Comorbidities in Older Persons with Controlled HIV Infection: Correlations with Frailty Index Subtypes
Bibliographic record
Abstract
Frailty is prevalent in persons with human immunodeficiency virus (PWH), but factors predisposing older PWH to frailty remain uncertain. We examined factors associated with frailty and determined whether there were multiple frailty subtypes in older adults with controlled HIV infection. This was a cross-sectional outpatient study in an urban HIV clinic. Twenty-nine clinical indicators were extracted from medical records to compute a Frailty Index (FI) for 389 older (age 50+) PWH (range = 50–93; mean = 61.1, standard deviation = 7.2; 85% men) receiving HIV treatment in Calgary, Canada. We used regressions to identify factors associated with FI values. Latent class analysis was used to identify FI subtypes. Age, employment status, and duration of known HIV infection were the strongest predictors of FI (p's < 0.05). Four FI subtypes were identified. Subtype 1 (severe metabolic dysfunction+polypharmacy) had the highest mean FI (0.30). Subtype 2 (less severe metabolic dysfunction+polypharmacy) and Subtype 3 (lung and liver dysfunction+polypharmacy) had lower but equivalent mean FIs (0.20 for each). Subtype 4 (least severe metabolic dysfunction) had the lowest mean FI (0.13; p's < 0.001). Sociodemographic and behavioral characteristics differed among the subtypes. Individuals with Subtype 1 were older and more frequently unemployed/retired, whereas those with Subtype 3 were more likely to smoke, use crack/cocaine, have heavy alcohol use, and live in temporary/unstable housing. The clinical presentation of frailty in older PWH is heterogeneous. The metabolic syndrome, hepatitis C virus coinfection, cirrhosis, lung disease, and polypharmacy were associated with frailty as were unemployment/retirement, unstable housing, and substance use. We certify that this work is novel and adds to the previous literature by identifying clinical subtypes associated with frailty in older persons living with HIV. Metabolic syndrome, liver and lung disease, and polypharmacy are associated with frailty in older persons living with HIV, as are socioeconomic factors including employment status, unstable housing, alcohol and drug use, and interpersonal violence. This knowledge will permit tailored approaches to addressing frailty in older persons with HIV.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".