Non-Pathogenic Anti-Heparin/PF4 Antibodies Generated by Low Molecular Weight Heparins Mediate Procoagulant Effects.
Bibliographic record
Abstract
Abstract Abstract 1091 Introduction: While the incidence of symptomatic heparin-induced thrombocytopenia (HIT) is relatively low with the use of low molecular weights heparins (LMWHs), these agents do generate anti-heparin/PF4 antibodies in 10–20% of treated patients. Dosage, duration, and the pathologic predisposition of the patient influence the quantitative and qualitative nature of these antibodies. It has been suggested that these non-pathogenic antibodies (NPAs) which do not produce symptomatic HIT may, nevertheless, be biologically active and mediate thrombogenic responses. The overall pathophysiologic role of NPAs is unknown at this time. Hypothesis: NPAs generated by LMWHs cause coagulation activation and compromise the anticoagulant effects of the administered LMWH. Study Design: Blood plasma samples collected at baseline and day 10 from patients enrolled in orthopedic surgery clinical trials of LMWHs for the prophylactic management of deep vein thrombosis (Lovenox enoxaparin, sanofi-aventis, n=352; Clivarin reviparin, Abbott, n=380) were retrospectively screened for the presence of anti-heparin/PF4 antibodies using the GTI ELISA method (Waukesha, WI). Positive samples were tested by the 14C-SRA to determine if the antibodies were capable of functionally activating platelets. Both ELISA positive and negative samples were evaluated in an assay of thrombin generation (Technothrombin TGA kit, diaPharma, West Chester, OH). Result: In the enoxaparin study, the baseline pre-treatment samples only showed one patient in the heparin control group to be positive by ELISA. On day 10, 11 of 175 (6.3%) enoxaparin patients had a positive ELISA response, whereas 22 of 177 (12.4%) heparin patients were ELISA positive. None of the samples were 14C-SRA positive. In the thrombin generation assay, the ELISA positive samples showed a lesser inhibition of thrombin generation for both the enoxaparin and heparin groups (270 ± 27 nM TGA enoxaparin group; 220 ± 21 nM TGA heparin group) compared to the thrombin generation response of the ELISA negative samples (190 ± 18 nM TGA enoxaparin group; 160 ± 20 nM TGA heparin group). In the reviparin study, none of the patients were ELISA positive at baseline. On day 10, in the reviparin group 19 of 200 (9.5%) patients were ELISA positive, whereas 28 of 180 (15.6%) heparin control patients had a positive ELISA titer. None of the samples were 14C-SRA positive. In comparison to the baseline (pre-treatment), both the reviparin and heparin treated patients showed an inhibition of thrombin generation (410 ± 27 nM TGA baseline vs 180–290 nM with treatment). However, consistent with the above study, those samples that were ELISA antibody positive showed a lesser inhibition of thrombin generation (240 ± 21 nM TGA reviparin group; 210 ± 16 nM TGA heparin group) in comparison to the ELISA negative samples (190 ± 12 nM TGA reviparin group; 180 ± 14 nM TGA heparin group). Interestingly, the D-dimer levels were found to be higher in the ELISA positive samples in all groups for both studies (p<0.05). Conclusion: These studies suggest a potential pathologic role of NPAs. The results of the thrombin generation studies strongly suggest that the generation of NPAs may result in a reduction of the antithrombotic potential of both LMWH and heparin in treated patients. While the exact mechanism of this process is not clear, dosage adjustment may be useful in those patients who generate NPAs. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".