4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Fit‐for‐Purpose Biometric Monitoring Technologies: Leveraging the Laboratory Biomarker Experience

      review-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Biometric monitoring technologies (BioMeTs) are becoming increasingly common to aid data collection in clinical trials and practice. The state of BioMeTs, and associated digitally measured biomarkers, is highly reminiscent of the field of laboratory biomarkers 2 decades ago. In this review, we have summarized and leveraged historical perspectives, and lessons learned from laboratory biomarkers as they apply to BioMeTs. Both categories share common features, including goals and roles in biomedical research, definitions, and many elements of the biomarker qualification framework. They can also be classified based on the underlying technology, each with distinct features and performance characteristics, which require bench and human experimentation testing phases. In contrast to laboratory biomarkers, digitally measured biomarkers require prospective data collection for purposes of analytical validation in human subjects, lack well‐established and widely accepted performance characteristics, require human factor testing, and, for many applications, access to raw (sample‐level) data. Novel methods to handle large volumes of data, as well as security and data rights requirements add to the complexity of this emerging field. Our review highlights the need for a common framework with appropriate vocabulary and standardized approaches to evaluate digitally measured biomarkers, including defining performance characteristics and acceptance criteria. Additionally, the need for human factor testing drives early patient engagement during technology development. Finally, use of BioMeTs requires a relatively high degree of technology literacy among both study participants and healthcare professionals. Transparency of data generation and the need for novel analytical and statistical tools creates opportunities for precompetitive collaborations.

          Related collections

          Most cited references70

          • Record: found
          • Abstract: not found
          • Article: not found

          Biomarkers and surrogate endpoints: preferred definitions and conceptual framework.

          (2001)
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network

            Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow1. Widely available digital ECG data and the algorithmic paradigm of deep learning2 present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, a comprehensive evaluation of an end-to-end deep learning approach for ECG analysis across a wide variety of diagnostic classes has not been previously reported. Here, we develop a deep neural network (DNN) to classify 12 rhythm classes using 91,232 single-lead ECGs from 53,549 patients who used a single-lead ambulatory ECG monitoring device. When validated against an independent test dataset annotated by a consensus committee of board-certified practicing cardiologists, the DNN achieved an average area under the receiver operating characteristic curve (ROC) of 0.97. The average F1 score, which is the harmonic mean of the positive predictive value and sensitivity, for the DNN (0.837) exceeded that of average cardiologists (0.780). With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes. These findings demonstrate that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists. If confirmed in clinical settings, this approach could reduce the rate of misdiagnosed computerized ECG interpretations and improve the efficiency of expert human ECG interpretation by accurately triaging or prioritizing the most urgent conditions.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found

              Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing

              As more clinically relevant cancer genes are identified, comprehensive diagnostic approaches are needed to match patients to therapies, raising the challenge of optimization and analytical validation of assays that interrogate millions of bases of cancer genomes altered by multiple mechanisms. Here we describe a test based on massively parallel DNA sequencing to characterize base substitutions, short insertions and deletions (indels), copy number alterations and selected fusions across 287 cancer-related genes from routine formalin-fixed and paraffin-embedded (FFPE) clinical specimens. We implemented a practical validation strategy with reference samples of pooled cell lines that model key determinants of accuracy, including mutant allele frequency, indel length and amplitude of copy change. Test sensitivity achieved was 95-99% across alteration types, with high specificity (positive predictive value >99%). We confirmed accuracy using 249 FFPE cancer specimens characterized by established assays. Application of the test to 2,221 clinical cases revealed clinically actionable alterations in 76% of tumors, three times the number of actionable alterations detected by current diagnostic tests.
                Bookmark

                Author and article information

                Contributors
                alan.godfrey@northumbria.ac.uk
                Journal
                Clin Transl Sci
                Clin Transl Sci
                10.1111/(ISSN)1752-8062
                CTS
                Clinical and Translational Science
                John Wiley and Sons Inc. (Hoboken )
                1752-8054
                1752-8062
                25 August 2020
                January 2021
                : 14
                : 1 ( doiID: 10.1111/cts.v14.1 )
                : 62-74
                Affiliations
                [ 1 ] Department of Computer and Information Sciences Northumbria University Newcastle‐upon‐Tyne UK
                [ 2 ] Byteflies Antwerp Belgium
                [ 3 ] Department of Electrical Computer, and Systems Engineering Case Western Reserve University Cleveland Ohio USA
                [ 4 ] Philips Monroeville Pennsylvania USA
                [ 5 ] ClinMed LLC Dayton New Jersey USA
                [ 6 ] Curis Advisors Cambridge Massachusetts USA
                [ 7 ] Cambridge Cognition Boston Massachusetts USA
                [ 8 ] Office of Clinical Pharmacology Office of Translational Sciences Center for Drug Evaluation and Research, US Food and Drug Administration Silver Spring Maryland USA
                [ 9 ] Early Clinical Development Pfizer Inc. Cambridge Massachusetts USA
                [ 10 ] Takeda Pharmaceuticals International Co. Cambridge Massachusetts USA
                [ 11 ] Lineberger Comprehensive Cancer Center University of North Carolina North Carolina USA
                [ 12 ] Department of Biostatistics Johns Hopkins Bloomberg School of Public Health Baltimore Maryland USA
                [ 13 ] Foresite Capital Boston Massachusetts USA
                [ 14 ] Koneksa Health Inc. New York New York USA
                Author notes
                [*] [* ] Correspondence: Alan Godfrey ( alan.godfrey@ 123456northumbria.ac.uk )

                Article
                CTS12865
                10.1111/cts.12865
                7877826
                32770726
                2c892bf9-4895-4baa-b161-f8261ce6a616
                © 2020 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of the American Society for Clinical Pharmacology and Therapeutics.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 09 June 2020
                : 22 July 2020
                Page count
                Figures: 2, Tables: 3, Pages: 13, Words: 11576
                Categories
                Review
                Reviews
                Review
                Custom metadata
                2.0
                January 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.7 mode:remove_FC converted:11.02.2021

                Medicine
                Medicine

                Comments

                Comment on this article