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Record W4391732864

Treatment Patterns and Predictors of Adherence in HIV Patients Receiving Single- or Multiple-Tablet Darunavir, Cobicistat, Emtricitabine, and Tenofovir Alafenamide

2020· article· en· W4391732864 on OpenAlexaboutno aff
Wing Chow, Prina Donga, Carmine Rossi, Patrick Lefèbvre, MH Lafeuille, Bruno Émond

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languageen
FieldMedicine
TopicHIV/AIDS drug development and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsDarunavirTenofovir alafenamideEmtricitabineCobicistatHuman immunodeficiency virus (HIV)MedicineVirologyAntiretroviral therapyViral load
DOInot available

Abstract

fetched live from OpenAlex

Wing Chow,1 Prina Donga,1 Aurélie Côté-Sergent,2 Carmine Rossi,2 Patrick Lefebvre,2 Marie-Hélène Lafeuille,2 Hélène Hardy,2 Bruno Emond3 1Real World Value & Evidence, Janssen Scientific Affairs, LLC, Titusville, NJ, USA; 2Analysis Group, Inc., Montréal, QC, Canada; 3Early Compound Development, Janssen Research & Development, LLC, Titusville, NJ, USACorrespondence: Bruno Emond Tel +1 514-394-4455Email Bruno.Emond@analysisgroup.comPurpose: Darunavir, cobicistat, emtricitabine, and tenofovir alafenamide can be used as a single-tablet regimen (STR, DRV/c/FTC/TAF) or multiple-tablet regimen (MTR, DRV/c+FTC/TAF) to treat patients with human immunodeficiency virus (HIV). This study described treatment patterns and predictors of adherence among patients with HIV initiated on DRV/c/FTC/TAF or DRV/c+FTC/TAF.Patients and Methods: A retrospective longitudinal study was conducted using linked claims and electronic medical records from Decision Resources Group’s Real World Data Repository (7/17/2017– 6/1/2019). Treatment-naïve and treatment-experienced virologically suppressed adults with HIV-1 prescribed DRV/c/FTC/TAF or DRV/c+FTC/TAF (index date) were included. Six-month persistence (no treatment gaps > 60 and > 90 days) and adherence (proportion of days covered [PDC]) to the index regimen were evaluated among patients with ≥ 6 months of observation post-index. Predictors of low adherence (PDC< 80%) were evaluated using a logistic regression model.Results: Among 2633 eligible patients (49.5 years old, 29% female, 37% African American/Black), 12% were treatment-naïve pre-index and 88% switched from a previous antiretroviral therapy; 84% initiated DRV/c/FTC/TAF and 16% initiated DRV/c+FTC/TAF. Among 822 DRV/c/FTC/TAF patients with ≥ 6 months of observation post-index, 80% and 86% had no > 60- and > 90-day gaps in DRV/c/FTC/TAF coverage, respectively, while among 204 DRV/c+FTC/TAF patients with ≥ 6 months of observation post-index, 69% and 75% had no > 60- and > 90-day gaps in DRV/c+FTC/TAF coverage, respectively. Mean (median) PDC for the index regimen was 81% (93%) for patients treated with DRV/c/FTC/TAF and 73% (83%) for patients treated with DRV/c+FTC/TAF. Predictors of low adherence included younger age (odds ratio [OR]=2.36, p=0.017), higher Quan-Charlson comorbidity index (OR=1.32, p=0.012), use of MTR regimen at index (OR=1.69, p=0.022), and prior low adherence (OR=2.56, p< 0.001).Conclusion: Among patients initiating a DRV/c-based regimen, those initiating STR had higher 6-month adherence/persistence than those initiating MTR, highlighting the potential benefits of the STR formulation, particularly among younger patients with multiple comorbidities and prior low adherence.Keywords: HIV, protease inhibitors, treatment adherence and compliance, patient compliance, administrative claims, healthcare, electronic health records

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.169
GPT teacher head0.456
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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