Cardiovascular risk evaluation and antiretroviral therapy effects in an HIV cohort: implications for clinical management: the CREATE 1 study
Bibliographic record
Abstract
AIMS: The aim of this study is to determine the cardiovascular disease (CVD) risk profile of a large UK HIV cohort and how highly active antiretroviral therapy (HAART) affects this. METHODS: It is a cross-sectional study within a large inner city hospital and neighbouring district hospital. A total of 1021 HIV positive outpatients representative of the complete cohort and 990 who had no previous CVD were included in CVD risk analysis. We recorded demographics, HAART history and CVD risk factors. CVD and coronary heart disease (CHD) risks were calculated using the Framingham (1991) algorithm adjusted for family history. RESULTS: The non-CVD cohort (n = 990) was 74% men, 51% Caucasian and 73.1% were on HAART. Mean age was 41 +/- 9 years, systolic blood pressure 120 +/- 14 mmHg, total cholesterol 4.70 +/- 1.05 mmol/l, high-density lipoprotein-C 1.32 +/- 0.48 mmol/l and 37% smoked. Median CVD risk was 4 (0-56) % in men and 1.4 (0-37) % in women; CHD risks were 3.5 (0-36) % and 0.6 (0-16) %. CVD risk was > 20% in 6% of men and 1% of women and > 10% in 12% of men and 4% of women. CVD risk was higher in Caucasians than other ethnicities; the risk factor contributing most was raised cholesterol. For patients on their first HAART, increased CHD risk (26.2% vs. 6.5%; odds ratio 4.03, p < 0.001) was strongly related to the duration of therapy. CONCLUSIONS: Modifiable risk factors, especially cholesterol, and also duration of HAART, were key determinants of CVD risk. DISCUSSION: Regular CHD and/or CVD risk assessment should be performed on patients with HIV, especially during HAART therapy. The effect of different HAART regimens on CHD risk should be considered when selecting therapy.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".