Framework of the strengths and challenges of clinically integrated trials: An expert panel report.

The limitations of the explanatory clinical trial framework include the high expense of implementing explanatory trials, restrictive entry criteria for participants, and redundant logistical processes. These limitations can result in slow evidence generation that is not responsive to population health needs, yielding evidence that is not generalizable. Clinically integrated trials, which integrate clinical research into routine care, represent a potential solution to this challenge and an opportunity to support learning health systems. The operational and design features of clinically integrated trials include a focused scope, simplicity in design and requirements, the leveraging of existing data structures, and patient participation in the entire trial process. These features are designed to minimize barriers to participation and trial execution and reduce additional research burdens for participants and clinicians alike. Broad adoption and scalability of clinically integrated trials are dependent, in part, on continuing regulatory, healthcare system, and payer support. This analysis presents a framework of the strengths and challenges of clinically integrated trials and is based on a multidisciplinary expert "Think Tank" panel discussion that included representatives from patient populations, academia, non-profit funding agencies, the U.S. Food and Drug Administration, and industry.

Adoption of Decentralization: Are Our Perceptions Holding Us Back?

Incorporating decentralized approaches into clinical trials is a critical innovation with potential implications for improved accessibility and diversity, as well as lower burden for participants and caregivers. As we move forward in a collective effort to modernize clinical trials, we consistently hear of hurdles that interfere with the adoption of decentralized approaches. But are these hurdles really the impediments we think they are? In this commentary, we offer three perceptions that are commonly heard as impediments to the adoption of digital and decentralized clinical trials. Leveraging the Clinical Trial Transformation Initiative's Digital Health Trial hub of work, interactions with members and regulators, and observations related to adoption, we address those perceptions and note some resources that exist to overcome them. In working through these barriers, we can instill confidence in sponsors and designers to leverage all the clinical trial design tools available to them to advance the use of decentralized approaches.

How Much Evidence Is Enough? Research Sponsor Experiences Seeking Regulatory Acceptance of Digital Health Technology-Derived Endpoints.

Introduction: Digital health technologies (DHTs) provide opportunities for real-time data collection and assessment of patient function. However, use of DHT-derived endpoints in clinical trials to support medical product labelling claims is limited. Methods: From November 2020 through March 2021, the Clinical Trials Transformation Initiative (CTTI) conducted a qualitative descriptive study using semi-structured interviews with sponsors of clinical trials that used DHT-derived endpoints. We aimed to learn about their experiences, including their interactions with regulators and the challenges they encountered. Using applied thematic analysis, we identified barriers to and recommendations for using DHT-derived endpoints in pivotal trials. Results: Sponsors identified five key challenges to incorporating DHT-derived endpoints in clinical trials. These included (1) a need for additional regulatory clarity specific to DHT-derived endpoints, (2) the official clinical outcome assessment qualification process being impractical for the biopharmaceutical industry, (3) a lack of comparator clinical endpoints, (4) a lack of validated DHTs and algorithms for concepts of interest, and (5) a lack of operational support from DHT vendors. Discussion/Conclusion: CTTI shared the interview findings with the US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) and during a multi-stakeholder expert meeting. Based on these discussions, we provide several new and revised tools to aid sponsors in using DHT-derived endpoints in pivotal trials to support labelling claims.

Learning from patient and site perspectives to develop better digital health trials: Recommendations from the Clinical Trials Transformation Initiative.

In order to harness the potential of digital health technologies to enhance the quality of clinical research, it is critical to first understand how to engage patients and research sites when planning and conducting digital health trials. To pave the way for the more effective use of digital health technologies in trials, the Clinical Trials Transformation Initiative has developed the first comprehensive, evidence-based set of recommendations for incorporating patient and site perspectives in digital health trials. While directed primarily at sponsors, these recommendations are expected to be valuable for all stakeholders including investigators.

Digital facial dysmorphology for genetic screening: Hierarchical constrained local model using ICA.

Down syndrome, the most common single cause of human birth defects, produces alterations in physical growth and mental retardation. If missed before birth, the early detection of Down syndrome is crucial for the management of patients and disease. However, the diagnostic accuracy for pediatricians prior to cytogenetic results is moderate and the access to specialists is limited in many social and low-economic areas. In this study, we propose a simple, non-invasive and automated framework for Down syndrome detection based on disease-specific facial patterns. Geometric and local texture features are extracted based on automatically detected anatomical landmarks to describe facial morphology and structure. To accurately locate the anatomical facial landmarks, a hierarchical constrained local model using independent component analysis (ICA) is proposed. We also introduce a data-driven ordering method for selecting dominant independent components in ICA. The hierarchical structure of the model increases the accuracy of landmark detection by fitting separate models to different groups. Then the most representative features are selected and we also demonstrate that they match clinical observations. Finally, a variety of classifiers are evaluated to discriminate between Down syndrome and healthy populations. The best performance achieved 0.967 accuracy and 0.956 F1 score using combined features and linear discriminant analysis. The method was also validated on a dataset with mixed genetic syndromes and high performance (0.970 accuracy and 0.930 F1 score) was also obtained. The promising results indicate that our method could assist in Down syndrome screening effectively in a simple, non-invasive way, and extensible to detection of other genetic syndromes.