Angina Severity, Mortality, and Healthcare Utilization Among Veterans With Stable Angina
Bibliographic record
Abstract
Background Canadian Cardiovascular Society (CCS) angina severity classification is associated with mortality, myocardial infarction, and coronary revascularization in clinical trial and registry data. The objective of this study was to determine associations between CCS class and all-cause mortality and healthcare utilization, using natural language processing to extract CCS classifications from clinical notes. Methods and Results In this retrospective cohort study of veterans in the United States with stable angina from January 1, 2006, to December 31, 2013, natural language processing extracted CCS classifications. Veterans with a prior diagnosis of coronary artery disease were excluded. Outcomes included all-cause mortality (primary), all-cause and cardiovascular-specific hospitalizations, coronary revascularization, and 1-year healthcare costs. Of 299 577 veterans identified, 14 216 (4.7%) had ≥1 CCS classification extracted by natural language processing. The mean age was 66.6±9.8 years, 99% of participants were male, and 81% were white. During a median follow-up of 3.4 years, all-cause mortality rates were 4.58, 4.60, 6.22, and 6.83 per 100 person-years for CCS classes I, II, III, and IV, respectively. Multivariable adjusted hazard ratios for all-cause mortality comparing CCS II, III, and IV with those in class I were 1.05 (95% CI, 0.95-1.15), 1.33 (95% CI, 1.20-1.47), and 1.48 (95% CI, 1.25-1.76), respectively. The multivariable hazard ratio comparing CCS IV with CCS I was 1.20 (95% CI, 1.09-1.33) for all-cause hospitalization, 1.25 (95% CI, 0.96-1.64) for acute coronary syndrome hospitalizations, 1.00 (95% CI, 0.80-1.26) for heart failure hospitalizations, 1.05 (95% CI, 0.88-1.25) for atrial fibrillation hospitalizations, 1.92 (95% CI, 1.40-2.64) for percutaneous coronary intervention, and 2.51 (95% CI, 1.99-3.16) for coronary artery bypass grafting surgery. Conclusions Natural language processing-extracted CCS classification was positively associated with all-cause mortality and healthcare utilization, demonstrating the prognostic importance of anginal symptom assessment and documentation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".