Opportunistic Genomic Screening for a Broad Range of Medically Relevant Secondary Findings: Molecular Findings, Clinical Utility, and Cost-Effectiveness
Notice bibliographique
Résumé
Background: When used as a diagnostic test, genomic sequencing can also be used to opportunistically screen for secondary findings (SFs) – medically relevant variants that are unrelated to the primary indication for testing. However, there is scarce evidence on the outcomes or costs of screening for SFs to inform decisions about whether, and which, secondary findings should be offered to patients. Through a randomized controlled trial (Incidental Genomics RCT; NCT03597165) we aimed to evaluate the clinical, economic, and laboratory outcomes of opportunistic screening for a broad range of medically relevant SFs, encompassing risks for medically actionable and non-medically actionable monogenic disorders, carrier status for recessive disorders, pharmacogenomic variants, and risk variants for common multifactorial disease.Methods: Adult cancer patients received germline exome sequencing with primary cancer findings only (control arm), or primary cancer findings and a choice of secondary findings (intervention arm). Through a chart review and patient-reported outcomes, the yield of reportable secondary findings was characterized, as well as the impact on patients’ medical management and correlations with clinical features and family history. Data from the RCT were linked to healthcare administrative databases, and cost-utility and cost-effectiveness analyses were performed. Laboratory exome analysis logs were analyzed to characterize all variants requiring manual curation, and the resources required for exome analysis. The time horizon for all analyses was the first year after return of exome sequencing results. Results: From the diagnostic laboratory perspective, analyzing all types of secondary findings required substantial effort. Across all intervention arm participants, in the monogenic secondary finding genes there were 4,441 unique variants, 5.0% (221) of which were classified as P/LP and were reportable, and 81.4% (3615) were classified as VUS and not reportable. There were on average 2.6 (SD 1.66, range 0-9) P/LP variants per case in the intervention arm, and 29.5 VUS (SD 13.2, range 2-74). Filtration, variant classification and report generation were substantially more time consuming in the intervention arm compared to the control arm given the greater number of variants being analyzed. All participants who elected to learn SFs had ≥1 variant reported (100% [139/139]). SFs across all categories prompted changes in management among 28.1% of participants, including SFs not a priori categorized as medically actionable. Moreover, a considerable proportion of participants had suggestive clinical features (49.0% [24/49]) or family history (21.7% [27/124]) potentially related to their SFs. Overall costs (costs associated with genomic sequencing, plus downstream healthcare costs) were on average $1290CAD higher per participant in the intervention arm, at $12,965 (SD$20,038) in the intervention arm compared to $11,676 (SD$21,776) in the control arm. However, we did not find strong evidence that these were statistically different (ratio of geometric means: 1.27, 95% CI 0.98 to 1.65, p=0.0652). QALYs were higher for participants in the intervention arm than in the control arm (β=0.04, 95% CI 0.01-0.06, p=0.005), and the odds of having a GS-informed change in management were substantially higher among participants in the intervention arm compared to the control arm (OR 11.2, 95% CI 4.3 to 29.5, p<0.0001). The ICER was $34,929CAD/QALY, falling below commonly accepted cost-effectiveness thresholds. Conclusions: This thesis provides evidence on the laboratory, clinical and health system outcomes of opportunistic screening for a broad range of secondary findings. While secondary findings were associated with higher costs, these may be justified in light of the associated benefits, namely, clinical utility and quality of life.
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 | 0,012 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».