Quantifying the impact of using Coronary Artery Calcium Score for risk categorization instead of Framingham Score or European Heart SCORE in lipid lowering algorithms in a Middle Eastern population
Bibliographic record
Abstract
BACKGROUND: The use of the Coronary Artery Calcium Score (CACS) for risk categorization instead of the Framingham Risk Score (FRS) or European Heart SCORE (EHS) to improve classification of individuals is well documented. However, the impact of reclassifying individuals using CACS on initiating lipid lowering therapy is not well understood. We aimed to determine the percentage of individuals not requiring lipid lowering therapy as per the FRS and EHS models but are found to require it using CACS and vice versa; and to determine the level of agreement between CACS, FRS and EHS based models. METHODS: Data was collected for 500 consecutive patients who had already undergone CACS. However, only 242 patients met the inclusion criteria and were included in the analysis. Risk stratification comparisons were conducted according to CACS, FRS, and EHS, and the agreement (Kappa) between them was calculated. RESULTS: In accordance with the models, 79.7% to 81.5% of high-risk individuals were down-classified by CACS, while 6.8% to 7.6% of individuals at intermediate risk were up-classified to high risk by CACS, with slight to moderate agreement. Moreover, CACS recommended treatment to 5.7% and 5.8% of subjects untreated according to European and Canadian guidelines, respectively; whereas 75.2% to 81.2% of those treated in line with the guidelines would not be treated based on CACS. CONCLUSION: In this simulation, using CACS for risk categorization warrants lipid lowering treatment for 5-6% and spares 70-80% from treatment in accordance with the guidelines. Current strong evidence from double randomized clinical trials is in support of guideline recommendations. Our results call for a prospective trial to explore the benefits/risks of a CACS-based approach before any recommendations can be made.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".