Explore the vast range of titles available.

Multicenter clinical trials are essential for evaluating interventions but often face significant challenges in study design, site coordination, participant recruitment, and regulatory compliance. To address these issues, the National Institutes of Health’s National Center for Advancing Translational Sciences established the Trial Innovation Network (TIN). The TIN offers a scientific consultation process, providing access to clinical trial and disease experts who provide input and recommendations throughout the trial’s duration, at no cost to investigators. This approach aims to improve trial design, accelerate implementation, foster interdisciplinary teamwork, and spur innovations that enhance multicenter trial quality and efficiency. The TIN leverages resources of the Clinical and Translational Science Awards (CTSA) program, complementing local capabilities at the investigator’s institution. The Initial Consultation process focuses on the study’s scientific premise, design, site development, recruitment and retention strategies, funding feasibility, and other support areas. As of 6/1/2024, the TIN has provided 431 Initial Consultations to increase efficiency and accelerate trial implementation by delivering customized support and tailored recommendations. Across a range of clinical trials, the TIN has developed standardized, streamlined, and adaptable processes. We describe these processes, provide operational metrics, and include a set of lessons learned for consideration by other trial support and innovation networks.

In 2016, the National Center for Advancing Translational Science launched the Trial Innovation Network (TIN) to address barriers to efficient and informative multicenter trials. The TIN provides a national platform, working in partnership with 60+ Clinical and Translational Science Award (CTSA) hubs across the country to support the design and conduct of successful multicenter trials. A dedicated Hub Liaison Team (HLT) was established within each CTSA to facilitate connection between the hubs and the newly launched Trial and Recruitment Innovation Centers. Each HLT serves as an expert intermediary, connecting CTSA Hub investigators with TIN support, and connecting TIN research teams with potential multicenter trial site investigators. The cross-consortium Liaison Team network was developed during the first TIN funding cycle, and it is now a mature national network at the cutting edge of team science in clinical and translational research. The CTSA-based HLT structures and the external network structure have been developed in collaborative and iterative ways, with methods for shared learning and continuous process improvement. In this paper, we review the structure, function, and development of the Liaison Team network, discuss lessons learned during the first TIN funding cycle, and outline a path toward further network maturity.

The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using electronic health records (EHRs). Based on scientific merit and predicted difficulty, eMERGE selected six existing phenotypes to enhance with NLP. We assessed performance, portability, and ease of use. We summarized lessons learned by: (1) challenges; (2) best practices to address challenges based on existing evidence and/or eMERGE experience; and (3) opportunities for future research. Adding NLP resulted in improved, or the same, precision and/or recall for all but one algorithm. Portability, phenotyping workflow/process, and technology were major themes. With NLP, development and validation took longer. Besides portability of NLP technology and algorithm replicability, factors to ensure success include privacy protection, technical infrastructure setup, intellectual property agreement, and efficient communication. Workflow improvements can improve communication and reduce implementation time. NLP performance varied mainly due to clinical document heterogeneity; therefore, we suggest using semi-structured notes, comprehensive documentation, and customization options. NLP portability is possible with improved phenotype algorithm performance, but careful planning and architecture of the algorithms is essential to support local customizations.

Genomic medicine holds great promise for improving health care, but integrating searchable and actionable genetic data into electronic health records (EHRs) remains a challenge. Here we describe Neptune, a system for managing the interaction between a clinical laboratory and an EHR system during the clinical reporting process. We developed Neptune and applied it to two clinical sequencing projects that required report customization, variant reanalysis, and EHR integration. Neptune has been applied for the generation and delivery of over 15,000 clinical genomic reports. This work spans two clinical tests based on targeted gene panels that contain 68 and 153 genes respectively. These projects demanded customizable clinical reports that contained a variety of genetic data types including single-nucleotide variants (SNVs), copy-number variants (CNVs), pharmacogenomics, and polygenic risk scores. Two variant reanalysis activities were also supported, highlighting this important workflow. Methods are needed for delivering structured genetic data to EHRs. This need extends beyond developing data formats to providing infrastructure that manages the reporting process itself. Neptune was successfully applied on two high-throughput clinical sequencing projects to build and deliver clinical reports to EHR systems. The software is open source and available at https://gitlab.com/bcm-hgsc/neptune.

Introduction Identifying key characteristics of exemplar genomic learning healthcare systems (gLHS) and knowledge gaps that can be explored by collaboration among them is likely to accelerate the sharing of best practices and generation of evidence that informs the use of genomics in clinical care. Methods Deliberations of an expert group convened by the National Human Genome Research Institute (NHGRI) supplemented by relevant literature. Results Recent advances in genomic data standardization, automated clinical decision support, increased interoperability, and improved genomic technologies have enabled the development of several robust gLHS. They remain concentrated in major academic centers, however, and operate largely independently. Sharing their methods and tools would increase access to these innovations and advance the field. Several gLHS have expressed willingness to collaborate in a coalition designed to gather, evaluate, and disseminate best practices and development needs. Such a coalition has recently been formed under the leadership of NHGRI. Conclusion Increased collaboration, interoperability, and sharing of genomic information and strategies across gLHS can help define, refine, and disseminate best practices. Such cooperation can improve genomic variant curation and interpretation, diagnostic accuracy, evidence generation, and ultimately patient care through seamless integration of research as an integral component of good clinical care.

For many rare diseases with no approved preventive interventions, promising interventions exist, yet it has been difficult to conduct a pivotal phase 3 trial that could provide direct evidence demonstrating a beneficial effect on the target disease outcome. When a promising putative surrogate endpoint(s) for the target outcome is available, surrogate-based provisional approval of an intervention may be pursued. We apply the Causal Roadmap rubric to define a surrogate endpoint based provisional approval causal roadmap, which combines observational study data that estimates the relationship between the putative surrogate and the target outcome, with a phase 3 surrogate endpoint study that collects the same data but is very under-powered to assess the treatment effect (TE) on the target outcome. The objective is conservative estimation/inference for the TE with an estimated lower uncertainty bound that allows (through two bias functions) for an imperfect surrogate and imperfect transport of the conditional target outcome risk in the untreated between the observational and phase 3 studies. Two estimators of TE (plug-in, nonparametric efficient one-step) with corresponding inference procedures are developed. Finite-sample performance of the plug-in estimator is evaluated in two simulation studies, with R code provided. The roadmap is illustrated with contemporary Group B Streptococcus vaccine development.

Summary statistics from genome-wide association studies (GWAS) represent a huge potential for research. A challenge for researchers in this field is the access and sharing of summary statistics data due to a lack of standards for the data content and file format. For this reason, the GWAS Catalog hosted a series of meetings in 2021 with summary statistics stakeholders to guide the development of a standard format. The key requirements from the stakeholders were for a standard that contained key data elements to be able to support a wide range of data analyses, required low bioinformatics skills for file access and generation, to have easily accessible metadata, and unambiguous and interoperable data. Here, we define the specifications for the first version of the GWAS-SSF format, which was developed to meet the requirements discussed with the community. GWAS-SSF consists of a tab-separated data file with well-defined fields and an accompanying metadata file.

Background: Next generation DNA sequencing (NGS) has been rapidly adopted by clinical testing laboratories for detection of germline and somatic genetic variants. The complexity of sample processing in a clinical DNA sequencing laboratory creates multiple opportunities for sample identification errors, demanding stringent quality control procedures. Methods: We utilized DNA genotyping via a 96-SNP PCR panel applied at sample acquisition in comparison to the final sequence, for tracking of sample identity throughout the sequencing pipeline. The 96-SNP PCR panel's inclusion of sex SNPs also provides a mechanism for a genotype-based comparison to recorded sex at sample collection for identification. This approach was implemented in the clinical genomic testing pathways, in the multi-center Electronic Medical Records and Genomics (eMERGE) Phase III program. Results: We identified 110 inconsistencies from 25,015 (0.44%) clinical samples, when comparing the 96-SNP PCR panel data to the test requisition-provided sex. The 96-SNP PCR panel genetic sex predictions were confirmed using additional SNP sites in the sequencing data or high-density hybridization-based genotyping arrays. Results identified clerical errors, samples from transgender participants and stem cell or bone marrow transplant patients and undetermined sample mix-ups. Conclusion: The 96-SNP PCR panel provides a cost-effective, robust tool for tracking samples within DNA sequencing laboratories, while the ability to predict sex from genotyping data provides an additional quality control measure for all procedures, beginning with sample collections. While not sufficient to detect all sample mix-ups, the inclusion of genetic versus reported sex matching can give estimates of the rate of errors in sample collection systems. Competing Interest Statement The authors have declared no competing interest.

Abstract Structured representation of clinical genetic results is necessary for advancing precision medicine. The Electronic Medical Records and Genomics (eMERGE) Network’s Phase III program initially used a commercially developed XML message format for standardized and structured representation of genetic results for electronic health record (EHR) integration. In a desire to move towards a standard representation, the network created a new standardized format based upon Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR), to represent clinical genomics results. These new standards improve the utility of HL7 FHIR as an international healthcare interoperability standard for management of genetic data from patients. This work advances the establishment of standards that are being designed for broad adoption in the current health information technology landscape. Competing Interest Statement Luke V. Rasmussen has a patent GENERATING DATA IN STANDARDIZED FORMATS AND PROVIDING RECOMMENDATIONS that is no longer being pursued. Samuel J. Aronson, Hana Zouk and Heidi L. Rehm are employed by Mass General Brigham which receives royalties on sales of GeneInsight software. David R. Crosslin is a consultant for UnitedHealth Group. Richard A. Gibbs declares that Baylor College of Medicine receives payments from Baylor Genetics Laboratories, which provides services for genetic testing; Baylor College of Medicine is part owner of Codified Genomics. Eric Venner is a cofounder of Codified Genomics, which provides variant interpretation services. All other authors declare no competing interests.

This paper considers the relationship between energy-efficient design and the leasing/sales markets for commercial real estate. An economic model is provided that considers lease rates and occupancy in simultaneous equilibrium. The behavior of both is predicted to be influenced by efficient design attributes. Selling price is determined by both rents and occupancy; therefore the impact of efficient design on commercial sales activity should be distributed through the leasing market. The model is tested empirically using a national sample of sales and leasing data for class A office buildings. The evidence indicates that “green” buildings achieve superior rents and sustain significantly higher occupancy. The improved performance in the rental market is reflected in a significant premium for the selling price of Energy Star-labeled and LEED-certified properties.