Estimated 10-year risk of recurrent cardiovascular events and potential health benefits of secondary prevention in people with HIV
Bibliographic record
Abstract
OBJECTIVE: People with HIV (PWH) with atherosclerotic cardiovascular disease (ASCVD) are at high risk of recurrent cardiovascular events. However, its drivers and impact of specific interventions are largely unknown. Therefore, we estimated the 10-year recurrence risk and the potential benefits of guideline-recommended interventions in a large cohort of PWH and controls. DESIGN: PWH from the ATHENA cohort with prior ASCVD were included and 1 : 1 matched for nonmodifiable risk factors (age, sex, type of ASCVD manifestation, and years since first event) to controls without HIV from the UCC-SMART cohort. METHODS: The SMART2 model was applied to estimate the 10-year risk of recurrent cardiovascular events. Subsequently, the effects of the following interventions were estimated: smoking cessation, initiation of antithrombotics, achieving systolic blood pressure <140 mmHg, and achieving low-density lipoprotein cholesterol (LDL-c) <1.4 mmol/l. RESULTS: A total of 1247 PWH and 1247 matched controls were included. The estimated 10-year recurrence risk in PWH was significantly higher [22% (IQR 16-31%) versus 20% (IQR 14%-29%)], primarily driven by a higher smoking prevalence. Attainment of targets potentially averts up to 113 events per 1000 PWH treated, largely attributed to smoking cessation and lipid-lowering treatment (35 and 46 averted events). In the highest-risk PWH, half of the recurrent events might be prevented with intensified treatment. CONCLUSIONS: The estimated 10-year risk of recurrent cardiovascular events in PWH with prior ASCVD exceeds that of matched individuals without HIV. Our results indicate that stringent adherence to risk factor-targeted interventions is key and could avert many events in this at-risk population.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".