Barriers and facilitators to next-generation sequencing use in United States oncology settings: a systematic review
Notice bibliographique
Résumé
Aim: Next-generation sequencing (NGS) of solid tumors can inform treatment decisions; however, uptake remains low. This objective of this systematic review was to identify barriers to and facilitators of NGS in US oncology settings. Materials & methods: Embase and MEDLINE were searched in March 2023 for articles published from 2012 to 2023 on barriers and facilitators of NGS adoption for solid tumors. Surveys, interviews and observational studies were eligible. Studies on genetic testing for hereditary cancers and non-US studies were excluded. The Motheral scale, Joanna Briggs Institute critical appraisal checklist and McGill Mixed Methods Appraisal Tool were used to assess study quality. Data were synthesized narratively. Results: Twenty-one studies were included. Study participants were clinicians, payers and administrators. Key barriers included complex reimbursement processes and uncertainties around clinical utility. Including recommendations for NGS in clinical practice guidelines was a key facilitator, although insurance policies were often more restrictive than guideline recommendations. Conclusion: Uptake of NGS is increasing but barriers remain. Changes to the current reimbursement frameworks are needed to increase access to NGS. The impact of implementing the 2018 National Coverage Determination, which allows access to NGS for all Medicare beneficiaries with advanced cancer, is not yet evident in the published literature. Next-generation sequencing (NGS) is used in oncology to detect gene mutations such as NTRK, ALK, MET, ROS1 and ALK in solid tumors and thereby inform treatment decisions. Actionable mutations identified by NGS can be treated with tumor-agnostic targeted therapies, such as larotrectinib and entrectinib. We conducted a systematic review to investigate the reasons underlying the documented under-use of NGS testing for predictive biomarkers in oncology in the United States. The 21 studies included in the review represent viewpoints from clinicians, lab personnel, commercial payers and patients with advanced cancer. Barriers to NGS testing included complex reimbursement processes, uncertainties around clinical utility, lack of staff training and knowledge and inadequate infrastructure. Facilitators were reported less frequently than barriers, but decision support tools for clinicians and the inclusion of NGS testing in clinical practice guidelines appear to aid the uptake of NGS testing. Despite clinical guidelines' preference for NGS testing, commercial insurers often do not offer coverage that is commensurate with the guidelines. Opportunities for increased adoption of NGS testing should be further explored by payers, clinicians and healthcare system administrators by, for example, revising the current payer framework and investing in staff resources and training. Future studies should investigate the impact of Medicare's National Coverage Determination for NGS testing, which came into effect in 2018 and covers NGS for patients with advanced cancer.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,036 | 0,163 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,007 |
| Bibliométrie | 0,009 | 0,011 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».