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Viswanathan VS, Parmar V, Madabhushi A. Towards equitable AI in oncology. Nat Rev Clin Oncol 2024; 21:628-637. [PMID: 38849530 DOI: 10.1038/s41571-024-00909-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2024] [Indexed: 06/09/2024]
Abstract
Artificial intelligence (AI) stands at the threshold of revolutionizing clinical oncology, with considerable potential to improve early cancer detection and risk assessment, and to enable more accurate personalized treatment recommendations. However, a notable imbalance exists in the distribution of the benefits of AI, which disproportionately favour those living in specific geographical locations and in specific populations. In this Perspective, we discuss the need to foster the development of equitable AI tools that are both accurate in and accessible to a diverse range of patient populations, including those in low-income to middle-income countries. We also discuss some of the challenges and potential solutions in attaining equitable AI, including addressing the historically limited representation of diverse populations in existing clinical datasets and the use of inadequate clinical validation methods. Additionally, we focus on extant sources of inequity including the type of model approach (such as deep learning, and feature engineering-based methods), the implications of dataset curation strategies, the need for rigorous validation across a variety of populations and settings, and the risk of introducing contextual bias that comes with developing tools predominantly in high-income countries.
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Affiliation(s)
| | - Vani Parmar
- Department of Breast Surgical Oncology, Punyashlok Ahilyadevi Holkar Head & Neck Cancer Institute of India, Mumbai, India
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
- Atlanta Veterans Administration Medical Center, Atlanta, GA, USA.
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2
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Petrie J, Loban A, Turton E, Derebecka J, North S, Herbert E, Hind D. "Reality is frequently inaccurate" A case study examining the whens and whys of post-live database changes in a UK clinical trials unit *Douglas Adams. Contemp Clin Trials 2024; 142:107573. [PMID: 38759865 DOI: 10.1016/j.cct.2024.107573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 04/16/2024] [Accepted: 05/11/2024] [Indexed: 05/19/2024]
Abstract
INTRODUCTION Accurately estimating the costs of clinical trials is challenging. There is currently no reference class data to allow researchers to understand the potential costs associated with database change management in clinical trials. METHODS We used a case-based approach, summarising post-live changes in eleven clinical trial databases managed by Sheffield Clinical Trials Research Unit. We reviewed the database specifications for each trial and summarised the number of changes, change type, change category, and timing of changes. We pooled our experiences and made observations in relation to key themes. RESULTS Median total number of changes across the eleven trials was 71 (range 40-155) and median number of changes per study week was 0.48 (range 0.32-1.34). The most common change type was modification (median 39, range 20-90), followed by additions (median 32, range 18-55), then deletions (median 7, range 1-12). In our sample, changes were more common in the first half of the trial's lifespan, regardless of its overall duration. Trials which saw continuous changes seemed more likely to be external pilots or trials in areas where the trial team was either less experienced overall or within the particular therapeutic area. CONCLUSIONS Researchers should plan trials with the expectation that clinical trial databases will require changes within the life of the trial, particularly in the early stages or with a less experienced trial team. More research is required to understand potential differences between clinical trial units and database types.
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Affiliation(s)
- Jennifer Petrie
- Clinical Trials Research Unit, School of Health and Related Research, The University of Sheffield, Sheffield, S1 4DA, UK.
| | - Amanda Loban
- Clinical Trials Research Unit, School of Health and Related Research, The University of Sheffield, Sheffield, S1 4DA, UK
| | - Emily Turton
- Clinical Trials Research Unit, School of Health and Related Research, The University of Sheffield, Sheffield, S1 4DA, UK
| | - Julia Derebecka
- Department of Computer Science, The University of Sheffield, Sheffield, S1 4DA, UK
| | - Siobhán North
- Department of Computer Science, The University of Sheffield, Sheffield, S1 4DA, UK
| | - Esther Herbert
- Clinical Trials Research Unit, School of Health and Related Research, The University of Sheffield, Sheffield, S1 4DA, UK
| | - Daniel Hind
- Clinical Trials Research Unit, School of Health and Related Research, The University of Sheffield, Sheffield, S1 4DA, UK
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Weishaar K, Shiu KB, Wright ZM. A Practical Guide to Clinical Studies in Veterinary Oncology. Vet Clin North Am Small Anim Pract 2024; 54:591-601. [PMID: 38176955 DOI: 10.1016/j.cvsm.2023.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2024]
Abstract
This article explains the authors' experiences about opportunities, perspectives, and considerations required to initiate clinical studies in a veterinary oncology practice. These details include the infrastructure required for appropriate study training for all staff. Negotiation of scope of work and fees for service with study sponsors is also discussed. Finally, although generally similar, the article also describes management of clinical studies in academic and private practice settings.
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Affiliation(s)
- Kristen Weishaar
- Colorado State University Flint Animal Cancer Center, CSU Veterinary Teaching Hospital, 300 West Drake Road, Fort Collins, CO 80523, USA.
| | - Kai-Biu Shiu
- VCA VESVSC, 1612 North High Point Road, Middleton, WI 53562, USA
| | - Zachary M Wright
- VCA Animal Diagnostic Clinic, 4444 Trinity Mills Suite 100, Dallas, TX 75287, USA
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Isere EE, Omorogbe NE. Quality Management in Clinical and Public Health Research: A Panacea for Minimising and Eliminating Protocol Deviations in Research Operations. Niger Med J 2024; 65:367-375. [PMID: 39022564 PMCID: PMC11249487 DOI: 10.60787/nmj-v65i3-421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/20/2024] Open
Abstract
A quality management system for clinical and public health research operations is indispensable because it ensures the integrity and reliability of research outcomes. By implementing a robust quality management practice in research implementation and operation, research teams can uphold the highest standard of research conduct, thereby enhancing the credibility and trustworthiness of research findings. This paper elucidates the significance and role of a quality management system in clinical and public health research operations and its efficacy in minimising and eliminating protocol deviations and highlights the key steps in setting up a quality management system for research operations.
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Affiliation(s)
- Elvis Efe Isere
- Department of Epidemiology and Medical Statistics, Faculty of Public Health, University of Ibadan, Nigeria
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Carmezim J, Satorra P, Peñafiel J, García-Lerma E, Pallarès N, Santos N, Tebé C. REDCapDM: An R package with a set of data management tools for a REDCap project. BMC Med Res Methodol 2024; 24:55. [PMID: 38429658 PMCID: PMC10905808 DOI: 10.1186/s12874-024-02178-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 02/12/2024] [Indexed: 03/03/2024] Open
Abstract
BACKGROUND Research Electronic Data CAPture (REDCap) is a web application for creating and managing online surveys and databases. Clinical data management is an essential process before performing any statistical analysis to ensure the quality and reliability of study information. Processing REDCap data in R can be complex and often benefits from automation. While there are several R packages available for specific tasks, none offer an expansive approach to data management. RESULTS The REDCapDM is an R package for accessing and managing REDCap data. It imports data from REDCap to R using either an API connection or the files in R format exported directly from REDCap. It has several functions for data processing and transformation, and it helps to generate and manage queries to clarify or resolve discrepancies found in the data. CONCLUSION The REDCapDM package is a valuable tool for data scientists and clinical data managers who use REDCap and R. It assists in tasks such as importing, processing, and quality-checking data from their research studies.
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Affiliation(s)
- João Carmezim
- Biostatistics Support and Research Unit, Germans Trias i Pujol Research Institute and Hospital (IGTP), Carretera de Can Ruti, Camí de Les Escoles S/N, 08916, Badalona, Spain
| | - Pau Satorra
- Biostatistics Support and Research Unit, Germans Trias i Pujol Research Institute and Hospital (IGTP), Carretera de Can Ruti, Camí de Les Escoles S/N, 08916, Badalona, Spain
| | - Judith Peñafiel
- Biostatistics Unit, Institut d'Investigació Biomèdica de Bellvitge, Hospitalet de Llobregat, Spain
- Department of Basic Clinical Practice, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Esther García-Lerma
- Biostatistics Unit, Institut d'Investigació Biomèdica de Bellvitge, Hospitalet de Llobregat, Spain
| | - Natàlia Pallarès
- Biostatistics Support and Research Unit, Germans Trias i Pujol Research Institute and Hospital (IGTP), Carretera de Can Ruti, Camí de Les Escoles S/N, 08916, Badalona, Spain
- Department of Basic Clinical Practice, School of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain
| | - Naiara Santos
- Biostatistics Unit, Institut d'Investigació Biomèdica de Bellvitge, Hospitalet de Llobregat, Spain
| | - Cristian Tebé
- Biostatistics Support and Research Unit, Germans Trias i Pujol Research Institute and Hospital (IGTP), Carretera de Can Ruti, Camí de Les Escoles S/N, 08916, Badalona, Spain.
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Liakos A, Pagkalidou E, Karagiannis T, Malandris K, Avgerinos I, Gigi E, Bekiari E, Haidich AB, Tsapas A. A Simple Guide to Randomized Controlled Trials. INT J LOW EXTR WOUND 2024:15347346241236385. [PMID: 38419478 DOI: 10.1177/15347346241236385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Abstract
Randomized controlled trials represent the cornerstone for the regulatory approval of drugs and evidence-based medicine and policy. Compared with observational studies random assignment of participants to each study arm guarantees an equal distribution of potential confounders thus achieving impartiality in the evaluation of between group differences and allowing for causal inferences to be drawn. These complex and costly medical experiments are tightly regulated and require substantial planning with great attention to several methodological aspects ranging from allocation concealment and blinding to sample size estimation, statistical analysis, and handling of protocol deviations. This brief guide offers useful insights into the design, conduct, and interpretation of clinical trial findings for beginners.
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Affiliation(s)
- Aris Liakos
- Second Medical Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Eirini Pagkalidou
- Department of Hygiene, Social Preventive Medicine and Medical Statistics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Thomas Karagiannis
- Second Medical Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | | | - Ioannis Avgerinos
- Second Medical Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Eleni Gigi
- Second Medical Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Eleni Bekiari
- Second Medical Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Anna-Bettina Haidich
- Department of Hygiene, Social Preventive Medicine and Medical Statistics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Apostolos Tsapas
- Second Medical Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Harris Manchester College, University of Oxford, Oxford, UK
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Alberti P, Argyriou AA, Bruna J, Damaj MI, Faithfull S, Harding A, Hoke A, Knoerl R, Kolb N, Li T, Park SB, Staff NP, Tamburin S, Thomas S, Smith EL. Considerations for establishing and maintaining international research collaboration: the example of chemotherapy-induced peripheral neurotoxicity (CIPN)-a white paper. Support Care Cancer 2024; 32:117. [PMID: 38244122 PMCID: PMC10799817 DOI: 10.1007/s00520-023-08301-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 12/28/2023] [Indexed: 01/22/2024]
Abstract
PURPOSE This white paper provides guidance regarding the process for establishing and maintaining international collaborations to conduct oncology/neurology-focused chemotherapy-induced peripheral neurotoxicity (CIPN) research. METHODS An international multidisciplinary group of CIPN scientists, clinicians, research administrators, and legal experts have pooled their collective knowledge regarding recommendations for establishing and maintaining international collaboration to foster advancement of CIPN science. RESULTS Experts provide recommendations in 10 categories: (1) preclinical and (2) clinical research collaboration; (3) collaborators and consortiums; (4) communication; (5) funding; (6) international regulatory standards; (7) staff training; (8) data management, quality control, and data sharing; (9) dissemination across disciplines and countries; and (10) additional recommendations about feasibility, policy, and mentorship. CONCLUSION Recommendations to establish and maintain international CIPN research collaboration will promote the inclusion of more diverse research participants, increasing consideration of cultural and genetic factors that are essential to inform innovative precision medicine interventions and propel scientific discovery to benefit cancer survivors worldwide. RELEVANCE TO INFORM RESEARCH POLICY Our suggested guidelines for establishing and maintaining international collaborations to conduct oncology/neurology-focused chemotherapy-induced peripheral neurotoxicity (CIPN) research set forth a challenge to multinational science, clinical, and policy leaders to (1) develop simple, streamlined research designs; (2) address logistical barriers; (3) simplify and standardize regulatory requirements across countries; (4) increase funding to support international collaboration; and (5) foster faculty mentorship.
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Affiliation(s)
- Paola Alberti
- University of Milano-Bicocca, School of Medicine and Surgery, Monza, Italy
- Fondazione IRCCS San Gerardo dei Tintori, Monza, Italy
| | | | - Jordi Bruna
- Hospital Universitari de Bellvitge, Neuro-Oncology Unit, Institut Catala d'Oncologia (IDIBELL), L'Hospitalet del Llobregat, Barcelona, Spain
| | - M Imad Damaj
- Department of Pharmacology and Toxicology and Translational Research Initiative for Pain and Neuropathy, Virginia Commonwealth University, Richmond, VA, USA
| | - Sara Faithfull
- Trinity College Dublin, School of Medicine, Dublin, Ireland
- University of Dublin, Trinity Centre for Health Sciences St. James's Hospital Campus, Dublin, Ireland
| | - Alice Harding
- University of Alabama at Birmingham, Office of Sponsored Programs, Birmingham, AL, USA
| | - Ahmet Hoke
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Robert Knoerl
- Department of Health Behavior and Biological Sciences, University of Michigan School of Nursing, Ann Arbor, MI, USA
| | - Noah Kolb
- Department of Neurological Sciences, University of Vermont Robert Larner College of Medicine, Burlington, VT, USA
| | - Tiffany Li
- Faculty of Medicine and Health, University of Sydney, Brain and Mind Centre and School of Medical Sciences, Sydney, Australia
| | - Susanna B Park
- Faculty of Medicine and Health, University of Sydney, Brain and Mind Centre and School of Medical Sciences, Sydney, Australia
| | - Nathan P Staff
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy
| | - Simone Thomas
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Ellen Lavoie Smith
- Department of Acute, Chronic & Continuing Care, University of Alabama at Birmingham School of Nursing, Birmingham, AL, USA.
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Pandey A, Borah S, Chaudhary B, Rana S, Singh H, Nadkarni A, Kaur H. NBSP: an online centralized database management system for a newborn sickle cell program in India. Front Digit Health 2023; 5:1204550. [PMID: 37781453 PMCID: PMC10534972 DOI: 10.3389/fdgth.2023.1204550] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 08/14/2023] [Indexed: 10/03/2023] Open
Affiliation(s)
- Apoorva Pandey
- Division of Epidemiological and Communicable Diseases, ICMR Hqrs., Ramalingaswami Bhavan, Delhi, India
| | - Sapan Borah
- Department of Haematogenetics, ICMR-National Institute of Immunohaematology, Mumbai, India
- Department of Biotechnology, National Institute of Pharmaceutical Education and Research (NIPER)-Ahmedabad, Gandhinagar, Gujarat, India
| | - Bhavik Chaudhary
- Division of Biomedical Informatics, ICMR Hqrs., Ramalingaswami Bhavan, Delhi, India
| | - Shweta Rana
- Division of Biomedical Informatics, ICMR Hqrs., Ramalingaswami Bhavan, Delhi, India
| | - Harpreet Singh
- Division of Biomedical Informatics, ICMR Hqrs., Ramalingaswami Bhavan, Delhi, India
| | - Anita Nadkarni
- Department of Haematogenetics, ICMR-National Institute of Immunohaematology, Mumbai, India
| | - Harpreet Kaur
- Division of Epidemiological and Communicable Diseases, ICMR Hqrs., Ramalingaswami Bhavan, Delhi, India
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Oronsky B, Burbano E, Stirn M, Brechlin J, Abrouk N, Caroen S, Coyle A, Williams J, Cabrales P, Reid TR. Data Management 101 for drug developers: A peek behind the curtain. Clin Transl Sci 2023; 16:1497-1509. [PMID: 37382299 PMCID: PMC10499417 DOI: 10.1111/cts.13582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 06/11/2023] [Accepted: 06/20/2023] [Indexed: 06/30/2023] Open
Abstract
In drug development a frequently used phrase is "data-driven". Just as high-test gas fuels a car, so drug development "runs on" high-quality data; hence, good data management practices, which involve case report form design, data entry, data capture, data validation, medical coding, database closure, and database locking, are critically important. This review covers the essentials of clinical data management (CDM) for the United States. It is intended to demystify CDM, which means nothing more esoteric than the collection, organization, maintenance, and analysis of data for clinical trials. The review is written with those who are new to drug development in mind and assumes only a passing familiarity with the terms and concepts that are introduced. However, its relevance may also extend to experienced professionals that feel the need to brush up on the basics. For added color and context, the review includes real-world examples with RRx-001, a new molecular entity in phase III and with fast-track status in head and neck cancer, and AdAPT-001, an oncolytic adenovirus armed with a transforming growth factor-beta (TGF-β) trap in a phase I/II clinical trial with which the authors, as employees of the biopharmaceutical company, EpicentRx, are closely involved. An alphabetized glossary of key terms and acronyms used throughout this review is also included for easy reference.
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Affiliation(s)
| | | | | | | | - Nacer Abrouk
- Clinical Trial InnovationsMountain ViewCaliforniaUSA
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Niangoran S, Journot V, Marcy O, Anglaret X, Alioum A. Performance of four centralized statistical monitoring methods for early detection of an atypical center in a multicenter study. Contemp Clin Trials Commun 2023; 34:101168. [PMID: 37425338 PMCID: PMC10328794 DOI: 10.1016/j.conctc.2023.101168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 06/02/2023] [Accepted: 06/18/2023] [Indexed: 07/11/2023] Open
Abstract
Background Ensuring the quality of data is essential for the credibility of a multicenter clinical trial. Centralized Statistical Monitoring (CSM) of data allows the detection of a center in which the distribution of a specific variable is atypical compared to other centers. The ideal CSM method should allow early detection of problem and therefore involve the fewest possible participants. Methods We simulated clinical trials and compared the performance of four CSM methods (Student, Hatayama, Desmet, Distance) to detect whether the distribution of a quantitative variable was atypical in one center in relation to the others, with different numbers of participants and different mean deviation amplitudes. Results The Student and Hatayama methods had good sensitivity but poor specificity, which disqualifies them for practical use in CSM. The Desmet and Distance methods had very high specificity for detecting all the mean deviations tested (including small values) but low sensitivity with mean deviations less than 50%. Conclusion Although the Student and Hatayama methods are more sensitive, their low specificity would lead to too many alerts being triggered, which would result in additional unnecessary control work to ensure data quality. The Desmet and Distance methods have low sensitivity when the deviation from the mean is low, suggesting that the CSM should be used alongside other conventional monitoring procedures rather than replacing them. However, they have excellent specificity, which suggests they can be applied routinely, since using them takes up no time at central level and does not cause any unnecessary workload in investigating centers.
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Affiliation(s)
- Serge Niangoran
- University of Bordeaux, National Institute for Health and Medical Research (INSERM) UMR 1219, Bordeaux Population Health Research Center, Bordeaux, France
- Research Institute for Sustainable Development (IRD) EMR 271, Bordeaux, France
- Programme PACCI, Abidjan, Côte d'Ivoire
| | - Valérie Journot
- University of Bordeaux, National Institute for Health and Medical Research (INSERM) UMR 1219, Bordeaux Population Health Research Center, Bordeaux, France
- Research Institute for Sustainable Development (IRD) EMR 271, Bordeaux, France
| | - Olivier Marcy
- University of Bordeaux, National Institute for Health and Medical Research (INSERM) UMR 1219, Bordeaux Population Health Research Center, Bordeaux, France
- Research Institute for Sustainable Development (IRD) EMR 271, Bordeaux, France
| | - Xavier Anglaret
- University of Bordeaux, National Institute for Health and Medical Research (INSERM) UMR 1219, Bordeaux Population Health Research Center, Bordeaux, France
- Research Institute for Sustainable Development (IRD) EMR 271, Bordeaux, France
| | - Amadou Alioum
- University of Bordeaux, National Institute for Health and Medical Research (INSERM) UMR 1219, Bordeaux Population Health Research Center, Bordeaux, France
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Bernardi FA, Mello de Oliveira B, Bettiol Yamada D, Artifon M, Schmidt AM, Machado Scheibe V, Alves D, Félix TM. The Minimum Data Set for Rare Diseases: Systematic Review. J Med Internet Res 2023; 25:e44641. [PMID: 37498666 PMCID: PMC10415943 DOI: 10.2196/44641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/24/2023] [Accepted: 06/27/2023] [Indexed: 07/28/2023] Open
Abstract
BACKGROUND The minimum data set (MDS) is a collection of data elements to be grouped using a standard approach to allow the use of data for clinical and research purposes. Health data are typically voluminous, complex, and sometimes too ambiguous to generate indicators that can provide knowledge and information on health. This complexity extends further to the rare disease (RD) domain. MDSs are essential for health surveillance as they help provide services and generate recommended population indicators. There is a bottleneck in international literature that reveals a global problem with data collection, recording, and structuring in RD. OBJECTIVE This study aimed to identify and analyze the MDSs used for RD in health care networks worldwide and compare them with World Health Organization (WHO) guidelines. METHODS The population, concept, and context methodology proposed by the Joanna Briggs Institute was used to define the research question of this systematic review. A total of 4 databases were reviewed, and all the processes were reported using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. The data elements were analyzed, extracted, and organized into 10 categories according to WHO digital health guidelines. The quality assessment used the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist. RESULTS We included 20 studies in our review, 70% (n=14) of which focused on a specific health domain and 30% (n=6) of which referred to RD in general. WHO recommends that health systems and networks use standard terminology to exchange data, information, knowledge, and intelligence in health. However, there was a lack of terminological standardization of the concepts in MDSs. Moreover, the selected studies did not follow the same standard structure for classifying the data from their MDSs. All studies presented MDSs with limitations or restrictions because they covered only a specific RD, or their scope of application was restricted to a specific context or geographic region. Data science methods and clinical experience were used to design, structure, and recommend a fundamental global MDS for RD patient records in health care networks. CONCLUSIONS Our study highlights the difficulties in standardizing and categorizing findings from MDSs for RD because of the varying structures used in different studies. The fundamental RD MDS designed in this study comprehensively covers the data needs in the clinical and management sectors. These results can help public policy makers support other aspects of their policies. We highlight the potential of our results to help strategic decisions related to RD. TRIAL REGISTRATION PROSPERO CRD42021221593; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221593. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1016/j.procs.2021.12.034.
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Affiliation(s)
- Filipe Andrade Bernardi
- Health Intelligence Laboratory, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirão Preto, Brazil
- Brazilian Rare Disease Network, Porto Alegre, Brazil
| | - Bibiana Mello de Oliveira
- Brazilian Rare Disease Network, Porto Alegre, Brazil
- Medical Genetics Service, Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil
- Federal University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Diego Bettiol Yamada
- Health Intelligence Laboratory, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirão Preto, Brazil
- Brazilian Rare Disease Network, Porto Alegre, Brazil
| | - Milena Artifon
- Brazilian Rare Disease Network, Porto Alegre, Brazil
- Medical Genetics Service, Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil
| | - Amanda Maria Schmidt
- Brazilian Rare Disease Network, Porto Alegre, Brazil
- Medical Genetics Service, Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil
| | - Victória Machado Scheibe
- Brazilian Rare Disease Network, Porto Alegre, Brazil
- Medical Genetics Service, Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil
- Faculty of Medicine, Lutheran University of Brazil, Canoas, Brazil
| | - Domingos Alves
- Health Intelligence Laboratory, Ribeirao Preto Medical School, University of Sao Paulo, Ribeirão Preto, Brazil
- Brazilian Rare Disease Network, Porto Alegre, Brazil
| | - Têmis Maria Félix
- Brazilian Rare Disease Network, Porto Alegre, Brazil
- Medical Genetics Service, Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil
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Lima VC, Rijo RPCL, Bernardi FA, Filho MEC, Barbosa-Junior F, Pellison FC, Galliez RM, Kritski AL, Alves D. REDbox: a comprehensive semantic framework for data collection and management in tuberculosis research. Sci Rep 2023; 13:7686. [PMID: 37169802 PMCID: PMC10173910 DOI: 10.1038/s41598-023-33492-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 04/13/2023] [Indexed: 05/13/2023] Open
Abstract
Clinical research outcomes depend on the correct definition of the research protocol, the data collection strategy, and the data management plan. Furthermore, researchers often need to work within challenging contexts, as is the case in tuberculosis services, where human and technological resources for research may be scarce. Electronic Data Capture Systems mitigate such risks and enable a reliable environment to conduct health research and promote result dissemination and data reusability. The proposed solution is based on needs pinpointed by researchers, considering the need for an accommodating solution to conduct research in low-resource environments. The REDbox framework was developed to facilitate data collection, management, sharing, and availability in tuberculosis research and improve the user experience through user-friendly, web-based tools. REDbox combines elements of the REDCap and KoBoToolbox electronic data capture systems and semantics to deliver new valuable tools that meet the needs of tuberculosis researchers in Brazil. The framework was implemented in five cross-institutional, nationwide projects to evaluate the users' perceptions of the system's usefulness and the information and user experience. Seventeen responses (representing 40% of active users) to an anonymous survey distributed to active users indicated that REDbox was perceived to be helpful for the particular audience of researchers and health professionals. The relevance of this article lies in the innovative approach to supporting tuberculosis research by combining existing technologies and tailoring supporting features.
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Affiliation(s)
- Vinícius Costa Lima
- Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil.
| | - Rui Pedro Charters Lopes Rijo
- Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
- School of Technology and Management, Polytechnic Institute of Leiria, Leiria, Portugal
- Institute for Systems Engineering and Computers at Coimbra, Coimbra, Portugal
- Center for Research in Health Technologies and Services, Faculty of Medicine, University of Porto, Porto, Portugal
| | | | | | | | | | - Rafael Mello Galliez
- Faculty of Medicine, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
| | | | - Domingos Alves
- Department of Social Medicine, Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto, Brazil
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Nourani A, Ayatollahi H, Solaymani-Dodaran M. Data management system for diabetes clinical trials: a pre-post evaluation study. BMC Med Inform Decis Mak 2023; 23:14. [PMID: 36670481 PMCID: PMC9854045 DOI: 10.1186/s12911-023-02110-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 01/13/2023] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND Data management system for diabetes clinical trials is used to support clinical data management processes. The purpose of this study was to evaluate the quality and usability of this system from the users' perspectives. METHODS This study was conducted in 2020, and the pre-post evaluation method was used to examine the quality and usability of the designed system. Initially, a questionnaire was designed and distributed among the researchers who were involved in the diabetes clinical trials (n = 30) to investigate their expectations. Then, the researchers were asked to use the system and explain their perspectives about it by completing two questionnaires. RESULTS There was no statistically significant differences between the users' perspectives about the information quality, service quality, achievements, and communication before and after using the system. However, in terms of the system quality (P = 0.042) and users' autonomy (P = 0.026), the users' expectations were greater than the system performance. The system usability was at a good level based on the users' opinions. CONCLUSION It seems that the designed system largely met the users' expectations in most areas. However, the system quality and users' autonomy need further attentions. In addition, the system should be used in multicenter trials and re-evaluated by a larger group of users.
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Affiliation(s)
- Aynaz Nourani
- grid.412763.50000 0004 0442 8645Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran
| | - Haleh Ayatollahi
- grid.411746.10000 0004 4911 7066Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran ,grid.411746.10000 0004 4911 7066Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Masoud Solaymani-Dodaran
- grid.411746.10000 0004 4911 7066Department of Epidemiology, Iran University of Medical Sciences, Tehran, Iran
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What does a department need to get involved in clinical trials? Injury 2023:S0020-1383(23)00075-X. [PMID: 36725487 DOI: 10.1016/j.injury.2023.01.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 12/20/2022] [Accepted: 01/18/2023] [Indexed: 01/22/2023]
Abstract
Clinical trials are conducted to evaluate the usefulness of a drug, medical treatment, surgical intervention, radiological procedure, behavioral intervention, or preventive health care strategy. Clinical trials are challenging, time-consuming, and need careful planning and execution. There are certain requirements for an academic unit to be ready for conducting quality research, especially clinical trials. These include logistical concerns, infrastructure, appropriately trained human resources, conformity with ethical compliances and regulatory compliances as per the laws governing the respective geographical regions. Infrastructure requirements include research division space, robust data archival system, archival of imaging data, laboratory services, information technology division and facilities for the appropriate consenting process. Human resources such as principal investigators, data managers, clinical research assistants and biostatisticians are needed to execute the clinical trial as per the study design. Adherence to ethical and regulatory standards; during the conduct of the clinical trial are imperative. Strict compliance with prevailing governing laws in the country is a prerequisite for a department to get involved in clinical trials. The department also needs to put in place an institutional ethics committee; the composition of which is often governed by regulatory laws. The ethical aspects of the trial undergo a formal scrutiny by an Institute ethics committee that ensures quality care to the patient, and safeguards the patient's rights and privileges. The academic unit of any department forms the core for possible research and pursuit of clinical trials. A department with rich clinical experience and expertise in the filed aids in the execution of quality research. The academic department must consider grant writing and multiple center collaborative research projects in due course of time to further enhance the quality of clinical trials and research output.
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Essentials of data management: an overview. Pediatr Res 2023; 93:2-3. [PMID: 33603212 PMCID: PMC8371066 DOI: 10.1038/s41390-021-01389-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 12/27/2020] [Accepted: 01/06/2021] [Indexed: 01/28/2023]
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Promoting the quality and transparency of health research in China. J Clin Epidemiol 2022; 152:209-217. [PMID: 36220624 DOI: 10.1016/j.jclinepi.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 09/13/2022] [Accepted: 10/02/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND AND OBJECTIVES To review the efforts and progress in promoting quality and transparency of health research in China and to discuss how to improve. METHODS We focused on three different types of health research: clinical trials, systematic reviews, and clinical practice guidelines, and summarized China's progress from their registration, implementation, and reporting stages. RESULTS In the last 3 decades, China's major works include the following: 1) For registration, the trials and CPGs registries have been set up, and released policies to mandate registration and submission of protocols for trials. 2) For implementation, multiple clinical research and EBM centers have been established. The ResMan platform for data management has been created and required researchers to share results data. 3) For reporting, the Chinese EQUATOR Center has been set up. Translations and extensions of the CONSORT, PRISMA, and RIGHT have been published. 4) For traditional Chinese medicine (TCM), specific guidelines for the registration and reporting of TCM research have been developed. CONCLUSION China has made progress in promoting research quality and transparency, especially in research registration and reporting. Nevertheless, more can be done in data management and sharing for research implementation, and good publication practices in TCM.
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Comparison of 0.12% Chlorhexidine and a New Bone Bioactive Liquid, BBL, in Mouthwash for Oral Wound Healing: A Randomized, Double Blind Clinical Human Trial. J Pers Med 2022; 12:jpm12101725. [PMID: 36294864 PMCID: PMC9605239 DOI: 10.3390/jpm12101725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 10/08/2022] [Accepted: 10/13/2022] [Indexed: 11/16/2022] Open
Abstract
Following surgery, healing within the oral cavity occurs in a hostile environment, and proper oral care and hygiene are required to accelerate recovery. The aim of the current study is to investigate and compare the bioreactivity characteristics of mouthwashes based on either chlorhexidine (CHX) or a novel bone bioactive liquid (BBL) in terms of oral healing within seven days application post-surgery. A randomized, double blind clinical trial was conducted in 81 patients, wherein the mouthwashes were applied twice a day for a period of 7 days. The visual analog scale (VAS) protocol was applied to determine pain index scores. Early wound healing index (EHI) score was determined for evaluating oral cavity healing progress. No adverse effects were observed using the mouthwashes, but CHX application resulted in stained teeth. Applications of both CHX and BBL were sufficient to reduce pain over a period of 7 days. However, the BBL group demonstrated a statistically significant reduction in VAS scores starting on day 4. The EHI scores were significantly higher in the BBL group compared with the CHX group, independent of tooth location. No differences in either VAS or EHI scores due to gender were observed. Compared with the commercially available CHX mouthwash, application of the BBL mouthwash reduced pain and accelerated oral cavity healing to a greater extent, suggesting it effectively improves the oral cavity microenvironment at the wound site in mediating soft tissue regeneration.
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Nourani A, Ayatollahi H, Solaymani Dodaran M. Data management in diabetes clinical trials: a qualitative study. Trials 2022; 23:187. [PMID: 35241149 PMCID: PMC8895796 DOI: 10.1186/s13063-022-06110-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 02/15/2022] [Indexed: 11/16/2022] Open
Abstract
Background Clinical trials play an important role in expanding the knowledge of diabetes prevention, diagnosis, and treatment, and data management is one of the main issues in clinical trials. Lack of appropriate planning for data management in clinical trials may negatively influence achieving the desired results. The aim of this study was to explore data management processes in diabetes clinical trials in three research institutes in Iran. Method This was a qualitative study conducted in 2019. In this study, data were collected through in-depth semi-structured interviews with 16 researchers in three endocrinology and metabolism research institutes. To analyze data, the method of thematic analysis was used. Results The five themes that emerged from data analysis included (1) clinical trial data collection, (2) technologies used in data management, (3) data security and confidentiality management, (4) data quality management, and (5) data management standards. In general, the findings indicated that no clear and standard process was used for data management in diabetes clinical trials, and each research center executed its own methods and processes. Conclusion According to the results, the common methods of data management in diabetes clinical trials included a set of paper-based processes. It seems that using information technology can help facilitate data management processes in a variety of clinical trials, including diabetes clinical trials.
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Affiliation(s)
- Aynaz Nourani
- Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran
| | - Haleh Ayatollahi
- Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran. .,Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
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A Clinical Data Management System for Diabetes Clinical Trials. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:8421529. [PMID: 35251579 PMCID: PMC8894039 DOI: 10.1155/2022/8421529] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 12/27/2021] [Accepted: 02/02/2022] [Indexed: 01/23/2023]
Abstract
Background The use of novel medications and methods to prevent, diagnose, treat, and manage diabetes requires confirmation of safety and efficacy in a well-designed study prior to widespread adoption. Diabetes clinical trials are the studies that examine these issues. The aim of the present study was to develop a web-based system for data management in diabetes clinical trials. Methods The present research was a mixed-methods study conducted in 2019. To identify the required data elements and functions to develop the system, 60 researchers completed a questionnaire. The designed system was evaluated using two methods. The usability of the system was initially evaluated by a group of researchers (n = 6) using the think-aloud method, and after system improvement, the system functions were evaluated by other researchers (n = 30) using a questionnaire. Results The main data elements which were required to develop a case report form included “study data,” “participant's personal data,” and “clinical data.” The functional requirements of the system were “managing the study,” “creating case report forms,” “data management,” “data quality control,” and “data security and confidentiality.” After using the system, researchers rated the system functions at a “good” level (6.3 ± 0.73) on a seven-point Likert scale. Conclusion Given the complexity of the data management processes in diabetes clinical trials and the widespread use of information technologies in research, the use of clinical data management systems in diabetes clinical trials seems inevitable. The system developed in the current study can facilitate and improve the process of creating and managing case report forms as well as collecting data in diabetes clinical trials.
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Williams TB, Schmidtke C, Roessger K, Dieffenderfer V, Garza M, Zozus M. Informing training needs for the revised certified clinical data manager (CCDMTM) exam: analysis of results from the previous exam. JAMIA Open 2022; 5:ooac010. [PMID: 35274085 PMCID: PMC8903177 DOI: 10.1093/jamiaopen/ooac010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 11/29/2021] [Accepted: 02/08/2022] [Indexed: 11/12/2022] Open
Abstract
Objective To inform training needs for the revised Certified Clinical Data Manager (CCDMTM) Exam. Introduction Clinical data managers hold the responsibility for processing the data on which research conclusions and regulatory decisions are based, highlighting the importance of applying effective data management practices. The use of practice standards such as the Good Clinical Data Management Practices increases confidence in data, emphasizing that the study conclusions likely hold much more weight when utilizing standard practices. Methods A quantitative, descriptive study, and application of classic test theory was undertaken to analyze past data from the CCDMTM Exam to identify potential training needs. Data across 952 sequential exam attempts were pooled for analysis. Results Competency domain-level analysis identified training needs in 4 areas: design tasks; data processing tasks; programming tasks; and coordination and management tasks. Conclusions Analysis of past CCDMTM Exam results using classic test theory identified training needs reflective of exam takers. Training in the identified areas could benefit CCDMTM Exam takers and improve their ability to apply effective data management practices. While this may not be reflective of individual or organizational needs, recommendations for assessing individual and organizational training needs are provided. This study analyzed past data from the Certified Clinical Data ManagerTM Exam (CCDMTM) to identify potential training needs. Data across 952 sequential exam attempts were pooled for analysis. The study identified training needs in 4 areas of clinical data management: design tasks; data processing tasks; programming tasks; and coordination and management tasks. Training in the 4 identified areas could benefit CCDMTM Exam takers and improve their ability to apply effective data management practices.
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Affiliation(s)
- Tremaine Brueon Williams
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Carsten Schmidtke
- Department of Human Resource & Workforce Development, University of Arkansas, Fayetteville, Arkansas, USA
| | - Kevin Roessger
- Department of Adult and Lifelong Learning, University of Arkansas, Fayetteville, Arkansas, USA
| | - Vicki Dieffenderfer
- Department of Human Resource & Workforce Development, University of Arkansas, Fayetteville, Arkansas, USA
| | - Maryam Garza
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Meredith Zozus
- Department of Population Health Sciences, Joe R. and Teresa Lozano Long School of Medicine, University of Texas Health Science Center San Antonio, San Antonio, Texas, USA
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Tao S, Cui L, Chou WC, Lhatoo S, Zhang GQ. DaT3M: A Data Tracker for Multi-faceted Management of Multi-site Clinical Research Data Submission, Curation, Master Inventorying, and Sharing. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2022:466-475. [PMID: 35854726 PMCID: PMC9285149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/01/2023]
Abstract
Managing research data is an important and challenging aspect of clinical studies, especially for multi-site collaboratives. To address this challenge, we designed, developed and deployed a multi-faceted, multi-level interactive data tracker (DaT3M) for multi-site clinical research data submission, curation, master inventorying, and sharing. Components of DaT3M include data overview, data portal, data status panel, data query engine, and data downloader. DaT3M managed clinical research data for the Center for SUDEP Research (CSR). The CSR instance of DaT3M includes 2,743 subjects from seven data contributing institutions, 7 data modalities and 10,678 data components: 3,398 Epilepsy Monitoring Unit reports, 3,440 electroencephalography recordings, 629 MRI imaging datasets, 177 bio-chemistry datasets, 722 DNA datasets, 2,289 follow-up forms, and 30 SUDEP forms. Preliminary, structured, one-on-one usability evaluations were performed with 7 researchers from four institutions. System Usability Score reached 85.3, showing that DaT3M has achieved high levels of user satisfaction based on our pilot evaluation.
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Affiliation(s)
- Shiqiang Tao
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, 77030
| | - Licong Cui
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, 77030
| | - Wei-Chun Chou
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, 77030
| | - Samden Lhatoo
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, 77030
| | - Guo-Qiang Zhang
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, 77030
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AIM in Medical Informatics. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_32] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Greulich L, Hegselmann S, Dugas M. An Open-Source, Standard-Compliant, and Mobile Electronic Data Capture System for Medical Research (OpenEDC): Design and Evaluation Study. JMIR Med Inform 2021; 9:e29176. [PMID: 34806987 PMCID: PMC8663450 DOI: 10.2196/29176] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 07/13/2021] [Accepted: 09/28/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Medical research and machine learning for health care depend on high-quality data. Electronic data capture (EDC) systems have been widely adopted for metadata-driven digital data collection. However, many systems use proprietary and incompatible formats that inhibit clinical data exchange and metadata reuse. In addition, the configuration and financial requirements of typical EDC systems frequently prevent small-scale studies from benefiting from their inherent advantages. OBJECTIVE The aim of this study is to develop and publish an open-source EDC system that addresses these issues. We aim to plan a system that is applicable to a wide range of research projects. METHODS We conducted a literature-based requirements analysis to identify the academic and regulatory demands for digital data collection. After designing and implementing OpenEDC, we performed a usability evaluation to obtain feedback from users. RESULTS We identified 20 frequently stated requirements for EDC. According to the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 25010 norm, we categorized the requirements into functional suitability, availability, compatibility, usability, and security. We developed OpenEDC based on the regulatory-compliant Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) standard. Mobile device support enables the collection of patient-reported outcomes. OpenEDC is publicly available and released under the MIT open-source license. CONCLUSIONS Adopting an established standard without modifications supports metadata reuse and clinical data exchange, but it limits item layouts. OpenEDC is a stand-alone web app that can be used without a setup or configuration. This should foster compatibility between medical research and open science. OpenEDC is targeted at observational and translational research studies by clinicians.
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Affiliation(s)
- Leonard Greulich
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Stefan Hegselmann
- Institute of Medical Informatics, University of Münster, Münster, Germany
| | - Martin Dugas
- Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany
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Hammer RD, Fowler D, Sheets LR, Siadimas A, Guo C, Prime MS. Digital Tumor Board Solutions Have Significant Impact on Case Preparation. JCO Clin Cancer Inform 2021; 4:757-768. [PMID: 32816529 PMCID: PMC7469605 DOI: 10.1200/cci.20.00029] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Multidisciplinary tumor boards (TBs) are the gold standard for decision-making in cancer care. Variability in preparation, conduction, and impact is widely reported. The benefit of digital technologies to support TBs is unknown. This study evaluated the impact of the NAVIFY Tumor Board solution (NTB) on TB preparation time across multiple user groups in 4 cancer categories: breast, GI, head and neck (ie, ear, nose, and throat, or ENT), and hematopathology. METHODS This prospective study evaluated TB preparation time in multiple phases pre- and post-NTB implementation at an academic health care center. TB preparation times were recorded for multiple weeks using a digital time tracker. RESULTS Preparation times for 59 breast, 61 GI, 36 ENT, and 71 hematopathology cancer TBs comparing a pre-NTB phase to 3 phases of NTB implementation were evaluated between February 2018 and July 2019. NTB resulted in significant reductions in overall preparation time (30%) across 3 TBs pre-NTB compared with the final post-NTB implementation phase. In the breast TB, NTB reduced overall preparation time by 28%, with a 76% decrease in standard deviation (SD). In the GI TB, a 23% reduction in average preparation time was observed for all users, with a 48% decrease in SD. In the ENT TB, a 33% reduction in average preparation time was observed for all users, with a 73% decrease in SD. The hematopathology TB, which was the cocreation partner and initial adopter of the solution, showed variable results. CONCLUSION This study showed a significant impact of a digital solution on time preparation for TBs across multiple users and different TBs, reflecting the generalizability of the NTB. Adoption of such a solution could improve the efficiency of TBs and have a direct economic impact on hospitals.
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Affiliation(s)
- Richard D Hammer
- Department of Pathology and Anatomical Sciences, University of Missouri, Columbia, MO
| | | | | | | | - Chaohui Guo
- Roche Diagnostics Information Solutions, Basel, Switzerland
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Lindner L, Weiß A, Reich A, Kindler S, Behrens F, Braun J, Listing J, Schett G, Sieper J, Strangfeld A, Regierer AC. Implementing an automated monitoring process in a digital, longitudinal observational cohort study. Arthritis Res Ther 2021; 23:181. [PMID: 34233730 PMCID: PMC8262053 DOI: 10.1186/s13075-021-02563-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 06/24/2021] [Indexed: 11/29/2022] Open
Abstract
Background Clinical data collection requires correct and complete data sets in order to perform correct statistical analysis and draw valid conclusions. While in randomized clinical trials much effort concentrates on data monitoring, this is rarely the case in observational studies- due to high numbers of cases and often-restricted resources. We have developed a valid and cost-effective monitoring tool, which can substantially contribute to an increased data quality in observational research. Methods An automated digital monitoring system for cohort studies developed by the German Rheumatism Research Centre (DRFZ) was tested within the disease register RABBIT-SpA, a longitudinal observational study including patients with axial spondyloarthritis and psoriatic arthritis. Physicians and patients complete electronic case report forms (eCRF) twice a year for up to 10 years. Automatic plausibility checks were implemented to verify all data after entry into the eCRF. To identify conflicts that cannot be found by this approach, all possible conflicts were compiled into a catalog. This “conflict catalog” was used to create queries, which are displayed as part of the eCRF. The proportion of queried eCRFs and responses were analyzed by descriptive methods. For the analysis of responses, the type of conflict was assigned to either a single conflict only (affecting individual items) or a conflict that required the entire eCRF to be queried. Results Data from 1883 patients was analyzed. A total of n = 3145 eCRFs submitted between baseline (T0) and T3 (12 months) had conflicts (40–64%). Fifty-six to 100% of the queries regarding eCRFs that were completely missing were answered. A mean of 1.4 to 2.4 single conflicts occurred per eCRF, of which 59–69% were answered. The most common missing values were CRP, ESR, Schober’s test, data on systemic glucocorticoid therapy, and presence of enthesitis. Conclusion Providing high data quality in large observational cohort studies is a major challenge, which requires careful monitoring. An automated monitoring process was successfully implemented and well accepted by the study centers. Two thirds of the queries were answered with new data. While conventional manual monitoring is resource-intensive and may itself create new sources of errors, automated processes are a convenient way to augment data quality. Supplementary Information The online version contains supplementary material available at 10.1186/s13075-021-02563-2.
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Affiliation(s)
- Lisa Lindner
- Epidemiology Unit, German Rheumatism Research Centre (DRFZ), Charitéplatz 1, 10117, Berlin, Germany.
| | - Anja Weiß
- Epidemiology Unit, German Rheumatism Research Centre (DRFZ), Charitéplatz 1, 10117, Berlin, Germany
| | - Andreas Reich
- Epidemiology Unit, German Rheumatism Research Centre (DRFZ), Charitéplatz 1, 10117, Berlin, Germany
| | - Siegfried Kindler
- Epidemiology Unit, German Rheumatism Research Centre (DRFZ), Charitéplatz 1, 10117, Berlin, Germany
| | | | | | - Joachim Listing
- Epidemiology Unit, German Rheumatism Research Centre (DRFZ), Charitéplatz 1, 10117, Berlin, Germany
| | - Georg Schett
- Rheumatology and Immunology, Universitätsklinikum Erlangen, Erlangen, Germany
| | - Joachim Sieper
- Charité - Universitätsmedizin Berlin, CBF, Berlin, Germany
| | - Anja Strangfeld
- Epidemiology Unit, German Rheumatism Research Centre (DRFZ), Charitéplatz 1, 10117, Berlin, Germany
| | - Anne C Regierer
- Epidemiology Unit, German Rheumatism Research Centre (DRFZ), Charitéplatz 1, 10117, Berlin, Germany
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Aldraimli M, Soria D, Grishchuck D, Ingram S, Lyon R, Mistry A, Oliveira J, Samuel R, Shelley LEA, Osman S, Dwek MV, Azria D, Chang-Claude J, Gutiérrez-Enríquez S, De Santis MC, Rosenstein BS, De Ruysscher D, Sperk E, Symonds RP, Stobart H, Vega A, Veldeman L, Webb A, Talbot CJ, West CM, Rattay T, Chaussalet TJ. A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy. Comput Biol Med 2021; 135:104624. [PMID: 34247131 DOI: 10.1016/j.compbiomed.2021.104624] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/24/2021] [Accepted: 06/28/2021] [Indexed: 11/20/2022]
Abstract
The prediction by classification of side effects incidence in a given medical treatment is a common challenge in medical research. Machine Learning (ML) methods are widely used in the areas of risk prediction and classification. The primary objective of such algorithms is to use several features to predict dichotomous responses (e.g., disease positive/negative). Similar to statistical inference modelling, ML modelling is subject to the class imbalance problem and is affected by the majority class, increasing the false-negative rate. In this study, seventy-nine ML models were built and evaluated to classify approximately 2000 participants from 26 hospitals in eight different countries into two groups of radiotherapy (RT) side effects incidence based on recorded observations from the international study of RT related toxicity "REQUITE". We also examined the effect of sampling techniques and cost-sensitive learning methods on the models when dealing with class imbalance. The combinations of such techniques used had a significant impact on the classification. They resulted in an improvement in incidence status prediction by shifting classifiers' attention to the minority group. The best classification model for RT acute toxicity prediction was identified based on domain experts' success criteria. The Area Under Receiver Operator Characteristic curve of the models tested with an isolated dataset ranged from 0.50 to 0.77. The scale of improved results is promising and will guide further development of models to predict RT acute toxicities. One model was optimised and found to be beneficial to identify patients who are at risk of developing acute RT early-stage toxicities as a result of undergoing breast RT ensuring relevant treatment interventions can be appropriately targeted. The design of the approach presented in this paper resulted in producing a preclinical-valid prediction model. The study was developed by a multi-disciplinary collaboration of data scientists, medical physicists, oncologists and surgeons in the UK Radiotherapy Machine Learning Network.
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Affiliation(s)
- Mahmoud Aldraimli
- The Health Innovation Ecosystem, University of Westminster, London, UK.
| | - Daniele Soria
- School of Computing, University of Kent, Canterbury, UK
| | | | - Samuel Ingram
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, UK
| | - Robert Lyon
- Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, UK
| | - Anil Mistry
- Guy's and St Thomas' NHS Foundation Trust, London, UK
| | | | - Robert Samuel
- University of Leeds, Leeds Cancer Centre, St. James's University Hospital, Leeds, UK
| | - Leila E A Shelley
- Edinburgh Cancer Centre, Western General Hospital, Crewe Road South, Edinburgh, UK
| | - Sarah Osman
- Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Miriam V Dwek
- School of Life Sciences, University of Westminster, London, UK
| | | | - Jenny Chang-Claude
- German Cancer Research Center (DKFZ) Division of Cancer Epidemiology, Unit of Genetic Epidemiology, Heidelberg, Germany
| | | | - Maria Carmen De Santis
- Dept of Radiation Oncology 1, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | | | - Dirk De Ruysscher
- Maastricht Radiation Oncology (MAASTRO Clinic) University Hospital Maastricht, the Netherlands
| | - Elena Sperk
- Department of Radiation Oncology, University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Germany
| | | | | | - Ana Vega
- Fundación Publica Galega Medicina Xenomica, Santiago de Compostela, Spain
| | - Liv Veldeman
- Department of Basic Medical Sciences, University Hospital Ghent, Belgium
| | - Adam Webb
- Department of Genetics and Genome Biology, University of Leicester, UK
| | | | - Catharine M West
- Institute of Cancer Sciences, Christie Hospital, Wilmslow Road, Manchester, UK
| | - Tim Rattay
- Cancer Research Centre, University of Leicester, Leicester, UK
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A digital tumor board solution impacts case discussion time and postponement of cases in tumor boards. HEALTH AND TECHNOLOGY 2021. [DOI: 10.1007/s12553-021-00533-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
AbstractMultidisciplinary tumor boards (TBs) is an integral part of cancer care. Emerging evidence shows that effective TB implementation is crucial. It remains largely unknown how digital solutions can assist effective TB conduction. This study aimed to evaluate the impact of a digital solution on case discussion during TB meetings in four cancer types: Breast, Gastrointestinal (GI), Ear, Nose & Throat (ENT), and Hematopathology. A prospective study was performed to evaluate case discussion time during TB meetings pre- and post-solution implementation, at an US academic healthcare cancer center. Data were recorded by a Nurse Navigator for each case during TB meetings. Case discussion times were recorded for 2312 patients, at a total of 286 TB meetings. Significant decreases were observed in the average case discussion time for the breast and GI TBs. We observed a trend for reduction in discussion time variance for all TBs, suggesting the potential of the digital solution to standardize case discussion via provision of uniform case presentation and data access. Postponement rate decreased from 23 to 10% for ENT TB. This study demonstrated that the digital solution enhanced effective TB implementation, with heterogeneity across cancer types.
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Bruno P, Calimeri F, Greco G. AIM in Medical Informatics. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_32-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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30
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Kaplan J, Block R, Gillard A, Putnam M. Inventory of Youth Adaptation to Loss (IYAL): Psychometric Testing of a New Instrument for Bereaved Youth to Assess Social Support and Coping. OMEGA-JOURNAL OF DEATH AND DYING 2020; 86:503-532. [PMID: 33283630 DOI: 10.1177/0030222820976299] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The Inventory of Youth Adaptation to Loss (IYAL) was developed to understand the feelings and social supports experienced by bereaved youth to develop an evidence-base for bereavement interventions. The sample included 400 youth ages 9-17, permitting robust psychometric testing of the IYAL. To evaluate the reliability of the IYAL, the sample framework was national in scope, encompassed a range of different types of youth bereavement programs, and purposively sought diversity in the sample. Exploratory factor analysis identified five factors with distinct sub-scales indicating that the IYAL is a valid and reliable assessment instrument of youth coping with grief and their social support relationships.
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Affiliation(s)
- Jennifer Kaplan
- Dissertation Research, Simmons College School of Social Work, Boston, MA, United States.,Jeff's Place Children's Bereavement Center, FRIENDS WAY, Experience Camps
| | | | - Ann Gillard
- Research and Evaluation, The Hole in the Wall Gang Camp
| | - Michelle Putnam
- Simmons College School of Social Work, Boston, MA, United States
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Pung J, Rienhoff O. Key components and IT assistance of participant management in clinical research: a scoping review. JAMIA Open 2020; 3:449-458. [PMID: 33215078 PMCID: PMC7660951 DOI: 10.1093/jamiaopen/ooaa041] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 07/16/2020] [Accepted: 08/24/2020] [Indexed: 01/05/2023] Open
Abstract
Objectives Managing participants and their data are fundamental for the success of a clinical trial. Our review identifies and describes processes that deal with management of trial participants and highlights information technology (IT) assistance for clinical research in the context of participant management. Methods A scoping literature review design, based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement, was used to identify literature on trial participant-related proceedings, work procedures, or workflows, and assisting electronic systems. Results The literature search identified 1329 articles of which 111 were included for analysis. Participant-related procedures were categorized into 4 major trial processes: recruitment, obtaining informed consent, managing identities, and managing administrative data. Our results demonstrated that management of trial participants is considered in nearly every step of clinical trials, and that IT was successfully introduced to all participant-related areas of a clinical trial to facilitate processes. Discussion There is no precise definition of participant management, so a broad search strategy was necessary, resulting in a high number of articles that had to be excluded. Nevertheless, this review provides a comprehensive overview of participant management-related components, which was lacking so far. The review contributes to a better understanding of how computer-assisted management of participants in clinical trials is possible.
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Affiliation(s)
- Johannes Pung
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
| | - Otto Rienhoff
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen, Germany
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32
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Zaki M, Galligan M, O'Sullivan L, Devane D, McAuliffe E. A protocol for a systematic review investigating the factors influencing the statistical planning, design, conduct, analysis and reporting of trials. HRB Open Res 2020; 3:36. [PMID: 32875270 PMCID: PMC7445680 DOI: 10.12688/hrbopenres.13068.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/18/2020] [Indexed: 12/04/2022] Open
Abstract
Trials can be defined as prospective human research studies to test the effectiveness and safety of interventions, such as medications, surgeries, medical devices and other interventions for the management of patient care. Statistics is an important and powerful tool in trials. Inappropriately designed trials and/or inappropriate statistical analysis produce unreliable results, with limited clinical use. The aim of this systematic literature review is to identify, describe and synthesise factors contributing to or influencing the statistical planning, design, conduct, analysis and reporting of trials. This protocol will describe the methodological approach taken for the following: conducting a systematic and comprehensive search for relevant articles, applying eligibility criteria for the inclusion of such articles, extracting data and information, appraising the quality of the articles, and thematically synthesizing the data to illuminate the key factors influencing statistical aspects of trials.
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Affiliation(s)
- Marina Zaki
- University College Dublin, Centre for Interdisciplinary Research, Education, and Innovation in Health Systems (IRIS Centre), School of Nursing, Midwifery and Health Systems, University College Dublin, Dublin 4, Ireland.,Health Research Board - Trials Methodology Research Network (HRB-TMRN), School of Nursing and Midwifery,, National University of Ireland, Galway, Galway, Ireland
| | - Marie Galligan
- School of Medicine and Medical Science, University College Dublin, Dublin 4, Ireland
| | - Lydia O'Sullivan
- Health Research Board - Trials Methodology Research Network (HRB-TMRN), School of Nursing and Midwifery,, National University of Ireland, Galway, Galway, Ireland.,School of Medicine and Medical Science, University College Dublin, Dublin 4, Ireland
| | - Declan Devane
- Health Research Board - Trials Methodology Research Network (HRB-TMRN), School of Nursing and Midwifery,, National University of Ireland, Galway, Galway, Ireland.,School of Nursing and Midwifery, National University of Ireland, Galway, Galway, Ireland.,Evidence Synthesis Ireland, National University of Ireland, Galway, Galway, Ireland
| | - Eilish McAuliffe
- University College Dublin, Centre for Interdisciplinary Research, Education, and Innovation in Health Systems (IRIS Centre), School of Nursing, Midwifery and Health Systems, University College Dublin, Dublin 4, Ireland
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Hennessy M, Dennehy R, Meaney S, Devane D, O'Donoghue K. A protocol for a systematic review of clinical practice guidelines for recurrent miscarriage. HRB Open Res 2020; 3:12. [DOI: 10.12688/hrbopenres.13024.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/16/2020] [Indexed: 11/20/2022] Open
Abstract
Recurrent miscarriage (RM) was recently re-defined by the European Society of Human Reproduction and Embryology (ESHRE) as the loss of two or more consecutive pregnancies. Before this, and indeed still in some countries, RM was defined as three or more consecutive pregnancy losses. While the incidence of RM depends on the definition employed and population studied, it is generally accepted to affect 1-6% of women of reproductive age. Clinical practice guidelines (CPGs) for RM have been published by some professional organisations. While there are CPGs on miscarriage in Ireland, there are none concerning RM specifically. The aim of this systematic review is to identify, appraise and describe published CPGs for the management, investigation and/or follow-up of RM within high-income countries. Electronic databases (MEDLINE (Ovid®; 1946), Embase® (Elsevier; 1980), CINAHL Complete (EBSCOhost; 1994), Web of Science™ (Thomson Reuters), Scopus (Elsevier; 2004), and Open Grey (INIST-CNRS; 2011)), selected guideline repositories, and the websites of professional societies will be searched to identify CPGs, published within the last 20 years, for potential inclusion. Two reviewers will review abstracts and full texts independently against the eligibility criteria. Characteristics and recommendations of included CPGs will be extracted by one reviewer and double-checked by another. Two reviewers will use the Appraisal of Guidelines for Research and Evaluation version 2 (AGREE II) instrument independently to assess the quality of the included CPGs. Narrative synthesis will be conducted to appraise and compare CPGs and their recommendations or guidance therein. The identification, appraisal and description of published CPGs in other high-income countries will be a valuable first step in informing efforts to promote the optimisation and standardisation of RM care.
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Houston L, Yu P, Martin A, Probst Y. Heterogeneity in clinical research data quality monitoring: A national survey. J Biomed Inform 2020; 108:103491. [DOI: 10.1016/j.jbi.2020.103491] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 05/19/2020] [Accepted: 06/16/2020] [Indexed: 01/21/2023]
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35
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Zaki M, Galligan M, O'Sullivan L, Devane D, McAuliffe E. A protocol for a systematic review investigating the factors influencing the statistical planning, design, conduct, analysis and reporting of clinical trials. HRB Open Res 2020; 3:36. [DOI: 10.12688/hrbopenres.13068.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/02/2020] [Indexed: 11/20/2022] Open
Abstract
Clinical trials can be defined as prospective human research studies to test the effectiveness and safety of interventions, such as medications, surgeries, medical devices and other interventions for the management of patient care. Statistics is an important and powerful tool in clinical trials. Inappropriately designed trials and/or inappropriate statistical analysis produce unreliable results, with limited clinical use. The aim of this systematic literature review is to identify, describe and synthesise factors contributing to or influencing the statistical planning, design, conduct, analysis and reporting of clinical trials. This protocol will describe the methodological approach taken for the following: conducting a systematic and comprehensive search for relevant articles, applying eligibility criteria for the inclusion of such articles, extracting data and information, appraising the quality of the articles, and thematically synthesizing the data to illuminate the key factors influencing statistical aspects of clinical trials.
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36
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Khin NA, Francis G, Mulinde J, Grandinetti C, Skeete R, Yu B, Ayalew K, Cho S, Fisher A, Kleppinger C, Ayala R, Bonapace C, Dasgupta A, Kronstein PD, Vinter S. Data Integrity in Global Clinical Trials: Discussions From Joint US Food and Drug Administration and UK Medicines and Healthcare Products Regulatory Agency Good Clinical Practice Workshop. Clin Pharmacol Ther 2020; 108:949-963. [DOI: 10.1002/cpt.1794] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 12/26/2019] [Indexed: 01/23/2023]
Affiliation(s)
- Ni A. Khin
- Division of Clinical Compliance Evaluation Office of Scientific Investigations (OSI) Office of Compliance Center for Drug Evaluation and Research (CDER) US Food and Drug Administration (FDA) Silver Spring Maryland USA
| | - Gail Francis
- Inspection, Enforcement and Standards Division Medicines and Healthcare Products Regulatory Agency (MHRA) London UK
| | - Jean Mulinde
- Division of Clinical Compliance Evaluation Office of Scientific Investigations (OSI) Office of Compliance Center for Drug Evaluation and Research (CDER) US Food and Drug Administration (FDA) Silver Spring Maryland USA
| | - Cheryl Grandinetti
- Division of Clinical Compliance Evaluation Office of Scientific Investigations (OSI) Office of Compliance Center for Drug Evaluation and Research (CDER) US Food and Drug Administration (FDA) Silver Spring Maryland USA
| | - Rachel Skeete
- Division of Clinical Compliance Evaluation Office of Scientific Investigations (OSI) Office of Compliance Center for Drug Evaluation and Research (CDER) US Food and Drug Administration (FDA) Silver Spring Maryland USA
| | - Bei Yu
- Division of Clinical Compliance Evaluation Office of Scientific Investigations (OSI) Office of Compliance Center for Drug Evaluation and Research (CDER) US Food and Drug Administration (FDA) Silver Spring Maryland USA
| | - Kassa Ayalew
- Division of Clinical Compliance Evaluation Office of Scientific Investigations (OSI) Office of Compliance Center for Drug Evaluation and Research (CDER) US Food and Drug Administration (FDA) Silver Spring Maryland USA
| | - Seongeun‐Julia Cho
- Division of Generic Drug Bioequivalence Evaluation Office of Study Integrity and Surveillance (OSIS) Office of Translational Sciences (OTS) Center for Drug Evaluation and Research (CDER) Food and Drug Administration (FDA) Silver Spring Maryland USA
| | - Andrew Fisher
- Inspection, Enforcement and Standards Division Medicines and Healthcare Products Regulatory Agency (MHRA) London UK
| | - Cynthia Kleppinger
- Division of Clinical Compliance Evaluation Office of Scientific Investigations (OSI) Office of Compliance Center for Drug Evaluation and Research (CDER) US Food and Drug Administration (FDA) Silver Spring Maryland USA
| | - Ruben Ayala
- Division of New Drug Bioequivalence Evaluation Office of Study Integrity and Surveillance (OSIS) Office of Translational Sciences (OTS) Center for Drug Evaluation and Research (CDER) Food and Drug Administration (FDA) Silver Spring Maryland USA
| | - Charles Bonapace
- Division of New Drug Bioequivalence Evaluation Office of Study Integrity and Surveillance (OSIS) Office of Translational Sciences (OTS) Center for Drug Evaluation and Research (CDER) Food and Drug Administration (FDA) Silver Spring Maryland USA
| | - Arindam Dasgupta
- Division of New Drug Bioequivalence Evaluation Office of Study Integrity and Surveillance (OSIS) Office of Translational Sciences (OTS) Center for Drug Evaluation and Research (CDER) Food and Drug Administration (FDA) Silver Spring Maryland USA
| | - Phillip D. Kronstein
- Division of Clinical Compliance Evaluation Office of Scientific Investigations (OSI) Office of Compliance Center for Drug Evaluation and Research (CDER) US Food and Drug Administration (FDA) Silver Spring Maryland USA
| | - Stephen Vinter
- Inspection, Enforcement and Standards Division Medicines and Healthcare Products Regulatory Agency (MHRA) London UK
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Wang X, Teng P, Ontiveros A, Goldin JG, Brown MS. High throughput image labeling on chest computed tomography by deep learning. J Med Imaging (Bellingham) 2020; 7:024501. [PMID: 32219151 DOI: 10.1117/1.jmi.7.2.024501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Accepted: 02/26/2020] [Indexed: 11/14/2022] Open
Abstract
When mining image data from PACs or clinical trials or processing large volumes of data without curation, the relevant scans must be identified among irrelevant or redundant data. Only images acquired with appropriate technical factors, patient positioning, and physiological conditions may be applicable to a particular image processing or machine learning task. Automatic labeling is important to make big data mining practical by replacing conventional manual review of every single-image series. Digital imaging and communications in medicine headers usually do not provide all the necessary labels and are sometimes incorrect. We propose an image-based high throughput labeling pipeline using deep learning, aimed at identifying scan direction, scan posture, lung coverage, contrast usage, and breath-hold types. They were posed as different classification problems and some of them involved further segmentation and identification of anatomic landmarks. Images of different view planes were used depending on the specific classification problem. All of our models achieved accuracy > 99 % on test set across different tasks using a research database from multicenter clinical trials.
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Affiliation(s)
- Xiaoyong Wang
- University of California, Los Angeles, Center for Computer Vision and Imaging Biomarkers, Los Angeles, California, United States.,University of California, Los Angeles, Department of Radiological Sciences, Los Angeles, California, United States
| | - Pangyu Teng
- University of California, Los Angeles, Center for Computer Vision and Imaging Biomarkers, Los Angeles, California, United States.,University of California, Los Angeles, Department of Radiological Sciences, Los Angeles, California, United States
| | - Ashley Ontiveros
- University of California, Los Angeles, Center for Computer Vision and Imaging Biomarkers, Los Angeles, California, United States.,University of California, Los Angeles, Department of Radiological Sciences, Los Angeles, California, United States
| | - Jonathan G Goldin
- University of California, Los Angeles, Center for Computer Vision and Imaging Biomarkers, Los Angeles, California, United States.,University of California, Los Angeles, Department of Radiological Sciences, Los Angeles, California, United States
| | - Matthew S Brown
- University of California, Los Angeles, Center for Computer Vision and Imaging Biomarkers, Los Angeles, California, United States.,University of California, Los Angeles, Department of Radiological Sciences, Los Angeles, California, United States
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Kumuthini J, Chimenti M, Nahnsen S, Peltzer A, Meraba R, McFadyen R, Wells G, Taylor D, Maienschein-Cline M, Li JL, Thimmapuram J, Murthy-Karuturi R, Zass L. Ten simple rules for providing effective bioinformatics research support. PLoS Comput Biol 2020; 16:e1007531. [PMID: 32214318 PMCID: PMC7098546 DOI: 10.1371/journal.pcbi.1007531] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Life scientists are increasingly turning to high-throughput sequencing technologies in their research programs, owing to the enormous potential of these methods. In a parallel manner, the number of core facilities that provide bioinformatics support are also increasing. Notably, the generation of complex large datasets has necessitated the development of bioinformatics support core facilities that aid laboratory scientists with cost-effective and efficient data management, analysis, and interpretation. In this article, we address the challenges-related to communication, good laboratory practice, and data handling-that may be encountered in core support facilities when providing bioinformatics support, drawing on our own experiences working as support bioinformaticians on multidisciplinary research projects. Most importantly, the article proposes a list of guidelines that outline how these challenges can be preemptively avoided and effectively managed to increase the value of outputs to the end user, covering the entire research project lifecycle, including experimental design, data analysis, and management (i.e., sharing and storage). In addition, we highlight the importance of clear and transparent communication, comprehensive preparation, appropriate handling of samples and data using monitoring systems, and the employment of appropriate tools and standard operating procedures to provide effective bioinformatics support.
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Affiliation(s)
- Judit Kumuthini
- H3ABioNet, Centre for Proteomic and Genomic Research, Cape Town, South Africa
| | - Michael Chimenti
- Iowa Institute of Human Genetics, Bioinformatics Division, Carver College of Medicine, University of Iowa, Iowa City, United States of America
| | - Sven Nahnsen
- Quantitative Biology Centre, Eberhard Karls University of Tübingen, Tübingen, Baden-Württemberg, Germany
| | - Alexander Peltzer
- Quantitative Biology Centre, Eberhard Karls University of Tübingen, Tübingen, Baden-Württemberg, Germany
| | - Rebone Meraba
- H3ABioNet, Centre for Proteomic and Genomic Research, Cape Town, South Africa
| | - Ross McFadyen
- H3ABioNet, Centre for Proteomic and Genomic Research, Cape Town, South Africa
| | - Gordon Wells
- H3ABioNet, Centre for Proteomic and Genomic Research, Cape Town, South Africa
| | - Deanne Taylor
- Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Mark Maienschein-Cline
- Research Informatics Core, University of Illinois at Chicago, Chicago, Illinois, United States of America
| | - Jian-Liang Li
- Integrative Bioinformatics Support Group, National Institute of Environmental Health Sciences, Durham, North Carolina, United States of America
| | - Jyothi Thimmapuram
- Bioinformatics Core, Purdue University, West Lafayette, Indiana, United States of America
| | - Radha Murthy-Karuturi
- Department of Computational Sciences, The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, United States of America
| | - Lyndon Zass
- H3ABioNet, Centre for Proteomic and Genomic Research, Cape Town, South Africa
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Gruszauskas NP, Armato SG. Critical Challenges to the Management of Clinical Trial Imaging: Recommendations for the Conduct of Imaging at Investigational Sites. Acad Radiol 2020; 27:300-306. [PMID: 31097377 DOI: 10.1016/j.acra.2019.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2019] [Revised: 04/03/2019] [Accepted: 04/04/2019] [Indexed: 11/15/2022]
Abstract
RATIONALE AND OBJECTIVES Participation in clinical research can be both highly rewarding and logistically demanding. As highlighted by recent Food and Drug Administration guidance, imaging has become an integral part of this research. The unique technical and administrative aspects of clinical trial imaging may differ substantially from those of standard-of-care imaging and thus burden the established clinical infrastructure at investigational sites. Failure to comply with requirements can lead to unusable data, repeat imaging, or the removal of patients from the trial. It is therefore imperative that all stakeholders address these challenges to engage in clinical research successfully. MATERIALS AND METHODS The authors' experiences in managing clinical trial imaging requirements at their institution were used to identify common challenges. The impact of these challenges was assessed from an operational perspective. RESULTS Although contract research organizations attempt to minimize these challenges, their efforts are necessarily limited and insufficient, and there is a lack of infrastructure available at investigational sites to address these issues. As such, recommendations are proposed for addressing these challenges at institutional and industry levels. CONCLUSION The challenges associated with clinical trial imaging require an investment of resources from all stakeholders. Investigational sites must confront these challenges to satisfy trial requirements effectively, maintain a superior level of patient care, and guarantee trial integrity. Similarly, sponsors must acknowledge the burden of clinical trial imaging and support the development of the necessary local infrastructure. The implementation of the recommendations described here will improve the conduct of clinical trial imaging.
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Affiliation(s)
| | - Samuel G Armato
- Human Imaging Research Office, University of Chicago, Chicago, Illinois; Department of Radiology, University of Chicago, Chicago, Illinois
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Albers PN, Wright CY. Clinical Trial Data Management in Environmental Health Tailored for an African Setting. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17020402. [PMID: 31936227 PMCID: PMC7013767 DOI: 10.3390/ijerph17020402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Revised: 12/08/2019] [Accepted: 12/19/2019] [Indexed: 11/16/2022]
Abstract
Clinical trial data management tools are widely available—some free to access and others relatively expensive, particularly for low- and middle-income countries. Such tools also do not always permit adaptation for local conditions nor include options to capture environmental and meteorological data. In the context of climate change and pressing environmental health threats, more studies that aim to assess the impacts of environmental change on public health are being carried out. Here, using freely available software, we tailor-made a clinical trial data management tool that managed all aspects of an intervention-based clinical trial to assess the impact of personal solar ultraviolet radiation exposure on vaccine effectiveness. Data captured and associated procedures included patient data, scheduling, reporting, analysis and data management. Moreover, patient enrolment, recruitment, follow-up and decision-making in response to patient data were managed. Given the multidisciplinary study approach, the tool also managed all environmental and meteorological data for the rural African study site. Application of the tool ensured efficient communication between rural sites, a relatively high overall participant response rate (87%) and minimal loss to follow-up. This study suggests that it is possible to tailor-make a clinical trial data management tool for environmental and public health studies.
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Affiliation(s)
- Patricia Nicole Albers
- Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0028, South Africa;
- Correspondence:
| | - Caradee Yael Wright
- Department of Geography, Geoinformatics and Meteorology, University of Pretoria, Pretoria 0028, South Africa;
- Environment and Health Research Unit, South African Medical Research Council, Pretoria 0084, South Africa
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Psotka MA, Fiuzat M, Carson PE, Kao DP, Cerkvenik J, Schaber DE, Verta P, Kazmierski RT, Shinnar M, Stockbridge N, Unger EF, Zuckerman B, Butler J, Felker GM, Konstam MA, Lindenfeld J, Solomon SD, Teerlink JR, O'Connor CM, Abraham WT. Design of a "Lean" Case Report Form for Heart Failure Therapeutic Development. JACC-HEART FAILURE 2019; 7:913-921. [PMID: 31401097 DOI: 10.1016/j.jchf.2019.07.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 07/01/2019] [Accepted: 07/08/2019] [Indexed: 01/11/2023]
Abstract
The development of treatments for heart failure (HF) is challenged by burdensome clinical trials. Reducing the need for extensive data collection and increasing opportunities for data compatibility between trials may improve efficiency and reduce resource burden. The Heart Failure Collaboratory (HFC) multi-stakeholder consortium sought to create a lean case report form (CRF) for use in HF clinical trials evaluating cardiac devices. The HFC convened patients, clinicians, clinical researchers, the U.S. Food and Drug Administration (FDA), payers, industry partners, and statisticians to create a consensus core CRF. Eight recent clinical trial CRFs for the treatment of HF from 6 industry partners were analyzed. All CRF elements were systematically reviewed. Those elements deemed critical for data collection in HF clinical trials were used to construct the final, harmonized CRF. The original CRFs included 176 distinct data items covering demographics, vital signs, physical examination, medical history, laboratory and imaging testing, device therapy, medications, functional and quality of life assessment, and outcome events. The resulting, minimally inclusive CRF device contains 75 baseline data items and 6 events, with separate modular additions that can be used depending on the additional detail required for a particular intervention. The consensus electronic form is now freely available for use in clinical trials. Creation of a core CRF is important to improve clinical trial efficiency in HF device development in the United States. This living document intends to reduce clinical trial administrative burden, increase evidence integrity, and improve comparability of clinical data between trials.
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Affiliation(s)
| | - Mona Fiuzat
- Department of Medicine, Duke University School of Medicine, Durham, North Carolina
| | - Peter E Carson
- Department of Cardiology, Washington Veterans Affairs Medical Center, Washington, DC
| | - David P Kao
- Division of Cardiology, University of Colorado School of Medicine, Aurora, Colorado
| | | | | | | | | | - Meir Shinnar
- U.S. Food and Drug Administration, Silver Spring, Maryland
| | | | - Ellis F Unger
- U.S. Food and Drug Administration, Silver Spring, Maryland
| | - Bram Zuckerman
- U.S. Food and Drug Administration, Silver Spring, Maryland
| | - Javed Butler
- Department of Medicine, University of Mississippi, Jackson, Mississippi
| | - G Michael Felker
- Division of Cardiology, Duke University Medical Center, Durham, North Carolina
| | - Marvin A Konstam
- CardioVascular Center of Tufts Medical Center, Boston, Massachusetts
| | - JoAnn Lindenfeld
- Heart Failure and Transplantation Section, Vanderbilt Heart and Vascular Institute, Nashville, Tennessee
| | - Scott D Solomon
- Cardiovascular Division, Brigham and Women's Hospital, Boston, Massachusetts
| | - John R Teerlink
- Section of Cardiology, San Francisco Veterans Affairs Medical Center and School of Medicine, University of California San Francisco, San Francisco, California
| | | | - William T Abraham
- Departments of Medicine, Physiology, and Cell Biology, Division of Cardiovascular Medicine, Davis Heart and Lung Research Institute, Ohio State University, Columbus, Ohio
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Babiker HM, Davis L, Larson K, Placencia C, Swensen C, Tenneti P, Lim M, Cañamar R, Curtis J, Castillo E, Mancuso J, Rensvold D, Martinez S, Macias L, Recio-Boiles A, Chandana SR, Mahadevan D. A Multidisciplinary Evaluation of Barriers to Enrolling Cancer Patients into Early Phase Clinical Trials: Challenges and Patient-centric Recommendations. Expert Opin Investig Drugs 2019; 28:675-686. [PMID: 31327293 DOI: 10.1080/13543784.2019.1646726] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Introduction: Early phase clinical trials are the first clinical research step to bringing new cancer therapeutics to patients. At this stage, a new drug's safety, dosing, and scheduling profiles are established as the main endpoints. However, excellent responses due to biomarker-guided and immune checkpoint trials in early phase have resulted in direct approvals of new anti-cancer drugs. Despite doubling of the success rate of new drug approvals, many barriers exist to expeditiously bring active new drugs to the clinic. Areas covered: This review covers roles of members of the early phase program and the challenges they face in enrolling advanced cancer patients to trials. Practical solutions are provided from the perspective of the investigators, regulatory, investigational pharmacy, research nurses, clinical research coordinators, budgets, contracts, and data management. Expert opinion: We are witnessing a burgeoning era in drug development with rapid approval of efficacious drugs. This is achieved by a strong collaboration between investigators, academic institutions, pharmaceutical sponsors, scientists, Food and Drug Administration (FDA), and community practices. Herein, we discuss some of the challenges faced by early phase clinical trials programs and discuss methods of improvement.
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Affiliation(s)
- Hani M Babiker
- a Department of Medicine, Division of Hematology-Oncology, University of Arizona Cancer Center, Early Phase Clinical Trials Program , Tucson , AZ , USA
| | - Lisa Davis
- a Department of Medicine, Division of Hematology-Oncology, University of Arizona Cancer Center, Early Phase Clinical Trials Program , Tucson , AZ , USA
| | - Kristian Larson
- a Department of Medicine, Division of Hematology-Oncology, University of Arizona Cancer Center, Early Phase Clinical Trials Program , Tucson , AZ , USA
| | - Crystal Placencia
- a Department of Medicine, Division of Hematology-Oncology, University of Arizona Cancer Center, Early Phase Clinical Trials Program , Tucson , AZ , USA
| | - Connor Swensen
- a Department of Medicine, Division of Hematology-Oncology, University of Arizona Cancer Center, Early Phase Clinical Trials Program , Tucson , AZ , USA
| | - Pavan Tenneti
- b Department of Medicine, Banner University Medical Center Phoenix , Phoenix , AZ , USA
| | - Melissa Lim
- a Department of Medicine, Division of Hematology-Oncology, University of Arizona Cancer Center, Early Phase Clinical Trials Program , Tucson , AZ , USA
| | - Ruth Cañamar
- a Department of Medicine, Division of Hematology-Oncology, University of Arizona Cancer Center, Early Phase Clinical Trials Program , Tucson , AZ , USA
| | - Jacqueline Curtis
- a Department of Medicine, Division of Hematology-Oncology, University of Arizona Cancer Center, Early Phase Clinical Trials Program , Tucson , AZ , USA
| | - Erica Castillo
- a Department of Medicine, Division of Hematology-Oncology, University of Arizona Cancer Center, Early Phase Clinical Trials Program , Tucson , AZ , USA
| | - James Mancuso
- c Department of Immunology, University of Texas MD Anderson Cancer Center , Houston , TX , USA
| | - Diane Rensvold
- a Department of Medicine, Division of Hematology-Oncology, University of Arizona Cancer Center, Early Phase Clinical Trials Program , Tucson , AZ , USA
| | - Sarah Martinez
- a Department of Medicine, Division of Hematology-Oncology, University of Arizona Cancer Center, Early Phase Clinical Trials Program , Tucson , AZ , USA
| | - Lora Macias
- a Department of Medicine, Division of Hematology-Oncology, University of Arizona Cancer Center, Early Phase Clinical Trials Program , Tucson , AZ , USA
| | - Alejandro Recio-Boiles
- a Department of Medicine, Division of Hematology-Oncology, University of Arizona Cancer Center, Early Phase Clinical Trials Program , Tucson , AZ , USA
| | - Sreenivasa R Chandana
- d Department of Gastrointestinal Medical Oncology, Cancer and Hematology Centers of Western Michigan, Phase I Program, START Midwest , Grand Rapids , MI , USA
| | - Daruka Mahadevan
- a Department of Medicine, Division of Hematology-Oncology, University of Arizona Cancer Center, Early Phase Clinical Trials Program , Tucson , AZ , USA
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Han J, Chen K, Fang L, Zhang S, Wang F, Ma H, Zhao L, Liu S. Improving the Efficacy of the Data Entry Process for Clinical Research With a Natural Language Processing-Driven Medical Information Extraction System: Quantitative Field Research. JMIR Med Inform 2019; 7:e13331. [PMID: 31313661 PMCID: PMC6672807 DOI: 10.2196/13331] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 05/13/2019] [Accepted: 05/29/2019] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND The growing interest in observational trials using patient data from electronic medical records poses challenges to both efficiency and quality of clinical data collection and management. Even with the help of electronic data capture systems and electronic case report forms (eCRFs), the manual data entry process followed by chart review is still time consuming. OBJECTIVE To facilitate the data entry process, we developed a natural language processing-driven medical information extraction system (NLP-MIES) based on the i2b2 reference standard. We aimed to evaluate whether the NLP-MIES-based eCRF application could improve the accuracy and efficiency of the data entry process. METHODS We conducted a randomized and controlled field experiment, and 24 eligible participants were recruited (12 for the manual group and 12 for NLP-MIES-supported group). We simulated the real-world eCRF completion process using our system and compared the performance of data entry on two research topics, pediatric congenital heart disease and pneumonia. RESULTS For the congenital heart disease condition, the NLP-MIES-supported group increased accuracy by 15% (95% CI 4%-120%, P=.03) and reduced elapsed time by 33% (95% CI 22%-42%, P<.001) compared with the manual group. For the pneumonia condition, the NLP-MIES-supported group increased accuracy by 18% (95% CI 6%-32%, P=.008) and reduced elapsed time by 31% (95% CI 19%-41%, P<.001). CONCLUSIONS Our system could improve both the accuracy and efficiency of the data entry process.
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Affiliation(s)
- Jiang Han
- Pediatric Translational Medicine Institute, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | | | | | - Shaodian Zhang
- Synyi Research, Shanghai, China.,APEX Data and Knowledge Management Lab, Shanghai Jiao Tong University, Shanghai, China
| | - Fei Wang
- Synyi Research, Shanghai, China.,Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY, United States
| | - Handong Ma
- Synyi Research, Shanghai, China.,Department of computer science, Shanghai Jiao Tong University, Shanghai, China
| | - Liebin Zhao
- School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Child Health Advocacy Institute, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shijian Liu
- Pediatric Translational Medicine Institute, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Hague D, Townsend S, Masters L, Rauchenberger M, Van Looy N, Diaz-Montana C, Gannon M, James N, Maughan T, Parmar MKB, Brown L, Sydes MR. Changing platforms without stopping the train: experiences of data management and data management systems when adapting platform protocols by adding and closing comparisons. Trials 2019; 20:294. [PMID: 31138292 PMCID: PMC6540437 DOI: 10.1186/s13063-019-3322-7] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 03/25/2019] [Indexed: 02/08/2023] Open
Abstract
Background There is limited research and literature on the data management challenges encountered in multi-arm, multi-stage platform and umbrella protocols. These trial designs allow both (1) seamless addition of new research comparisons and (2) early stopping of accrual to individual comparisons that do not show sufficient activity. FOCUS4 (colorectal cancer) and STAMPEDE (prostate cancer), run from the Medical Research Council Clinical Trials Unit (CTU) at UCL, are two leading UK examples of clinical trials implementing adaptive platform protocol designs. To date, STAMPEDE has added five new research comparisons, closed two research comparisons following pre-planned interim analysis (lack of benefit), adapted the control arm following results from STAMPEDE and other relevant trials, and completed recruitment to six research comparisons. FOCUS4 has closed one research comparison following pre-planned interim analysis (lack of benefit) and added one new research comparison, with a number of further comparisons in the pipeline. We share our experiences from the operational aspects of running these adaptive trials, focusing on data management. Methods We held discussion groups with STAMPEDE and FOCUS4 CTU data management staff to identify data management challenges specific to adaptive platform protocols. We collated data on a number of case report form (CRF) changes, database amendments and database growth since each trial began. Discussion We found similar adaptive protocol-specific challenges in both trials. Adding comparisons to and removing them from open trials provides extra layers of complexity to CRF and database development. At the start of an adaptive trial, CRFs and databases must be designed to be flexible and scalable in order to cope with the continuous changes, ensuring future data requirements are considered where possible. When adding or stopping a comparison, the challenge is to incorporate new data requirements while ensuring data collection within ongoing comparisons is unaffected. Some changes may apply to all comparisons; others may be comparison-specific or applicable only to patients recruited during a specific time period. We discuss the advantages and disadvantages of the different approaches to CRF and database design we implemented in these trials, particularly in relation to use and maintenance of generic versus comparison-specific CRFs and databases. The work required to add or remove a comparison, including the development and testing of changes, updating of documentation, and training of sites, must be undertaken alongside data management of ongoing comparisons. Adequate resource is required for these competing data management tasks, especially in trials with long follow-up. A plan is needed for regular and pre-analysis data cleaning for multiple comparisons that could recruit at different rates and periods of time. Data-cleaning activities may need to be split and prioritised, especially if analyses for different comparisons overlap in time. Conclusions Adaptive trials offer an efficient model to run randomised controlled trials, but setting up and conducting the data management activities in these trials can be operationally challenging. Trialists and funders must plan for scalability in data collection and the resource required to cope with additional competing data management tasks. Electronic supplementary material The online version of this article (10.1186/s13063-019-3322-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Dominic Hague
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK. .,MRC London Hub for Trials Methodology Research, London, UK.
| | - Stephen Townsend
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK.,MRC London Hub for Trials Methodology Research, London, UK
| | - Lindsey Masters
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK.,MRC London Hub for Trials Methodology Research, London, UK
| | - Mary Rauchenberger
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK.,MRC London Hub for Trials Methodology Research, London, UK
| | - Nadine Van Looy
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK.,MRC London Hub for Trials Methodology Research, London, UK
| | - Carlos Diaz-Montana
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK.,MRC London Hub for Trials Methodology Research, London, UK
| | - Melissa Gannon
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK.,Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK
| | - Nicholas James
- Institute of Cancer and Genomic Sciences, University of Birmingham, Edgbaston, Birmingham, UK
| | - Tim Maughan
- Cancer Research UK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford, UK
| | - Mahesh K B Parmar
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK.,MRC London Hub for Trials Methodology Research, London, UK
| | - Louise Brown
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK.,MRC London Hub for Trials Methodology Research, London, UK
| | - Matthew R Sydes
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, UCL, London, UK.,MRC London Hub for Trials Methodology Research, London, UK
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Sul JU, Kim MK, Leem J, Jo HG, Yoon SH, Kim J, Lee EJ, Yoo JE, Park SJ, Kim YI, Kim E, Jung IC, Jeon JH, Park YC. Efficacy and safety of gyejigachulbutang (Gui-Zhi-Jia-Shu-Fu-Tang, Keishikajutsubuto, TJ-18) for knee pain in patients with degenerative knee osteoarthritis: a randomized, placebo-controlled, patient and assessor blinded clinical trial. Trials 2019; 20:140. [PMID: 30782208 PMCID: PMC6381693 DOI: 10.1186/s13063-019-3234-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Accepted: 01/29/2019] [Indexed: 01/04/2023] Open
Abstract
Background Degenerative knee osteoarthritis is a leading cause of disability in the elderly. If patients do not respond to pharmacological or nonpharmacological intervention, total knee replacement surgery is recommended. However, owing to the contraindications and adverse effects of surgery, the need for a new treatment strategy is emerging. Traditional herbal medicine is a widely used intervention in east Asia to treat knee osteoarthritis. Gyejigachulbutang is one of the frequently prescribed herbal formulae. The aim of our study is to evaluate the efficacy and safety of gyejigachulbutang for knee osteoarthritis. Methods This study is a randomized, placebo-controlled, patient and assessor blinded, superiority clinical trial. A total of 80 patients with knee osteoarthritis will be enrolled. The participants will be randomly assigned to the gyejigachulbutang or placebo group in a 1:1 ratio in two Korean medical hospitals. Every participant will take gyejigachulbutang or placebo at a dose of 2.5 g three times a day for 4 weeks. Additional follow-up will be conducted 4 weeks after treatment completion. Any concomitant treatment to relive knee pain will not be allowed except for rescue medicine (acetaminophen). The primary outcome will be a comparison of the change in the visual analogue scale score for knee pain from baseline to visit 3 (week 4) for both the treatment and placebo groups. Secondary outcomes include clinical relevance, minimal clinically important difference, disability, quality of life, and safety. Discussion This protocol presents a research methodology for clinical trials of gyejigachulbutang for knee osteoarthritis. Various secondary outcomes make this trial more informative. Our trial will provide fundamental evidence for knee osteoarthritis management via herbal medicine treatment. Trial registration Clinical Research Information Service (CRIS), KCT0003024. Registered on 25 July 2018. Electronic supplementary material The online version of this article (10.1186/s13063-019-3234-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jae-Uk Sul
- Chung-Yeon Central Institute, 64, Sangmujungang-ro, Seo-gu, Gwangju, 61949, South Korea.,Chung-Yeon Korean Medicine Hospital, 64, Sangmujungang-ro, Seo-gu, Gwangju, 61949, South Korea
| | - Myung Kwan Kim
- Department of Acupuncture & Moxibustion Medicine, College of Korean Medicine, Daejeon University, 62, Daehak-ro, Dong-gu, Daejeon, 34520, South Korea
| | - Jungtae Leem
- Chung-Yeon Central Institute, 64, Sangmujungang-ro, Seo-gu, Gwangju, 61949, South Korea.,Dongshin Korean Medicine Hospital, 351, Omok-ro, Yangcheon-gu, Seoul, 07999, South Korea
| | - Hee-Geun Jo
- Chung-Yeon Central Institute, 64, Sangmujungang-ro, Seo-gu, Gwangju, 61949, South Korea.,Chung-Yeon Korean Medicine Hospital, 64, Sangmujungang-ro, Seo-gu, Gwangju, 61949, South Korea
| | - Sang-Hoon Yoon
- Chung-Yeon Central Institute, 64, Sangmujungang-ro, Seo-gu, Gwangju, 61949, South Korea.,Chung-Yeon Korean Medicine Hospital, 64, Sangmujungang-ro, Seo-gu, Gwangju, 61949, South Korea
| | - Jeeyong Kim
- Chung-Yeon Central Institute, 64, Sangmujungang-ro, Seo-gu, Gwangju, 61949, South Korea.,Chung-Yeon Korean Medicine Hospital, 64, Sangmujungang-ro, Seo-gu, Gwangju, 61949, South Korea
| | - Eun-Jung Lee
- Department of Korean Rehabilitation Medicine, College of Korean Medicine, Daejeon University, 62, Daehak-ro, Dong-gu, Daejeon, 34520, South Korea
| | - Jeong-Eun Yoo
- Department of Korean Medicine Obstetrics & Gynecology, College of Korean Medicine, Daejeon University, 62, Daehak-ro, Dong-gu, Daejeon, 34520, South Korea
| | - So Jung Park
- East West Cancer Center, Dunsan Korean Medicine Hospital of Daejeon University, 75, 176 Bun-gil, Daedeok-daero, Seo-gu, Daejeon City, 35235, South Korea
| | - Young Il Kim
- Department of Acupuncture & Moxibustion Medicine, College of Korean Medicine, Daejeon University, 62, Daehak-ro, Dong-gu, Daejeon, 34520, South Korea
| | - Eunseok Kim
- Department of Acupuncture & Moxibustion Medicine, College of Korean Medicine, Daejeon University, 62, Daehak-ro, Dong-gu, Daejeon, 34520, South Korea
| | - In Chul Jung
- Department of Neuropsychiatry, Dunsan Korean Medicine Hospital of Daejeon University, 75, Daedeok-daero 176 beon-gil, Seo-gu, Daejeon, 35235, South Korea
| | - Ju-Hyun Jeon
- Department of Acupuncture & Moxibustion Medicine, College of Korean Medicine, Daejeon University, 62, Daehak-ro, Dong-gu, Daejeon, 34520, South Korea.
| | - Yang-Chun Park
- Department of Internal Medicine, Dunsan Korean Medicine Hospital of Daejeon University, 75, Daedeok-daero 176 beon-gil, Seo-gu, Daejeon, 35235, South Korea.
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Nourani A, Ayatollahi H, Dodaran MS. Clinical Trial Data Management Software: A Review of the Technical Features. Rev Recent Clin Trials 2019; 14:160-172. [PMID: 30734683 DOI: 10.2174/1574887114666190207151500] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2019] [Revised: 01/25/2019] [Accepted: 01/29/2019] [Indexed: 11/22/2022]
Abstract
BACKGROUND Data management is an important, complex and multidimensional process in clinical trials. The execution of this process is very difficult and expensive without the use of information technology. A clinical data management system is software that is vastly used for managing the data generated in clinical trials. The objective of this study was to review the technical features of clinical trial data management systems. METHODS Related articles were identified by searching databases, such as Web of Science, Scopus, Science Direct, ProQuest, Ovid and PubMed. All of the research papers related to clinical data management systems which were published between 2007 and 2017 (n=19) were included in the study. RESULTS Most of the clinical data management systems were web-based systems developed based on the needs of a specific clinical trial in the shortest possible time. The SQL Server and MySQL databases were used in the development of the systems. These systems did not fully support the process of clinical data management. In addition, most of the systems lacked flexibility and extensibility for system development. CONCLUSION It seems that most of the systems used in the research centers were weak in terms of supporting the process of data management and managing clinical trial's workflow. Therefore, more attention should be paid to design a more complete, usable, and high quality data management system for clinical trials. More studies are suggested to identify the features of the successful systems used in clinical trials.
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Affiliation(s)
- Aynaz Nourani
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Haleh Ayatollahi
- Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran
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47
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Read KB. Adapting data management education to support clinical research projects in an academic medical center. J Med Libr Assoc 2019; 107:89-97. [PMID: 30598653 PMCID: PMC6300223 DOI: 10.5195/jmla.2019.580] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2018] [Accepted: 09/01/2018] [Indexed: 02/07/2023] Open
Abstract
Background Librarians and researchers alike have long identified research data management (RDM) training as a need in biomedical research. Despite the wealth of libraries offering RDM education to their communities, clinical research is an area that has not been targeted. Clinical RDM (CRDM) is seen by its community as an essential part of the research process where established guidelines exist, yet educational initiatives in this area are unknown. Case Presentation Leveraging my academic library's experience supporting CRDM through informationist grants and REDCap training in our medical center, I developed a 1.5 hour CRDM workshop. This workshop was designed to use established CRDM guidelines in clinical research and address common questions asked by our community through the library's existing data support program. The workshop was offered to the entire medical center 4 times between November 2017 and July 2018. This case study describes the development, implementation, and evaluation of this workshop. Conclusions The 4 workshops were well attended and well received by the medical center community, with 99% stating that they would recommend the class to others and 98% stating that they would use what they learned in their work. Attendees also articulated how they would implement the main competencies they learned from the workshop into their work. For the library, the effort to support CRDM has led to the coordination of a larger institutional collaborative training series to educate researchers on best practices with data, as well as the formation of institution-wide policy groups to address researcher challenges with CRDM, data transfer, and data sharing.
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Affiliation(s)
- Kevin B Read
- Data Services Librarian and Data Discovery Lead, NYU Health Sciences Library, New York University School of Medicine, 577 First Avenue, New York, NY 10016,
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48
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Nourani A, Ayatollahi H, Dodaran MS. A Review of Clinical Data Management Systems Used in Clinical Trials. Rev Recent Clin Trials 2019; 14:10-23. [PMID: 30251611 DOI: 10.2174/1574887113666180924165230] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Revised: 09/09/2018] [Accepted: 09/18/2018] [Indexed: 04/13/2023]
Abstract
BACKGROUND A clinical data management system is a software supporting the data management process in clinical trials. In this system, the effective support of clinical data management dimensions leads to the increased accuracy of results and prevention of diversion in clinical trials. The aim of this review article was to investigate the dimensions of data management in clinical data management systems. METHODS This study was conducted in 2017. The used databases included Web of Science, Scopus, Science Direct, ProQuest, Ovid Medline and PubMed. The search was conducted over a period of 10 years from 2007 to 2017. The initial number of studies was 101 reaching 19 in the final stage. The final studies were described and compared in terms of the year, country and dimensions of the clinical data management process in clinical trials. RESULTS The research findings indicated that none of the systems completely supported the data management dimensions in clinical trials. Although these systems were developed for supporting the clinical data management process, they were similar to electronic data capture systems in many cases. The most significant dimensions of data management in such systems were data collection or entry, report, validation, and security maintenance. CONCLUSION Seemingly, not sufficient attention has been paid to automate all dimensions of the clinical data management process in clinical trials. However, these systems could take positive steps towards changing the manual processes of clinical data management to electronic processes.
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Affiliation(s)
- Aynaz Nourani
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Haleh Ayatollahi
- Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran
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49
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Farnum MA, Ashok M, Kowalski D, Du F, Mohanty L, Konstant P, Ciervo J, Lobanov VS, Agrafiotis DK. A cross-source, system-agnostic solution for clinical data review. Database (Oxford) 2019; 2019:baz017. [PMID: 30773591 PMCID: PMC6378235 DOI: 10.1093/database/baz017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 12/24/2018] [Accepted: 01/19/2019] [Indexed: 01/23/2023]
Abstract
Assembly of complete and error-free clinical trial data sets for statistical analysis and regulatory submission requires extensive effort and communication among investigational sites, central laboratories, pharmaceutical sponsors, contract research organizations and other entities. Traditionally, this data is captured, cleaned and reconciled through multiple disjointed systems and processes, which is resource intensive and error prone. Here, we introduce a new system for clinical data review that helps data managers identify missing, erroneous and inconsistent data and manage queries in a unified, system-agnostic and efficient way. Our solution enables timely and integrated access to all study data regardless of source, facilitates the review of validation and discrepancy checks and the management of the resulting queries, tracks the status of page review, verification and locking activities, monitors subject data cleanliness and readiness for database lock and provides extensive configuration options to meet any study's needs, automation for regular updates and fit-for-purpose user interfaces for global oversight and problem detection.
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Affiliation(s)
- Michael A Farnum
- Covance, the Drug Development Division of LabCorp, 210 Carnegie Center, Princeton, NJ, USA
| | - Mathangi Ashok
- Covance, the Drug Development Division of LabCorp, 210 Carnegie Center, Princeton, NJ, USA
| | - Daniel Kowalski
- Covance, the Drug Development Division of LabCorp, 210 Carnegie Center, Princeton, NJ, USA
| | - Fang Du
- Covance, the Drug Development Division of LabCorp, 210 Carnegie Center, Princeton, NJ, USA
| | - Lalit Mohanty
- Covance, the Drug Development Division of LabCorp, 210 Carnegie Center, Princeton, NJ, USA
| | - Paul Konstant
- Covance, the Drug Development Division of LabCorp, 210 Carnegie Center, Princeton, NJ, USA
| | - Joseph Ciervo
- Covance, the Drug Development Division of LabCorp, 210 Carnegie Center, Princeton, NJ, USA
| | - Victor S Lobanov
- Covance, the Drug Development Division of LabCorp, 210 Carnegie Center, Princeton, NJ, USA
| | - Dimitris K Agrafiotis
- Covance, the Drug Development Division of LabCorp, 210 Carnegie Center, Princeton, NJ, USA
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Ahmed Z, Kim M, Liang BT. MAV-clic: management, analysis, and visualization of clinical data. JAMIA Open 2018; 2:23-28. [PMID: 31984341 PMCID: PMC6951942 DOI: 10.1093/jamiaopen/ooy052] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 07/18/2018] [Accepted: 11/22/2018] [Indexed: 11/12/2022] Open
Abstract
Objectives Develop a multifunctional analytics platform for efficient management and analysis of healthcare data. Materials and Methods Management, Analysis, and Visualization of Clinical Data (MAV-clic) is a Health Insurance Portability and Accountability Act of 1996 (HIPAA)-compliant framework based on the Butterfly Model. MAV-clic extracts, cleanses, and encrypts data then restructures and aggregates data in a deidentified format. A graphical user interface allows query, analysis, and visualization of clinical data. Results MAV-clic manages healthcare data for over 800 000 subjects at UConn Health. Three analytic capabilities of MAV-clic include: creating cohorts based on specific criteria; performing measurement analysis of subjects with a specific diagnosis and medication; and calculating measure outcomes of subjects over time. Discussion MAV-clic supports clinicians and healthcare analysts by efficiently stratifying subjects to understand specific scenarios and optimize decision making. Conclusion MAV-clic is founded on the scientific premise that to improve the quality and transition of healthcare, integrative platforms are necessary to analyze heterogeneous clinical, epidemiological, metabolomics, proteomics, and genomics data for precision medicine.
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Affiliation(s)
- Zeeshan Ahmed
- Department of Genetics and Genome Sciences, Institute for Systems Genomics, School of Medicine, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - Minjung Kim
- The Pat and Jim Calhoun Cardiology Center, School of Medicine, University of Connecticut Health Center, Farmington, Connecticut, USA
| | - Bruce T Liang
- Ray Neag Distinguished Professor of Cardiovascular Biology and Medicine, Director Pat and Jim Calhoun Cardiology Center, Dean UConn School of Medicine, University of Connecticut Health Center, Farmington, Connecticut, USA
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