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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., Competing Interests: Disclosures Dr. Peters is supported by the National Heart Lung and Blood Institute (T32HL069749) and has received honoraria from Cytokinetics. Dr. Jones has received grants from Bayer, Boehringer Ingelheim, and Merck. Dr. Broedl is an employee of Boehringer Ingelheim. Dr. Hornik received a research grant from Before Brands and consulting fees from SC Pharma. Dr. Knowlton received a salary from Intermountain Health Care. Dr. Krofah had leadership roles in Protas, the Reagan Udall Foundation, Clinical Trials Transformation Initiative (CTTI), Duke Margolis Center for Health Policy, Alliance for a Stronger FDA, Patient Focused Medicines Development (PFMD), and Medical Device Innovation Consortium (MDIC). Dr. Landray received grants from Janssen, FluLab, Schmidt Futures, Google Ventures, the National Institute for Health Research, UK Research & Innovation, Wellcome, and the Bill & Melinda Gates Foundation, as well as study drugs from Regeneron, Roche, AbbVie, and GSK. Dr. Rockhold received grants from the NIH, PCORI, BMS, AstraZeneca, American Regent, the Gates Foundation, and Eidos and consulting fees from Janssen, Clover, Doctor Evidence, and Intercept. He also participated on Data Safety Monitoring Boards for Lilly, AstraZeneca, Merck, Gilead, Novartis, Icosavax, Sanofi, UCB, Amgen, Biogen, BMS, Pulmocide, Alkermes, and Diurnal. He had an unpaid leadership role for the Frontier Science Foundation. He has stock or stock options for GSK, Clover, Athira, Doctor Evidence, DataVant, Spencer Health Solutions, and Adaptic Health. Dr. Roessig is a full-time employee of Bayer AG. Dr. Rothman has received grants from PCORI, the CDC, NIH, and AHRQ. Lesley Schofield is an employee of Novartis and has stock or stock options from Novartis. Dr. Tenaerts is an employee of Medable, Inc., and has a stock option grant with Medable, Inc. Dr. Tenaerts also received an honorarium for participation on a data monitoring committee., (Copyright © 2024. Published by Elsevier Inc.)

Importance: Pragmatic randomized clinical trials (RCTs) often use multiple data sources to examine clinical events, but the relative contribution of data sources to clinical end-point rates is understudied., Objective: To assess the contribution of data sources (electronic health records [EHRs], public/private insurance claims, and/or participant-reported data) to clinical end points among ADAPTABLE participants who had available data., Design, Setting, and Participants: The ADAPTABLE study was an open-label, pragmatic RCT from April 2016 through June 2019 conducted in research networks within clinical practice. Participants had existing atherosclerotic cardiovascular disease and available data to analyze. The characteristics of patients by combinations of data source availability were compared to examine the contribution of each of the data sources to end-point ascertainment. Data for this prespecified analysis were examined from January 2022 to June 2023., Exposures: Randomized exposure to 81 mg or 325 mg of aspirin daily., Main Outcomes and Measures: Number of events for the primary end point (composite of death, hospitalization for myocardial infarction, and hospitalization for stroke) that were contributed by EHR or claims data and then number of events contributed by each additional data source., Results: Of 15 006 participants randomized with at least 1 other source of data available beyond participant-reported data, there were 8756 (58.3%) with participant-reported and EHR data; 4291 (28.6%) with participant-reported, EHR, and claims data; 1412 (9.4%) with EHR-only data; 262 (1.7%) with participant-reported and claims data; 202 (1.3%) with EHR and claims data; and 83 (0.6%) with claims-only data. Participants with EHR-only data were younger (median age, 63.7 years; IQR, 55.8-71.4) compared with the other groups (range, 65.6-71.9 years). Among participants with both EHR and claims data, with or without participant-reported data (n = 4493), for each outcome, most events (92%-100%) were identified in the EHR or in claims data. For all clinical end points, participant-reported data contributed less than 10% of events not otherwise available from claims or EHR data., Conclusions and Relevance: In this analysis of a pragmatic RCT, claims and EHR data provided the most clinical end-point data when compared with participant-reported events. These findings provide a framework for collecting end points in pragmatic clinical trials. Further work is needed to understand the data source combinations that most effectively provide clinical end-point data in RCTs.

Importance: The US leads the world in bringing new medical products to market, but the ability to generate evidence to inform clinical practice in postmarket settings needs improvement. Although a diverse group of stakeholders is working to improve postmarket evidence generation, the role of private payers has been underappreciated., Observations: Payers are crucial allies in improving evidence generation because better data would better inform coverage decisions, their policies and practices influence the conduct of care and research, and their claims data are a source of real-world evidence used in medical product evaluation. In addition, payers have a stake in improving evidence generation because the kinds of evidence needed to inform health care and coverage decisions are often not available when a product enters the market and may not be generated without their involvement. Here, we describe several key steps payers could take to improve evidence generation, including participating in efforts to reduce administrative and financial barriers to the conduct of clinical trials, directly incentivizing evidence generation on high-priority questions by funding potential cost-saving trials, increasing engagement with the medical products industry on evidentiary needs for coverage decisions, and improving usability of claims data by reducing data lags and routinely recording unique device identifiers. Broad payer engagement with US Food and Drug Administration recommendations regarding evidence generation will ensure that the opportunities to participate in clinical research are extended to all communities and that evidence needed to inform care is generated in trials and surveillance systems that reflect the clinical reality across the US., Conclusions and Relevance: Increasing payer involvement in evidence generation can benefit all participants in the medical innovation ecosystem. The importance of payers in these efforts will continue to grow in response to imperatives to increase integration of care and research, engage a diverse set of communities in clinical research, and move toward alternative payment models.

The global pharmaceutical industry portfolio is skewed towards cancer and rare diseases due to more predictable development pathways and financial incentives. In contrast, drug development for major chronic health conditions that are responsible for a large part of mortality and disability worldwide is stalled. To examine the processes of novel drug development for common chronic health conditions, a multistakeholder Think Tank meeting, including thought leaders from academia, clinical practice, non-profit healthcare organizations, the pharmaceutical industry, the Food and Drug Administration (FDA), payors as well as investors, was convened in July 2022. Herein, we summarize the proceedings of this meeting, including an overview of the current state of drug development for chronic health conditions and key barriers that were identified. Six major action items were formulated to accelerate drug development for chronic diseases, with a focus on improving the efficiency of clinical trials and rapid implementation of evidence into clinical practice., Competing Interests: Conflict of interest Konstantin A. Krychtiuk: advisory boards Novartis, Sanofi, Amgen; speaker fees Amgen, Daichii Sankyo, Zoll Medical Tomas LG Andersson: employed by AstraZeneca, owns stock in AstraZeneca, travel support by AstraZeneca Ulrike Bodesheim employed by Bayer AG; Javed Butler: Consulting fees: 3ivelabs, Abbott, Amgen, American Regent, Applied Therapeutics, AstraZeneca, Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Cardiac Dimension, Cardior, CVRx, Cytokinetics, Edwards, Element, Faraday, G3 Pharmaceutical, Imbria, Impulse Dynamics, Innolife, Inventiva, Ionis, Janssen, LivaNova, Lexicon, Medtronic, Merck, Novartis, Novo Nordisk, Otsuka, Occlutech, PharmaCosmos, Roche, Sanofi, Secretome, Sequana, Tricog, Vifor; Speaker Fees: Novartis, Boehringer Ingelheim-Lilly, Astra Zeneca, Impulse Dynamics; Lesley H Curtis: Fees Boehringer Ingleheim, NIH, and Regeneron Pharmaceuticals Mitchell Elkind: Grants: Roche; Lecture Fees: Atria Academy of Science and Medicine; Travel Support: American Heart Association; Adrian F. Hernandez: Consulting fees Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Boston Scientific, CSL Behring, Cytokinetics, Eli Lilly, Intellia Therapeutics, Merck, Novartis, Novo Nordisk, Prolaio; Grants/Contracts: American Regent, AstraZeneca, Bayer, Boehringer Ingelheim, Intellia Therapeutics, Merck, Novartis, Novo Nordisk, Verily; Christoph Hornik: None declared; Gary H Lyman: Consulting Fees: Sandoz, Astra Zeneca, Beyond Spring, G1 Therapeutics, Merck, Partners Healthcare, ER Squibb, Samsung, Fresenius Kabi; Pooja Khatri: grants from Cerenovus; serving on the scientific advisory boards of Lumosa; Shionogi, Bayer, and Basking Biosciences; andreceiving online publication royalties from UpToDate; Michael Mbagwu: Stocks in Verana Health as employee Masahiro Murakami: employed by Eli Lilly; Gwen Nichols: None declared; Lothar Roessig: employed by Bayer; Anne Quinn Young: None declared; Richard L. Schilsky Consulting Fees: Cellworks, EQRx, Zephyr AI, Clarified Precision Medicine, Illumina, Member, DSMB, Baseline Study sponsored by Verily Chairman, Scientific Advisory Board, Ontario Institute for Cancer Research Member, Strategic External Advisory Committee for Canadian Cancer Trials Group Chairman, Board of Directors, Reagan-Udall Foundation for the FDA, Member, Board of Directors, Friends of Cancer Research, Member, Board of Directors, Leap Therapeutics; Neha Pagidipati: Research support from Alnylam, Amgen, Boehringer Ingelheim, Eggland's Best, Eli Lilly, Novartis, Novo Nordisk, Verily Life Sciences; Consultation/Advisory Panels for Bayer, Boehringer Ingelheim, CRISPR Therapeutics, Eli Lilly, Esperion, AstraZeneca, Merck, Novartis, and Novo Nordisk; Executive Committee member for trials sponsored by Novo Nordisk and by Amgen; DSMB for trials sponsored by J+J and Novartis; Medical advisory board for Miga Health., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Several years ago, the US News and World Report changed their risk-adjustment methodology, now relying almost exclusively on chronic conditions for risk adjustment. The impacts of adding selected acute conditions like pneumonia, sepsis, and electrolyte disorders ("augmented") to their current risk models ("base") for 4 specialties-cardiology, neurology, oncology, and pulmonology-on estimates of hospital performance are reported here. In the augmented models, many acute conditions were associated with substantial risks of mortality. Compared to the base models, the discrimination and calibration of the augmented models for all specialties were improved. While estimated hospital performance was highly correlated between the 2 models, the inclusion of acute conditions in risk-adjustment models meaningfully improved the predictive ability of those models and had noticeable effects on hospital performance estimates. Measures or conditions that address disease severity should always be included when risk-adjusting hospitalization outcomes, especially if the goal is provider profiling., (Copyright © The Authors 2024.)

While the coronavirus disease 2019 (COVID-19) pandemic continues to present global challenges, sufficient time has passed to reflect on lessons learned and use those insights to inform policy and approaches to prepare for the next pandemic. In May 2022, the Duke Clinical Research Institute convened a think tank with thought leaders from academia, clinical practice, the pharmaceutical industry, patient advocacy, the National Institutes of Health, the US Food and Drug Administration, and the Centers for Disease Control and Prevention to share, firsthand, expert knowledge of the insights gained from the COVID-19 pandemic and how this acquired knowledge can help inform the next pandemic response. The think tank focused on pandemic preparedness, therapeutics, vaccines, and challenges related to clinical trial design and scale-up during the early phase of a pandemic. Based on the multi-faceted discussions, we outline 10 key steps to an improved and equitable pandemic response., Competing Interests: Potential conflicts of interest. A. F. H. reports contracts with Pfizer and Merck and consulting fees from Merck. B. L. is an employee of and owns stock in Moderna and reports support for travel to a meeting from Moderna. C. W. W. reports consulting fees from Arena Pharmaceuticals, BioFire, FHI Clinical, Giner, Karius, and SeLux Diagnostics; grants or contracts (paid to institution) from Defense Advanced Research Projects Agency, National Institutes of Health (NIH)-Antibacterial Resistance Leadership Group/National Institute of Allergy and Infectious Diseases/NIH Vaccine and Treatment Evaluation Units/National Institute of Mental Health/National Institute of General Medical Sciences, Sanofi, Najit, the Centers for Disease Control and Prevention (CDC), Patient-Centered Outcomes Research, United States Army Medical Research Acquisition Activity, Department of Defence, Abbott, and Pfizer; support for attending meetings and/or travel from the American Society for Microbiology (ASM); participation on a data and safety monitoring board (DSMB) or advisory board for IDbyDNA, Janssen, Regeneron, Roche Molecular Sciences; a leadership or fiduciary role for ASM and the American Society of Tropical Medicine and Hygiene; being employed by Duke University, Durham Veterans Affairs Hospital, Biomeme, and Equity/Founder of Predigen, Inc; and planned, issued, or pending patents for biomarkers for the molecular classification of bacterial infection, methods to diagnose and treat acute respiratory infections, gene expression signatures useful to predict or diagnose sepsis and methods of using the same, host-based molecular signatures of human infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 (coronavirus disease 2019 [COVID-19]), methods of identifying infectious disease and assays for identifying infectious disease, and nasopharyngeal protein biomarkers of acute respiratory virus infection and methods of using same. H. C. and S. C. B. are employees of and hold or may hold stock in AstraZeneca. J. W. reports being an employee of and owns stock/stock options in and received equipment, materials, drugs, medical writing, gifts, and other services from Johnson & Johnson and, as CMO, has been deposed in litigation; patents issued that do not relate to the current article (available on request); and a role on the Brooklyn College Cancer Center Advisory Board, a Brooklyn College Foundation Trustee, and on the Fellowships at Auschwitz for the Study of Professional Ethics, Academic Committee. J. R. D. is an employee and shareholder in Eli Lilly and Company. L. H. C. reports consulting fees from Regeneron for the NFL Players Association and grants or contracts from NIH for PCORI. L. S. reports stock/stock options in Novartis. L. R. is an employee of and owns stock/stock options in and received support for attending meetings and/or travel from Bayer AG. M. S. reports consulting fees from AbbVie, Assembly Bio, Antios, Arbutus, Gilead Sciences, Precision Bio, and GSK for scientific advisory boards and participation on a DSMB for Gilead Sciences and AbbVie and a role as editor of the Journal Viral Hepatitis. O. V. is an employee of Boehringer Ingelheim. P. P. has a patent pending for sotrovimab and is an employee and shareholder of Vir Biotechnology. S. N. reports consulting fees from Pardes Biosciences, Theratechnologies, and Silverback Therapeutics; stock/stock options in Vir Biotechnology; grants or contracts (paid to institution) from Gilead Sciences and AbbVie; participation on an advisory board for Vir Biotechnology, a DSMB for Personal Health Insights, Inc; and serving on an event adjudication committee for Bristol–Myers Squibb/PRA. M. A. M. reports consulting fees from Abcam; stock/stock options in Grid Therapeutics; a role as an advisory board member for GSK Belimumab Pregnancy Registry; and funding to Duke from NIAD (U01 AI151378): Centers for Research in Emerging Infectious Disease (CREID) Network Coordinating Center, HHS 75N93019C00050: Duke Collaborative Influenza Vaccine Innovation Centers (CIVICs) A: Vaccine Center, HHS 75N93019C00054: Duke CIVICs C: Clinical Core, U19 AI144177: A Global Syphilis Vaccine Targeting Outer Membrane Proteins Of Treponema pallidum, 2P01 AI089618: Structure-Function Analysis Of Infection- And Vaccine-Induced B Cell Repertoires. S. E. T. reports royalties from UptoDate; grants or contracts (paid to institution) from the CDC; payment or honoraria (to author) from the Infectious Disease Society of America (IDSA) and Emerson Hospital; support from DCRI to attend the meeting upon which this article was based. L. R. B. is involved in human immunodeficiency virus (HIV) and SARS-CoV-2 vaccine clinical trials conducted in collaboration with the NIH, HIV Vaccine Trials Network, COVID Vaccine Prevention Network, International AIDS Vaccine Initiative, Crucell/Janssen, Moderna, Military HIV Research Program, the Gates Foundation, and Harvard Medical School. reports participation on a DSMB for NIH and AMDAC Committee for the FDA (research funded by the NIH, Welcome Trust, and the Gates Foundation). R. T. G. reports a leadership or fiduciary role on the NIH COVID Treatment Guidelines Panel and IDSA COVID Treatment Guidelines Panel. R. G. reports grants (to institution) from NIH/NIAID, book chapter royalties from Elsevier, and section editor royalties from UpToDate. All remaining authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Translational research is a data-driven process that involves transforming scientific laboratory- and clinic-based discoveries into products and activities with real-world impact to improve individual and population health. Successful execution of translational research requires collaboration between clinical and translational science researchers, who have expertise in a wide variety of domains across the field of medicine, and qualitative and quantitative scientists, who have specialized methodologic expertise across diverse methodologic domains. While many institutions are working to build networks of these specialists, a formalized process is needed to help researchers navigate the network to find the best match and to track the navigation process to evaluate an institution's unmet collaborative needs. In 2018, a novel analytic resource navigation process was developed at Duke University to connect potential collaborators, leverage resources, and foster a community of researchers and scientists. This analytic resource navigation process can be readily adopted by other academic medical centers. The process relies on navigators with broad qualitative and quantitative methodologic knowledge, strong communication and leadership skills, and extensive collaborative experience. The essential elements of the analytic resource navigation process are as follows: (1) strong institutional knowledge of methodologic expertise and access to analytic resources, (2) deep understanding of research needs and methodologic expertise, (3) education of researchers on the role of qualitative and quantitative scientists in the research project, and (4) ongoing evaluation of the analytic resource navigation process to inform improvements. Navigators help researchers determine the type of expertise needed, search the institution to find potential collaborators with that expertise, and document the process to evaluate unmet needs. Although the navigation process can create a basis for an effective solution, some challenges remain, such as having resources to train navigators, comprehensively identifying all potential collaborators, and keeping updated information about resources as methodologists join and leave the institution., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Association of American Medical Colleges.)

Real-world data (RWD)-derived external controls can be used to contextualize efficacy findings for investigational therapies evaluated in uncontrolled trials. As the number of submissions to regulatory and health technology assessment (HTA) bodies using external controls rises, and in light of recent regulatory and HTA guidance on the appropriate use of RWD, there is a need to address the operational and methodological challenges impeding the quality of real-world evidence (RWE) generation and the consistency in evaluation of RWE across agencies. This systematic review summarizes publicly available information on the use of external controls to contextualize outcomes from uncontrolled trials for all indications from January 1, 2015, through August 20, 2021, that were submitted to the European Medicines Agency, the US Food and Drug Administration, and/or select major HTA bodies (National Institute for Health and Care Excellence (NICE), Haute Autorité de Santé (HAS), Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen (IQWiG), and Gemeinsamer Bundesausschuss (G-BA)). By systematically reviewing submissions to regulatory and HTA bodies in the context of recent guidance, this study provides quantitative and qualitative insights into how external control design and analytic choices may be viewed by different agencies in practice. The primary operational and methodological aspects identified for discussion include, but are not limited to, engagement of regulators and HTA bodies, approaches to handling missing data (a component of data quality), and selection of real-world endpoints. Continued collaboration and guidance to address these and other aspects will inform and assist stakeholders attempting to generate evidence using external controls., (© 2023 Regeneron Pharmaceuticals Inc and The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Regulators and Health Technology Assessment (HTA) bodies are increasingly familiar with, and publishing guidance on, external controls derived from real-world data (RWD) to generate real-world evidence (RWE). We recently conducted a systematic literature review (SLR) evaluating publicly available information on the use of RWD-derived external controls to contextualize outcomes from uncontrolled trials submitted to the European Medicines Agency (EMA), the US Food and Drug Administration (FDA), and/or select HTA bodies. The review identified several key operational and methodological aspects for which more detailed guidance and alignment within and between regulatory agencies and HTA bodies is necessary. This paper builds on the SLR findings by delineating a set of key takeaways for the responsible generation of fit-for-purpose RWE. Practical methodological and operational guidelines for designing, conducting, and reporting RWD-derived external control studies are explored and discussed. These considerations include: (i) early engagement with regulators and HTA bodies during the study planning phase; (ii) consideration of the appropriateness and comparability of external controls across multiple dimensions, including eligibility criteria, temporality, population representation, and clinical evaluation; (iii) ensuring adequate sample sizes, including hypothesis testing considerations; (iv) implementation of a clear and transparent strategy for assessing and addressing data quality, including data missingness across trials and RWD; (v) selection of comparable and meaningful endpoints that are operationalized and analyzed using appropriate analytic methods; and (vi) conduct of sensitivity analyses to assess the robustness of findings in the context of uncertainty and sources of potential bias., (© 2023 Regeneron Pharmaceuticals Inc and The Authors. Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Importance: Patient-reported health data can facilitate clinical event capture in pragmatic clinical trials. However, few data are available on the fitness for use of patient-reported data in large-scale health research., Objective: To evaluate the concordance of a set of variables reported by patients and available in the electronic health record as part of a pragmatic clinical trial., Design, Setting, and Participants: Data from ADAPTABLE (Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-term Effectiveness), a pragmatic clinical trial, were used in a concordance substudy of a comparative effectiveness research trial. The trial randomized 15 076 patients with existing atherosclerotic cardiovascular disease in a 1:1 ratio to low- or high-dose aspirin from April 2016 through June 30, 2019., Main Outcomes and Measures: Concordance of data was evaluated from 4 domains (demographic characteristics, encounters, diagnoses, and procedures) present in 2 data sources: patient-reported data captured through an online portal and data from electronic sources (electronic health record data). Overall agreement, sensitivity, specificity, positive predictive value, negative predictive value, and κ statistics with 95% CIs were calculated using patient report as the criterion standard for demographic characteristics and the electronic health record as the criterion standard for clinical outcomes., Results: Of 15 076 patients with complete information, the median age was 67.6 years (range, 21-99 years), and 68.7% were male. With the use of patient-reported data as the criterion standard, agreement (κ) was high for Black and White race and ethnicity but only moderate for current smoking status. Electronic health record data were highly specific (99.6%) but less sensitive (82.5%) for Hispanic ethnicity. Compared with electronic health record data, patient report of clinical end points had low sensitivity for myocardial infarction (33.0%), stroke (34.2%), and major bleeding (36.6%). Positive predictive value was similarly low for myocardial infarction (40.7%), stroke (38.8%), and major bleeding (21.9%). Coronary revascularization was the most concordant event by data source, with only moderate agreement (κ = 0.54) and positive predictive value. Agreement metrics varied by site for all demographic characteristics and several clinical events., Conclusions and Relevance: In a concordance substudy of a large, pragmatic comparative effectiveness research trial, sensitivity and chance-corrected agreement of patient-reported data captured through an online portal for cardiovascular events were low to moderate. Findings suggest that additional work is needed to optimize integration of patient-reported health data into pragmatic research studies., Trial Registration: ClinicalTrials.gov Identifier: NCT02697916.

Background: Despite the extensive use of real-world data for retrospective, observational clinical research, our understanding of how real-world data might increase the efficiency of data collection in patient-level randomized clinical trials is largely unknown. The structure of real-world data is inherently heterogeneous, with each source electronic health record and claims database different from the next. Their fitness-for-use as data sources for multisite trials in the United States has not been established., Methods: For a subset of participants in the HARMONY Outcomes Trial, we obtained electronic health record data from recruiting sites or Medicare claims data from the Centers for Medicare & Medicaid Services. For baseline characteristics and follow-up events, we assessed the level of agreement between these real-world data and data documented in the trial database., Results: Real-world data-derived demographic information tended to agree with trial-reported demographic information, although real-world data were less accurate in identifying medical history. The ability of real-world data to identify baseline medication usage differed by real-world data source, with claims data demonstrating substantially better performance than electronic health record data. The limited number of lab results in the collected electronic health record data matched closely with values in the trial database. There were not enough follow-up events in the ancillary study population to draw meaningful conclusions about the performance of real-world data for identification of events. Based on the conduct of this ancillary study, the challenges and opportunities of using real-world data within clinical trials are discussed., Conclusion: Based on a subset of participants from the HARMONY Outcomes Trial, our results suggest that electronic health record or claims data, as currently available, are unlikely to be a complete substitute for trial data collection of medical history or baseline lab results, but that Medicare claims were able to identify most medications. The limited size of the study population prevents us from drawing strong conclusions based on these results, and other studies are clearly needed to confirm or refute these findings.

Background: The COVID-19 pandemic has considerably disrupted nearly all aspects of daily life, including healthcare delivery and clinical research. Because pragmatic clinical trials are often embedded within healthcare delivery systems, they may be at high risk of disruption due to the dual impacts on the conduct of both care and research., Methods: We collected qualitative data using multiple methods to characterize the impact of COVID-19 on the research activities of 14 active pragmatic clinical trials in the National Institutes of Health (NIH) Health Care Systems Research Collaboratory. A COVID-19 impact questionnaire was administered electronically to principal investigators in June 2020. Text responses were analyzed thematically, and qualitative summaries were subsequently reviewed by five independent reviewers, who made iterative revisions. Additional COVID-19-related impacts were identified during virtual meetings with trial teams during April-July 2020 and combined with questionnaire responses for analysis., Results: Impacts of the pandemic were broadly classified into two main types: healthcare operations and social distancing. In some instances, trial delays created statistical challenges, particularly with trials using stepped-wedge designs, and necessitated changing data collection strategies or modifying interventions. The majority of projects used existing stakeholder-driven approaches to adapt interventions. Several benefits of these adaptions were identified, including expanded outreach capabilities and ability to study virtual intervention delivery. All trial teams were able to adapt to pandemic-related modifications., Conclusion: In a group of 14 ongoing pragmatic clinical trials, there was significant impact of COVID-19 on trial activities. Engaging appropriate stakeholders was critical to designing and implementing trial modifications and making continued safe progress toward meeting research objectives., (© 2022. The Author(s).)

Background: Improving diversity in clinical trials is essential in order to produce generalizable results. Although the importance of representation has become increasingly recognized, identifying strategies to approach this work remains elusive. This article reviews the proceedings of a multi-stakeholder conference about the current state of diversity in clinical trials and outlines actionable steps for improvement., Methods: Conference attendees included representatives from the United States Food and Drug Administration (FDA), National Institutes of Health (NIH), practicing clinical investigators, pharmaceutical and device companies, community-based organizations, data analytics companies, and patient advocacy groups. At this virtual event, attendees were asked to consider key questions around best practices for engagement of underrepresented populations., Results: Community engagement is an integral part of recruitment and retention of underrepresented groups. Decentralization of sites and use of digital tools can enhance the accessibility of clinical research. Finally, improving representation among investigators and clinical research staff may translate to diverse clinical trial participants., Conclusion: Improving diversity in clinical trials is an ethical and scientific imperative, which requires a multifaceted approach., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Objective: To empirically explore how pragmatic clinical trials (PCTs) that used real-world data (RWD) assessed study-specific fitness-for-use., Methods: We conducted interviews and surveys with PCT teams who used electronic health record (EHR) data to ascertain endpoints. The survey cataloged key concerns about RWD, activities used to assess data fitness-for-use, and related barriers encountered by study teams. Patterns and commonalities across trials were used to develop recommendations for study-specific fitness-for-use assessments., Results: Of 15 invited trial teams, 7 interviews were conducted. Of 31 invited trials, 15 responded to the survey. Most respondents had prior experience using RWD (93%). Major concerns about EHR data were data reliability, missingness or incompleteness of EHR elements, variation in data quality across study sites, and presence of implausible or incorrect values. Although many PCTs conducted fitness-for-use activities (eg, data quality assessments, 11/14, 79%), less than a quarter did so before choosing a data source. Fitness-for-use activities, findings, and resulting study design changes were not often publically documented. Overall costs and personnel costs were barriers to fitness-for-use assessments., Discussion: These results support three recommendations for PCTs that use EHR data for endpoint ascertainment. Trials should detail the rationale and plan for study-specific fitness-for-use activities, conduct study-specific fitness-for-use assessments early in the prestudy phase to inform study design changes before the trial begins, and share results of fitness-for-use assessments and description of relevant challenges and facilitators., Conclusion: These recommendations can help researchers and end-users of real-world evidence improve characterization of RWD reliability and relevance in the PCT-specific context., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Understanding the relationship between patterns of peripheral artery disease and outcomes is an essential step toward improving care and outcomes. We hypothesized that clinician specialty would be associated with occurrence of major adverse vascular events (MAVE)., Methods: Patients with at least 1 peripheral artery disease-related encounter in our health system and fee-for-service Medicare were divided into groups based on the specialty of the clinician (ie, cardiologist, surgeon, podiatrist, primary care, or other) providing a plurality of peripheral artery disease-coded care in the year prior to index encounter. The primary outcome was MAVE (a composite of all-cause mortality, myocardial infarction, stroke, lower extremity revascularization, and lower extremity amputation)., Results: The cohort included 1768 patients, of whom 30.0% were Black, 23.9% were Medicaid dual-enrollment eligible, and 31.1% lived in rural areas. Patients receiving a plurality of their care from podiatrists had the highest 1-year rates of MAVE (34.4%, P <.001), hospitalization (65.9%, P <.001), and amputations (22.6%, P <.001). Clinician specialty was not associated with outcomes after adjustment. Patients who were Medicaid dual-eligible had higher adjusted risks of mortality (adjusted hazard ratio [HR adj ] 1.54, 95% confidence interval [CI] 1.11-2.14) and all-cause hospitalization (HR adj 1.20, 95% CI 1.03-1.40) and patients who were Black had a higher adjusted risk of amputation (HR adj 1.49, 95% CI 1.03-2.15)., Conclusions: Clinician specialty was not associated with worse outcomes after adjustment, but certain socioeconomic factors were. The effects of clinician specialty and socioeconomic status were likely attenuated by the fact that all patients in this study had health insurance; these analyses require confirmation in a more representative cohort., (Copyright © 2021 Elsevier Inc. All rights reserved.)

The Sentinel System is a major component of the United States Food and Drug Administration's (FDA) approach to active medical product safety surveillance. While Sentinel has historically relied on large quantities of health insurance claims data, leveraging longitudinal electronic health records (EHRs) that contain more detailed clinical information, as structured and unstructured features, may address some of the current gaps in capabilities. We identify key challenges when using EHR data to investigate medical product safety in a scalable and accelerated way, outline potential solutions, and describe the Sentinel Innovation Center's initiatives to put solutions into practice by expanding and strengthening the existing system with a query-ready, large-scale data infrastructure of linked EHR and claims data. We describe our initiatives in four strategic priority areas: (1) data infrastructure, (2) feature engineering, (3) causal inference, and (4) detection analytics, with the goal of incorporating emerging data science innovations to maximize the utility of EHR data for medical product safety surveillance., (© 2021. The Author(s).)

Background: Individuals with chronic kidney disease (CKD), hypertension (HTN), or diabetes mellitus (DM) are at increased risk for cardiovascular disease (CVD). The extent to which psychosocial factors are associated with increased CVD risk within these individuals is unclear. Black individuals experience a high degree of psychosocial stressors due to socioeconomic factors, environment, racism, and discrimination. We examined the association between psychosocial factors and risk of CVD events among Black men and women with CKD and CKD risk factors in the Jackson Heart Study., Methods and Results: We identified 1919 participants with prevalent CKD or CKD risk factors at baseline. We used rotated principal component analysis - a form of unsupervised machine learning that may identify constructs not intuitively identified by a person - to describe five groups of psychosocial components (including negative moods, religiosity, discrimination, negative outlooks, and negative coping resources) based on a battery of questionnaires. Multiple imputation by chained equation (MICE) was used to impute missing covariate data. Cox models were used to quantify the association between psychosocial components and incident CVD, defined as a fatal coronary heart disease event, myocardial infarction, cardiac procedure (angiography or revascularization procedure), or stroke. Of the 929 participants in the analysis, 67% were female, 28% were current/former smokers with mean age of 56 years and mean BMI of 33 kg/m 2 . Over a median follow-up of 8 years, 6% had an incident CVD event. In multivariable models, each standard deviation (SD) increase in the religiosity component was associated with an increased hazard for CVD event (hazard ratio [HR] = 1.52, 95% CI: 1.09-2.13)., Conclusions: Religiosity was associated with CVD among participants with prevalent CKD or CKD risk factors. Studies to better understand the mechanisms of this relationship are needed., (© 2021. The Author(s).)

Background: PAD increases the risk of cardiovascular mortality and limb loss, and disparities in treatment and outcomes have been described. However, the association of patient-specific characteristics with variation in outcomes is less well known., Methods: Patients with PAD from Duke University Health System (DUHS) between January 1, 2015 and March 31, 2016 were identified. PAD status was confirmed through ground truth adjudication and predictive modeling using diagnosis codes, procedure codes, and other administrative data. Symptom severity, lower extremity imaging, and ankle-brachial index (ABI) were manually abstracted from the electronic health record (EHR). Data was linked to Centers for Medicare and Medicaid Services data to provide longitudinal follow up. Primary outcome was major adverse vascular events (MAVE), a composite of all-cause mortality, myocardial infarction (MI), stroke, lower extremity revascularization and amputation., Results: Of 1,768 patients with PAD, 31.6% were asymptomatic, 41.2% had intermittent claudication (IC), and 27.3% had chronic limb-threatening ischemia (CLTI). At 1 year, patients with CLTI had higher rates of MAVE compared with asymptomatic or IC patients. CLTI and Medicaid dual eligibility were independent predictors of mortality. CLTI and Black race were associated with amputation., Conclusions: Rates of MAVE were highest in patients with CLTI, but patients with IC or asymptomatic disease also had high rates of adverse events. Black and Medicaid dual-eligible patients were disproportionately present in the CLTI subgroup and were at higher risk of amputation and mortality, respectively. Future studies must focus on early identification of high-risk patient groups to improve outcomes in patients with PAD., (Copyright © 2021. Published by Elsevier Inc.)

Background Sacubitril/Valsartan has been highly efficacious in randomized trials of heart failure with reduced ejection fraction (HFrEF). However, the effectiveness of sacubitril/valsartan in older patients hospitalized for HFrEF in real-world US practice is unclear. Methods and Results This study included Medicare beneficiaries age ≥65 years who were hospitalized for HFrEF ≤40% in the Get With The Guidelines-Heart Failure registry between October 2015 and December 2018, and eligible for sacubitril/valsartan. Associations between discharge prescription of sacubitril/valsartan and clinical outcomes were assessed after inverse probability of treatment weighting and adjustment for other HFrEF medications. Overall, 1551 (10.9%) patients were discharged on sacubitril/valsartan. Of those not prescribed sacubitril/valsartan, 7857 (62.0%) were prescribed an angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker. Over 12-month follow-up, compared with a discharge prescription of angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker, sacubitril/valsartan was independently associated with lower all-cause mortality (adjusted hazard ratio [HR], 0.82; 95% CI, 0.72-0.94; P =0.004) but not all-cause hospitalization (adjusted HR, 0.97; 95% CI, 0.89-1.07; P =0.55) or heart failure hospitalization (adjusted HR, 1.04; 95% CI, 0.91-1.18; P =0.59). Patients prescribed sacubitril/valsartan versus those without a prescription had lower risk of all-cause mortality (adjusted HR, 0.69; 95% CI, 0.60-0.79; P <0.001), all-cause hospitalization (adjusted HR, 0.90; 95% CI, 0.82-0.98; P =0.02), but not heart failure hospitalization (adjusted HR, 0.94; 95% CI, 0.82-1.08; P =0.40). Conclusions Among patients hospitalized for HFrEF, prescription of sacubitril/valsartan at discharge was independently associated with reduced postdischarge mortality compared with angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker, and reduced mortality and all-cause hospitalization compared with no sacubitril/valsartan. These findings support the use of sacubitril/valsartan to improve postdischarge outcomes among older patients hospitalized for HFrEF in routine US clinical practice.

Background: The electronic health record (EHR) contains a wealth of clinical data that may be used to streamline the identification of potential clinical trial participants. However, there is little empirical information on site-level facilitators of and barriers to optimal use of EHR systems with respect to trial recruitment., Methods: We conducted qualitative focus groups and quantitative surveys as part of the EHR Ancillary Study, which is being conducted alongside the multicenter, global, Harmony Outcomes Trial comparing albiglutide to standard care for the prevention of cardiovascular events in type 2 diabetes. Subject matter experts used findings from focus groups to draft a 20-question survey examining the use of the EHR for participant identification, common site recruitment strategies, and variation in perceived barriers to optimal use of the EHR. The final survey was fielded with 446 site investigators actively enrolling participants in the main trial., Results: Nearly two-thirds of respondents were study coordinators (63.2%), 23.1% were principal investigators, and 13.7% held other research roles. Approximately half of the respondents reported using the EHR to find potential trial participants. Of these, 79.4% reported using EHR searches in conjunction with other recruitment methods, including reviewing of upcoming clinic schedules (75.3%) and contacting past trial participants (71.2%). Important barriers to optimal use of the EHR included the lack of availability of certain research-focused EHR modules and limitations on the ability to contact patients cared for by other providers. Of survey respondents who did not use the EHR to find potential participants, one-quarter reported that the EHR was not accessible in their country; this finding varied from 2.6% of respondents in North America to 50% of respondents in the Asia Pacific., Conclusions: While EHR screening was commonly used for recruitment in a cardiovascular outcomes trial, important technical, governance, and regulatory barriers persist. Multifaceted, scalable, and customizable strategies are needed to support the optimal use of the EHR for trial participant identification., Trial Registration: ClinicalTrials.gov NCT02465515. Registered on 8 June 2015., (© 2021. The Author(s).)

Introduction: In the 2016 U.S. Presidential election, voters in communities with recent stagnation or decline in life expectancy were more likely to vote for the Republican candidate than in prior Presidential elections. We aimed to assess the association between change in life expectancy and voting patterns in the 2020 Presidential election., Methods: With data on county-level life expectancy from the Institute for Health Metrics and Evaluation and voting data from a GitHub repository of results scraped from news outlets, we used weighted multivariable linear regression to estimate the association between the change in life expectancy from 1980 to 2014 and the proportion of votes for the Republican candidate and change in the proportion of votes cast for the Republican candidate in the 2020 Presidential election., Results: Among 3110 U.S counties and Washington, D.C., change in life expectancy at the county level was negatively associated with Republican share of the vote in the 2020 Presidential election (parameter estimate -7.2, 95% confidence interval, -7.8 to -6.6). With the inclusion of state, sociodemographic, and economic variables in the model, the association was attenuated (parameter estimate -0.8; 95% CI, -1.5 to -0.2). County-level change in life expectancy was positively associated with change in Republican vote share 0.29 percentage points (95% CI, 0.23 to 0.36). The association was attenuated when state, sociodemographic, and economic variables were added (parameter estimate 0.24; 95% CI, 0.15 to 0.33)., Conclusion: Counties with a less positive trajectory in life expectancy were more likely to vote for the Republican candidate in the 2020 U.S. Presidential election, but the Republican candidate's share improved in some counties that experienced marked gains in life expectancy. Associations were moderated by demographic, social and economic factors., Competing Interests: LC, MN and BH declare no conflicts of interest. RMC reports employment from Google Health and Verily Life Sciences and Corporate Board for Cytokinetics and Centessa with payment and stock., (© 2021 Published by Elsevier Ltd.)

Background: The appropriate dose of aspirin to lower the risk of death, myocardial infarction, and stroke and to minimize major bleeding in patients with established atherosclerotic cardiovascular disease is a subject of controversy., Methods: Using an open-label, pragmatic design, we randomly assigned patients with established atherosclerotic cardiovascular disease to a strategy of 81 mg or 325 mg of aspirin per day. The primary effectiveness outcome was a composite of death from any cause, hospitalization for myocardial infarction, or hospitalization for stroke, assessed in a time-to-event analysis. The primary safety outcome was hospitalization for major bleeding, also assessed in a time-to-event analysis., Results: A total of 15,076 patients were followed for a median of 26.2 months (interquartile range [IQR], 19.0 to 34.9). Before randomization, 13,537 (96.0% of those with available information on previous aspirin use) were already taking aspirin, and 85.3% of these patients were previously taking 81 mg of daily aspirin. Death, hospitalization for myocardial infarction, or hospitalization for stroke occurred in 590 patients (estimated percentage, 7.28%) in the 81-mg group and 569 patients (estimated percentage, 7.51%) in the 325-mg group (hazard ratio, 1.02; 95% confidence interval [CI], 0.91 to 1.14). Hospitalization for major bleeding occurred in 53 patients (estimated percentage, 0.63%) in the 81-mg group and 44 patients (estimated percentage, 0.60%) in the 325-mg group (hazard ratio, 1.18; 95% CI, 0.79 to 1.77). Patients assigned to 325 mg had a higher incidence of dose switching than those assigned to 81 mg (41.6% vs. 7.1%) and fewer median days of exposure to the assigned dose (434 days [IQR, 139 to 737] vs. 650 days [IQR, 415 to 922])., Conclusions: In this pragmatic trial involving patients with established cardiovascular disease, there was substantial dose switching to 81 mg of daily aspirin and no significant differences in cardiovascular events or major bleeding between patients assigned to 81 mg and those assigned to 325 mg of aspirin daily. (Funded by the Patient-Centered Outcomes Research Institute; ADAPTABLE ClinicalTrials.gov number, NCT02697916.)., (Copyright © 2021 Massachusetts Medical Society.)

Introduction: Transfusion-related acute lung injury (TRALI), an adverse event occurring during or within 6 hours of transfusion, is a leading cause of transfusion-associated fatalities reported to the US Food and Drug Administration. There is limited information on the validity of diagnosis codes for TRALI recorded in inpatient electronic medical records (EMRs)., Study Designs and Methods: We conducted a validation study to establish the positive predictive value (PPV) of TRALI International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes recorded within a large hospital system between 2013 and 2015. A physician with critical care expertise confirmed the TRALI diagnosis. As TRALI is likely underdiagnosed, we used the specific code (518.7), and codes for respiratory failure (518.82) in combination with transfusion reaction (999.80, 999.89, E934.7)., Results: Among almost four million inpatient stays, we identified 208 potential TRALI cases with ICD-9-CM codes and reviewed 195 medical records; 68 (35%) met clinical definitions for TRALI (26 [38%] definitive, 15 [22%] possible, 27 [40%] delayed). Overall, the PPV for all inpatient TRALI diagnoses was 35% (95% confidence interval (CI), 28-42). The PPV for the TRALI-specific code was 44% (95% CI, 35-54)., Conclusion: We observed low PPVs (<50%) for TRALI ICD-9-CM diagnosis codes as validated by medical charts, which may relate to inconsistent code use, incomplete medical records, or other factors. Future studies using TRALI diagnosis codes in EMR databases may consider confirming diagnoses with medical records, assessing TRALI ICD, Tenth Revision, Clinical Modification codes, or exploring alternative ways for of accurately identifying TRALI in EMR databases., Key Points: In 169 hospitals, we identified 208 potential TRALI cases, reviewed 195 charts, and confirmed 68 (35%) cases met TRALI clinical definitions. As many potential TRALI cases identified with diagnosis codes did not meet clinical definitions, medical record confirmation may be prudent., (© 2021 AABB.)

In late 2018, the Food and Drug Administration (FDA) outlined a framework for evaluating the possible use of real-world evidence (RWE) to support regulatory decision-making. This framework was created to facilitate studies that would generate high-quality RWE, including pragmatic clinical trials (PCTs), which are randomized trials designed to inform clinical or policy decisions by assessing the real-world effectiveness of an intervention. There is general agreement among experts that the use of existing healthcare and patient-generated data holds promise for making randomized trials more efficient, less costly, and more generalizable. Yet the benefits of relying on real-world data sources must be weighed against difficulties with ensuring data integrity and completeness. Additionally, appropriately monitoring patient safety in randomized trials of new drugs using healthcare system data that might not be available in real time can be quite difficult. Recognizing that these and other concerns are critical to the development and acceptability of PCTs, a group of stakeholders from academia, industry, professional organizations, regulatory bodies, government agencies, and patient advocates discussed a path forward for PCT growth and sustainability at a think tank meeting entitled "Monitoring and Analyzing Data from Pragmatic Streamlined Randomized Clinical Trials," which took place in January 2019 (Washington, DC). The goals of this meeting were to: (1) evaluate study design and methodological options specific to PCTs that have the potential to yield high-quality evidence; (2) discuss best practices to ensure data quality in PCTs; and (3) identify appropriate methods for study monitoring. Proceedings from the think tank meeting are summarized in this manuscript.

Patient-reported health data provide information for pragmatic clinical trials that may not be readily available from electronic health records or administrative claims data. In this report, we present key considerations for collecting patient-reported health information in pragmatic clinical trials, which are informed by best practices from patient-reported outcome research. We focus on question design and administration via electronic data collection platforms with respect to 3 types of patient-reported health data: medication use, utilization of health care services, and comorbid conditions. We summarize key scientific literature on the accuracy of these patient-reported data compared with electronic health record data. We discuss question design in detail, specifically defining the concept to be measured, patient understanding of the concept, recall periods of the question, and patient willingness to report. In addition, we discuss approaches for question administration and data collection platforms, which are key aspects of successful patient-reported data collection., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: Peripheral artery disease (PAD) affects 8 to 10 million Americans, who face significantly elevated risks of both mortality and major limb events such as amputation. Unfortunately, PAD is relatively underdiagnosed, undertreated, and underresearched, leading to wide variations in treatment patterns and outcomes. Efforts to improve PAD care and outcomes have been hampered by persistent difficulties identifying patients with PAD for clinical and investigatory purposes., Objective: The aim of this study is to develop and validate a model-based algorithm to detect patients with peripheral artery disease (PAD) using data from an electronic health record (EHR) system., Methods: An initial query of the EHR in a large health system identified all patients with PAD-related diagnosis codes for any encounter during the study period. Clinical adjudication of PAD diagnosis was performed by chart review on a random subgroup. A binary logistic regression to predict PAD was built and validated using a least absolute shrinkage and selection operator (LASSO) approach in the adjudicated patients. The algorithm was then applied to the nonsampled records to further evaluate its performance., Results: The initial EHR data query using 406 diagnostic codes yielded 15,406 patients. Overall, 2500 patients were randomly selected for ground truth PAD status adjudication. In the end, 108 code flags remained after removing rarely- and never-used codes. We entered these code flags plus administrative encounter, imaging, procedure, and specialist flags into a LASSO model. The area under the curve for this model was 0.862., Conclusions: The algorithm we constructed has two main advantages over other approaches to the identification of patients with PAD. First, it was derived from a broad population of patients with many different PAD manifestations and treatment pathways across a large health system. Second, our model does not rely on clinical notes and can be applied in situations in which only administrative billing data (eg, large administrative data sets) are available. A combination of diagnosis codes and administrative flags can accurately identify patients with PAD in large cohorts., (©Elizabeth Hope Weissler, Steven J Lippmann, Michelle M Smerek, Rachael A Ward, Aman Kansal, Adam Brock, Robert C Sullivan, Chandler Long, Manesh R Patel, Melissa A Greiner, N Chantelle Hardy, Lesley H Curtis, W Schuyler Jones. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 19.08.2020.)

Background: Many large-scale cardiovascular clinical trials are plagued with escalating costs and low enrollment. Implementing a computable phenotype, which is a set of executable algorithms, to identify a group of clinical characteristics derivable from electronic health records or administrative claims records, is essential to successful recruitment in large-scale pragmatic clinical trials. This methods paper provides an overview of the development and implementation of a computable phenotype in ADAPTABLE (Aspirin Dosing: a Patient-Centric Trial Assessing Benefits and Long-Term Effectiveness)-a pragmatic, randomized, open-label clinical trial testing the optimal dose of aspirin for secondary prevention of atherosclerotic cardiovascular disease events., Methods and Results: A multidisciplinary team developed and tested the computable phenotype to identify adults ≥18 years of age with a history of atherosclerotic cardiovascular disease without safety concerns around using aspirin and meeting trial eligibility criteria. Using the computable phenotype, investigators identified over 650 000 potentially eligible patients from the 40 participating sites from Patient-Centered Outcomes Research Network-a network of Clinical Data Research Networks, Patient-Powered Research Networks, and Health Plan Research Networks. Leveraging diverse recruitment methods, sites enrolled 15 076 participants from April 2016 to June 2019. During the process of developing and implementing the ADAPTABLE computable phenotype, several key lessons were learned. The accuracy and utility of a computable phenotype are dependent on the quality of the source data, which can be variable even with a common data model. Local validation and modification were required based on site factors, such as recruitment strategies, data quality, and local coding patterns. Sustained collaboration among a diverse team of researchers is needed during computable phenotype development and implementation., Conclusions: The ADAPTABLE computable phenotype served as an efficient method to recruit patients in a multisite pragmatic clinical trial. This process of development and implementation will be informative for future large-scale, pragmatic clinical trials. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02697916.

Purpose: Chemotherapy-induced peripheral neuropathy (CIPN) is a disabling complication of many chemotherapies. We investigated the feasibility of using health plan claims and administrative data to identify CIPN occurrence by comparing patients who received neurotoxic and non-neurotoxic chemotherapies., Methods: The sample included over 53,000,000 patients from two regional and one national insurer in the USA (> 400,000 exposed to chemotherapy). Peripheral neuropathy was identified using a broad definition (definition 1) and a specific definition (i.e., drug-induced polyneuropathy code) (definition 2)., Results: CIPN incidence as measured by definition 1 within 6 months of chemotherapy initiation was 18.1% and 6.2% for patients who received neurotoxic and non-neurotoxic chemotherapy, respectively (relative risk neurotoxic vs. non-neurotoxic (RR), 2.93 (95% CI, 2.87-2.98)). For definition 2, these incidences were 3.6% and 0.1% (RR, 25.2 (95% CI, 22.8-27.8)). The incidences of new analgesic prescriptions for neurotoxic and non-neurotoxic groups were as follows: gabapentin, 7.1%/1.7%; pregabalin, 0.69%/0.31%; and duloxetine, 0.78%/0.76%. The incidence of CIPN as defined by definitions 1 and 2 was low compared with that of published research studies, but the relative risk of CIPN among patients who received neurotoxic chemotherapies compared with those who received non-neurotoxic chemotherapies was high using definition 2., Conclusions: These data suggest that as used currently by clinicians, administrative codes likely underestimate CIPN incidence. Thus, studies using administrative data to estimate CIPN incidence are not currently feasible. However, the drug-induced polyneuropathy code is a specific indicator of CIPN in administrative data and may be useful for investigating predictors or potentially preventive therapies of CIPN.

Objectives: The purpose of this study was to evaluate the association of physical activity (PA) level and longitudinal PA trajectory with a composite heart failure hospitalization and mortality endpoint over a 5-year follow-up period following implantation., Background: Low device measured PA early after implantation of an implantable cardioverter-defibrillator (ICD) or cardiac resynchronization therapy defibrillator (CRT-D) is associated with poor outcomes., Methods: We linked daily PA data from the Boston Scientific ALTITUDE dataset of patients with ICD or CRT-D implantation to Medicare claims data. We used a joint model to investigate the association of the composite endpoint with 1) the time-varying point estimate of PA and 2) the time-varying trajectory/slope of PA during follow-up., Results: Among 20,927 patients with median activity level 85 min/day, 14.1% and 49.6% experienced the composite endpoint at 1 and 5 years. Adjusted joint model results showed that there was a 1.13 (95% confidence interval: 1.12 to 1.13)-fold increase in the hazard of the composite endpoint for 75 min of daily PA relative to 85 min of PA; and a within-patient 10-min decrease in average daily PA over an 8-week period from 85 to 75 min was associated with a hazard ratio of 4.02 (95% confidence interval: 3.82 to 4.22) for the composite endpoint., Conclusions: Patients with large decreases in PA have significantly higher risk of experiencing heart failure hospitalization or death. PA data from implantable devices may identify patients before clinical decompensation., (Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background: Older adults with cardiometabolic conditions are typically seen by multiple providers. Management by multiple providers may compromise care continuity and increase health expenditures for older adults, which may partly explain the inverse association between continuity and Medicare expenditures found in prior studies. This study sought to examine whether all-cause admission, outpatient expenditures or total expenditures were associated with the number of prescribers of cardiometabolic medications., Methods: Medicare fee-for-service beneficiaries with diabetes (n = 100,191), hypertension (n = 299,949) or dyslipidemia (n = 243,598) living in 10 states were identified from claims data. The probability of an all-cause hospital admission in 2011 was estimated via logistic regression and Medicare (outpatient, total) expenditures in 2011 were estimated using generalized linear models, both as a function of the number of prescribers in 2010. Regressions were adjusted for demographic characteristics, Medicaid status, number of prescriptions, and 17 chronic conditions., Results: In all three cohorts, older adults with more prescribers in 2010 had modestly higher adjusted odds of all-cause inpatient admission than older adults with a single prescriber. Compared to a single prescriber, outpatient and total expenditures in 2011 were 3-10% higher for older adults with diabetes and multiple prescribers, 2-6% higher for older adults with hypertension and multiple prescribers, and 2-5% higher for older adults with dyslipidemia and multiple prescribers., Conclusions and Implications: These results provide some evidence that older adults with multiple prescribers also have modestly higher Medicare utilization than those with a single prescriber; thus care continuity may impact patient utilization., Level of Evidence: Level III (retrospective cohort analysis)., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Stakeholders in the clinical research enterprise are aligned around the need to make clinical research in general, and randomized controlled trials in particular, more meaningful and efficient. To that end, we have built distributed research networks (DRNs) for the Sentinel System, the National Institutes of Health (NIH) Collaboratory, and the National Patient-Centered Clinical Research Network (PCORnet). DRNs reuse electronic health record (EHR) and claims data for research. The design and use of health data DRNs is complicated by lack of uniformity in data collection, a fragmented healthcare system, and the imperative to protect research participants. We describe the key elements of successful DRNs, as well as methods, challenges, and solutions we have encountered in using DRNs to support different phases of randomized, multi-site, clinical research. This work supports "real-world" efforts to build a learning health system and will enable others to conduct randomized clinical trial research using a DRN., Competing Interests: This work is supported within the National Institutes of Health (NIH) Health Care Systems Research Collaboratory by the NIH Common Fund through cooperative agreement U24AT009676 from the Office of Strategic Coordination within the Office of the NIH Director. The views presented here are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health. JB, BH and RP have no conflicts of interest to disclose; BS reports grants from NIH during the conduct of the study; KM reports grants from PCORI, during the conduct of the study; personal fees from Novartis, outside the submitted work; AH reports grants and personal fees from AstraZeneca, grants and personal fees from Bayer, personal fees from Boehringer Ingelheim, grants from American Regent, grants and personal fees from Novartis, grants and personal fees from Merck, grants from Verily, outside the submitted work; LC reports grants from GlaxoSmithKline, grants from Novartis, grants from Boston Scientific, from St. Jude, outside the submitted work; SR reports grants from GSK, outside the submitted work. There are no other financial relationships with any organisations that might have an interest in the submitted work in the previous three years and no other relationships or activities that could appear to have influenced the submitted work., (© 2019 Published by Elsevier Inc.)

Background: Data from randomized controlled trials and observational studies on older adults who take statins for primary prevention of atherosclerotic cardiovascular disease are limited. To determine the incidence of statin use in older adults with and without cardiovascular disease (CVD) and/or diabetes (DM), we conducted a descriptive observational study., Methods: The cohort consisted of health plan members in the NIH Collaboratory Distributed Research Network aged >75 years who had continuous drug and medical benefits for ≥183 days during the study period, January 1, 2008- March 31, 2018. We defined DM and CVD using diagnosis codes, and identified statins using dispensing data. Statin use was considered incident if a member had no evidence of statin exposure in the claims during the previous 183 days, and the use was considered long-term if statins were supplied for ≥180 days. Incidence rates were reported among members with and without CVD and/or diabetes, and stratified by year, sex, and age group., Results: Among 757,569 eligible members, 109,306 older adults initiated statins and 54,624 became long-term users. Health plan members with CVD had the highest incidence of statin use (143.9 initiators per 1,000 member-years for CVD & DM; 114.5 initiators per 1,000 member-years for CVD & No DM). Among health plan members without CVD, those with DM had rates of statin use that were over two times higher than members without DM (76.1 versus 34.5 initiators per 1,000 member-years, respectively). Statin initiation remained steady throughout 2008-2016, was slightly higher in males, and declined with increasing age., Conclusion: Incidence of statin use varied by CVD and DM comorbidity, and was lowest among those without CVD. These results highlight the potential clinical equipoise to conduct large pragmatic clinical trials to generate evidence that could be used to inform future blood cholesterol guidelines., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: One of the authors [KH] is employed by HealthCore, Inc. Dr. Curtis has research contracts with Glaxo Smith Kline, Medtronic, and Novartis. None of the work is related to this manuscript. Dr. Hernandez consults and conducts research for AstraZeneca, Merck, and Novartis. He consults for Bayer. The other authors have declared that no competing interests exist. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials.

Primary care clinicians care for an extremely diverse range of patients, and they therefore have numerous opportunities to measure and act to improve the health of various populations. In order to take effective actions to improve the health of their patient populations, primary care clinicians must measure health. Strong population health metrics are characterized by their high validity, consistency, feasibility, and interpretability. Population health metrics should be applied longitudinally to obtain the most information from available data. Optimal population health metrics are actionable and facilitate the implementation of effective strategies to improve population health through administrative or clinical programs., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Medicare insurance claims may provide an efficient means to ascertain follow-up of older participants in clinical research. We sought to determine the accuracy and completeness of claims- versus site-based follow-up with clinical event committee (+CEC) adjudication of cardiovascular outcomes., Methods: We performed a retrospective study using linked Medicare and Duke Database of Clinical Trials data. Medicare claims were linked to clinical data from 7 randomized cardiovascular clinical trials. Of 52,476 trial participants, linking resulted in 5,839 (of 10,497 linkage-eligible) Medicare-linked trial participants with fee-for-service A and B coverage. Death, myocardial infarction (MI), stroke, and revascularization incidences were compared using Medicare inpatient claims only, site-reported events (+CEC) only, or a combination of the 2. Randomized treatment effects were compared as a function of whether claims-based, site-based (+CEC), or a combined system was used for event detection., Results: Among the 5,839 study participants, the annual event rates were similar between claims- and site-based (+CEC) follow-up: death (overall rate 5.2% vs 5.2%; adjusted κ 0.99), MI (2.2% vs 2.3%; adjusted κ 0.96), stroke (0.7% vs 0.7%; adjusted κ 0.99), and any revascularization (7.4% vs 7.9%; adjusted κ 0.95). Of events detected by claims yet not reported by CEC, a minority were reported by sites but negatively adjudicated by CEC (39% of MIs and 18% of strokes). Differences in individual case concordance led to higher event rates when claims- and site-based (+CEC) systems were combined. Randomized treatment effects were similar among the 3 approaches for each outcome of interest., Conclusions: Claims- versus site-based (+CEC) follow-up identified similar overall cardiovascular event rates despite meaningful differences in the events detected. Randomized treatment effects were similar using the 2 methods, suggesting claims data could be used to support clinical research leveraging routinely collected data. This approach may lead to more effective evidence generation, synthesis, and appraisal of medical products and inform the strategic approaches toward the National Evaluation System for Health Technology., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Objectives: This study sought to describe clinical outcomes among patients with atrial fibrillation (AF) and contraindications to oral anticoagulation (OAC)., Background: Treatment with OAC prevents stroke and death in patients with AF, but may be contraindicated among patients at high bleeding risk., Methods: This was an observational, longitudinal analysis of a nationally representative 5% Medicare sample of patients with chronic AF and CHA 2 DS 2 -VASc (congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, prior stroke or transient ischemic attack or thromboembolism, vascular disease, age 65-74 years, sex category) score ≥2. They were stratified by both the presence of high bleeding risk contraindications to OAC and by OAC use. We assessed 3-year ischemic and bleeding outcomes using multivariable Cox proportional hazards models adjusted for relevant patient characteristics., Results: Among 26,684 AF patients not treated with OAC, 8,283 (31%) had a high bleeding risk contraindication, primarily a blood dyscrasia (75%) or history of gastrointestinal bleeding (40%). Without OAC, patients with contraindications had worse ischemic and bleeding outcomes at 3 years compared with those without contraindications. We also identified 12,454 patients with OAC contraindications who received OAC. Compared with patients not receiving OAC, use of OAC was associated with reduced mortality (adjusted hazard ratio [HR]: 0.79; 95% confidence interval [CI]: 0.76 to 0.83), stroke (adjusted HR: 0.90; 95% CI: 0.83 to 0.99), and all-cause hospitalization (adjusted HR: 0.93; 95% CI: 0.90 to 0.96) but increased risk of intracranial hemorrhage (adjusted HR: 1.42; 95% CI: 1.17 to 1.72)., Conclusions: High bleeding risk contraindications to OAC are common among older patients with AF, and these patients have higher mortality compared with untreated patients without OAC contraindications. The use of OAC in these patients is associated with lower rates of all-cause stroke, hospitalization, and death but higher risk of intracranial hemorrhage., (Copyright © 2019 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Congestion is associated with adverse outcomes in heart failure (HF) patients. We characterized congestion in patients hospitalized for HF and examined the association between congestion severity at admission and postdischarge outcomes. Using the OPTIMIZE-HF registry linked to Medicare claims, we analyzed patients ≥65 years old hospitalized for HF from 2003 to 2004. Congestion severity was measured using a 15-point scale that scores dyspnea, orthopnea, fatigue, jugular venous pressure, rales, and edema. Patient characteristics and outcomes were described by congestion strata. Proportional hazards models were fit to examine associations between congestion and 1-year outcomes. Congestion scores for the 24,724 patients ranged from 0 to 14, with a median of 5 (Q1, Q3: 3, 7). At baseline, patients with the highest scores (≥7) had the highest rates of recent HF hospitalizations, EF ≤40%, and co-morbidities, including arrhythmias, diabetes mellitus, and renal insufficiency. Adjusting for patient characteristics, a 3-point congestion score increase was positively associated with mortality (hazard ratio [HR] 1.06, 95% confidence interval [CI] 1.03, 1.09), all-cause rehospitalization (HR 1.02, 95% CI 1.00, 1.04), and HF rehospitalization (HR 1.09, 95% CI 1.06, 1.12), but not emergency department visits (HR 0.99, 95% CI 0.97, 1.01). In conclusion, for patients hospitalized with HF, congestion was associated with rehospitalization and mortality., (Copyright © 2019 Elsevier Inc. All rights reserved.)

While conducting a set of large-scale multi-site pragmatic clinical trials involving high-impact public health issues such as end-stage renal disease, opioid use, and colorectal cancer, there were substantial changes to both policies and guidelines relevant to the trials. These external changes gave rise to unexpected challenges for the trials, including decisions regarding how to respond to new clinical practice guidelines, increased difficulty in implementing trial interventions, achieving separation between treatment groups, and differential responses across sites. In this article, we describe these challenges and the approaches used to address them. When deliberating appropriate action in the face of external changes during a pragmatic clinical trial, we recommend considering the well-being of the participants, clinical equipoise, and the strength and quality of the evidence associated with the change; involving those charged with data and safety monitoring; and where possible, planning for potential external changes as the trial is being designed. Any solution must balance the primary obligation to protect the well-being of participants with the secondary obligation to protect the integrity of the trial in order to gain meaningful answers to important public health questions.

Background: Emerging data suggest that neck circumference (NC) is associated with cardiometabolic risk factors. Limited research is available regarding the association between NC and cardiovascular outcomes in African Americans., Methods: Using data from the Jackson Heart Study, we included participants with recorded NC measurements at baseline (2000-2004). Baseline characteristics for the included population were summarized by tertiles of NC. We then calculated age- and sex-adjusted cumulative incidence of clinical cardiovascular outcomes and performed Cox proportional-hazards with stepwise models., Results: Overall, 5,290 participants were categorized into tertiles of baseline NC defined as ≤37 cm (n = 2179), 38-40 cm (n = 1552), and >40 cm (n = 1559). After adjusting for age and sex, increasing NC was associated with increased risk of heart failure (HF) hospitalization (cumulative incidence = 13.4% [99% CI, 10.7-16.7] in the largest NC tertile vs 6.5% [99% CI, 4.7-8.8] in the smallest NC tertile), but not mortality, stroke, myocardial infarction, or coronary heart disease (all P ≥ .1). Following full risk adjustment, there was a nominal increase in the risk of HF hospitalization with increasing NC, but this was not statistically significant (hazard ratio per 1-cm increase, 1.04 [99% CI, 0.99-1.10], P = .06)., Conclusions: In this large cohort of African American individuals, a larger NC was associated with increased risk for HF hospitalization following adjustment for age and sex, but this risk was not statistically significant after adjusting for other clinical variables. Although NC is not independently associated with increased risk for cardiovascular events, it may offer prognostic information particularly related to HF hospitalization., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Clinical event committees are commonly employed for event validation in clinical studies, but little is known about the comparative performance of administrative claims data versus clinician-triggered event adjudication for ascertainment of adverse events in structural heart disease studies., Methods and Results: Medicare claims were linked to 418 patients >65 years of age who underwent transcatheter mitral valve repair (MitraClip) for severe mitral regurgitation from 2007 to 2013 as part of the EVEREST II (Endovascular Valve Edge-to-Edge Repair Study II) High-Risk Registry or the REALISM (Real World Expanded Multicenter Study of the MitraClip System) Continued-Access Registry. Each registry adjudicated mortality, heart failure hospitalization, renal failure, ventilation, and bleeding/transfusion within 1 year. Concordance of claims-based outcomes with events was assessed in 3 ways: 1-year occurrence, cumulative incidence, and synchrony of first events. For event occurrence, positive predictive value (PPV) of claims versus adjudication was the highest for mortality (PPV=97%) and heart failure hospitalization (PPV=69%) but lower for bleeding (PPV=40%) and renal failure (PPV=19%). Whereas claims-based cumulative incidence for mortality, heart failure hospitalization, and renal failure were consistent with clinician-triggered adjudication, incidence curves for bleeding events and ventilation diverged, with claims identifying a greater number of events. When events were detected by both methods, however, over 75% of event dates matched exactly. Mitral valve reinterventions were identified through claims with perfect sensitivity and specificity relative to physician adjudication., Conclusions: Ascertainment of mortality, heart failure hospitalization, and renal failure was highly concordant between physician adjudication and administrative claims. Further work is necessary to determine the role of administrative claims in event ascertainment in both prospective and retrospective studies of structural heart disease.

Importance: In response to rapidly growing interest in population health, academic medical centers are launching department-level initiatives that focus on this evolving discipline. This trend, with its potential to extend the scope of academic medicine, has not been well characterized., Objective: To describe the emergence of departments of population health at academic medical centers in the United States, including shared areas of focus, opportunities, and challenges., Design, Setting, and Participants: This qualitative study was based on a structured in-person convening of a working group of chairs of population health-oriented departments on November 13 and 14, 2017, complemented by a survey of core characteristics of these and additional departments identified through web-based review of US academic medical centers. United States medical school departments with the word population in their name were included. Centers, institutes, and schools were not included., Main Outcomes and Measures: Departments were characterized by year of origin, areas of focus, organizational structure, faculty size, teaching programs, and service engagement. Opportunities and challenges faced by these emerging departments were grouped thematically and described., Results: Eight of 9 population health-oriented departments in the working group were launched in the last 6 years. The 9 departments had 5 to 97 full-time faculty. Despite varied organizational structures, all addressed essential areas of focus spanning the missions of research, education, and service. Departments varied significantly in their relationships with the delivery of clinical care, but all engaged in practice-based and/or community collaboration. Common attributes include core attention to population health-oriented research methods across disciplines, emphasis on applied research in frontline settings, strong commitment to partnership, interest in engaging other sectors, and focus on improving health equity. Tensions included defining boundaries with other academic units with overlapping areas of focus, identifying sources of sustainable extramural funding, and facilitating the interface between research and health system operations., Conclusions and Relevance: Departments addressing population health are emerging rapidly in academic medical centers. In supporting this new framing, academic medicine affirms and strengthens its commitment to advancing population health and health equity, to improving the quality and effectiveness of care, and to upholding the social mission of medicine.

The National Institutes of Health (NIH) Health Care Systems (HCS) Research Collaboratory hosted a workshop to explore challenges and strategies for the dissemination, implementation, and sustainability of findings from pragmatic clinical trials (PCTs) embedded in HCS. PCTs are designed to assess the impact of interventions delivered in usual or real-world conditions and leverage existing infrastructure to answer important clinical questions. The goal of the workshop was to discuss strategies for conducting impactful future PCTs that bridge the gap between evidence, practice, and policy. This paper summarizes presentations about how to design and conduct PCTs embedded in HCS and use dissemination and implementation strategies during the planning and conduct of projects, emphasizing the ever-changing world of care delivery and the need for pragmatic trial operations to adapt at various levels of operation., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Background The angiotensin-receptor/neprilysin inhibitor ( ARNI ) sacubitril/valsartan reduces hospitalization and mortality for patients with heart failure with reduced ejection fraction. However, adoption of ARNI into clinical practice has been slow. Factors influencing use of ARNI have not been fully elucidated. Using data from the Get With The Guidelines-Heart Failure registry, Hospital Compare, Dartmouth Atlas, and the American Hospital Association Survey, we sought to identify hospital characteristics associated with patient-level receipt of an ARNI prescription. Methods and Results We analyzed patients with heart failure with reduced ejection fraction who were eligible for ARNI prescription (ejection fraction≤40%, no contraindications) and hospitalized from October 1, 2015 through December 31, 2016. We used logistic regression to estimate the associations between hospital characteristics and patient ARNI prescription at hospital discharge, accounting for clustering of patients within hospitals using generalized estimating equation methods and adjusting for patient-level covariates. Of 16 674 eligible hospitalizations from 210 hospitals, 1020 patients (6.1%) were prescribed ARNI at discharge. The median hospital-level proportion of patients prescribed ARNI was 3.3% (Q1, Q3: 0%, 12.6%). After adjustment for patient-level covariates, for-profit hospitals had significantly higher odds of ARNI prescription compared with not-for-profit hospitals (odds ratio, 2.53; 95% CI , 1.05-6.10; P=0.04), and hospitals located in the Western United States had lower odds of ARNI prescription compared with those in the Northeast (odds ratio, 0.33; 95% CI , 0.13-0.84; P=0.02). Conclusions Relatively few hospital characteristics were associated with ARNI prescription at hospital discharge, in contrast to what has been observed in early adoption in other disease areas. Additional evaluation of barriers to implementing new evidence into heart failure practice is needed.

Background The 2013 American College of Cardiology/American Heart Association Cholesterol Treatment Guideline increased the number of primary prevention patients eligible for statin therapy, yet uptake of these guidelines has been modest. Little is known of how primary care provider ( PCP ) beliefs influence statin prescription. Methods and Results We surveyed 164 PCP s from a community-based North Carolina network in 2017 about statin therapy. We evaluated statin initiation among the PCP s' statin-eligible patients between 2014 and 2015 without a previous prescription. Seventy-two PCP s (43.9%) completed the survey. The median estimate of the relative risk reduction for high-intensity statins was 45% (interquartile range, 25%-50%). A minority of providers (27.8%) believed statins caused diabetes mellitus, and only 16.7% reported always/very often discussing this with patients. Most PCPs (97.2%) believed that statins cause myopathy, and 72.3% reported always/very often discussing this with patients. Most (77.7%) reported always/very often using the 10-year atherosclerotic cardiovascular disease risk calculator, although many reported that in most cases other risk factors or patient preferences influenced prescribing (59.8% and 43.1%, respectively). Of 6172 statin-eligible patients, 22.3% received a prescription for a moderate- or high-intensity statin at follow-up. Providers reporting greater reliance on risk factors beyond atherosclerotic cardiovascular disease risk were less likely to prescribe statins. Conclusions Although beliefs and approaches to statin discussions vary among community PCP s, new prescription rates are low and minimally associated with those beliefs. These results highlight the complexity of increasing statin prescriptions for primary prevention and suggest that strategies to facilitate standardized discussions and to address external influences on patient beliefs warrant future study.