Prevalence and outcomes of type 2 myocardial infarction in patients with cancer: A retrospective analysis from the National Inpatient Sample dataset
Bibliographic record
Abstract
BACKGROUND: This study aimed to investigate the prevalence, clinical characteristics and outcomes of type 2 myocardial infarction (T2AMI) in patients with versus without cancer. METHODS: All hospitalizations with a primary discharge diagnosis of T2AMI were stratified according to cancer status (secondary diagnosis of any-cancer vs cancer-free) using data from the US National Inpatient Sample (2016-2019). The primary outcome was in-hospital all-cause mortality while secondary outcomes were in-hospital major adverse cardiovascular and cerebrovascular events (MACCE). RESULTS: Among 61,305 included hospitalizations with primary diagnosis of T2AMI, 3745 (6.1%) were associated with a diagnosis of cancer. Patients with T2AMI and cancer presented more frequently with acute respiratory failure (23.2% vs 18.1%), acute pulmonary embolism (3.7% v 1.3%), major bleeding (6.8% vs 4.1%) and renal failure (51.0% vs 46.8%), compared to patients without. On adjusted analysis, diagnosis of cancer was associated with lower odds of invasive coronary angiography (aOR 0.75, 95% CI 0.60 to 0.93, p = 0.009) but greater odds of mortality (aOR 1.95, 95% C.I. 1.26-2.99 p = 0.002). Among the different types of cancer, adjusted risk of all-cause mortality was higher in patients with colorectal (aOR 4.17 95% CI 1.68-10.32, p = 0.002), lung (aOR 3.63, 95% CI 1.83-7.18, p < 0.001) and haematologic (aOR 2.48, 95% CI 1.22-5.05, p = 0.001) cancer. CONCLUSIONS: Patients with cancer presenting with T2AMI have lower odds of management with invasive diagnostic coronary angiography and have higher rates of in-hospital all-cause death. Further studies are warranted to improve overall care and outcomes of cancer patients and cardiovascular diseases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".