Identifying Drugs Implicated in Drug-Induced Immune Thrombocytopenia Using Levels of Evidence Applied to Laboratory Tests,
Bibliographic record
Abstract
Abstract Abstract 3304 Introduction: Many drugs can cause platelet counts to decrease. However, only relatively few cause severe drug-induced immune thrombocytopenia (DITP), a hemorrhagic syndrome characterized by drug-dependent, platelet-reactive antibodies. Laboratory testing for DITP is important to confirm the diagnosis, however test methods have evolved over the years and results are often difficult to interpret. We applied hierarchical grading methodology to published reports of DITP test methods to evaluate their validity and reliability. Using this grading system, we identified drugs that were implicated in DITP reactions with the highest level of evidence. Methods: All drugs implicated in DITP reactions based on clinical criteria were compiled from a previous systematic review (Swisher KK, Drug Safety 2009). Primary publications and additional reports associated with each drug were retrieved by searching MEDLINE and EMBASE to identify those drugs for which in vitro DITP testing had been performed. In duplicate and independently, the validity of DITP test methods was assessed based on whether or not they demonstrated: 1) drug or drug metabolite-dependence; 2) platelet specificity; and 3) IgG-binding. Reliability of test methods was assessed based on whether or not DITP test results were confirmed by more than one laboratory. Discrepancies were adjudicated by a third party. Assessors were experienced in DITP test methods. Results: We identified 149 drugs that were associated with DITP reactions by clinical criteria alone. Of those, 92 were excluded because testing was either not performed or was negative, or primary reports were irretrievable. Publications associated with the remaining 57 drugs were reviewed in duplicate. Of those, 27 were excluded because testing did not confirm drug-dependence (N= 15), platelet specificity (N=1) or IgG binding (N=11); 19 were included; and 11 were sent for adjudication. In the end, 22 drugs (abciximab, acetaminophen, cephamandole, diazepam, diphenylhydantoin, eptifibatide, gold, ibuprofen, mirtazapine, naproxen, oxaliplatin, penicillin, quinidine, quinine, rifampin, rosiglitazole, roxifiban, sulfisoxazole, tirofiban, tranilast, trimethoprim/sulfamethoxazole and vancomycin) met all validity criteria. Only 6 (gold, quinine, quinidine, tirofiban, rifampin and vancomycin) were confirmed positive by more than one laboratory. Conclusion: Based on assessments of validity and reproducibility of laboratory test methods, we identified 6 drugs – gold, quinine, quinidine, tirofiban, rifampin and vancomycin – that have been implicated in DITP reactions with a high level of evidence. This type of methodological approach can improve the likelihood of DITP diagnosis with a given drug. Disclosures: Warkentin: Sanofi-Aventis: Speakers Bureau; Pfizer Canada: Speakers Bureau; GlaxoSmithKline: Consultancy, Research Funding; GTI Diagnostics: Consultancy, Research Funding; Canyon Pharma: Consultancy, Speakers Bureau; Informa: Patents & Royalties.
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 | 0.024 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.039 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".