The Pandemic H1N1 Influenza Vaccine Results In Low Rates of Seroconversion In Patients with Hematological Malignancies
Bibliographic record
Abstract
Abstract Abstract 3110 Background: Patients with hematological malignancies are at increased risk of influenza and its complications. However, evidence for the efficacy of influenza vaccination in this population is limited and contradictory [Pollyea et al., J Clin Oncol. 2010]. The adjuvanted pandemic H1N1 vaccine has been shown to be highly effective in healthy adults, with reported rates of seroprotection and seroconversion of over 90% [Plennevaux et al., Lancet 2010]. We sought to determine whether patients being treated for hematological malignancies were able to mount a protective antibody response to the H1N1 pandemic influenza vaccine. Methods: Patients being treated for hematological malignancies at the London Regional Cancer Program during the 2009–2010 influenza season were invited to participate. Patients who had received the vaccine prior to the commencement of the study in November 2009 were excluded. Pre-vaccination plasma samples were collected in November 2009, and post-vaccination samples were collected from January through March 2010. At the time of second sample collection, patients were asked to complete a questionnaire asking if and when they had received the H1N1 influenza vaccine. Plasma samples from patients who elected not to be vaccinated formed a control group. Antibody titration was performed by the hemagglutinin inhibition test. Our primary outcome was the rate of seroconversion, as defined by a fourfold increase in antibody titres. We also measured geometric mean titres (GMT), geometric mean titre ratios, (GMTR, defined as the ratio of the post-vaccination titre to the pre-vaccination titre), and rates of seroprotection (titre ≥ 1:80). Statistical analysis was done using Mann-Whitney U, chi-squared, or Fisher's Exact Tests, as appropriate. Results: Sixty-two patients received the H1N1 vaccine and 41 patients chose not to be vaccinated. The rate of seroconversion among vaccinated patients was 21%, which was significantly higher than that in unvaccinated patients (0%) and significantly lower than that in healthy individuals. The GMTR was significantly higher in the vaccinated group than the unvaccinated group (2.2 ± 2.5 vs. 1.2 ± 0.6, p = 0.041). There were no significant differences in the geometric mean titres or the rates of seroprotection between the vaccinated and unvaccinated groups. Of the 46 patients on active chemotherapy who received the vaccine, 10 (22%) seroconverted and 16 (35%) mounted seroprotective titres. Of the 12 patients on active Rituximab who received the vaccine, 2 (17%) seroconverted and 4 (33%) mounted seroprotective titres. There were no significant differences in the rates of seroconversion and seroprotection between patients on or off chemotherapy or between patients on or off Rituximab. Conclusions: Only 21% of patients with hematological malignancies were able to produce a fourfold increase in antibody titres in response to the H1N1 influenza vaccine, a rate significantly lower than that previously reported for healthy patients. We were unable to identify any clinical factors predictive of a response to the vaccine. Physicians should be aware that patients with hematological malignancies are less likely to receive protection from the influenza vaccine, and should consider alternate strategies to minimize the morbidity and mortality from influenza in this population. Larger studies are indicated to confirm these results. 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".