Selection of individuals for genetic testing for familial hypercholesterolaemia: development and external validation of a prediction model for the presence of a mutation causing familial hypercholesterolaemia
Bibliographic record
Abstract
AIMS: Familial hypercholesterolaemia (FH) is an autosomal dominant disease that warrants early diagnosis to prevent premature cardiovascular disease (CVD). However, genetic testing to make a definite diagnosis is costly, and careful selection of eligible subjects is important. Unfortunately, accuracy of current diagnostic criteria is poor, especially in young individuals. We therefore developed and validated a model to predict the presence of an FH causing mutation in persons referred by general practitioners. METHODS AND RESULTS: All participants in the Dutch FH screening programme from 1994 to 2014 were included in the development cohort. The validation cohort consisted of consecutive patients, suspected for FH, attending the outpatient lipid clinic in Saguenay (Quebec) from 1993 to 2014. Cross-sectional data were available on medical history, lipid profile, and DNA analysis. Multivariable logistic regression analysis was used for model development. The primary outcome was the presence of a deleterious FH mutation. The development cohort comprised 26 167 FH patients and 37 939 unaffected relatives. Our final model included age; sex; levels of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides; history and age of CVD; use of statins; smoking; alcohol; and presence of hypertension. The area under the receiver operating characteristic curve (AUC) was 85.4% (95% CI: 85.0-85.9). The calibration slope was 1.02 (where 1.00 is optimal). In the validation cohort (1436 FH patients and 1767 unaffected persons), the AUC was 95.4% (95% CI: 94.7-96.1%) and the calibration slope 1.06. CONCLUSION: Our model showed good discrimination and calibration. We specifically expect our model to be of added value for young persons set against current diagnostic criteria, since LDL-C and age are now used as continuous predictors. The equation will be available as an online calculator to estimate the probability of the presence of an FH mutation in individual patients. This tool might aid physicians in the decision for referral of patients for molecular testing.
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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 | 0.024 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".