Familial Hypercholesterolemia-Risk-Score: A New Score Predicting Cardiovascular Events and Cardiovascular Mortality in Familial Hypercholesterolemia
Bibliographic record
Abstract
Objective: Familial hypercholesterolemia (FH) is associated with a high risk of premature atherosclerotic cardiovascular disease (ASCVD). However, this risk is highly heterogeneous and current risk prediction algorithms for FH suffer from limitations. The primary objective of this study was to develop a score predicting incident ASCVD events over 10 years in a large multinational FH cohort. The secondary objective was to investigate the prediction of major adverse cardiovascular events and cardiovascular mortality using this score. Approach and Results: We prospectively followed 3881 patients with adult heterozygous FH with no prior history of ASCVD (32 361 person-years of follow-up) from 5 registries in Europe and North America. The FH-Risk-Score incorporates 7 clinical variables: sex, age, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, hypertension, smoking, and lipoprotein (a) (Lp(a)) with a Harrell C-index for 10-year ASCVD event of 0.75, which was superior to the SAFEHEART-RE (Spanish Familial Hypercholesterolemia Cohort; 0.69). Subjects with an elevated FH-Risk-Score had decreases in 10-year ASCVD-free survival, 10-year major adverse cardiovascular event-free survival, and 30-year survival for CV mortality compared with the low-risk group, with hazard ratios of 5.52 (3.94-7.73), 4.64 (2.66-8.11), and 10.73 (2.51-45.79), respectively. The FH-Risk-Score showed a similar performance in subjects with and without an FH-causing mutation. Conclusions: The FH-Risk-Score is a stronger predictor of future ASCVD than the SAFEHEART-RE and was developed in FH subjects with no prior cardiovascular event. Furthermore, the FH-Risk-Score is the first score to predict CV death and could offer personalized cardiovascular risk assessment and treatment for patients with FH. Future studies are required to validate the FH-Risk-Score in different ethnic groups.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 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".