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Enregistrement W4297262532

Genetic Testing for Familial Hypercholesterolemia: Health Technology Assessment.

2022· article· en· W4297262532 sur OpenAlexaboutno aff

Notice bibliographique

RevuePubMed · 2022
Typearticle
Langueen
DomaineMedicine
ThématiqueLipoproteins and Cardiovascular Health
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésGenetic testingFamilial hypercholesterolemiaMedicineGrading (engineering)Genetic counselingPersonalized medicineRisk assessmentIntensive care medicineBioinformaticsInternal medicineGeneticsCholesterolBiologyComputer science
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,061
score de la tête « metaresearch » (Gemma)0,270
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,061
Score d'incertitude au seuil0,320

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0610,270
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0060,014
Bibliométrie0,0220,016
Études des sciences et des technologies0,0010,001
Communication savante0,0050,003
Science ouverte0,0030,003
Intégrité de la recherche0,0040,004
Charge utile insuffisante (le modèle a refusé de juger)0,0100,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,044
Tête enseignante GPT0,292
Écart entre enseignants0,248 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2022
Routes d'admission1
Résumé présentoui

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