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A scoping review of the assessment reports of genetic or genomic tests reveals inconsistent consideration of key dimensions of clinical utility

2025-02-20Journal article Open access
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dc.contributor.authorPezzullo, Angelo Maria
dc.contributor.authorGris, Angelica Valz
dc.contributor.authorScarsi, Nicolò
dc.contributor.authorTona, Diego Maria
dc.contributor.authorPorcelli, Martina
dc.contributor.authorDi Pumpo, Matteo
dc.contributor.authorPiko, Peter
dc.contributor.authorAdany, Roza
dc.contributor.authorKannan, Pragathy
dc.contributor.authorPerola, Markus
dc.contributor.authorCardoso, Maria Luis
dc.contributor.authorCosta, Alexandra
dc.contributor.authorVicente, Astrid M.
dc.contributor.authorReigo, Anu
dc.contributor.authorVaht, Mariliis
dc.contributor.authorMetspalu, Andres
dc.contributor.authorKroese, Mark
dc.contributor.authorPastorino, Roberta
dc.contributor.authorBoccia, Stefania
dc.date.accessioned2025-10-27T15:50:36Z
dc.date.available2025-10-27T15:50:36Z
dc.date.issued2025-02-20
dc.description.abstractObjectives: Genetic and genomic tests are the cornerstone of personalized preventive approaches. Inconsistency in evaluating their clinical utility is often cited as a reason for their limited implementation in clinical practice. Previous reviews have primarily focused on theoretical frameworks used for clinical utility evaluations of genetic tests, rather than actual assessments and examined dimensions, rather than specific indicators within these dimensions. We aimed to review the dimensions and the specific indicators measured in published assessment reports of genetic or genomic tests. Study design and setting: We conducted a scoping review of assessment reports of genetic and genomic tests used for prevention, searching through PubMed, Web of Science, Scopus, the websites of 20 different organizations, Google, and Google Scholar. From the included assessments, we extracted the reported indicators of clinical utility, compiling a list of disease-specific indicators that detailed their numerator, denominator, and calculation methods. We analyzed the extracted indicators by stratifying them according to ten comprehensive dimensions of clinical utility, the assessment framework used, and the type of indicator (categorized as quantitative, qualitative, reference, or no evidence reported). From these indicators, we then distilled a list of general indicators. Results: We reviewed 3054 unique references and 12,000 results from gray literature searches, ultimately selecting 57 assessment reports. The reference frameworks used were health technology assessment (HTA) (42%), Evaluation of Genomic Applications in Practice and Prevention (EGAPP) (25%), ACCE (21%), and others (12%). We identified 951 disease-specific indicators. The dimensions most frequently evaluated (ie, had at least one indicator) were analytic validity (60%), clinical validity (79%), clinical efficacy (79%), and economic impact (58%). Only 12 assessments compared health outcomes between tested and untested groups, and fewer than 15% of the assessments addressed equity, acceptability, legitimacy, and personal value. Conclusion: Our study illustrates that, although dimensions such as equity and acceptability, are significantly emphasized in traditional evaluation frameworks, these are often not considered in the assessments. Additionally, our study has underscored a significant dearth of reported primary evidence concerning the clinical efficacy of these tests.eng
dc.description.abstractPlain language summary: Genetic and genomic tests analyze a person's genes to predict health risks and guide healthcare decisions, potentially identifying who might benefit from certain treatments or check-ups. However, determining whether these tests are genuinely useful for wide use in health services is complex, because there is no standard way to define "clinical utility" of a genetic test. To understand how these tests are evaluated, we reviewed 57 evaluation reports from high-income countries, most of which focused on cancer-related genetic tests. We found that many evaluations looked mainly at how well a test predicted a condition (validity) and considered some form of effectiveness, yet often failed to measure whether the test truly improved patient health outcomes, such as lowering death rates or enhancing the quality of life. Moreover, factors like patient acceptance, equity, and personal relevance (eg, reducing anxiety) were frequently overlooked. Without including these broader considerations, evaluations risk missing critical evidence that would indicate whether a test is helpful, fair, and worth using. From over 900 unique indicators used to measure clinical utility, we created a simpler list of about 150 general indicators that can guide future evaluations. This consolidated list can help test developers decide which factors to investigate, evaluators determine what to measure, and policymakers identify what might be missing before deciding if a test should be adopted in healthcare. By highlighting the gaps-areas that should be assessed but currently are not-our study encourages a more comprehensive approach to evaluating genetic tests. If we fail to consider issues like equity, patient preferences, and proven health benefits, we risk investing in tests that may do little good or even harm patients. Ultimately, recognizing these shortcomings can lead to better-informed decisions, ensuring that genetic testing is used in ways that truly benefit patients and deliver safer, more personalized, and fairer healthcare for everyone.eng
dc.description.abstractHighlights: - Few genetic test evaluations measure personal value, equity, and acceptability; - Genetic test evaluations rarely include evidence that show direct clinical efficacy; - We offer a catalog of indicators used for test evaluations.por
dc.description.sponsorshipThis research is supported by the ‘‘PROPHET - a PeRsOnalised Prevention roadmap for the future HEalThcare’’ project funded by the European Union (HORIZON Research and Innovation Actions no. 101057721). The UK participant in Horizon Europe Project PROPHET is supported by UKRI grant number 10040946 (Foundation for Genomics & Population Health).
dc.identifier.citationJ Clin Epidemiol . 2025 May:181:111729. doi: 10.1016/j.jclinepi.2025.111729. Epub 2025 Feb 20
dc.identifier.doi10.1016/j.jclinepi.2025.111729
dc.identifier.issn0895-4356
dc.identifier.pmid39986491
dc.identifier.urihttp://hdl.handle.net/10400.18/10570
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationA PeRsOnalized Prevention roadmap for the future HEalThcare
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S0895435625000629?via%3Dihub
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectClinical Utility
dc.subjectGenetic Testing
dc.subjectGenomics
dc.subjectHealth Technology Assessment
dc.subjectPersonalized Prevention
dc.subjectPrecision Medicine
dc.subjectScoping Review
dc.subjectPrecision Medicine
dc.subjectMedicina Personalizada
dc.titleA scoping review of the assessment reports of genetic or genomic tests reveals inconsistent consideration of key dimensions of clinical utilityeng
dc.typejournal article
dcterms.referenceshttps://ars.els-cdn.com/content/image/1-s2.0-S0895435625000629-mmc1.docx
dcterms.referencesThe protocol, data, and code used for this analysis are available (or will be made upon publication) at the Open Science Framework: https://osf.io/h3cxn
dspace.entity.typePublication
oaire.awardTitleA PeRsOnalized Prevention roadmap for the future HEalThcare
oaire.awardURIhttp://hdl.handle.net/10400.18/10569
oaire.citation.startPage111729
oaire.citation.titleJournal of Clinical Epidemiology
oaire.citation.volume181
oaire.fundingStreamHORIZON Coordination and Support Actions
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
relation.isProjectOfPublication05cec19b-2fd2-4e63-8cdf-05a4da7d874f
relation.isProjectOfPublication.latestForDiscovery05cec19b-2fd2-4e63-8cdf-05a4da7d874f

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