Effectiveness of clinical scores in predicting coronary artery disease in familial hypercholesterolemia: a coronary computed tomography angiography study
Bibliographic record
Abstract
PURPOSE: One of the major challenges in the management of familial hypercholesterolemia (FH) is the stratification of cardiovascular risk in asymptomatic subjects. Our purpose is to investigate the performance of clinical scoring systems, Montreal-FH-score (MFHS), SAFEHEART risk (SAFEHEART-RE) and FH risk score (FHRS) equations and Dutch Lipid Clinic Network (DLCN) diagnostic score, in predicting extent and severity of CAD at coronary computed tomography angiography (CCTA) in asymptomatic FH. MATERIAL AND METHODS: One-hundred and thirty-nine asymptomatic FH subjects were prospectively enrolled to perform CCTA. MFHS, FHRS, SAFEHEART-RE and DLCN were assessed for each patient. Atherosclerotic burden scores at CCTA (Agatston score [AS], segment stenosis score [SSS]) and CAD-RADS score were calculated and compared to clinical indices. RESULTS: Non-obstructive CAD was found in 109 patients, while 30 patients had a CAD-RADS ≥ 3. Classifying the two groups according to AS, values varied significantly for MFHS (p < 0.001), FHRS (p < 0.001) and SAFEHEART-RE (p = 0.047), while according to SSS only MFHS and FHRS showed significant differences (p < 0.001). MFHS, FHRS and SAFEHEART-RE, but not DLCN, showed significant differences between the two CAD-RADS groups (p < .001). MFHS proved to have the best discriminatory power (AUC = 0.819; 0.703-0.937, p < 0.001) at ROC analysis, followed by FHRS (AUC = 0.795; 0.715-0.875, p < .0001) and SAFEHEART-RE (AUC = .725; .61-.843, p < .001). CONCLUSIONS: Greater values of MFHS, FHRS and SAFEHEART-RE are associated to higher risk of obstructive CAD and might help to select asymptomatic patients that should be referred to CCTA for secondary prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".