Evaluation of the Atherogenic Index of Plasma to Predict All-Cause Mortality in Elderly With Acute Coronary Syndrome: A Long-Term Follow-Up
Bibliographic record
Abstract
The Atherogenic Index of Plasma (AIP) is associated with coronary artery disease (CAD) and acute coronary syndrome (ACS), but the relationship between AIP and ACS in elderly patients remains unclear. We investigated the prognostic capability of AIP for in-hospital and long-term mortality in elderly patients with ACS undergoing coronary angiography (CA). We analyzed 627 patients with ACS over 75 years of age who were admitted to our clinic between April 2015 and December 2022 and underwent CA. The primary clinical endpoints were in-hospital, 30-day, 1-year, and long-term mortality. The median follow-up time was 27 months. AIP was defined as log (triglyceride/high-density lipoprotein cholesterol). In-hospital mortality rates for patients with AIP ≤.1 and AIP >.1 were 4.7% and 17.6% ( P < .001), 30-day mortality rates were 8.7% and 32.2% ( P = .01), 1-year mortality rates were 12.1% and 45.1% ( P < .001), and long-term mortality rates were 47.3% and 67.5% ( P < .001), respectively. Multivariate Cox regression analysis revealed AIP, age, left ventricle ejection fraction (LVEF), admission creatinine, and Killip ≥2 as independent predictors for long-term mortality. AIP can predict in-hospital and long-time all-cause mortality in elderly patients with ACS undergoing CA. Age, LVEF, admission creatinine, and Killip ≥2 are additional factors that predict long-term all-cause mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".