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Record W4312192520 · doi:10.1016/j.eclinm.2022.101792

Meaningful cognitive decline is uncommon in virally suppressed HIV, but sustained impairment, subtle decline and abnormal cognitive aging are not

2022· article· en· W4312192520 on OpenAlexfundno aff
Htein Linn Aung, Krista J. Siefried, Thomas M. Gates, Bruce J. Brew, Limin Mao, Andrew Carr, Lucette A. Cysique

Bibliographic record

VenueEClinicalMedicine · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilBalnaves FoundationDepartment of Health, State Government of VictoriaQueensland HealthMinistry of Health, British ColumbiaGilead SciencesU.S. Department of Health and Human Services
KeywordsMedicineCognitive declineContext (archaeology)CognitionHuman immunodeficiency virus (HIV)Antiretroviral therapyCognitive impairmentCognitive agingDiseaseGerontologyDementiaImmunologyViral loadPsychiatryPathology

Abstract

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Background: High antiretroviral therapy (ART) coverage and viral suppression among people with HIV (PWH) in Australia provide a unique context to study individual cognitive trajectories, cognitive aging and factors associated with longitudinal cognitive function during chronic and stable HIV disease. Methods: Participants from the Predictors of Adherence to Antiretroviral Therapy study (n = 457, recruited between September 2013 and November 2015, median age = 52 years, and all with HIV RNA <50 copies mL) completed a cognitive assessment with CogState Computerized Battery (CCB) at baseline, Month-12, and Month-24. Demographics, psycho-social and socioeconomic factors, healthcare seeking behaviors, HIV disease characteristics and comorbidities were assessed. The CCB data were corrected for age, sex and practice effect and averaged into a global z-score (GZS). Cognitive impairment was defined with the global deficit score method (GDS>0.5). Meaningful cognitive change was statistically defined (decline or improvement versus stability, i.e., 90% CI, that is p < 0.05, 2-tailed) using a novel evidence-based change score: the linear mixed-effect regression (LMER)-based GZS change score. A separate LMER model with a top-down variable selection approach identified the independent effects of age and other demographic, HIV disease characteristics, socioeconomic and health-related factors on the demographically corrected GZS. The combined definitions of change and cross-sectional impairment enabled the identification of cognitive trajectories. Findings: At Month-12 and Month-24, 6% and 7% showed meaningful cognitive decline and 4% and 3% improved respectively. Only 1% showed sustained decline. Incident impairment due to subtle cognitive decline (i.e., below the threshold of meaningful cognitive decline) was 31% and 25% at Month-12 and Month-24, while 14% showed sustained impairment (i.e., cognitively impaired at all study visits). Older age (≥50 years) and time interaction was associated with lower demographically corrected GZS (β = -0.31, p < 0.001). Having a regular relationship, excellent English proficiency, and perceived stigma (avoidance) were associated with higher GZS (all p < 0.05). Relying on government subsidy, severe depression, and lower belief in ART necessity and higher concerns were associated with lower GZS (all p < 0.05). No HIV disease characteristics had a significant effect. Interpretations: Meaningful cognitive decline was not different from normal expectation in chronic stable HIV disease. Despite this, subtle cognitive decline, sustained cognitive impairment, and greater than normative-age cognitive aging were evident. Funding: Funding for the PAART study was provided in part by unrestricted educational grants from Gilead Sciences (www.gilead.com) (Grant Number: IN-AU-264- 0131), the Balnaves Foundation (www.balnavesfoundation.com), the Victorian Department of Health and Human Services (Australia) (www.dhs.vic.gov.au/home), Western Australia Health (www.health.wa.gov.au), the ACT Ministry of Health (Australia) (www.health.act.gov.au), and in-kind support from the Queensland Department of Health (Australia) (www.health.qld.gov.au), and NHMRC Partnership grant APP1058474 (PI: Carr, Andrew).

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.317
Teacher spread0.293 · 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 teacher head, not a consensus.

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

Citations29
Published2022
Admission routes1
Has abstractyes

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