Geographic variations in prevalence and management of cardiovascular risk factors in 33 283 outpatients with CAD: data from the contemporary CLARIFY registry from 45 countries
Bibliographic record
Abstract
Background: Reduction of risk factors to prevent atherosclerotic events is important for the management of coronary artery disease (CAD). We analyzed geographic variations in the prevalence of major cardiovascular risk factors, and therapeutic risk factor control in outpatients with stable CAD enrolled in the CLARIFY registry from different geographic zones. Methods and results: CLARIFY is an international, prospective, observational, longitudinal registry in outpatients with proven stable CAD from 45 countries (enrolled Nov 2009–Jul 2010). The present cross-sectional baseline analysis was conducted in 33 283 patients (77% male, mean age: 64 years) from: Continental Europe (CE) (n=15 388), Russia and Ukraine (n=3026), Middle East (n=1511), Asia (n=5360), Central and South America (C&S-America) (n=2235), India (n=809), Canada, South Africa, Australia, and UK (Can/SA/Aust/UK) (n=4954). Overall, 12% of patients smoke (range: 6% in C&S-America to 19% in Russia and Ukraine). 78% of patients are overweight (from 73% in Asia, C&S-America to 85% in Russia and Ukraine) and 30% obese (20% in Asia and 42% in Middle East). 29% of patients have diabetes (from 17% in Russia and Ukraine to 60% in Middle East). 71% of patients have hypertension, ranging from 64% Can/SA/Aust/UK to 79% Russia and Ukraine). Dyslipidemia was reported in 75% of patients, ranging from 59% in Asia to 81% in CE. Heart rate was ≥70 bpm in 44% of patients (from 38% in CE to 78% in India). Patients receive aspirin (84%-96%), lipid-lowering drugs (88%-97%), including statins (73%-89%), ACE inhibitors (35%-75%), ARBs (14%-35%). Blood pressure control (defined as <140/90 mmHg) in hypertensive patients was less frequent in Russia and Ukraine, India (47-52%) and more frequent in C&S-America, Asia and Can/SA/Aust/UK (63-66%). Glucose control in patients with diabetes (defined as HbA1c<7%) was less frequent in India (23%) and more frequent in CE, Asia, and C&S-America (50-51%). In 22 536 patients with values available, LDL cholesterol <1 g/L was more frequent in Can/SA/Aust/UK (76%) and less frequent in Russia and Ukraine (35%). Heart rate <70 bpm was less frequent in India (22%) and more frequent in CE (62%) and Can/SA/Aust/UK (60%). Heart rate ≤60 bpm was achieved overall in 22% of patients with angina (from 2% in India to 28-29% in CE, C&S-America, and in Can/SA/Aust/UK). Conclusion: In this contemporary registry, there were major variations in the prevalence and control of major cardiovascular risk factors in outpatients with CAD. These data may be useful to target additional interventions to improve secondary prevention.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".