P5329Montreal score and coronary artery calcification in the prediction of cardiovascular event in patients with familial hypercholesterolemia
Bibliographic record
Abstract
Abstract Introduction Familial hypercholesterolemia (FH) is characterized by elevated levels of LDL-C and early cardiovascular disease (CVD). However, the risk of CVD in HF is variable. The Montreal score was designed to stratify cardiovascular risk in the FH population. Coronary calcium score (CAC) is a tool that can be used to optimize CVD risk assessment in FH. Purpose The objective of this study is to evaluate whether CAC is superior to the Montreal score in cardiovascular risk discrimination in FH. Methods We Included 206 patients with molecular diagnosis of FH (36.4% men, mean age 45±14 years, mean baseline LDL-C: 269±70 mg/dL). All patients underwent CAC and were treated with maximum tolerated statin therapy. We evaluated cardiovascular risk factors and calculated Montreal score as prior publication. Cox regression analysis was performed to test the association of CAC with the incidence of cardiovascular events. CAC was transformed into LogCAC + 1 to optimize the distribution of the CAC as previously described. Area under the ROC curve was calculated for Montreal score and CAC. Results Patients were followed by a median of 3.7 years (interquartile range: 2.7 to 6.8 years). Mean Montreal score was 22±8, median of 22. CAC was positive in 105 individuals (51%) and 15 CVD events (7.2%) had occurred. Montreal score above the median was associated with CAC (OR: 8.36, 95% CI: 4.47–15.62, p<0.001), and there was a gradient of increase in the Montreal score with CAC increase (mean Montreal score for CAC = 0, CAC 1–100, CAC>100: 17±7, 23±7, 30±4, p<0.001). Univariate analysis showed that the following variables were associated with CVD occurrence: male gender, family history of early coronary disease, corneal arcus, HDL-c (protective), logCAC + 1 and Montreal score. Multivariate analysis was performed: model 1 with Montreal score and logCAC + 1, only logCAC + 1 was associated with the occurrence of CVD (RR: 3.886; 95% CI: 2.112–7.148, p<0.001). Model 2 with family history of early coronary disease, corneal arcus, logCAC + 1 and Montreal score, only the latter was not associated with the occurrence of CVD. CAC presents greater area under the ROC curve for CVD event discrimination compared to the Montreal score: 0.839 versus 0.685, p=0.0074. Conclusion The Montreal score is associated with CAC in FH, however CAC is superior than this clinical score in predicting the occurrence of CVD in FH.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".