Cancer and the risk of perioperative arterial ischaemic events
Bibliographic record
Abstract
BACKGROUND AND AIMS: Most cancer patients require surgery for diagnosis and treatment. This study evaluated whether cancer is a risk factor for perioperative arterial ischaemic events. METHODS: The primary cohort included patients registered in the National Surgical Quality Improvement Program (NSQIP) between 2006 and 2016. The secondary cohort included Healthcare Cost and Utilization Project (HCUP) claims data from 11 US states between 2016 and 2018. Study populations comprised patients who underwent inpatient (NSQIP, HCUP) or outpatient (NSQIP) surgery. Study exposures were disseminated cancer (NSQIP) and all cancers (HCUP). The primary outcome was a perioperative arterial ischaemic event, defined as myocardial infarction or stroke diagnosed within 30 days after surgery. RESULTS: Among 5 609 675 NSQIP surgeries, 2.2% involved patients with disseminated cancer. The perioperative arterial ischaemic event rate was 0.96% among patients with disseminated cancer vs. 0.48% among patients without (hazard ratio [HR], 2.01; 95% confidence interval [CI], 1.90-2.13). In Cox analyses adjusting for demographics, functional status, comorbidities, surgical specialty, anesthesia type, and clinical factors, disseminated cancer remained associated with higher risk of perioperative arterial ischaemic events (HR, 1.37; 95% CI, 1.28-1.46). Among 1 341 658 surgical patients in the HCUP cohort, 11.8% had a diagnosis of cancer. A perioperative arterial ischaemic event was diagnosed in 0.74% of patients with cancer vs. 0.54% of patients without cancer (HR, 1.35; 95% CI, 1.27-1.43). In Cox analyses adjusted for demographics, insurance, comorbidities, and surgery type, cancer remained associated with higher risk of perioperative arterial ischaemic events (HR, 1.31; 95% CI, 1.21-1.42). CONCLUSION: Cancer is an independent risk factor for perioperative arterial ischaemic events.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".