Sword 1 and 2: Subgroup Analysis of 48 Week Results by Age, Race and Gender
Bibliographic record
Abstract
Abstract Background Switching to the 2-drug regimen (2DR) of DTG+RPV was proven non-inferior to continuing a suppressive PI-, INI- or NNRTI- based current antiretroviral regimen (CAR) at Week 48. This analysis evaluated the efficacy and safety of switching from CAR to DTG+RPV by age, race and gender subgroups. Methods Two identically designed, open-label, multicenter, global, phase III, non-inferiority studies compared the efficacy and safety of switching from a 3 or 4-drug CAR to DTG + RPV once daily in HIV-1-infected adults, with HIV-1 RNA<50 c/mL. Primary endpoint was proportion of patients with VL<50 c/mL at Wk48 using FDA Snapshot. Additional analysis were performed to summarize efficacy base on age, race and gender subgroups for each individual study and pooled. Results 1024 patients were randomized and exposed (DTG+RPV 513; CAR 511), across both studies. Treatment arms were well matched for demographic and baseline characteristics. Median age across both arms was 43.4 years, with 29% and 28% ≥ 50 years in DTG+RPV and CAR, respectively. 23% and 21% were female while 18% and 22% were non-white for DTG+RPV and CAR. For the pooled studies and for SWORD-1 and SWORD-2 individually, switching to DTG+RPV was non-inferior to CAR at Wk48. Similar response rates were observed in the DTG+RPV arm compared with CAR across subgroups (Table 1). More AEs were reported in the DTG+/RPV arm across all subgroups except Asian race; no unexpected AEs were identified for either drug. Conclusion Switch to a novel, once daily 2DR of DTG+RPV in patients with a suppressed viral load, was an effective and well tolerated treatment option across age, race, and gender subgroups which were consistent with overall results. Disclosures S. Walmsley, Merck: Board Member, Consultant, Grant Investigator, Investigator, Scientific Advisor and Speaker’s Bureau, Consulting fee, Grant recipient, Research grant and Speaker honorarium; ViiV Healthcare: Board Member, Consultant, Grant Investigator, Investigator, Scientific Advisor and Speaker’s Bureau, Consulting fee, Grant recipient, Research grant and Speaker honorarium; Gilead Sciences: Board Member, Consultant, Grant Investigator, Investigator, Scientific Advisor and Speaker’s Bureau, Consulting fee, Grant recipient, Research grant and Speaker honorarium; GSK: Board Member, Consultant, Grant Investigator, Investigator, Scientific Advisor and Speaker’s Bureau, Consulting fee, Grant recipient, Research grant and Speaker honorarium; Janssen: Board Member, Consultant, Grant Investigator, Investigator, Scientific Advisor and Speaker’s Bureau, Grant recipient, Research grant and Speaker honorarium; BMS: Grant Investigator, Investigator, Scientific Advisor and Speaker’s Bureau, Consulting fee, Grant recipient, Research grant and Speaker honorarium; G. Richmond, Viiv Healthcare: Investigator, Research support; F. Bredeek, ViiV Healthcare: Investigator and Scientific Advisor, Consulting fee and Research support; M. Ramgopal, viiv: Investigator, Consulting fee; C. C. Hung, Gilead Sciences: Board Member and Speaker’s Bureau, Consulting fee and Speaker honorarium; ViiV: Board Member and Investigator, Consulting fee and Research support; Abbvie: Board Member and Investigator, Consulting fee and Research grant; Bristol-Myers Squibb: Investigator, Research support; Jassen: Board Member and Investigator, Consulting fee and Research support; E. Blair, ViiV Healthcare: Employee and Shareholder, Salary; L. Kahl, ViiV Healthcare: Employee and Shareholder, Salary; M. Underwood, ViiV Healthcare: Employee, Salary; K. Angelis, GlaxoSmithKline: Employee, Salary; K. Vandermeulen, Jansen: Employee, Salary; B. Wynne, ViiV Healthcare: Employee, Salary; M. Aboud, ViiV Healthcare: Employee, Salary
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.012 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".