Prevalence and Extent of Obstructive Coronary Artery Disease Among Patients Undergoing Elective Coronary Catheterization in New York State and Ontario
Bibliographic record
Abstract
IMPORTANCE: Prior studies have shown that physicians in New York State (New York) perform twice as many cardiac catheterizations per capita as those in Ontario for stable patients. However, the role of patient selection in these findings and their implications for detection of obstructive coronary artery disease (CAD) are largely unknown. OBJECTIVE: To evaluate the extent of obstructive CAD and to compare the probability of detecting obstructive CAD for patients undergoing cardiac catheterization. DESIGN, SETTING, AND PATIENTS: An observational study was conducted involving patients without a history of cardiac disease who underwent elective cardiac catheterization between October 1, 2008, and September 30, 2011. Obstructive CAD was defined as diameter stenosis of 50% or more in the left main coronary artery or stenosis of 70% or more in a major epicardial vessel. MAIN OUTCOMES AND MEASURES: Observed rates and predicted probabilities of obstructive CAD. Predicted probabilities were estimated using logistic regression models. RESULTS: A total of 18,114 patients from New York and 54,933 from Ontario were included. The observed rate of obstructive CAD was significantly lower in New York at 30.4% (95% CI, 29.7%-31.0%) than in Ontario at 44.8% (95% CI, 44.4%-45.3%; P < .001). The percentage of patients with left main or 3-vessel CAD was also significantly lower in New York than in Ontario (7.0% [95% CI, 6.6%-7.3%] vs 13.0% [95% CI, 12.8%-13.3%]; P < .001). In New York, a substantially higher percentage of patients with low predicted probability of obstructive CAD underwent cardiac catheterization; for example, only 19.3% (95% CI, 18.7%-19.9%) of patients undergoing cardiac catheterization in New York had a greater than 50% predicted probability of having obstructive CAD than those in Ontario at 41% (95% CI, 40.6%-41.4%; P < .001). At 30 days, crude mortality for patients undergoing cardiac catheterization was slightly higher in New York at 0.65% (90 of 13,824; 95% CI, 0.51%-0.78%) than in Ontario at 0.38% (153 of 40,794; 95% CI, 0.32%-0.43%; P < .001). CONCLUSIONS AND RELEVANCE: In Ontario compared with New York State, patients undergoing elective cardiac catheterization were significantly more likely to have obstructive CAD. This appears to be related to a higher percentage of patients in New York with low predicted probability of CAD undergoing cardiac catheterization.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".