and low-molecular-weight heparin (LMWH, 0.2 U/mL), supratherapeutic concentrations of UFH (100 U/mL), and with 0.1 U/mL UFH in the presence of a platelet Fc receptor-blocking monoclonal antibody. Various technical aspects of the assay optimize test sensitivity and specificity (e.g., using heat-inactivated patient serum; washing the platelets with apyrase; resuspending the platelets in albumin-free Tyrode’s buffer; using platelets from healthy volunteers known to react well to IgG platelet agonists, etc.). The classic HIT platelet activation profile is strong platelet activation (>50% serotonin release) in the presence of therapeutic UFH or LMWH that is inhibited by supratherapeutic heparin and the platelet Fc receptor-blocking monoclonal antibody. One drawback to the assay is that some patient sera activate platelets via the Fc receptors in a heparin-independent fashion, i.e., the platelets are activated at all heparin concentrations. This is known as an “indeterminate” reaction profile, since the presence of in vitro immune complexes (generated by the heat-inactivation process) or in vivo immune complexes or other platelet-activating factors could “mask” the presence of a true HIT antibody. We developed an algorithm for dealing with such indeterminate reaction profiles. First, we repeat the SRA using another aliquot of patient serum that is newly heat-inactivated, and also use different platelet donors to perform the assay. Often, this results in an interpretable test result. However, if the repeat SRA also gives an indeterminate result, we then use an in-house anti-PF4/heparin ELISA (that detects only IgG class antibodies) to determine whether HIT antibodies could be present. From 2091 patient serum samples tested for HIT antibodies using the SRA, we identified 199 (9.5%) samples that gave an initial indeterminate result. Using our algorithm, 81 samples subsequently gave clearly negative results, and 35 samples gave clearly positive results. However, 83 samples (representing 41.7% of the retested samples, or 4.0% of the total samples) gave a repeat indeterminate test result. When this last group of samples was tested using the anti-PF4/heparin-IgG ELISA, 53 of the samples tested negative (OD<0.45) and 30 tested positive (OD >0.45). With this algorithmic approach, about 96% of patients can be classified as negative or positive using the SRA. However, 4% of patients require the use of a complementary assay- the anti-PF4/heparin-IgG ELISA, to evaluate for the presence of HIT antibodies. Further studies are required to determine the causes of persistent indeterminate results in the SRA, which may lead to new approaches to further optimize this assay.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.016 |
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".