Frequency of Heparin‐Induced Thrombocytopenia in Critical Care Patients
Bibliographic record
Abstract
The diagnosis of heparin-induced thrombocytopenia (HIT) in critically ill patients is complicated by lack of information on the frequency of HIT relative to thrombocytopenia from other causes. In addition, results from HIT diagnostic tests have not been clearly evaluated for clinical utility. In this prospective study, we estimated the frequency of HIT and the predictive performance of the heparin-platelet factor 4 enzyme-linked immunosorbent assay (heparin-PF4 ELISA) in 748 consecutive, heparin-treated patients in a combined intensive and coronary care unit. The criteria for diagnosis were as follows: two or more consecutive platelet counts below 150 x 10(3)/mm3 or a 33% or greater decrease in platelet count 5 or more days after beginning heparin, or any time after starting heparin for patients exposed to the agent within the previous 8 weeks; and a positive 14C-serotonin release assay (SRA), the reference standard. Specificity and predictive values for the heparin-PF4 ELISA were estimated in patients who met the clinical criteria for HIT. Of 748 patients, 267 were exposed to heparin for a sufficient length of time to be considered to be at risk for HIT. Forty of these patients (15.0%, 95% confidence interval [CI] 10.7%-19.3%) met the clinical criteria for HIT. Serum samples were available for 32 of these patients, one of whom tested positive by the SRA, yielding a HIT frequency of 0.39% (95% CI 0.01-2.1%). The specificity of the heparin-PF4 ELISA among thrombocytopenic patients with negative SRA results was 71%, and the positive (PPV) and negative (NPV) predictive values of this test were estimated to be 10% and 100%, respectively. The point estimate of the frequency of HIT in critically ill patients was less than 1% in this cohort. The low PPV and high NPV of the heparin-PF4 ELISA suggest that it can be used to exclude HIT as a cause of thrombocytopenia in this patient population.
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 | 0.001 | 0.007 |
| 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 | 0.001 |
| 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".