985. Impact of Age and Medical Comorbidities on Renal Outcomes in the DISCOVER Trial
Bibliographic record
Abstract
Abstract Background In DISCOVER, emtricitabine/tenofovir alafenamide (F/TAF) was noninferior to F/tenofovir disoproxil fumarate (TDF) for preexposure prophylaxis (PrEP) in men who have sex with men and transgender women, with a superior renal laboratory profile. The differential impact of F/TAF and F/TDF on renal parameters among older individuals and those with medical comorbidities is unknown. Methods DISCOVER randomized participants 1:1 to daily blinded F/TAF or F/TDF. We examined renal outcomes at week 48 including estimated glomerular filtration rate (eGFR) by Cockcroft-Gault, β2 microglobulin (M):Creatinine (Cr) and retinol binding protein (RBP):Cr ratios (markers of proximal renal tubular function), and discontinuations due to investigator reported study drug-related renal adverse events (AEs). Results Median age was 34 years (yrs)(range 18-76), with 12.5% vs 10.9% < 25yrs, 12.2% vs 14.4% ≥ 50yrs, and 1.1% vs 0.9% > 65yrs for F/TAF and F/TDF, respectively. The prevalence of medical comorbidities at baseline were; eGFR < 90mL/min= 9.1% vs 9.3%, diabetes= 2.9% vs 3.3%, and hypertension= 10.5 vs 11.1%, for F/TAF and F/TDF, respectively. eGFR changes by age category and medical comorbidity status are found in the Table. Forty participants had study drug-related renal AEs; 14 with F/TAF and 26 with F/TDF. Of these, 25% were > 50yrs, 20% had baseline eGFR < 90mL/min, 7.5% had history of diabetes, and 22.5% had history of hypertension. β2M:Cr and RBP:Cr changes were more favorable in participants receiving F/TAF, with greater magnitude of difference in older participants (data not shown). Table. Conclusion The DISCOVER trial allows for a large single variable comparison of the two tenofovir prodrugs in the absence of underlying HIV infection and in the absence of third antiretroviral agents. F/TAF was associated with favorable changes in renal biomarkers regardless of age or medical comorbidity. Participants ≥50yrs or with comorbidities were proportionately more likely to develop study drug related renal AEs, but these were present in the minority of cases. Disclosures Eric Daar, MD, BMS (Consultant)Gilead Sciences Inc. (Consultant, Grant/Research Support, Scientific Research Study Investigator)Janssen (Consultant)Merck (Consultant, Grant/Research Support)Teva (Consultant)Theratechnology (Consultant)Viiv Healthcare (Consultant, Grant/Research Support) Jason Brunetta, MD, AbbVie (Consultant)Gilead Sciences Inc. (Consultant, Grant/Research Support, Scientific Research Study Investigator, Speaker’s Bureau, Other Financial or Material Support, Conference attendance sponsorship)Janssen (Other Financial or Material Support, Conference attendance sponsorship)Merck (Consultant, Speaker’s Bureau, Other Financial or Material Support, Conference attendance sponsorship)Viiv Healthcare (Consultant, Speaker’s Bureau, Other Financial or Material Support, Conference attendance sponsorship) Eric Cua, MD, Gilead Sciences Inc. (Scientific Research Study Investigator) David Asmuth, MD, Gilead Sciences Inc. (Scientific Research Study Investigator) Christoph C. Carter, MD, Gilead Sciences Inc. (Employee, Shareholder) Yongwu Shao, PhD, Gilead Sciences Inc. (Employee, Shareholder) Pamela Wong, MPH, Gilead Sciences Inc. (Employee, Shareholder) Ramin Ebrahimi, MSc, Gilead Sciences Inc. (Employee, Shareholder) Moupali Das, MD, Gilead Sciences Inc. (Employee, Shareholder) Diana M. Brainard, MD, Gilead Sciences (Employee) Amanda Clarke, MD, Gilead Sciences Inc. (Consultant, Scientific Research Study Investigator, Other Financial or Material Support, Conference attendance sponsorship)Viiv Healthcare (Consultant, Other Financial or Material Support, Conference travel sponsorship)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".