Abstract 11790: New and Old Cardiovascular Disease Risk Models to Predict Events in HIV Infected Patients
Bibliographic record
Abstract
Background: The high incidence of cardiovascular events in HIV infected patients creates the important challenge of performing an accurate risk assessment and initiating treatment in the most appropriate patients. The new Pooled Equations (PE) recommended by the recent ACC/AHA guidelines have not been tested in HIV infected patients and compared with the older Framingham Risk Score (FRS). Method: Cohort of 2,550 HIV infected patients (34% women) followed prospectively for a total of 10,695 patient-years. We compared the 10-year risk of events of 7.5% with PE versus a FRS level of 6%, for the dual purpose of assessing the validity of the new algorithm in HIV and verify whether lowering the threshold of the older algorithm may be equivalent to the new method. Results: Mean age was 49.7+/-7 years, and mean HIV exposure was 16.6+/-6 years. Total follow-up was 10,695 patient-years and 67 non-fatal myocardial infarctions and 2 CV deaths were recorded. PE>7.5% and FRS>6% predicted (44/69 and 49/69) and missed (25/69 and 20/69) the same number of true events. The Net Reclassification Index showed that PE>7.5% is weaker than FRS>6% to predict events (7% fewer events predicted) but better to predict non-events (14% more cases predicted to not occur). Table 1 shows the recommendation for statin therapy according to PE and FRS compared to current clinical practice within our institution: the 2 algorithms were not superior to what is currently implemented at our institution. Conclusions: In HIV infected patients the new PE>7.5% algorithm performs similarly to a FRS>6% to predict events but is better than the older algorithm to predict non-events. Neither model is superior to clinical practice as a method to select patients who should receive risk reduction therapies. (p-value for comparison of recommended vs actual statin prescription)
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".