Value of serum pregnancy-associated plasma protein A for predicting cardiovascular events among patients presenting with cardiac chest pain
Bibliographic record
Abstract
BACKGROUND: Pregnancy-associated plasma protein A (PAPP-A) has been suggested as a candidate marker for the identification of unstable plaques in coronary arteries. We assessed the value of PAPP-A for predicting short-term cardiovascular events in a large cohort of patients presenting with cardiac chest pain. METHODS: We included consecutive patients who presented to a teaching hospital in Germany with chest pain of cardiac origin confirmed by coronary angiography. We analyzed PAPP-A levels from serum samples drawn within 30 minutes after arrival in the emergency department or in the catheterization laboratory. Patients were followed for 90 days or until death for major adverse cardiovascular events, defined as the combined outcome of stent thrombosis, myocardial (re)infarction, ischemic stroke or cardiovascular-related death. RESULTS: A total of 2568 patients (mean age [± standard deviation (SD)] 68 ± 11 years; 74% male) presented with cardiac chest pain: 55% had stable angina and 45% had acute coronary syndrome. The PAPP-A levels ranged from 4 to 2154 mIU/L (median 14.0 mIU/L, interquartile range 9.3-25.2 mIU/L). Major adverse cardiovascular events occurred in 203 patients (7.9%). The mean PAPP-A level was higher among patients who had an event than among those who did not (62 ± 156 v. 21 ± 23 mIU/L, p < 0.001). In a multivariable analysis, PAPP-A remained a significant independent predictor of the primary outcome within 90 days (hazard ratio per 1 SD increase in PAPP-A level 1.96, 95% confidence interval 1.74-2.21). The optimal prognostic cutoff value was a PAPP-A level of 34.6 mIU/L. INTERPRETATION: Higher levels of serum PAPP-A were independently associated with an increased short-term risk of cardiovascular events in patients presenting with cardiac chest pain. Further studies are required to validate the use of PAPP-A in routine clinical practice to predict future cardiovascular events.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".