Evaluating Diagnostic Algorithms for Heparin-Induced Thrombocytopenia using Two Combined Automated Rapid Immunoassays
Bibliographic record
Abstract
Heparin-induced thrombocytopenia (HIT) is an autoimmune disorder caused by antibodies against platelet factor 4 (PF4) and heparin complexes. Rapid immunoassays (IAs) for detection of these antibodies mark a milestone in HIT diagnosis, despite a higher false-positive rate compared with functional platelet-activation assays. However, combining different rapid IAs may help to improve their diagnostic specificity. Here, we compared the individual performance of the latex immunoturbidimetric assay (LIA; HemosIL HIT-Ab [PF4-H]; sensitivity 91.7%, specificity 68.4%) and chemiluminescence immunoassay (CLIA; HemosIL AcuStarHIT-Ab [PF4-H]; sensitivity 92.4%, specificity 85.8%) with their combined performance using two unique diagnostic algorithms in a single prospective cohort of suspected HIT patients. Using the simultaneous algorithm adapted from Warkentin et al, the combined LIA-CLIA had a sensitivity of 99.0% and specificity of 64.3%. The sequential algorithm adapted from Rittener-Ruff et al was applied in two theoretical scenarios to reflect real-world circumstances in diagnostic laboratories where access to clinical information is limited: (1) assuming all patients had an intermediate 4Ts score and (2) assuming all patients had a high 4Ts score. This algorithm correctly predicted HIT in 94.5% (high 4Ts) and 96.0% (intermediate 4Ts) and excluded HIT in 82.6% (high 4Ts) and 80.1% (intermediate 4Ts) of patients in either scenario, respectively. Although both combined algorithms improved diagnostic performance of individual IAs, the simultaneous algorithm showed fewer false predictions (7.9%) than the sequential algorithm (intermediate 4Ts: 37.6% and high 4Ts: 41.5%) and proved more practical as it does not rely on physician evaluations. Our findings highlight the importance of accounting for clinician and interlaboratory variability when evaluating diagnostic tests for HIT.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".