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Multi-gene Pharmacogenomic Testing That Includes Decision-Support Tools to Guide Medication Selection for Major Depression: A Health Technology Assessment.

2021· article· en· W4287460293 sur OpenAlexaboutno aff

Notice bibliographique

RevuePubMed · 2021
Typearticle
Langueen
DomainePharmacology, Toxicology and Pharmaceutics
ThématiquePharmacogenetics and Drug Metabolism
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPharmacogenomicsSelection (genetic algorithm)Depression (economics)Clinical decision support systemMedicineDecision support systemPsychologyComputer sciencePharmacologyData miningArtificial intelligence
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Major depression is a substantial public health concern that can affect personal relationships, reduce people's ability to go to school or work, and lead to social isolation. Multi-gene pharmacogenomic testing that includes decision-support tools can help predict which depression medications and dosages are most likely to result in a strong response to treatment or to have the lowest risk of adverse events on the basis of people's genes.We conducted a health technology assessment of multi-gene pharmacogenomic testing that includes decision-support tools for people with major depression. Our assessment evaluated effectiveness, safety, cost-effectiveness, the budget impact of publicly funding multi-gene pharmacogenomic testing, and patient preferences and values. METHODS: We performed a systematic literature search of the clinical evidence. We assessed the risk of bias of each included study using the Cochrane Risk of Bias Tool and the Risk of Bias Assessment Tool for Nonrandomized studies (RoBANS) 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 literature search of the economic evidence to review published cost-effectiveness studies on multi-gene pharmacogenomic testing that includes a decision-support tool in people with major depression. We developed a state-transition model and conducted a probabilistic analysis to determine the incremental cost of multi-gene pharmacogenomic testing versus treatment as usual per quality-adjusted life-year (QALY) gained for people with major depression who had inadequate response to one or more antidepressant medications. In the reference case (with GeneSight-guided care), we considered a 1-year time horizon with an Ontario Ministry of Health perspective. We also estimated the 5-year budget impact of publicly funding multi-gene pharmacogenomic testing for people with major depression in Ontario.To contextualize the potential value of multi-gene pharmacogenomic testing that includes decision-support tools, we spoke with people who have major depression and their families. RESULTS: We included 14 studies in the clinical evidence review that evaluated six multi-gene pharmacogenomic tests. Although all tests included decision-support tools, they otherwise differed greatly, as did study design, populations included in studies, and outcomes reported. Little or no improvement was observed on change in HAM-D17 depression score compared with treatment as usual for any test evaluated (GRADE: Low-Very Low). GeneSight- and NeuroIDgenetix-guided medication selection led to statistically significant improvements in response (GRADE: Low-Very Low) and remission (GRADE: Low-Very Low), while treatment guided by CNSdose led to significant improvement in remission rates (GRADE: Low), but the study did not report on response. Results were inconsistent and uncertain for the impact of Neuropharmagen, and no significant improvement was observed for Genecept or another unspecified test for either response or remission (GRADE: Low-Very Low). Neuropharmagen may reduce adverse events and CNSDose may reduce intolerability to medication, while no difference was observed in adverse events with GeneSight, Genecept, or another unspecified test (GRADE: Moderate-Very Low). No studies reported data on suicide, treatment adherence, relapse, recovery, or recurrence of depression symptoms.Our review included four model-based economic studies and found that multi-gene pharmacogenomic testing was associated with greater effectiveness and cost savings than treatment as usual, over long-term (i.e., 3-,5-year and lifetime) time horizons. Since none of the included studies was fully applicable to the Ontario health care system, we conducted a primary economic evaluation.Our reference case analysis over the 1-year time horizon found that multi-gene pharmacogenomic testing (with GeneSight) was associated with additional QALYs (0.03, 95% credible interval [CrI]: 0.005; 0.072) and additional costs ($1,906, 95% Crl: $688; $3,360). An incremental cost-effectiveness ratio was $60,564 per QALY gained. The probability of the intervention being cost-effective (vs. treatment as usual) was 36.8% at a willingness-to-pay amount of $50,000 per QALY (i.e., moderately likely not to be cost-effective), rising to 70.7% at a willingness-to-pay amount of $100,000 per QALY (i.e., moderately likely to be cost-effective). Evidence informing economic modeling of the reference case with GeneSight and other multi-gene pharmacogenomic tests was of low to very low quality, implying considerable uncertainty or low confidence in the effectiveness estimates. The price of the test, efficacy of the intervention on remission, time horizon, and analytic perspective were major determinants of the cost-effectiveness results. If the test price were assumed to be $2,162 (compared with $2,500 in the reference case), the intervention would be cost-effective at a willingness-to-pay amount of $50,000 per QALY; moreover, if the price decreased to $595, the intervention would be cost saving (or dominant) compared with treatment as usual.At an increasing uptake of 1% per year and a test price of $2,500, the annual budget impact of publicly funding multi-gene pharmacogenomic testing in Ontario over the next 5 years ranged from an additional $3.5 million in year 1 (at uptake of 1%) to $16.8 million in year 5. The 5-year budget impact was estimated at about $52 million.People with major depression and caregivers generally supported multi-gene pharmacogenomic testing because they believed it could provide guidance that fit their values. They hoped such guidance would speed symptom relief, would reduce side effects and help inform their medication choices. Some patients expressed concerns over maintaining confidentiality of test results and the possibility that physicians would sacrifice patient-centred care to follow pharmacogenomic guidance. CONCLUSIONS: Multi-gene pharmacogenomic testing that includes decision-support tools to guide medication selection for depression varies widely. Differences between individual tests must be considered, as clinical utility observed with one test might not apply to other tests. Overall, effectiveness was inconsistent among the six multi-gene pharmacogenomic tests we identified. Multi-gene pharmacogenomic tests may result in little or no difference in improvement in depression scores compared with treatment as usual, but some tests may improve response to treatment or remission from depression. The impact on adverse events is uncertain. The evidence, however, is uncertain, and therefore our confidence that these observed effects reflect the true effects is low to very low.For the management of major depression in people who had inadequate response to at least one medication, some multi-gene pharmacogenomic tests that include decision support tools are associated with additional costs and QALYs over the 1-year time horizon, and maybe be cost-effective at the willingness-to-pay amount of $100,000 per QALY. Publicly funding multi-gene pharmacogenomic testing in Ontario would result in additional annual costs of between $3.5 million and $16.8 million, with a total budget impact of about $52 million over the next 5 years.People with major depression and caregivers generally supported multi-gene pharmacogenomic testing because they believed it could provide guidance that fit their values. They hoped such guidance would speed symptom relief, would reduce side and help inform their medication choices. Some patients expressed concerns over maintaining confidentiality of test results and the possibility that physicians would sacrifice patient-centred care to follow pharmacogenomic guidance.

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)0,055
score de la tête « metaresearch » (Gemma)0,172
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,055
Score d'incertitude au seuil0,290

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0550,172
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0060,015
Bibliométrie0,0110,006
Études des sciences et des technologies0,0000,001
Communication savante0,0040,003
Science ouverte0,0030,003
Intégrité de la recherche0,0040,003
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

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 Opus0,248
Tête enseignante GPT0,484
Écart entre enseignants0,235 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations19
Publié2021
Routes d'admission1
Résumé présentoui

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