Diagnostic accuracy of cardiovascular and imaging biomarkers to identify index patients with familial hypercholesterolaemia
Bibliographic record
Abstract
Objective To determine the utility of secondary stratification measures in ascertainment of index cases for monogenic familial hypercholesterolaemia (FH).Methods Referrals from primary care were screened by methods for the potential diagnosis of FH, including Simon Broome (SB) or Dutch Lipid Clinic Network score (DLCN) criteria, initial LDL-C, lipoprotein (a) (Lp(a)) > 125 nM, troponin-T (hsTnT), imaging using carotid intima-media thickness and plaque assessment and a single nucleotide polymorphism (SNP) polygenic hypercholesterolaemia panel (12 loci).Results The population comprised 793 patients aged 55 ± 17 years, of whom 3% had tendon xanthomata, 7% coronary artery disease, and with pre-treatment LDL-C 5.84 ± 1.47 mmol/L. Genotyping was performed in 793 patients and 36% had monogenic FH. Dutch lipid score assessment was associated with a positive likelihood ratio (PLR) for FH 3.91 with a net reclassification index (NRI) of 8% while addition of negative modification for triglycerides (Welsh lipid score) had a PLR 6.88 (NRI 30%). In the whole cohort, the SNP12 score had a negative LR (NLR) of 1.32 (NRI −16%) above the 75th centile while Lp(a) > 125nmol/L had a NLR of 1.18 (NRI −29%) and raised hsTnT a PLR of 1.08 (NRI −16%). In a non-pre-stratified primary care cohort (n = 236), imaging had a PLR 1.70 (NRI 14%) for identifying patients with FH.Conclusions A clinical algorithm based on Welsh Lipid score criteria modifying DLCN score for triglycerides allied with stratification for the presence of tendon xanthomata, highly elevated LDL-C (>7 mmol/L) or positive imaging provides an efficient system to raise the yield of diagnosis of FH with a low chance of missing cases.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".