5. How Does Frailty Impact the Efficacy, Reactogenicity, Immunogenicity and Safety of the Adjuvanted Recombinant Zoster Vaccine? A Secondary Analysis of the ZOE-50 and ZOE-70 Studies
Bibliographic record
Abstract
Abstract Background Herpes zoster can negatively impact older adults’ health and quality of life. An adjuvanted recombinant zoster vaccine (RZV) has excellent vaccine efficacy (VE), including in older adults. Given that frailty is strongly associated with vulnerability to illness and adverse health outcomes, we studied how frailty impacts RZV VE, immunogenicity, reactogenicity, and safety. Methods In the ZOE-50 and ZOE-70 pivotal Phase 3 efficacy studies of RZV, 29,305 participants aged 50–96 received 2 doses of RZV vs. placebo in 1:1 randomization. In this secondary analysis (NCT03563183), a baseline frailty index (FI) was created retrospectively following previously validated methods using pre-existing comorbidities and patient reported outcomes. Participants were categorized as non-frail (FI≤ 0.08), pre-frail (FI=0.08–0.25) or frail (FI≥ 0.25) for stratified analyses. Results FI was calculated for 99.8% of participants included in this secondary analysis (n=26,976), and was balanced between RZV and placebo groups. 45.6% were pre-frail and 11.3% were frail. Mean age was 68.8 years; 58.1% were women. RZV VE against HZ was consistently above 90% for all frailty categories [non-frail: 95.8% (95%CI: 91.6–98.2), pre-frail: 90.4% (84.4–94.4), frail: 90.2% (75.4–97.0)]. The RZV group demonstrated robust antibody responses post-dose 2 across frailty categories. In the RZV group, the percentage of participants reporting solicited adverse events decreased with increasing frailty. Unsolicited medically attended visits and serious adverse events increased with frailty and were balanced between placebo and RZV groups. Conclusion The ZOE studies included older adults who were frail and pre-frail, and VE was high across frailty categories. Reactogenicity decreased with increasing frailty, and no safety concerns were identified in any frailty group. Disclosures Melissa K. Andrew, MD, PhD, MSc(Ph), GSK (Grant/Research Support, Research Grant or Support) Joon Hyung Kim, MD, GSK (Employee, Shareholder) Sean Matthews, MSc, GSK (Consultant) Christophe Dessart, MSc, GSK (Employee) myron J. levin, MD, Curevo (Advisor or Review Panel member)GlaxoSmithKline (Grant/Research Support, Advisor or Review Panel member)GlaxoSmithKline (Grant/Research Support, Advisor or Review Panel member)Merck Research Laboratories (Advisor or Review Panel member, GlaxoSmithKline)Merck Research Laboratories (Advisor or Review Panel member)Merck ResearchLaboratories (Advisor or Review Panel member) Lidia Oostvogels, MD, GSK (Shareholder) Megan Riley, PhD, GSK (Employee) Shelly McNeil, FRCPC, MD, GSK (Grant/Research Support, Scientific Research Study Investigator, Research Grant or Support, Other Financial or Material Support, honoraria for talks) Anne Schuind, MD, GSK (Employee, Other Financial or Material Support, own GSK stock options or restricted shares as part of renumeration) Desmond Curran, PhD, GSK (Employee, Shareholder)
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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 | 0.039 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.012 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".