Prevalence of atherosclerotic cardiovascular disease and subsequent major adverse cardiovascular events in <scp>Alberta, Canada</scp>: A real‐world evidence study
Bibliographic record
Abstract
BACKGROUND: Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of morbidity and mortality worldwide. Data from Canadian populations regarding the burden of ASCVD are limited. Therefore, we describe the 5-year period prevalence of ASCVD and subsequent major adverse cardiovascular event (MACE) outcomes among patients with ASCVD in Alberta, Canada. METHODS: A retrospective, observational study was conducted by linking provincial health services data, vital statistics, and pharmaceutical dispenses data. Five-year period prevalence of clinical ASCVD was captured between 2011 and 2016, and a cohort of adult patients with an initial clinical ASCVD event were identified between 2012 and 2016. One-year incidence rates (IRs) of subsequent MACE outcomes were calculated as composite and individual measures. A subgroup of patients with acute myocardial infarction (AMI) as their index event was examined. RESULTS: There were 198 573 patients (mean [standard deviation] age: 63.9 [15.6] years; 56.6% males) identified with clinical ASCVD between 2012 and 2016. Overall, the 5-year period prevalence of ASCVD in Alberta was 89.9 per 1000 persons and the 1-year IR for a primary MACE outcome was 6.15 (95% confidence interval [CI]: 6.03-6.26) per 100 person-years. Among the ASCVD cohort, 9465 had an AMI as their index event and the IR for a primary MACE outcome was 14.30 (95% CI: 13.45-15.20) per 100 person-years. CONCLUSIONS: This study found that the prevalence of ASCVD and the rate of subsequent MACE outcomes 1 year following the initial ASCVD event are substantial, particularly among patients with an AMI. Secondary prevention strategies aimed at lowering this risk are needed for patients with ASCVD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".