18. An Easy-to-Implement Clinical-Trial Frailty Index Based on Accumulation of Deficits: Validation in Zoster Clinical Trials
Bibliographic record
Abstract
Abstract Background The impact of frailty on the efficacy and safety of vaccines and therapeutic interventions is increasingly recognized, yet assessment of frailty in clinical trials is often considered logistically challenging. We developed the retrospective Clinical Trial Frailty Index (CT-FI), using baseline medical history and patient reported outcomes collected via standard instruments (Short Form Survey-36 and Euro Quality of Life-5 Dimension) in two clinical trials of the adjuvanted recombinant zoster vaccine (RZV, ZOE-50 [NCT01165177] and ZOE-70 [NCT01165229]). This post-hoc analysis aimed to show that CT-FI is a robust measure that may be used in any analysis where sufficient patient data has been collected in a clinical trial. Methods Items included in the CT-FI were scored from 0 to 1, summed for each participant and divided by the total number of potential deficits. CT-FI was validated using descriptive methods verifying distribution and age- and sex-associations in relation to established FI characteristics, Cox regressions in relation to fatal outcomes hypothetically related to frailty, and re-sampling methods (Jackknife and Bootstrap procedures) within the FI to demonstrate robustness to inclusion/exclusion of specific individual variables. Results The CT-FI distribution followed a gamma distribution with a range of 0 to 0.695; the distribution shifted to the right with age. The age-related slope of mean deficit accumulation per year increased with chronological age and was higher for women than men. The rate of mean deficit accumulation was 0.0025 for women vs 0.0016 for men < 70 years of age, and this increased to 0.0058 for women vs 0.0047 for men ≥70 years of age. In univariate and multivariate Cox regression survival analyses, FI, chronological age and sex were significant predictor factors for mortality. The Jackknife and Bootstrap re-sampling methods showed that the performance of CT-FI was not sensitive to inclusion/exclusion of specific individual or groups of variables, demonstrating the robustness of this methodology. Conclusion The current analysis validates that CT-FI, an easy-to-implement FI, is a robust method which allows retrospective/prospective evaluation of clinical outcomes by frailty status in clinical trials. Disclosures Melissa K. Andrew, MD, PhD, GSK (Grant/Research Support)Pfizer (Grant/Research Support, Advisor or Review Panel member)Sanofi (Consultant, Grant/Research Support, Advisor or Review Panel member)Seqirus (Advisor or Review Panel member) Sean Matthews, MSc, GSK (Independent Contractor) Joon Hyung Kim, MD, GSK group of companies (Employee, Shareholder) Megan Riley, PhD, GSK group of companies (Employee, Shareholder) Desmond Curran, PhD, The GSK group of companies (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 | 0.148 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".