Investigating ethnic differences in risk factors and severity of developing premature coronary artery disease: Predicting the effect of risk factors through decision tree analysis in a multicenter case-control study; Results from Iran Premature Coronary Artery Disease (IPAD study)
Bibliographic record
Abstract
Introduction: Premature coronary artery disease (PCAD) has an ascending trend especially in developing countries. This study have investigated the risk factors and severity of developing CAD across various Iranian ethnicities. Methods: This case-control study was done on 3015 Iranian patients undergoing coronary artery angiography, across highly populated Iranian ethnicities including Bakhtiari, Azari, Qashqai, Arab, Fars, Kurd, Gilak, and Lur. This study was performed over three years in 14 capitals of provinces in Iran headed by Isfahan Cardiovascular Research Center, by including men≤60 years old and women≤70 years undergoing coronary artery angiography. If they had coronary stenosis above 75% (more than 50% in the left main), they were categorized as Case group.The effects of conventional risk factors as well as psychosocial ones including age, gender, weight, Body mass index (BMI), economic status, cigarette smoking, drugs of abuse, stress, anxiety, diabetes, hypertension, etc. were determined in each ethnicity using decision tree statistical method. Also, via logistic regression method, the odds of incidence of CAD in each ethnicity were specified against the Fars ethnicity (the predominant ethnicity in Iran). Results: The most common risk factor among different ethnicities was age and male gender. Also, among the Iranian ethnicities, Kurd had the lowest chance while Gilak and Azari had the highest chance of developing PCAD as compared to the Fars ethnicity. Investigation of the behavioral and psychological dimensions indicated that stress was significantly higher among those without coronary artery involvement as compared to those with this involvement. The decision tree model could predict that among Gilakis, Fasting blood sugar (FBS) above 126 and in Lurs opium as well as diastolic blood pressure above 85, and in Kurds male gender would considerably increase the odds of developing CAD. Conclusion: The model obtained from the decision tree indicated that although variables of age, gender, cigarette, and opium are among the main risk factors for involvement of coronary arteries among young adult patients, in different ethnicities, the risk level of each of these risk factors in incidence of PCAD is different. This means among Kurds, age, among Gilakis diabetes, and among Lurs opium are more important.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".