Reducing the hospital burden of heparin-induced thrombocytopenia: impact of an avoid-heparin program
Bibliographic record
Abstract
Heparin-induced thrombocytopenia (HIT) is an adverse drug reaction occurring in up to 5% of patients exposed to unfractionated heparin (UFH). We examined the impact of a hospital-wide strategy for avoiding heparin on the incidence of HIT, HIT with thrombosis (HITT), and HIT-related costs. The Avoid-Heparin Initiative, implemented at a tertiary care hospital in Toronto, Ontario, Canada, since 2006, involved replacing UFH with low-molecular-weight heparin (LMWH) for prophylactic and therapeutic indications. Consecutive cases with suspected HIT from 2003 through 2012 were reviewed. Rates of suspected HIT, adjudicated HIT, and HITT, along with HIT-related expenditures were compared in the pre-intervention (2003-2005) and the avoid-heparin (2007-2012) phases. The annual rate of suspected HIT decreased 42%, from 85.5 per 10 000 admissions in the pre-intervention phase to 49.0 per 10 000 admissions in the avoid-heparin phase ( ITALIC! P< .001). The annual rate of patients with a positive HIT assay decreased 63% from 16.5 to 6.1 per 10 000 admissions ( ITALIC! P< .001), adjudicated HIT decreased 79% from 10.7 to 2.2 per 10 000 admissions ( ITALIC! P< .001), and HITT decreased 91% from 4.6 to 0.4 per 10 000 admissions ( ITALIC! P< .001). Hospital HIT-related expenditures decreased by $266 938 per year in the avoid-heparin phase. To the best of our knowledge, this is the first study demonstrating the success and feasibility of a hospital-wide HIT prevention strategy.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".