Comparison of Diagnostic Parameters of Acute Coronary Syndromes in Patients with and without Cancer: A Multifactorial Analysis
Bibliographic record
Abstract
Background: The simultaneous occurrence of cancer and acute coronary syndromes (ACSs) presents a complex clinical challenge. This study clarifies variances in diagnostic parameters among ACS patients with and without concurrent cancer. Methods: This retrospective study included 320 individuals diagnosed with ACS, stratified equally into two cohorts—one with cancer and the other cancer-free. We evaluated risk factors, symptom profiles, coronary angiography results, echocardiographic evaluations, and laboratory diagnostics. Statistical analysis was performed using Student’s t-test, the Mann–Whitney U test, and the chi-square test. Results: Cancer patients were older (mean age 71.03 vs. 65.13 years, p < 0.001) and had a higher prevalence of chronic kidney disease (33.1% vs. 15.0%, p < 0.001) but a lower prevalence of hyperlipidemia (59.7% vs. 82.5%, p < 0.001). Chest pain was less frequent in cancer patients (72.5% vs. 90%, p < 0.001), while hypotension was more common (41.9% vs. 28.8%, p = 0.022). NSTEMI was more common in cancer patients (41.9% vs. 30.6%, p = 0.048), while STEMI was less common (20.6% vs. 45.3%, p < 0.001). RCA and LAD involvement were less frequent in cancer patients (RCA: 18.1% vs. 30.0%, p = 0.018; LAD: 18.8% vs. 30.0%, p = 0.026). Conclusions: This study demonstrates differences in the clinical presentation of ACS between patients with and without cancer. Cancer patients were less likely to present with chest pain and more likely to experience hypotension. Additionally, they had a higher prevalence of chronic kidney disease and they were less likely to have hyperlipidemia. These findings highlight the need for a careful approach to diagnosing ACS in oncology patients, considering their distinct symptomatology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".