Characteristics, management, and outcome of patients with acute coronary syndromes and cancer: Insights from the EYESHOT-2 registry
Bibliographic record
Abstract
Abstract Background An increasing proportion of patients with acute coronary syndrome (ACS) presents with concomitant active or recent cancer (1). The contemporary in-hospital management and outcome of these patients have been poorly investigated (2-6). Purpose to compare the characteristics, management and outcome of ACS patients with and without cancer enrolled in the EYESHOT-2 registry (7). Methods The EYESHOT-2 was an observational, nationwide study conducted over a 4-week period (February 1st-29th, 2024) in 183 Italian coronary care units. In the present analysis, the 2806 consecutive ACS patients enrolled, were divided into 3 groups: - active cancer or history of cancer < 2 years (cancer group); - previous cancer >2 years (previous cancer group); no cancer in history (no cancer group). Results There were 98 patients with cancer (3.5%), 135 patients with previous cancer (4.8%), and 2564 patients with no history of cancer (91.4%). The "cancer" group was older and had comparable cardiovascular risk profiles and comorbidities to the other patients (Table 1). Of the 98 patients with active cancer, 85.3% had a solid tumor and 14.7% had a hematologic tumor. In addition, 32.6% of patients with active cancer had stage IV disease, and 60% were treated with pharmacologic therapy, 30% with surgery, and 7.1% with radiotherapy. The most commonly used oncological drug was androgen deprivation therapy (15.2%), followed by endocrine therapy (12.1%). Among STEMI patients, a percutaneous coronary intervention (PCI) was performed in 94%, 97% and 96% of patients without, with previous and with cancer, respectively (p=0.7), with a median time between hospital arrival and coronary angiography of 3.6 (interquartile range (IQR) 0.5-2.2)(no cancer), 3 (IQR 0.4-1.9) (previous cancer), 8 (IQR 0.4-2.4) hours (cancer) (p= 0.6). In NSTEMI, a PCI was performed in 73%, 74% and 70% of patients without, with previous and with active cancer, respectively (p=0.9); the median time between hospital arrival and coronary angiography was 37 (IQR 10-45) (no cancer), 40 (IQR 11-52) (previous cancer) and 53 (IQR 13-52) hours (cancer) (p= 0.2). In both STEMI and NSTEMI settings, all-cause in-hospital mortality and major bleeding rates were comparable between the 3 groups (Table 1). Conclusion Differently from previous findings of the literature, in the present analysis of a contemporary, nationwide registry of ACS we find no difference in the characteristics, management, and outcomes of patients with and without cancer.Table 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".