Comprehensive coronary CT angiography in people living with HIV: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: People living with HIV (PLWH) were previously shown to have a higher prevalence of non-calcified coronary plaque with discrepant results for coronary stenosis and any plaque prevalence. This systematic review and meta-analysis summarise and estimate pooled effect sizes for observational studies among PLWH using comprehensive coronary CT angiography (CTA). DESIGN: Preferred Reporting Items for Systematic Review and Meta-analysis reporting guidelines were used. DATA SOURCES: PubMed, Embase, Web of Science, CINAHL, Cochrane Clinical Trials and EBM were searched from inception to 23 February 2024. ELIGIBILITY CRITERIA: We included studies evaluating coronary atherosclerosis in adult PLWH with controls and CTA results for plaque prevalence, extent, severity and high-degree stenosis. DATA EXTRACTION AND SYNTHESIS: Two independent reviewers used standardised methods to screen for relevance by title, abstract and full-text review. Two unblinded independent reviewers manually extracted data and rated study quality using the Newcastle-Ottawa Scale. Meta-analysis was conducted using random effects models. A sensitivity analysis was performed with a fixed effects model. Publication bias was assessed by visual inspection of funnel plots and formal testing by Egger's and Begg's tests. Segment scores were evaluated using the difference of medians. RESULTS: PLWH showed a significantly higher prevalence of non-calcified plaque (34% (95% CI: 15% to 53%)) compared with controls (22% (95% CI: 6% to 38%)) with an OR of 1.61 (1.13-2.30, p=0.009). There was no significant difference in partially calcified plaque prevalence (OR=1.20, 0.96-1.49), stenosis prevalence (OR=1.34, 0.92-1.96) and median difference in Segment Involvement Score (SIS; 0.39, -0.01 to 0.79) in PLWH compared with controls who have a non-significant difference in calcified plaque (OR=0.80, 0.61-1.04). The prevalence of any plaque (OR=1.22, 0.93-1.61) and difference in median Segment Stenosis Score (-0.12, -0.60 to 0.35) did not differ between groups. CONCLUSION: This study demonstrates a greater prevalence of non-calcified plaque in PLWH than controls. Additional research is needed to assess quantitative CTA measurements. Increased power may reveal a difference in the prevalence of high-degree stenosis and median SIS score in PWLH, while controls may have a greater prevalence of calcified plaque.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".