Predictors of clinical outcome in patients with heparin-induced thrombocytopenia treated with direct thrombin inhibition
Bibliographic record
Abstract
We aimed to identify predictors of poor outcome in patients with heparin-induced thrombocytopenia, a serious immune-mediated reaction to heparin. All patients were treated with direct thrombin inhibition therapy, as part of two prospective studies. We performed a risk factor analysis of adverse outcomes (defined as death, amputation, new thrombosis, or their composite within a 37-day study period) in 809 patients from two reported prospective studies of the direct thrombin inhibitor argatroban in clinically diagnosed heparin-induced thrombocytopenia. We initially identified from among 14 baseline variables the significant predictors of poor outcome in the first study (304 patients), and then tested our resultant hypothesis in the second, independent study (505 patients), using multivariate analysis. Seven significant predictors were identified in the first study; three were confirmed in the second study. The strongest relationship occurred between the baseline platelet count and the composite of death, amputation, or new thrombosis (P = 0.0001), with the most severely thrombocytopenic patients being at greatest risk. The other significant associations were between renal impairment and death (odds ratio = 2.13, 95% confidence interval = 1.23-3.66, P = 0.007), and between cardiovascular surgery (particularly peripheral vascular surgery) and amputation (odds ratio = 3.39, 95% confidence interval = 1.65-6.95, P = 0.0009). In conclusion, in patients with clinically diagnosed heparin-induced thrombocytopenia, the severity of the baseline thrombocytopenia is the best predictor of death, amputation or thrombotic progression. The identification of higher risk subgroups for poor outcomes, such as patients with more severe thrombocytopenia or a history of renal impairment or peripheral vascular surgery, could allow more targeted therapy.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".