Abstract 7382: Trends in next generation sequencing (NGS) testing by social determinants of health for metastatic cancer patients, 2016-2022
Notice bibliographique
Résumé
Abstract Introduction: Next Generation Sequencing (NGS) testing is a precision medicine tool that uses genomic information from patient tumors to inform treatment decisions and direct patient access to targeted therapies. Social determinants of health (SDOH) partially explain barriers to healthcare access, which can impact clinical outcomes. Real-world studies examining impact of SDOH on receipt of genomic testing are limited. Objective: To examine annual incidence rates (IR) of NGS testing overall and by SDOH indices in endometrial, lung, and colorectal cancer patients. Methods: This retrospective cohort study included patients diagnosed with advanced and recurrent endometrial cancer (aEC), metastatic non-small cell lung cancer (mNSCLC), or metastatic colorectal cancer (mCRC), from July 2016 to September 2022, in the Optum Research Database. Annual incident rates (per 365, 000 person-days) were calculated for NGS testing and stratified by 5 SDOH domains: social isolation, financial stress, food insecurity, housing insecurity, and transportation difficulty. SDOH measures were categorized as low, medium, or high unmet needs. Full-year NGS testing IRs are reported for trend analysis. Results: The study examined 14, 624 aEC, 33, 608 mNSCLC, and 20, 437 mCRC mostly White patients (69%), with median age (years): 69 aEC, 72 mNSCLC, and 70 mCRC. NGS testing rates varied across tumors during the study follow up-period with the highest overall rates observed in mNSCLC (146.9), followed by CRC (73.8) and aEC (19.3). IRs increased over time for all cancer groups (1.5X higher in 2021 than 2017 for mNSCLC, and 2X higher for aEC and mCRC). Annual NGS testing trends were similar for racial/ethnic groups in aEC and mCRC, but, Black and Hispanic mNSCLC patients had the two lowest testing rates (IR: 139.3 and 134.8 respectively) compared to White (IR: 147.3), and Asian patients (IR: 186.3). In mCRC patients, those with the highest level of unmet need had the lowest IRs for all SDOH domains except food insecurity; a similar pattern was seen in mNSCLC. Before 2019, rates were lower in aEC patients with high financial stress and food insecurity vs. with those with low unmet need; this trend reversed from 2019-2021. Changing testing trends in aEC patients may result from the issuance of a Medicare national coverage determination for NGS testing. Conclusion: NGS testing rates have increased for mCRC, mNCSLC and aEC patients since 2017, however, rates differed by level of SDOH. Targeted interventions focused on increasing NGS testing among patients with more unmet SDOH needs could be impactful. Further investigation into the social factors that influence patient access and treatment decisions may help remove existing barriers to optimal patient care. Citation Format: Clementine Adeyemi, Louise B. Murphy, Tim Bancroft, Kristin Moore, Stephanie Gallagher, Amy Nguyen, Gieira S. Jones. Trends in next generation sequencing (NGS) testing by social determinants of health for metastatic cancer patients, 2016-2022 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7382.
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 | 0,001 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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.
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 ».