Expanded Multivariable Models to Assist Patient Selection for Long-Acting Cabotegravir + Rilpivirine Treatment: Clinical Utility of a Combination of Patient, Drug Concentration, and Viral Factors Associated With Virologic Failure
Bibliographic record
Abstract
BACKGROUND: Previously reported post hoc multivariable analyses exploring predictors of confirmed virologic failure (CVF) with cabotegravir + rilpivirine long-acting (CAB + RPV LA) were expanded to include data beyond week 48, additional covariates, and additional participants. METHODS: Pooled data from 1651 participants were used to explore dosing regimen (every 4 or every 8 weeks), demographic, viral, and pharmacokinetic covariates as potential predictors of CVF. Prior dosing regimen experience was accounted for using 2 populations. Two models were conducted in each population-baseline factor analyses exploring factors known at baseline and multivariable analyses exploring baseline factors plus postbaseline model-predicted CAB/RPV trough concentrations (4 and 44 weeks postinjection). Retained factors were evaluated to understand their contribution to CVF (alone or in combination). RESULTS: Overall, 1.4% (n = 23/1651) of participants had CVF through 152 weeks. The presence of RPV resistance-associated mutations, human immunodeficiency virus-1 subtype A6/A1, and body mass index ≥30 kg/m2 were associated with an increased risk of CVF (P < .05 adjusted incidence rate ratio), with participants with ≥2 of these baseline factors having a higher risk of CVF. Lower model-predicted CAB/RPV troughs were additional factors retained for multivariable analyses. CONCLUSIONS: The presence of ≥2 baseline factors (RPV resistance-associated mutations, A6/A1 subtype, and/or body mass index ≥30 kg/m2) was associated with increased CVF risk, consistent with prior analyses. Inclusion of initial model-predicted CAB/RPV trough concentrations (≤first quartile) did not improve the prediction of CVF beyond the presence of a combination of ≥2 baseline factors, reinforcing the clinical utility of the baseline factors in the appropriate use of CAB + RPV LA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".