Age and risk scores for detecting coronary artery calcification in heterozygous familial hypercholesterolemia
Bibliographic record
Abstract
Abstract Introduction Familial hypercholesterolemia (FH) is a genetic disorder characterized by elevated low-density lipoprotein cholesterol (LDL-c) levels and an increased risk of atherosclerotic cardiovascular disease (ASCVD). However, after statin treatment, the cardiovascular risk in these individuals becomes heterogeneous and justifies further risk stratification. FH risk scores were developed to predict ASCVD risk specifically for this population. Coronary artery calcium score (CAC) is a marker of subclinical atherosclerosis burden and a robust predictor of ASCVD in FH, however, is not widely available. Purpose To evaluate the association and discrimination of FH risk scores with the presence of CAC to improve the selection of the FH individuals who should benefit the most from this imaging test. Methods We reviewed patients treated at a specialized lipid clinic with genetically confirmed FH in this retrospective cross-sectional study. Risk assessment was conducted using the Familial Hypercholesterolemia Risk Score (FH Risk Score), the Spanish Familial Hypercholesterolemia Cohort (SAFEHEART), and the Montreal score. A logistic regression model evaluated the association between variables and CAC > 0. Discrimination and optimal cutoff point analysis for detecting CAC > 0 were conducted using the area under the receiver operating characteristic (ROC) curve and the Youden Index, respectively. A pairwise comparison of ROC curves was performed using the DeLong method. Results 802 patients with heterozygous FH were evaluated, and we excluded seven under 18 years and 376 patients who did not have CAC available. It was possible to calculate the Montreal score in 408, the FH risk score in 229, and the SAFEHEART score in 362 patients. Table 1 shows comparison of clinical and laboratory variables and risk scores for CAC zero versus CAC>0. Multivariate logistic regression analysis adjusted for age and hypertension showed that all three scores persisted associated with CAC>0 (p<0.05). All three scores showed AUC > 0.70 for discrimination of presence of CAC (Table 2); however, a pairwise comparison of ROC curves demonstrated that Montreal and FH risk scores have higher AUC than SAFEHEART. On the other hand, age alone presented the same discrimination value as the three FH risk scores for CAC presence. Conclusion Montreal, SAFEHEART, and FH risk scores are all associated with CAC in FH but do not offer additional discrimination compared to age alone. Age could be a suitable clinical parameter in clinical practice to indicate CAC presence in heterozygous FH.Table 1 Table 2
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".