Systematic identification of familial hypercholesterolaemia: An updated systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Familial hypercholesterolaemia (FH) is an inherited lipid disorder characterised by raised LDL-C and increased risk of premature atherosclerotic cardiovascular disease. Despite effective treatments, FH remains substantially underdiagnosed. Electronic health records (EHRs) enable systematic case-finding, but evidence on their effectiveness remains limited. This review aimed to evaluate EHR-based strategies for FH identification. METHODS: Seven databases and grey literature were systematically searched for relevant studies. Eligible studies reported on systematic EHR-based case-finding in adults (≥18 years). Meta-analysis of FH prevalence was conducted using random-effects modelling. Risk of bias was assessed using ROBINS-I; evidence certainty with GRADE. RESULTS: Of 831 citations screened, 12 eligible studies were included, including three from a prior review. Case-finding approaches included traditional diagnostic criteria (Simon-Broome, DLCN, MEDPED), hybrid models, and machine-learning algorithms (FAMCAT, FIND FH, TARB-Ex). FH prevalence estimates varied: 1.2% (95% CI 0.0%-3.0%; p=0.06) in general population studies, 41% (95% CI 2%-90%; p=0.02) in high-risk CVD populations, and 15% (95% CI 2%-34%; p=0.00) in genetically confirmed cohorts. Novel algorithmic approaches such as FAMCAT 2 and incorporating EHR-genomic data models demonstrated superior performance to traditional criteria. Secondary outcomes were inconsistently reported, though cholesterol levels at diagnosis were consistently higher in probable/confirmed FH, and markedly elevated in genetically confirmed cohorts. Certainty of evidence was moderate due to heterogeneity, non-randomised design, and potential publication bias. CONCLUSIONS: Algorithmic/genomics augmented EHR-based methods can enhance FH identification, but evidence remains limited. Standardised, scalable approaches validated in diverse populations are required to inform equitable FH screening and policy development.
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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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".