Anti–protamine-heparin antibodies: incidence, clinical relevance, and pathogenesis
Bibliographic record
Abstract
Protamine, which is routinely used after cardiac surgery to reverse the anticoagulant effects of heparin, is known to be immunogenic. Observing patients with an otherwise unexplained rapid decrease in platelet count directly after protamine administration, we determined the incidence and clinical relevance of protamine-reactive antibodies in patients undergoing cardiac-surgery. In vitro, these antibodies activated washed platelets in a FcγRIIa-dependent fashion. Using a nonobese diabetic/severe combined immunodeficiency mouse model, those antibodies induced thrombocytopenia only when protamine and heparin were present but not with protamine alone. Of 591 patients undergoing cardiopulmonary bypass surgery, 57 (9.6%) tested positive for anti-protamine-heparin antibodies at baseline and 154 (26.6%) tested positive at day 10. Diabetes was identified as a risk factor for the development of anti-protamine-heparin antibodies. In the majority of the patients, these antibodies were transient and titers decreased substantially after 4 months (P < .001). Seven patients had platelet-activating, anti-protamine-heparin antibodies at baseline and showed a greater and more prolonged decline in platelet counts compared with antibody-negative patients (P = .003). In addition, 2 of those patients experienced early arterial thromboembolic complications vs 9 of 584 control patients (multivariate analysis: odds ratio, 21.58; 95% confidence interval, 2.90-160.89; P = .003). Platelet-activating anti-protamine-heparin antibodies show several similarities with anti-platelet factor 4-heparin antibodies and are a potential risk factor for early postoperative thrombosis.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".