Pitfalls of Clinical Diagnosis Strategies for Heparin-Induced Thrombocytopenia (HIT) After Cardiac Surgery (CS).
Bibliographic record
Abstract
Abstract Abstract 3516 Poster Board III-453 Background HIT is an infrequent but potentially serious complication of heparin therapy. Its diagnosis is complex and depends on a combination of clinical suspicion and laboratory confirmation through ELISA and functional tests such as the serotonin release assay (SRA). The 4Ts score comprises 4 clinical parameters (severity and timing of onset of thrombocytopenia, development of thrombosis, and clinician's appraisal of the likelihood of alternate causes for thrombocytopenia) and has been proposed to predict the probability of HIT in patients deemed to be at risk. However, the validity of the 4Ts score in patients undergoing cardiac surgery (CS) is questionable considering the numerous other factors that predispose such patients to thrombocytopenia and thrombosis. In addition, in CS patients the HIT ELISA assay has been reported to have 25 - 50% false positive results making it less useful. Objectives To determine the usefulness of the 4T score in the post cardiac surgical population and the value of the HIT ELISA optical density for predicting HIT. Methods Retrospective case-control study of patients admitted for cardiac surgery to the London Health Sciences Centre between January 2006 and December 2008 and for whom a HIT ELISA assay was requested. Patients with an equivocal or positive ELISA test were tested by SRA which considered the gold standard. Information collected included clinical variables related to the surgery and post-operative period, calculated 4T scores, ELISA optical density (OD) and SRA results. Categorical variables were compared using chi2 or Fisher's exact tests as appropriate. Continuous variables were compared using a Mann-Whitney U test. Covariates achieving a p value ≤0.1 in univariate analysis and the components of the 4Ts score were incorporated in logistic regression models using stepwise forward selection. Finally, we constructed a Receiver Operating Characteristic (ROC) curve for the ELISA OD. Results 73 patients were included in the analysis. Results of the univariate analysis are shown in the table. On regression analysis only the ELISA optical density (per each OD arbitrary unit increase) was correlated with a diagnosis of HIT (OR 37.266; 95% CI 2.342-593.013; p=0.010). For the ELISA OD the area under the ROC curve was 0.990 (SE 0.013) (Figure). A cutoff value for the OD of 0.475 had a Sensitivity of 1, a specificity of 0.9, a positive likelihood ratio (LR) of 10 and a negative LR of 0.00. Assuming a prevalence proportion of 0.082 the posterior probability of HIT if the ELISA has an OD <0.475 is 0 (95% CI 0 – 9). On the other hand, an OD >0.92 resulted in a LR+ of 20 with a posterior probability of 64% (95% CI 35 – 80). Conclusions In this study, we found that the 4T score does not accurately predict HIT in post CS patients. Limitations of this study include a reduced sample size and its retrospective nature. Our findings suggest that in post CS patients developing thrombocytopenia between 10 and 100 × 109 or a platelet drop of 50% or more (100% of our population) a HIT ELISA with an OD < 0.475 could be used to rule out HIT. Our findings need to be confirmed in prospective studies. Disclosures: No relevant conflicts of interest to declare.
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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".