Improving the Diagnosis and Management of Patients with Coronary Artery Disease
Bibliographic record
Abstract
Despite significant therapeutic advances, coronary artery disease remains the leading cause of death worldwide. Patients with chronic coronary artery disease can be managed conservatively or invasively. Conservative management involves guideline-directed medical therapy (both lifestyle and pharmacological interventions), while the invasive approach also includes undergoing coronary angiography and, if needed, revascularization with percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG). This thesis aims to address knowledge gaps in the diagnosis and management of coronary artery disease. The thesis comprises three projects, conducted using population-based registries from Ontario, Canada. These registries were linked to aggregate information on clinical characteristics, laboratory and cardiac tests, medication use, and coronary revascularization procedures. A wide range of statistical methods was employed, including regression models, propensity score-based methods, and a machine learning approach. The first project addresses a gap in coronary artery disease diagnosis. This study attempted to derive a prediction model for left main coronary artery disease, a high-risk lesion that would benefit from invasive management. Despite using a wide range of predictors and a large sample size, the models were unlikely to have sufficient accuracy to be deployed in clinical practice. The second project focuses on the medical management of coronary artery disease. This study investigated if beta-blockers, widely used for angina control, could improve cardiovascular outcomes in patients with chronic coronary artery disease without heart failure or a recent myocardial infarction. After a median follow-up of five years, beta-blockers were associated with a small but significant reduction in the composite of all-cause mortality, hospitalization for heart failure, or myocardial infarction. The third project discusses the invasive management of coronary artery disease. This study compared PCI versus CABG in patients with diabetes and multivessel disease hospitalized for a non-ST elevation myocardial infarction, a situation in which the optimal revascularization strategy is unknown. While CABG was associated with reduced all-cause mortality compared to PCI, the relative benefit of CABG was attenuated when CABG-ineligible patients were excluded from the PCI group. Combined, the projects in this thesis can help inform clinical practice and plan future randomized trials on the management of coronary artery disease.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".