National Patient-Centered Clinical Research Network Research Articles

Early Antibiotic Exposure and Weight Outcomes in Young Children.

Health care data were available from 2009 to 2016 for 35 institutions participating in the National Patient-Centered Clinical Research Network. Participant inclusion required same-day height and weight measurements at 0 to <12, 12 to <30, and 48 to <72 months of age. We assessed the association between any antibiotic use at <24 months of age with BMI z score and overweight or obesity prevalence at 48 to <72 months (5 years) of age, with secondary assessments of antibiotic spectrum and age-period exposures. We included children with and without complex chronic conditions. Among 1 792 849 children with a same-day height and weight measurement at <12 months of age, 362 550 were eligible for the cohort. One-half of children (52%) were boys, 27% were African American, 18% were Hispanic, and 58% received ≥1 antibiotic prescription at <24 months of age. At 5 years, the mean BMI z score was 0.40 (SD 1.19), and 28% of children had overweight or obesity. In adjusted models for children without a complex chronic condition at 5 years, we estimated a higher mean BMI z score by 0.04 (95% confidence interval [CI] 0.03 to 0.05) and higher odds of overweight or obesity (odds ratio 1.05; 95% CI 1.03 to 1.07) associated with obtaining any (versus no) antibiotics at <24 months. Antibiotic use at <24 months of age was associated with a slightly higher body weight at 5 years of age.

Combining distributed regression and propensity scores: a doubly privacy-protecting analytic method for multicenter research

PurposeSharing of detailed individual-level data continues to pose challenges in multi-center studies. This issue can be addressed in part by using analytic methods that require only summary-level information to perform the desired multivariable-adjusted analysis. We examined the feasibility and empirical validity of 1) conducting multivariable-adjusted distributed linear regression and 2) combining distributed linear regression with propensity scores, in a large distributed data network.Patients and methodsWe compared percent total weight loss 1-year postsurgery between Roux-en-Y gastric bypass and sleeve gastrectomy procedure among 43,110 patients from 36 health systems in the National Patient-Centered Clinical Research Network. We adjusted for baseline demographic and clinical variables as individual covariates, deciles of propensity scores, or both, in three separate outcome regression models. We used distributed linear regression, a method that requires only summary-level information (specifically, sums of squares and cross products matrix) from sites, to fit the three ordinary least squares linear regression models. A comparison set of analyses that used pooled deidentified individual-level data from sites served as the reference.ResultsDistributed linear regression produced results identical to those from the corresponding pooled individual-level data analysis for all variables in all three models. The maximum numerical difference in the parameter estimate or standard error for all the variables was 3×10−11 across three models.ConclusionDistributed linear regression analysis is a feasible and valid analytic method in multicenter studies for one-time continuous outcomes. Combining distributed regression with propensity scores via modeling offers more privacy protection and analytic flexibility.

Comparative Effectiveness and Safety of Bariatric Procedures for Weight Loss: A PCORnet Cohort Study.

There has been a dramatic shift in use of bariatric procedures, but little is known about their long-term comparative effectiveness. To compare weight loss and safety among bariatric procedures. Retrospective observational cohort study, January 2005 to September 2015. (ClinicalTrials.gov: NCT02741674). 41 health systems in the National Patient-Centered Clinical Research Network. 65093 patients aged 20 to 79 years with body mass index (BMI) of 35 kg/m2 or greater who had bariatric procedures. 32208 Roux-en-Y gastric bypass (RYGB), 29693 sleeve gastrectomy (SG), and 3192 adjustable gastric banding (AGB) procedures. Estimated percent total weight loss (TWL) at 1, 3, and 5 years; 30-day rates of major adverse events. Total numbers of eligible patients with weight measures at 1, 3, and 5 years were 44978 (84%), 20783 (68%), and 7159 (69%), respectively. Thirty-day rates of major adverse events were 5.0% for RYGB, 2.6% for SG, and 2.9% for AGB. One-year mean TWLs were 31.2% (95% CI, 31.1% to 31.3%) for RYGB, 25.2% (CI, 25.1% to 25.4%) for SG, and 13.7% (CI, 13.3% to 14.0%) for AGB. At 1 year, RYGB patients lost 5.9 (CI, 5.8 to 6.1) percentage points more weight than SG patients and 17.7 (CI, 17.3 to 18.1) percentage points more than AGB patients, and SG patients lost 12.0 (CI, 11.6 to 12.5) percentage points more than AGB patients. Five-year mean TWLs were 25.5% (CI, 25.1% to 25.9%) for RYGB, 18.8% (CI, 18.0% to 19.6%) for SG, and 11.7% (CI, 10.2% to 13.1%) for AGB. Patients with diabetes, those with BMI less than 50 kg/m2, those aged 65 years or older, African American patients, and Hispanic patients lost less weight than patients without those characteristics. Potential unobserved confounding due to nonrandomized design; electronic health record databases had missing outcome data. Adults lost more weight with RYGB than with SG or AGB at 1, 3, and 5 years; however, RYGB had the highest 30-day rate of major adverse events. Small subgroup differences in weight loss outcomes were observed. Patient-Centered Outcomes Research Institute.

Using a deliberative forum for engaging health system and health plan leaders to prioritize research topics

Background: Including stakeholders in the process and outcomes of comparative effectiveness research (CER) can help ensure that research questions are relevant and findings are communicated to individuals who need them for decision-making. Yet limited strategies are available to assist researchers with stakeholder engagement.While health system leaders’ perspectives are increasingly recognized as valuable for CER planning, their inclusion in the stakeholder pool raises challenges due to differences in culture, training, incentives, priorities and language norms.Objective: To convene and evaluate a deliberative forum for engaging health system leaders and other stakeholders in order to shape health system research priorities for the PaTH Clinical Data Research Network, a member of the National Patient-Centered Clinical Research Network (PCORnet).Methods: Breakout sessions and large group deliberation solicited diverse perspectives and explored benefits and challenges of different research questions. Topic reframing, narrative integration and dynamic updating techniques facilitated communication across diverse backgrounds. Participants included 29 health system and health plan leaders, clinicians, clinical researchers and patients from the network’s 6 participating health systems. Main measures were audience response system (ARS) polling on general topic preferences and survey data on measures of engagement and deliberation success.Results: A slate of 10 specific research topics was vetted; after deliberation, the group converged to favor the characterization of high utilizers of healthcare. Audience response polling revealed opinion shifts. Participants reported high levels of satisfaction with the experience and rated it highly for markers of deliberative quality (e.g., opportunity for active participation and adequate discussion, respect for others’ opinions and awareness of different perspectives). Fifty-four percent noted their views on the issues changed. Most participants learned from the experience (93%) and agreed that the process helped them to empathize with the challenges of others (85%).Conclusions: A deliberation forum can incorporate diverse stakeholders into CER, enabling participants to inform and learn from each other’s perspectives while shaping a person-centered research trajectory.

Evaluating Foundational Data Quality in the National Patient-Centered Clinical Research Network (PCORnet®).

Introduction:Distributed research networks (DRNs) are critical components of the strategic roadmaps for the National Institutes of Health and the Food and Drug Administration as they work to move toward large-scale systems of evidence generation. The National Patient-Centered Clinical Research Network (PCORnet®) is one of the first DRNs to incorporate electronic health record data from multiple domains on a national scale. Before conducting analyses in a DRN, it is important to assess the quality and characteristics of the data.Methods:PCORnet’s Coordinating Center is responsible for evaluating foundational data quality, or assessing fitness-for-use across a broad research portfolio, through a process called data curation. Data curation involves a set of analytic and querying activities to assess data quality coupled with maintenance of detailed documentation and ongoing communication with network partners. The first cycle of PCORnet data curation focused on six domains in the PCORnet common data model: demographics, diagnoses, encounters, enrollment, procedures, and vitals.Results:The data curation process led to improvements in foundational data quality. Notable improvements included the elimination of data model conformance errors; a decrease in implausible height, weight, and blood pressure values; an increase in the volume of diagnoses and procedures; and more complete data for key analytic variables. Based on the findings of the first cycle, we made modifications to the curation process to increase efficiencies and further reduce variation among data partners.Discussion:The iterative nature of the data curation process allows PCORnet to gradually increase the foundational level of data quality and reduce variability across the network. These activities help increase the transparency and reproducibility of analyses within PCORnet and can serve as a model for other DRNs.

Abstract P059: Utilizing Electronic Health Records to Evaluate Racial Disparities in Metabolic Syndrome

Purpose: Metabolic syndrome is defined as a clustering of clinical metabolic conditions (increased blood pressure, high blood sugar, increased body fat, abnormal cholesterol or triglycerides) and has been associated with an increased risk for several chronic diseases, such as cardiovascular disease. The aim of this project was to identify individuals presenting with metabolic syndrome using a computational patient phenotype definition derived from electronic medical records (EHR) clinical outcomes data. Secondly, this project evaluated racial disparities in metabolic syndrome across Southeast Louisiana. Methods: Data was obtained through Research Action for Health Network (REACHnet). Using the National Patient-Centered Clinical Research Network Common Data Model, REACHnet has standardized and made usable EHR data for patient-centered research across Louisiana and Texas. The computational patient phenotype definition for metabolic syndrome was developed based on the National Cholesterol Education Program Expert Panel in Adult Treatment Panel III (NCEP III) guidelines. The presence of metabolic conditions was established using ICD9 Diagnosis codes, patient vitals and lab results that are routinely available in EHR data. Logistic regression models to assess racial disparities were executed using SAS 9.4. Results: We analyzed 18,664 patient EHRs for individuals 18 years or older with complete clinical data spanning the years 2013 to 2014. The sample was 43.28% male (n=8,077) and 29.35% black (n=5,477). Based on the patient phenotype definition, the prevalence of metabolic syndrome in the sample was 39.09%. Controlling for age, the odds of metabolic syndrome were twice as high for black women than for white women (OR= 2 (1.83, 2.18)), while the odds were 15% greater for black men than for white men (OR: 1.15 (1.04, 1.28)). Conclusion: We observed significant disparities in the prevalence of clinically evident metabolic syndrome in southeast Louisiana. Racial disparities were greatest among women. It has been increasingly recognized that differential exposure to chronic social and nutritive stress from living in a disadvantaged neighborhood may be contributing to racial health disparities. Further research in this sample will link ancillary sources of neighborhood data to the successfully developed metabolic syndrome phenotype to explore potential mechanisms for racial disparities in cardiovascular disease among a clinically-rich, state-wide sample.

PCORnet Antibiotics and Childhood Growth Study: Process for Cohort Creation and Cohort Description

ObjectivesThe National Patient-Centered Clinical Research Network (PCORnet) supports observational and clinical research using health care data. The PCORnet Antibiotics and Childhood Growth Study is one of PCORnet's inaugural observational studies. We sought to describe the processes used to integrate and analyze data from children across 35 participating institutions, the cohort characteristics, and prevalence of antibiotic use. MethodsWe included children in the cohort if they had at least one same-day height and weight measured in each of 3 age periods: 1) before 12 months, 2) 12 to 30 months, and 3) after 24 months. We distributed statistical queries that each institution ran on its local version of the PCORnet Common Data Model, with aggregate data returned for analysis. We defined overweight or obesity as age- and sex-specific body mass index ≥85th percentile, obesity ≥95th percentile, and severe obesity ≥120% of the 95th percentile. ResultsA total of 681,739 children met the cohort inclusion criteria, and participants were racially/ethnically diverse (24.9% black, 17.5% Hispanic). Before 24 months of age, 55.2% of children received at least one antibiotic prescription; 21.3% received a single antibiotic prescription; 14.3% received 4 or more; and 33.3% received a broad-spectrum antibiotic. Overweight and obesity prevalence was 27.6% at age 4 to <6 years (n = 362,044) and 36.2% at 9 to <11 years (n = 58,344). ConclusionsThe PCORnet Antibiotics and Childhood Growth Study is a large national longitudinal observational study in a diverse population that will examine the relationship between early antibiotic use and subsequent growth patterns in children.

The National Patient-Centered Clinical Research Network (PCORnet) Bariatric Study Cohort: Rationale, Methods, and Baseline Characteristics.

BackgroundAlthough bariatric procedures are commonly performed in clinical practice, long-term data on the comparative effectiveness and safety of different procedures on sustained weight loss, comorbidities, and adverse effects are limited, especially in important patient subgroups (eg, individuals with diabetes, older patients, adolescents, and minority patients).ObjectiveThe objective of this study was to create a population-based cohort of patients who underwent 3 commonly performed bariatric procedures—adjustable gastric band (AGB), Roux-en-Y gastric bypass (RYGB), and sleeve gastrectomy (SG)—to examine the long-term comparative effectiveness and safety of these procedures in both adults and adolescents.MethodsWe identified adults (20 to 79 years old) and adolescents (12 to 19 years old) who underwent a primary (first observed) AGB, RYGB, or SG procedure between January 1, 2005 and September 30, 2015 from 42 health systems participating in the Clinical Data Research Networks within the National Patient-Centered Clinical Research Network (PCORnet). We extracted information on patient demographics, encounters with healthcare providers, diagnoses recorded and procedures performed during these encounters, vital signs, and laboratory test results from patients’ electronic health records (EHRs). The outcomes of interest included weight change, incidence of major surgery-related adverse events, and diabetes remission and relapse, collected for up to 10 years after the initial bariatric procedure.ResultsA total of 65,093 adults and 777 adolescents met the eligibility criteria of the study. The adult subcohort had a mean age of 45 years and was predominantly female (79.30%, 51,619/65,093). Among adult patients with non-missing race or ethnicity information, 72.08% (41,248/57,227) were White, 21.13% (12,094/57,227) were Black, and 20.58% (13,094/63,637) were Hispanic. The average highest body mass index (BMI) recorded in the year prior to surgery was 49 kg/m2. RYGB was the most common bariatric procedure among adults (49.48%, 32,208/65,093), followed by SG (45.62%, 29,693/65,093) and AGB (4.90%, 3192/65,093). The mean age of the adolescent subcohort was 17 years and 77.5% (602/777) were female. Among adolescent patients with known race or ethnicity information, 67.3% (473/703) were White, 22.6% (159/703) were Black, and 18.0% (124/689) were Hispanic. The average highest recorded BMI in the year preceding surgery was 53 kg/m2. The majority of the adolescent patients received SG (60.4%, 469/777), followed by RYGB (30.8%, 239/777) and AGB (8.9%, 69/777). A BMI measurement (proxy for follow-up) was available in 84.31% (44,978/53,351), 68.09% (20,783/30,521), and 68.56% (7159/10,442) of the eligible adult patients at 1, 3, and 5 years of follow-up, respectively. The corresponding proportion was 82.0% (524/639), 49.9% (174/349), and 38.8% (47/121) in the adolescent subcohort.ConclusionsOur study cohort is one of the largest cohorts of patients with bariatric procedures in the United States. Patients are geographically and demographically diverse, which improves the generalizability of the research findings and allows examination of treatment effect heterogeneity. Ongoing and planned investigations will provide real-world evidence on the long-term benefits and risks of these most commonly used bariatric procedures in current clinical practice.

A proposed approach to accelerate evidence generation for genomic-based technologies in the context of a learning health system.

Genomic technologies should demonstrate analytical and clinical validity and clinical utility prior to wider adoption in clinical practice. However, the question of clinical utility remains unanswered for many genomic technologies. In this paper, we propose three building blocks for rapid generation of evidence on clinical utility of promising genomic technologies that underpin clinical and policy decisions. We define promising genomic tests as those that have proven analytical and clinical validity. First, risk-sharing agreements could be implemented between payers and manufacturers to enable temporary coverage that would help incorporate promising technologies into routine clinical care. Second, existing data networks, such as the Sentinel Initiative and the National Patient-Centered Clinical Research Network (PCORnet) could be leveraged, augmented with genomic information to track the use of genomic technologies and monitor clinical outcomes in millions of people. Third, endorsement and engagement from key stakeholders will be needed to establish this collaborative model for rapid evidence generation; all stakeholders will benefit from better information regarding the clinical utility of these technologies. This collaborative model can create a multipurpose and reusable national resource that generates knowledge from data gathered as part of routine care to drive evidence-based clinical practice and health system changes.

Engagement, Research, and Evidence: Leveraging the National Patient-Centered Clinical Research Network for Better Cardiovascular Health.

A deluge of information and new approaches spanning the continuum from discovery to implementation and policy promises to fundamentally alter our understanding of cardiovascular health and healthcare. Although the existing system for cardiovascular research has yielded impressive results, it remains fractured, slow, costly, and hindered by systematic gaps. Further, even when it delivers answers, they are often accompanied by uncertainty about how to interpret or apply that knowledge across different populations. If cardiovascular medicine is to maintain its traditional excellence in innovation and evidence generation, then our current research paradigms must evolve. At the same time, external forces are affecting healthcare, including the imperatives to improve the value of care by increasing quality and reducing costs, grapple with disparities in care and outcomes, and achieve meaningful integration with other technologies using electronic health data and sophisticated analytics. Also medical practice needs to be reoriented toward considering the needs, preferences, and values of individual patients and consumers instead of focusing narrowly on providing products and services. This approach is especially poignant for US healthcare, whose decades-old reimbursement model is transforming into a more patient-centered system. These challenges present a daunting prospect. But what if the move toward integrated, patient-centered, accountable healthcare could be harnessed to simultaneously drive improvements in our clinical research enterprise? What if building systems for electronic health data can also provide robust scaffolding for efficient evidence generation while breaking down silos that impede the flow of information and knowledge among all stakeholders, including patients? Throughout the …

Abstract P5-08-23: The share thoughts on breast cancer study: A Greater Plains collaborative cohort characterization project

Abstract Background. The Greater Plains Collaborative (GPC) is a new clinical data research network in PCORNet, the National Patient-Centered Clinical Research Network sponsored by the Patient-Centered Outcomes Research Institute to conduct patient-centered outcomes research and comparative effectiveness research. The Phase I GPC network brought together a diverse population of 10 million people across 1300 miles covering seven states with a combined area of 679 159 square miles. Using input from community members, breast cancer was selected as a focus for cohort building activities. Objective. To test the GPC's integrated data sources, technical components, and governance by demonstrating the ability to select, characterize, and achieve at least a 50% response rate for a cohort of breast cancer patients from across the GPC. Methods. Each participating medical center extracted, transformed and loaded North American Association of Central Cancer Registries (NAACCR)-formatted data from their institution's tumor registry into its Informatics for Integrating Biology and the Bedside (i2b2) research warehouse. The GPC i2b2 research warehouse is fully de-identified with re-identification possible when accompanied by an approved IRB protocol. From these data, each medical center selected a cohort of all patients age 18 or older diagnosed with breast cancer between January 2013 and May 2014. De-identified data files were submitted to the GPC Honest Broker who applied eligibility criteria and selected a random sample of 250 eligible patients from each center's file. Eligible patients were women with microscopically-confirmed ductal carcinoma in situ or invasive (but not metastatic) breast cancer diagnosed during the study period. Women who had previously been diagnosed with cancer per tumor registry records were excluded as were women known to be deceased at the time the sample was selected. The sample of patients plus a list of up to 10 replacement patients was provided to each center for re-identification and mailing. Patients participated in designing the study materials. Results. IRBs at 7 of 8 collaborating medical centers ceded IRB review to the University of Iowa IRB pursuant to the GPC reliance agreement. Upon patient advice to ensure a representative cohort, a mailed questionnaire with patient incentive was employed. All study materials were mailed in a single packet containing a cover letter from the participating medical center, a 21-page questionnaire, medical record consent and $10 incentive. Questionnaires were mailed over a six-week period beginning June 19, 2015 and one remailing to nonrespondents was conducted 4 weeks after the initial mailing. A total of 1,986 patients were invited and 1,235 (62.2%) responded. Signed consent to obtain information from medical records was obtained for 852 (69%). The survey dataset for consented subjects was linked to the research warehouse to obtain tumor and treatment data and diagnosis and procedure codes from billing sources. Conclusion. The GPC's integrated data sources, honest broker-mediated approach to extract data and apply standardized clinical criteria, IRB reliance model, and patient study design partners were implemented successfully. Citation Format: Chrischilles EA, Gryzlak B, McDowell BD, Connolly DW, Waitman LR. The share thoughts on breast cancer study: A Greater Plains collaborative cohort characterization project [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P5-08-23.

Patient-Centered Network of Learning Health Systems: Developing a resource for clinical translational research.

The Learning Health System Network clinical data research network includes academic medical centers, health-care systems, public health departments, and health plans, and is designed to facilitate outcomes research, pragmatic trials, comparative effectiveness research, and evaluation of population health interventions. The Learning Health System Network is 1 of 13 clinical data research networks assembled to create, in partnership with 20 patient-powered research networks, a National Patient-Centered Clinical Research Network. Herein, we describe the Learning Health System Network as an emerging resource for translational research, providing details on the governance and organizational structure of the network, the key milestones of the current funding period, and challenges and opportunities for collaborative science leveraging the network.

Data interchange using i2b2.

Objective Reinventing data extraction from electronic health records (EHRs) to meet new analytical needs is slow and expensive. However, each new data research network that wishes to support its own analytics tends to develop its own data model. Joining these different networks without new data extraction, transform, and load (ETL) processes can reduce the time and expense needed to participate. The Informatics for Integrating Biology and the Bedside (i2b2) project supports data network interoperability through an ontology-driven approach. We use i2b2 as a hub, to rapidly reconfigure data to meet new analytical requirements without new ETL programming.Materials and Methods Our 12-site National Patient-Centered Clinical Research Network (PCORnet) Clinical Data Research Network (CDRN) uses i2b2 to query data. We developed a process to generate a PCORnet Common Data Model (CDM) physical database directly from existing i2b2 systems, thereby supporting PCORnet analytic queries without new ETL programming. This involved: a formalized process for representing i2b2 information models (the specification of data types and formats); an information model that represents CDM Version 1.0; and a program that generates CDM tables, driven by this information model. This approach is generalizable to any logical information model.Results Eight PCORnet CDRN sites have implemented this approach and generated a CDM database without a new ETL process from the EHR. This enables federated querying within the CDRN and compatibility with the national PCORnet Distributed Research Network.Discussion We have established a way to adapt i2b2 to new information models without requiring changes to the underlying data. Eight Scalable Collaborative Infrastructure for a Learning Health System sites vetted this methodology, resulting in a network that, at present, supports research on 10 million patients’ data.Conclusion New analytical requirements can be quickly and cost-effectively supported by i2b2 without creating new data extraction processes from the EHR.

PBRNs or RBPNs or Both?

In this issue, two reports by Kelly and colleagues describe the development of the Recovery-Oriented Care Collaborative, a practice-based research network (PBRN) focused on improving services for people with serious mental illness. In the Research & Services Partnerships column, these authors detail the stepwise process of developing infrastructure, clarifying priorities, and executing an initial researchproject. In a related letter to the editor, the authors report project findings regarding the impact of integrating mental health and general medical care. As Kelly and colleagues describe, developing an effective PBRN requires attention to both infrastructure and culture. They note that the necessary infrastructure to support research includes administrative processes to facilitate communication, as well as technical tools or processes to collect and organize data. But establishing that infrastructure requires a cultural foundation of transparency and trust. Although the interests of clinical practice and research are often closely aligned, they are rarely identical. Serving those varying interests usually requires some compromise and sacrifice, which can be purchased only with trust. As Kelly and colleagues describe, developing transparency and trust is an iterative process. Trust facilitates successful collaboration, which further strengthens trust. Repeated successfully, this process expands beyond individual relationships to change the cultures of both clinical and research organizations. This process of mutual engagement characterizes community-based participatory research or what Van de Ven has called “engaged scholarship.” This iterative process of collaboration enables another important cultural shift: the establishment of continuous measurement as a core function of effective health care. Although measurement-based care in mental health has lagged behind many areas of general medicine, use of standard depression outcome measures is being rapidly implemented in community practice. The spread of electronicmedical records systems allows those measurement data to be organized in support of both care for individual patients and populationbased research. As always, the most important barriers to this transformation are cultural rather than technical. Patients, providers, and health systems must have confidence that systematic measurement of care processes, care experience, and patient-centered outcomes will support improvement for all rather than punish those already disadvantaged. A culture of continuous measurement can then enable a culture of continuous inquiry. As Kelly and colleagues describe, a learning health care system depends on engaging patients, clinicians, and health system leaders in a collaborative process of identifying and answering research questions. Behavioral scientists understand the power of immediate reinforcement. If the objective is to reinforce the behavior of asking questions, then a learning health care system should aim to provide timely answers to questions that arise from practice. Disagreements about best practices are healthy. In a culture of continuous inquiry, however, a disagreement leads to the questions: “What informationwould we need to actually know the answer?How can we find that information in our practice?” It must be admitted, however, that a culture of continuous inquiry and rapid-cycle learningwould create challenges for traditional researchers and traditional journals such as this one. Our current practice of disseminating research findings through journal publicationmay serve the career interests of researchers and the business interests of publishers. But delayed reporting of answers until publication does not always serve the interests of patients, clinicians, and leaders of health care systems. Notably, legislation establishing the Patient-CenteredOutcomes Research Institute (PCORI) required that findings of PCORIfunded research be made public within 90 days. Significant changes to our research traditions may be in the works. We can certainly lament the limited reach of research into community practice and the resulting delay in dissemination of evidence-based practices. Butwe can point to some advances on the national scene. PCORI’s National Patient-Centered Clinical Research Network (PCORnet) is developing infrastructure to “liberate” data from large health care systems and engage patients and patient-led organizations in the research process. NIH’s Health Care Systems Research Collaboratory is supporting a series of large pragmatic clinical trials based in real-world health care systems. The NIMH-funded Mental Health Research Network supports an infrastructure for clinical trials andobservational research in 13 large health care systems across theUnited States. The ultimate goal is the seamless integration of research and practice. In such a learning health care system, collaboration between patients, clinicians, health system leaders, and researchers would not be a special event. A culture of continuous measurement and continuous inquiry would extend to every placewherepeople livingwithpsychiatric disorders receive care, including rural practices and those serving traditionally disadvantaged groups. If this goal were realized, an observer might ask, “Is this a practice-based research network or a researchbased practice network?” And the answer would be “Yes!”

Exploring the ethical and regulatory issues in pragmatic clinical trials.

The need for high-quality evidence to support decision making about health and health care by patients, physicians, care providers, and policy-makers is well documented. However, serious shortcomings in evidence persist. Pragmatic clinical trials that use novel techniques including emerging information and communication technologies to explore important research questions rapidly and at a fraction of the cost incurred by more "traditional" research methods promise to help close this gap. Nevertheless, while pragmatic clinical trials can bridge clinical practice and research, they may also raise difficult ethical and regulatory challenges. In this article, the authors briefly survey the current state of evidence that is available to inform clinical care and other health-related decisions and discuss the potential for pragmatic clinical trials to improve this state of affairs. They then propose a new working definition for pragmatic research that centers upon fitness for informing decisions about health and health care. Finally, they introduce a project, jointly undertaken by the National Institutes of Health Health Care Systems Research Collaboratory and the National Patient-Centered Clinical Research Network (PCORnet), which addresses 11 key aspects of current systems for regulatory and ethical oversight of clinical research that pose challenges to conducting pragmatic clinical trials. In the series of articles commissioned on this topic published in this issue of Clinical Trials, each of these aspects is addressed in a dedicated article, with a special focus on the interplay between ethical and regulatory considerations and pragmatic clinical research aimed at informing "real-world" choices about health and health care.

The ADAPTABLE Trial and PCORnet: Shining Light on a New Research Paradigm.

This commentary discusses the National Patient-Centered Clinical Research Network (PCORnet), a “network of networks” to facilitate research that addresses important clinical questions, and the firs...

Anticipation Runs High for Coming Megatrials in General Cardiology

trials due to r Preventive cardiovascular medicine will get a big boost from ongoing major randomized controlled eport results during the next several years, Eric D. Peterson, MD, MPH, predicted at the annual meeting of the American College of Cardiology in San Diego. He presented an overview of eagerly anticipated clinical trials approaching completion in three of the hottest areas of research in general cardiology: new cardiovascular disease prevention agents and strategies; innovative health policy initiatives; and optimal antithrombotic regimens across the range of chronic coronary artery disease, peripheral arterial disease, patients with atrial fibrillation who’ve undergone percutaneous coronary intervention, and cerebrovascular disease. Dr. Peterson is executive director of the Duke Clinical Research Institute (DCRI), and professor of medicine at Duke University in Durham, N C.

Anticipated Ethics and Regulatory Challenges in PCORnet: The National Patient-Centered Clinical Research Network

ABSTRACTPCORnet, the National Patient-Centered Clinical Research Network, seeks to establish a robust national health data network for patient-centered comparative effectiveness research. This article reports the results of a PCORnet survey designed to identify the ethics and regulatory challenges anticipated in network implementation. A 12-item online survey was developed by leadership of the PCORnet Ethics and Regulatory Task Force; responses were collected from the 29 PCORnet networks. The most pressing ethics issues identified related to informed consent, patient engagement, privacy and confidentiality, and data sharing. High priority regulatory issues included IRB coordination, privacy and confidentiality, informed consent, and data sharing. Over 150 IRBs and five different approaches to managing multisite IRB review were identified within PCORnet. Further empirical and scholarly work, as well as practical and policy guidance, is essential if important initiatives that rely on comparative effectiveness research are to move forward.

Building electronic data infrastructure for comparative effectiveness research: accomplishments, lessons learned and future steps.

There are large gaps in our knowledge on the potential impact of diagnostics and therapeutics on outcomes of patients treated in the real world. Comparative effectiveness research aims to fill these gaps to maximize effectiveness of these interventions. Health information technology has the potential to dramatically improve the practice of medicine and of research. This is an overview of about US$100 million of American Recovery and Reinvestment Act investment in 12 projects managed by the Agency for Healthcare Research and Quality to build an electronic clinical data infrastructure that connects research with healthcare delivery. The achievements and lessons learned from these projects provided a foundation for the National Patient-Centered Clinical Research Network (PCORnet)and will help to guide future infrastructure development needed to build an efficient, scalable and sustainable learning health system.

Four health data networks illustrate the potential for a shared national multipurpose big-data network.

Information in electronic health data that are drawn from large populations of patients is transforming health care, public health practice, and clinical research. This article describes our experience in developing data networks that repurpose electronic health records and administrative data. The four programs we feature are the Food and Drug Administration's Mini-Sentinel program (which focuses on medical product safety), the National Patient-Centered Clinical Research Network (PCORnet, comparative effectiveness research), the National Institutes of Health's Health Care Systems Research Collaboratory Distributed Research Network (biomedical research), and ESPnet (public health surveillance). Challenges to these uses of electronic health data include understanding the factors driving the collection, coding, and preservation of the data; the extensive customization of different systems that collect similar data; the fragmentation of the US health care delivery system and its records; and privacy and proprietary considerations. We view these four programs as examples of the first stage in the development of a shared national big-data resource that leverages the investments of many agencies and organizations for the benefit of multiple networks and users.