Diagnostics Reform and Harmonization of Clinical Laboratory Testing

Schreier, Jeff; Feeney, Robert E.; Keeling, Peter

doi:10.1016/j.jmoldx.2019.04.002

Cited by 24 publications

(19 citation statements)

References 5 publications

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“…Global harmonization of clinical laboratory tests will, therefore, improve the efficiency of the clinical diagnostic testing landscape. Incorporating harmonization of LDTs into any future legislative regulatory framework will require the standardization of LDTs, with evidence of concordance with each other and with IVD kits [28].…”

Section: Step 2: Support Global Harmonization Of Clinical Laboratory mentioning

confidence: 99%

Challenges in the clinical implementation of precision medicine companion diagnostics

Keeling¹,

Clark²,

Finucane³

2020

Expert Review of Molecular Diagnostics

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Introduction: The pace of biomarker discovery has increased exponentially over the last few years, ushering in an era of precision medicine (PM) with a growing arsenal of treatments tailored to specific patient populations. To accurately identify patients, companion diagnostics (CDx) are developed and launched alongside these treatments. However, even with a timely launch of therapies and CDx tests, patients are not guaranteed optimal access to these tests because of the inefficiencies embedded within the clinical diagnostic testing landscape supporting PM. Areas covered: This commentary describes implementation challenges facing CDx tests and delaying clinical uptake. We also assess the 'siloed thinking' perpetuating these challenges and propose steps toward resolution. Our research is based on published literature and findings from the Diaceutics proprietary patient testing database. Expert opinion: The clinical and economic ecosystem underpinning the diagnostic journey of patients remains severely underdeveloped. Patients are denied suitable therapies because of delayed identification or failings in real-world testing deployment. Progress is needed in clinical collaborations, integrator platforms, economic value sharing, and ownership of the patient testing journey to PM. We need to consider that better precision testing will deliver an equal or greater outcome to patients than new precision treatments alone.

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Section: Step 2: Support Global Harmonization Of Clinical Laboratory mentioning

confidence: 99%

Challenges in the clinical implementation of precision medicine companion diagnostics

Keeling¹,

Clark²,

Finucane³

2020

Expert Review of Molecular Diagnostics

Self Cite

View full text Add to dashboard Cite

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“…Regardless of the data sharing model, data generators and stewards bear a significant degree of responsibility for data curation. There are also efforts to harmonize clinical assays as evidenced by the proposed Diagnostic Accuracy and Innovation Act [ 39 ]. Where copies of data are distributed, researchers can participate to various degrees in curating data to render it usable for specific purposes.…”

Section: Scientific Governance Of Model-to-datamentioning

confidence: 99%

Bringing Code to Data: Do Not Forget Governance

Suver¹,

Thorogood²,

Doerr³

et al. 2020

J Med Internet Res

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Developing or independently evaluating algorithms in biomedical research is difficult because of restrictions on access to clinical data. Access is restricted because of privacy concerns, the proprietary treatment of data by institutions (fueled in part by the cost of data hosting, curation, and distribution), concerns over misuse, and the complexities of applicable regulatory frameworks. The use of cloud technology and services can address many of the barriers to data sharing. For example, researchers can access data in high performance, secure, and auditable cloud computing environments without the need for copying or downloading. An alternative path to accessing data sets requiring additional protection is the model-to-data approach. In model-to-data, researchers submit algorithms to run on secure data sets that remain hidden. Model-to-data is designed to enhance security and local control while enabling communities of researchers to generate new knowledge from sequestered data. Model-to-data has not yet been widely implemented, but pilots have demonstrated its utility when technical or legal constraints preclude other methods of sharing. We argue that model-to-data can make a valuable addition to our data sharing arsenal, with 2 caveats. First, model-to-data should only be adopted where necessary to supplement rather than replace existing data-sharing approaches given that it requires significant resource commitments from data stewards and limits scientific freedom, reproducibility, and scalability. Second, although model-to-data reduces concerns over data privacy and loss of local control when sharing clinical data, it is not an ethical panacea. Data stewards will remain hesitant to adopt model-to-data approaches without guidance on how to do so responsibly. To address this gap, we explored how commitments to open science, reproducibility, security, respect for data subjects, and research ethics oversight must be re-evaluated in a model-to-data context.

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“…[ 39 40 41 42 43 ] Clinical laboratories face additional challenges when implementing high complexity and/or high-risk medical devices coupled with software solutions as laboratory-developed tests (LDTs). [ 44 45 46 ] For example, even when using an FDA-authorized whole-slide imaging device, the approval or clearance does not eliminate the need for an individual laboratory to verify the performance of these systems for the specific intended diagnostic purpose. Specifically, Clinical Laboratory Improvement Amendments of 1988 or CLIA '88 in the US requires at least verification[ 47 ] and substantial adaptation to implement.…”

Section: T He M Ultifaceted N mentioning

confidence: 99%

A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients

Marble

Huang

Dudgeon

et al. 2020

Journal of Pathology Informatics

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Unlocking the full potential of pathology data by gaining computational access to histological pixel data and metadata (digital pathology) is one of the key promises of computational pathology. Despite scientific progress and several regulatory approvals for primary diagnosis using whole-slide imaging, true clinical adoption at scale is slower than anticipated. In the U.S., advances in digital pathology are often siloed pursuits by individual stakeholders, and to our knowledge, there has not been a systematic approach to advance the field through a regulatory science initiative. The Alliance for Digital Pathology ( the Alliance ) is a recently established, volunteer, collaborative, regulatory science initiative to standardize digital pathology processes to speed up innovation to patients. The purpose is: (1) to account for the patient perspective by including patient advocacy; (2) to investigate and develop methods and tools for the evaluation of effectiveness, safety, and quality to specify risks and benefits in the precompetitive phase; (3) to help strategize the sequence of clinically meaningful deliverables; (4) to encourage and streamline the development of ground-truth data sets for machine learning model development and validation; and (5) to clarify regulatory pathways by investigating relevant regulatory science questions. The Alliance accepts participation from all stakeholders, and we solicit clinically relevant proposals that will benefit the field at large. The initiative will dissolve once a clinical, interoperable, modularized, integrated solution (from tissue acquisition to diagnostic algorithm) has been implemented. In times of rapidly evolving discoveries, scientific input from subject-matter experts is one essential element to inform regulatory guidance and decision-making. The Alliance aims to establish and promote synergistic regulatory science efforts that will leverage diverse inputs to move digital pathology forward and ultimately improve patient care.

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Diagnostics Reform and Harmonization of Clinical Laboratory Testing

Cited by 24 publications

References 5 publications

Challenges in the clinical implementation of precision medicine companion diagnostics

Challenges in the clinical implementation of precision medicine companion diagnostics

Bringing Code to Data: Do Not Forget Governance

A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients

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