Correlation of ELISA Optic Density with Clinical Diagnosis of Heparin-Induced Thrombocytopenia: A Retrospective Study of 104 Patients with Positive Anti-PF4/Heparin Antibodies.
Bibliographic record
Abstract
Abstract Abstract 2242 Introduction Heparin-induced thrombocytopenia (HIT), which is characterized by thrombotic events, is a serious complication of heparin use. Its diagnosis is primarily clinical but can be supported by several laboratory tests. ELISA for anti-PF4/heparin antibodies, which is the most widely-available technique, is expressed in terms of optical density (OD) results. This test was shown to have good sensitivity but poor positive predictive value. The goal of this study is to correlate OD levels with the probability of HIT diagnosis. Contrarily to previous studies where the diagnosis of HIT was mainly based on laboratory findings, we have defined HIT based on the strength of the original diagnosis, the retrospective adjudication performed by one or two clinicians familiar with HIT diagnosis, the absence of thrombosis and the absence of a clearly identified alternative diagnosis for the thrombocytopenia. Method We conducted a retrospective study involving 104 patients with a positive ELISA for anti-PF4/heparin antibodies (Stago Asserachrom HPIA essay) between 2008 and May 2012. For all patients who were hospitalized at the CHUM, an extensive chart review was performed from the day of admission and for a period of 3 months following the positive ELISA assay. For each patient that was included in the study, the Greinacher clinical score was calculated. According to the clinical evolution and the laboratory results, a final, clinical, retrospective diagnosis was made for each patient (which was either HIT-positive or HIT-negative). The OD result was collected only after diagnosis was made. Results In our study, 28.8% of the patients were HIT-positive and 71.2% HIT-negative. There was a statistically significant difference in ELISA results between these two groups (Figure 1). Mean OD was 0.83 (SD ± 0.62) for HIT-negative patients, versus 2.15 (SD ± 0.76) for HIT-positive ones (P< 0.001). Figure 2 shows a distribution of patients according to Greinacher score and final diagnosis. In patients with low clinical probability, HIT-positive patients had statistically higher mean OD than HIT-negative patients (3.0 ± 0.14 versus 0.66 ± 0.36, P<0.01). This was also true for patients with intermediate probability score (1.88 ± 0.78 versus 0.67 ± 0.29, P<0.01). In high probability patients, there was only one patient with negative diagnosis. Patients were divided into 3 groups according to their OD result. There were 59 patients who had an OD of less than 1.0. Of these, only 2 (3.4%) had a positive diagnosis for HIT. In comparison, the percentage of HIT-positive patients was 45.5% (10 patients out of 22) for the group with OD 1.0–2.0, and 78.2% (18 out of 23) when OD was more than 2.0. A Roc curve (figure 3) showed that specificity of the test increased from 28% to 78% when the cut-off for OD was moved from baseline threshold to 1.0 unit. In this situation, the sensitivity of the test was only decreased to 93%. Conclusion This study is a clinical confirmation that ELISA OD results are directly correlated with the probability of a clinical diagnosis of HIT. An OD of less than 1.0 was shown to be rarely associated with clinical HIT. To the opposite, OD values above 2.0 are highly correlated with the diagnosis, and should be considered as strong evidence for clinical HIT. Furthermore, this study showed that OD results are useful when used in addition to the clinical scores. In fact, OD could discriminate between positive and negative diagnosis in patients with low and moderate clinical suspicion. Finally, the use of baseline threshold (which corresponds to OD of 0.4) was associated with a very low specificity and likelihood ratio for HIT diagnosis. By increasing the threshold to 1.0, the specificity was increased to 78.4% with a sensitivity of 93.3%. ELISA specificity and likelihood ratio were directly proportional to optic density level. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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".