Testing for Coronary Artery Disease in Patients Newly Diagnosed with Heart Failure in Alberta, Canada
Bibliographic record
Abstract
Background: Early identification of coronary artery disease (CAD) in patients newly diagnosed with heart failure (HF) has prognostic and therapeutic implications. We evaluated frequency and predictors of CAD testing in Alberta between April 1, 2004 and March 31, 2023. Methods: Population-level retrospective cohort study using linked administrative health datasets and previously validated case definitions. Results: Of 166,447 adults with newly diagnosed HF, 64.2% first presented in the outpatient setting. Within the first month of diagnosis, patients were most likely to be seen by a primary care physician only (PCP, 41.8%); co-management with PCP and a specialist was the second most common management strategy (31.6%). Within 6-months of diagnosis, 46,143 (27.7%) patients had at least one diagnostic evaluation for CAD; coronary catheterization was more common in patients diagnosed in hospital while non-invasive imaging was more common in non-hospitalized patients. Testing was strongly associated with specialist involvement: 54.4% if co-managed with PCP [aOR 5.19, 95% confidence interval 4.96-5.43], 39.6% if saw specialist alone [aOR 2.86, 2.75- 2.97], and 13.8% if managed by PCP alone [referent]). Although frequency of echocardiography and CAD non-invasive imaging rose sharply in 2017, the majority of patients with new HF in all years were not tested for CAD. Conclusion: Despite its prognostic importance, CAD testing is performed in a minority of patients with newly diagnosed heart failure and is heavily influenced by specialist involvement. Optimizing CAD testing patterns for all patients newly diagnosed with HF should be a priority for clinicians and policy makers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".