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Record W4297262532

Genetic Testing for Familial Hypercholesterolemia: Health Technology Assessment.

2022· article· en· W4297262532 on OpenAlexaboutno aff

Bibliographic record

VenuePubMed · 2022
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsnot available
Fundersnot available
KeywordsGenetic testingFamilial hypercholesterolemiaMedicineGrading (engineering)Genetic counselingPersonalized medicineRisk assessmentIntensive care medicineBioinformaticsInternal medicineGeneticsCholesterolBiologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

Background: Familial hypercholesterolemia (FH) is an inherited disorder characterized by abnormally elevated low-density lipoprotein (LDL) cholesterol serum levels from birth, which increases the risk of premature atherosclerotic cardiovascular disease. Genetic testing is a type of a medical test that looks for changes in genes or chromosome structure to discover genetic differences, anomalies, or mutations that may prove pathological. It is regarded as the gold standard for screening and diagnosing FH. We conducted a health technology assessment on genetic testing for people with FH and their relatives (i.e., cascade screening). The assessment included an evaluation of clinical utility (the ability of a test to improve health outcomes), the diagnostic yield (ability of a test to identify people with FH), cost-effectiveness, the budget impact of publicly funding genetic testing for FH, and patient preferences and values. Methods: We performed a systematic literature search of the clinical evidence. For evaluation of clinical utility, we assessed the risk of bias of each included study using the ROBINS-I tool and the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria.We performed a systematic economic literature search and conducted a cost-effectiveness and cost-utility analysis with a lifetime horizon from a public payer perspective. We assessed the cost-effectiveness of using genetic testing both for confirming a FH clinical diagnosis and for cascade screening in relatives of genetically confirmed cases. We evaluated the cost effectiveness of cascade screening strategies with genetic testing, sequential testing, and lipid testing approaches. We also analyzed the budget impact of publicly funding genetic testing in Ontario. Results: We included 11 studies in the clinical evidence review. Overall, our review found that genetic testing to diagnose FH improves several health outcomes (GRADE: Moderate) compared with clinical evaluation without a genetic test. We also found that genetic cascade screening leads to a high diagnostic yield of FH.According to our primary economic evaluation, genetic testing is a dominant strategy (more effective and less costly) compared with no genetic testing for individuals with a FH clinical diagnosis. It reduced the number of FH diagnoses, led to fewer cardiovascular events, and improved QALYs. For first-degree relatives of genetically confirmed cases, all cascade screening strategies (genetic testing, sequential testing, and lipid testing) were cost-effective when compared with no cascade screening in a pairwise fashion. The ICERs of cascade screening with genetic, sequential, and lipid testing compared with no cascade screening were $58,390, $50,220, and $45,754 per QALY gained, respectively. When comparing all screening strategies together, cascade screening with lipid testing was the most cost-effective strategy. At commonly used willingness-to-pay values of $50,000 and $100,000 per QALY gained, the probability of lipid cascade screening being cost-effective was 53.5% and 71.5%, respectively.The annual budget impact of publicly funding genetic testing for individuals with a clinical FH diagnosis in Ontario ranged from a cost saving of $2 million in year 1 to $64 million in year 5, for a total of $141 million saved over the next 5 years, assuming the cost of genetic testing remains at $490 per person. If only testing-related costs were considered, the budget impact was estimated to be an additional cost of $7 million in year 1, increasing to $20 million in year 5, for a total cost of $64 million over the next 5 years. For relatives of genetically confirmed cases, publicly funding genetic cascade screening would lead to an additional cost of $5 million in year 1, increasing to $27 million in year 5, for a total cost of $73 million over the next 5 years. If only testing-related costs were considered, the budget impact was estimated to be an additional of $66 million. Conclusions: Genetic testing for FH has a higher clinical utility than clinical evaluation without a genetic test. It also results in a high diagnostic yield of FH through cascade screening. For individuals with a clinical diagnosis of FH, genetic testing would be a cost-saving and more effective diagnostic strategy. For relatives of index cases confirmed through genetic testing, genetic and lipid cascade screening are both cost-effective compared with no screening, but genetic cascade screening is less cost-effective than lipid cascade screening. We estimated that publicly funding genetic testing for individuals with a clinical diagnosis of FH in Ontario would save $141 million, and publicly funding genetic testing in a cascade screening program for relatives would cost an additional $73 million over the next five years.Most people with a positive genetic test perceived the screening, diagnosis, and treatment for FH more positively. The discovery of the condition can lead people to adhere to relevant treatments in an effort to control their cholesterol levels. People we spoke with felt that greater awareness and education would allow for more efficient uptake of cascade screening.

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 imitation

Not 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.

metaresearch head score (Codex)0.061
metaresearch head score (Gemma)0.270
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.061
Threshold uncertainty score0.320

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0610.270
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.014
Bibliometrics0.0220.016
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0030.003
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.044
GPT teacher head0.292
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

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Citations4
Published2022
Admission routes1
Has abstractyes

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