Impact of cancer diagnosis on distribution and trends of cardiovascular hospitalizations in the USA between 2004 and 2017
Bibliographic record
Abstract
BACKGROUND AND AIMS: There is limited data on temporal trends of cardiovascular hospitalizations and outcomes amongst cancer patients. We describe the distribution, trends of admissions, and in-hospital mortality associated with key cardiovascular diseases among cancer patients in the USA between 2004 and 2017. METHODS: Using the Nationwide Inpatient Sample we, identified admissions with five cardiovascular diseases of interest: acute myocardial infarction (AMI), pulmonary embolism (PE), ischaemic stroke, heart failure, atrial fibrillation (AF) or atrial flutter, and intracranial haemorrhage. Patients were stratified by cancer status and type. We estimated crude annual rates of hospitalizations and annual in-hospital all-cause mortality rates. RESULTS: From >42.5 million hospitalizations with a primary cardiovascular diagnosis, 1.9 million (4.5%) had a concurrent record of cancer. Between 2004 and 2017, cardiovascular admission rates increased by 23.2% in patients with cancer, whilst decreasing by 10.9% in patients without cancer. The admission rate increased among cancer patients across all admission causes and cancer types except prostate cancer. Patients with haematological (9.7-13.5), lung (7.4-8.9), and GI cancer (4.6-6.3) had the highest crude rates of cardiovascular hospitalizations per 100 000 US population. Heart failure was the most common reason for cardiovascular admission in patients across all cancer types, except GI cancer (crude admission rates of 13.6-16.6 per 100 000 US population for patients with cancer). CONCLUSIONS: In contrast to declining trends in patients without cancer, primary cardiovascular admissions in patients with cancer is increasing. The highest admission rates are in patients with haematological cancer, and the most common cause of admission is heart failure.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".