Coronary Angiography: Indications, Results and Cost-Effectiveness in the Diagnosis of Stable Angina Pectoris in Two Hospitals in Senegal
Bibliographic record
Abstract
Background: The current gold standard for the diagnosis of stable coronary artery disease (CAD) is invasive coronary angiography. But a large proportion of patients undergoing coronary angiography don’t have obstructive coronary artery disease. Objective: The aim of the present study was to evaluate the diagnostic performance of invasive coronary angiography for patients without known coronary artery disease presenting with stable chest pain syndrome at two hospitals structures in Senegal. Method: We conducted a prospective, descriptive, and analytical study from March 1, 2019, to December 31, 2020 in the Cardiology Departments of General Hospital Idrissa Pouye (HOGIP) and Aristide Le Dantec Hospital (HALD). During the study period a cohort of patients referred to angiography coronary for diagnostic CAD because of suspected stable angina were enrolled. Demographic characteristics, risk factors, symptoms, and noninvasive test results were correlated with the presence of obstructive coronary artery disease. Results: A total of 143 patients were included in our study with a median age of 60.91 ± 10.58 years; men were 96 (67.13%) and women 47 (32.87%). The prevalence of hypertension was 60.84%; diabetes was 34.27%; dyslipidemia was 32.17% and sedentary was 26.57% in our study population. Typical and atypical angina symptoms were present in 37.76% (n = 54) and 49.65% (n = 71) respectively, while 10.49% had dyspnea. Coronary angiography revealed 59 (41.26%) patients with no CAD as well as 27 (18.88%) with one-vessel; 28 (19.58%) with two-vessel, and 29 (20.28%) with three-vessel disease. Independent predictive factors for stable angina with the presence of obstructive lesion were: patient age (OR, 2.36; 95% CI, 1.05 - 5.29; p = 0.036); male gender (OR, 1.6; 95% CI, 0.72 - 3.57; p = 0.24); diabetes (OR, 2.14; 95% CI, 0.96 - 4.75; p = 0.06) and necrosis Q waves (4.75; CI, 0.98 - 23.09; p = 0.05). Conclusion: In our study, more than half of the patients (58.74%) referred for coronary angiography had a confirmed diagnosis. A better clinical and non-invasive assessment is needed to improve the efficiency of patient selection for coronary angiography.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".