Goal 3: Advance Translational Research

Advancing translational research requires timely in-depth analysis of large datasets

1. NHLBI investments over the last decade in terms of genomic approaches have yielded many research findings. 2. Rapid analyses of early data identified the "low hanging" fruit (and perhaps some/many important results were missed); this limited the scope of translation (partly because of relatively limited discovery?) 3. Important data are being generated at much greater rate than the data are processed/analyzed thoughtfully. ...more »

Submitted by (@rao000)

Is this idea a Compelling Question (CQ) or Critical Challenge (CC)? : Critical Challenge (CC)

Details on the impact of addressing this CQ or CC :

If we invest in a few multi-disciplinary data analysis centers (integrating biology with statistical genetics) involving active NHLBI participation (cooperative agreements), the massive amounts of data generated at huge cost could be analyzed more thoughtfully if time and resources are made available. This way, we may be able to identify many more research findings of much greater potential for translation.

Feasibility and challenges of addressing this CQ or CC :

It is feasible to pursue this challenge through cooperative agreements whereby NHLBI scientists can actively participate and ensure that strategic investing is following strategic paths for deep discovery.

Name of idea submitter and other team members who worked on this idea : DC Rao

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Goal 3: Advance Translational Research

Embedding Clinical Trials in Learning Health Systems

What are the best methods for using genotype information and other EMR data to randomize heart, lung, blood, sleep patients to different treatment strategies? One big challenge is how to consent patients for this sort of trial. Must patients be consented separately for every such trial or could there be blanket consent for participating in the learning health care model? This would also require a paradigm shift in how ...more »

Submitted by (@nhlbiforumadministrator1)

Is this idea a Compelling Question (CQ) or Critical Challenge (CC)? : Compelling Question (CQ)

Details on the impact of addressing this CQ or CC :

If successful this approach should enable the conduct of cheap pragmatic trials that are fueled by data from clinical care. The integration into clinical care helps assure efficiency and generalizability of results.

Feasibility and challenges of addressing this CQ or CC :

The advent of electronic medical records and the explosion of big data technology has made it possible to gain access to and analyze data in a manner that would have been unthinkable 10 years ago. This is already going on in other fields.

Health care systems are increasingly using "big data" approaches to track outcomes in the patients treated with different strategies and drugs, and apply the knowledge gained from outcomes in previous patients to inform decision making in subsequent patients ("learning"). This approach could be used to personalize treatment. A recent example from cancer is to genotype lung tumors, and tailor the treatment of a new patients to drugs producing good results in patients with similar tumor genotypes. When two or more treatments produce similar results, one could randomize. Cardiovascular disease presents a challenge in using genotyping information to personalize treatment, because the manifestations are the results of complex genetic and environmental risk factors.

Name of idea submitter and other team members who worked on this idea : NHLBI Staff

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15 up votes
11 down votes
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Goal 3: Advance Translational Research

NHLBI Cohort Populations for T4 Implementation Research

How best can NHLBI observational cohorts be utilized to study observational T4 Implementation Research among both general and vulnerable US populations?

Submitted by (@nhlbiforumadministrator)

Is this idea a Compelling Question (CQ) or Critical Challenge (CC)? : Compelling Question (CQ)

Details on the impact of addressing this CQ or CC :

• Would help identify key factor associated with successful implementation that could be studied in interventional T4 implementation research

• Result would refine implementation strategies and health and social policy aimed to reduce heart, lung, blood, sleep diseases and conditions

• Builds on excellent established platform of research with high quality outcomes in well characterized study populations over long term follow-up.

Feasibility and challenges of addressing this CQ or CC :

• Big data is developing methods to link large data sets from national, state, and community level surveys – surveys that can define exposures to various policies and interventions in place, time, and population.

• A family of high quality cohorts are available for ancillary observation studies

• Collection of community level and more broad policy level exposures is feasible through data already collected and through potentially new data collection.

Name of idea submitter and other team members who worked on this idea : NHLBI Staff

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11 up votes
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Goal 2: Reduce Human Disease

Data from regulatory studies a barrier to evidence-based medicine

Alignment of regulatory, healthcare, and research arms of the government is poor. There is a need to improve the design, quality and usefulness of data from regulatory studies to address major clinical questions and also to facilitate scientific inquiry. This is a barrier to evidence based medicine and improved treatments.

Submitted by (@societyforvascularsurgery)

Is this idea a Compelling Question (CQ) or Critical Challenge (CC)? : Critical Challenge (CC)

Name of idea submitter and other team members who worked on this idea : Society for Vascular Surgery

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Goal 2: Reduce Human Disease

Harnessing the Tsunami of Patient-Generated Health Data

How can the NHLBI foster the development of effective tools and methodologies to harness the tsunami of patient-generated health data into a valuable resource for conducting patient-centered research? Some challenges to overcome might include a) how to enable the collaboration of the more traditional clinical scientists with scientists from other disciplines such as informatics or computational and data-enabled science ...more »

Submitted by (@nhlbiforumadministrator)

Is this idea a Compelling Question (CQ) or Critical Challenge (CC)? : Compelling Question (CQ)

Details on the impact of addressing this CQ or CC :

Methodological research results in this area could be translated into clinical settings – specifically, they could help physicians interpret such data without being overwhelmed by their sheer volume, and even further use them as helpful adjunct tools to more traditional ways of diagnosing and/or treating diseases.

Feasibility and challenges of addressing this CQ or CC :

The increasing number of smartphones, mobile apps, and remote monitoring devices are producing a vast amount of patient-generated health-related data. However, there are no widely established tools, methodologies, or strategies to ensure optimal use and management of these data. The time is right to move forward quickly in furthering research in this field over the next 5 to 10 years.

Name of idea submitter and other team members who worked on this idea : NHLBI Staff

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10 up votes
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Goal 3: Advance Translational Research

Genome Profiling

How can proper infrastructure be designed to host sequencing data from hematologic diseases so as to enable its efficient interpretation and use in clinical care?

Is this idea a Compelling Question (CQ) or Critical Challenge (CC)? : Compelling Question (CQ)

Details on the impact of addressing this CQ or CC :

Accurate and consistent analysis of genetic data is crucial for both basic research and clinical applications, however, the complexity of sequence mutations in several blood disorders as well as the immense amounts of raw data produced during the sequencing and analysis process, make accurate bioinformatics analysis a challenge. Furthermore, the lack of consistency in the analysis of the non-coding genome and variations in correlating this information with transcriptional and epigenetic data pose an additional challenge in obtaining a comprehensive portrait of various hematologic diseases. To overcome these challenges, content-rich portals that can offer cost-effective and regulated access to raw genomic data for interrogating and sharing sequencing results without compromising patient privacy must be designed. Also, the biologic and clinical relevance of genetic alterations found in these portals must be reliable and sufficiently comprehensive in order to foster proper interpretation.

Name of idea submitter and other team members who worked on this idea : Alice Kuaban on behalf of the American Society of Hematology (ASH)

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10 up votes
18 down votes
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Goal 3: Advance Translational Research

Maximizing Previous Investment in Existing Cohorts

Everyone would like to see integration of genomic, metabolomic, epigenomic, proteomic, transcriptomic, etc. data analyzed in the context of clinical disease, environmental influences, and even end-organ effects (lung versus heart or blood as an example). Rarely can this occur on small cohorts, but rarely are funds available to take maximum use of existing large cohorts and the samples and information collected within ...more »

Submitted by (@nhlbiforumadministrator)

Is this idea a Compelling Question (CQ) or Critical Challenge (CC)? : Critical Challenge (CC)

Details on the impact of addressing this CQ or CC :

The impact would be huge as it would leverage already extremely expensive cohorts to maximum potential, allowing for exploration into clinical subphenotyping, disease mechanisms, personalized medicine, surrogate endpoints, biomarker exploration, etc. Maximizine output on previous investment is the clearest impact, since even simple analysis in a large number of samples adds up to a very hefty sum. Additionally, data from samples becomes more valuable with longitudinal follow-up of available subjects.

Feasibility and challenges of addressing this CQ or CC :

The challenges include the expense of analysis in large cohorts and the ability to attract and fund high level biostatistical faculty at top-notch institutions and get them engaged fully in the problem. Biostatisticians of high caliber will not engage without funding and without an ability to “train” students using the data and explore their own research interests within the context of the overall clinical problem. Funding mechanisms that are large (to allow for deep phenotyping of cohort samples on multiple platforms in multiple sample types) and that seek to generate solid and ongoing collaborations between the data generators and the data analyzers must emerge.

Name of idea submitter and other team members who worked on this idea : Wanda K. O’Neal, PhD

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34 up votes
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Goal 1: Promote Human Health

leveraging EHR through improved partnerships

Large medical delivery systems have an abundance of information stored in their EHR systems. What steps are necessary to develop partnerships between NHLBI and medical delivery systems or other agencies such as AHRQ to access these EHR systems, develop a common terminology (if icd 9 and other common codes are ineffective for merging data) and add that data to dbGap for NIH/community use?

Submitted by (@nhlbiforumadministrator)

Is this idea a Compelling Question (CQ) or Critical Challenge (CC)? : Compelling Question (CQ)

Details on the impact of addressing this CQ or CC :

Accessing the EHR systems of the large care systems would provide a wealth of knowledge/data for the research community.

Feasibility and challenges of addressing this CQ or CC :

The stored data will only expand in the EHR systems and it would certainly be of value and interest to any number of research projects.

Name of idea submitter and other team members who worked on this idea : NHLBI Staff

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13 up votes
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Goal 3: Advance Translational Research

Build a National Surveillance of Chronic CV and Lung Diseases

There is a need to build a robust coordinated surveillance system on the incidence and prevalence of chronic diseases. Surveillance data are needed to: •Describe and monitor the burden, trends, and patterns of these diseases •Set parameters and metrics of research priorities •Identify where to target resources for prevention, treatment, and delivery of care •Track and monitor progress toward public health disease ...more »

Submitted by (@nhlbiforumadministrator)

Is this idea a Compelling Question (CQ) or Critical Challenge (CC)? : Critical Challenge (CC)

Details on the impact of addressing this CQ or CC :

The high prevalence of chronic cardiovascular and lung diseases has created burden in increasing healthcare costs and high mortality rates in the US compared to other developed countries. Even so, they remain among the most preventable health problems. A national surveillance system for chronic cardiovascular and lung diseases would enable data-driven decision-making about public health strategies for prevention, management, and cost containment.

Feasibility and challenges of addressing this CQ or CC :

A 2011 Institute of Medicine (IOM) report concluded that a coordinated surveillance system is needed. It proposed a framework for such a system that would integrate existing information through collective efforts of multiple stakeholders. The time is right to gain from and build upon numerous ongoing broad initiatives in biomedical Big Data, including growing health IT adoption mandated by the HITECH Act, ONCHIT efforts to achieve health IT interoperability, the NIH BD2K initiative, and the multiorganizational network participating in FDA Mini-Sentinel, HCS Collaboratory, and PCORnet, among others. The NHLBI is well-positioned to lead, develop and implement the IOM’s recommended framework and system. (IOM report - http://www.iom.edu/Reports/2011/A-Nationwide-Framework-for-Surveillance-of-Cardiovascular-and-Chronic-Lung-Diseases.aspx)

Existing data sources (i.e., population surveys, registries, cohort studies, administrative data, and vital statistics) do not individually provide nationally representative data, cannot be linked, and are not currently readily accessible to all levels of users. One potential way to build such a system is to integrate and expand existing data sources.

Name of idea submitter and other team members who worked on this idea : NHLBI Staff

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13 up votes
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Goal 3: Advance Translational Research

Create a National Action Plan for COPD

Lead a coordinated effort of government, patient advocacy organizations, professional organizations, payers and others to plan and implement a coordinated plan to improve COPD awareness, education for patients and healthcare professionals, treatment strategies, research and data collection, policies and public health infrastructure and programs.

Submitted by (@jsullivan)

Is this idea a Compelling Question (CQ) or Critical Challenge (CC)? : Critical Challenge (CC)

Details on the impact of addressing this CQ or CC :

Unlike most leading causes of death and disability, there is no coordinated effort to lower disease burden associated with COPD. Coordinated plans provide a forum for identifying the most pressing issues that must be tackled, for setting goals and convening partners from different disciplines and are a framework upon which policy change can be achieved. In order to make meaningful progress in the impact that COPD is having on patients, health systems and payers, coordinated planning and action is needed and NHLBI can lead the way but time is of the essence.

Feasibility and challenges of addressing this CQ or CC :

There are proven models of multi-stakeholder, public and private partnerships to tackle disease burden and create national plans. There are also multiple national and regional organizations standing ready to assist in these efforts.

Name of idea submitter and other team members who worked on this idea : COPD Foundation Board of Directors, COPDF MASAC, COPDF State Advocacy Captains

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6 up votes
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Goal 3: Advance Translational Research

Leveraging big data for T4 translation research

What approaches can help leverage the emerging big data in health and health care for observational and interventional implementation research in heart, lung, blood, sleep diseases?

Submitted by (@nhlbiforumadministrator)

Is this idea a Compelling Question (CQ) or Critical Challenge (CC)? : Compelling Question (CQ)

Details on the impact of addressing this CQ or CC :

• Integration of big data analytics into T4 research study design and interventions development

• Innovative linkages across multiple health and non-health sector data

• Innovative methods to analyze big data linked across sectors

• Various communities are using big data analytics to understand population health data (e.g. electronic medical records s) and opportunities exist for consolidation of these efforts and standardization of methodologies

Feasibility and challenges of addressing this CQ or CC :

• NIH now has focus on big data in its formative stages

• Significant amount of NIH’s budget is/will be dedicated to big data research

• NHLBI can leverage NIH’s investment by foster research in D&I big data analytics and systems science

• Future investment in big data should yield opportunities and focus efforts

Name of idea submitter and other team members who worked on this idea : NHLBI Staff

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16 up votes
16 down votes
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Goal 4: Develop Workforce and Resources

Credible Data and Analysis of the Biomedical Research Workforce

There is a need for sensible policies that require collection and scientific analysis of credible data relating to the biomedical workforce. The data currently available are weak – for example no one knows, to a factor of 2X, the actual number of postdocs in the United States. The absence of credible human resource and labor market data on the biomedical research workforce is very surprising. NIH could contribute greatly ...more »

Submitted by (@teitelbaum)

Is this idea a Compelling Question (CQ) or Critical Challenge (CC)? : Critical Challenge (CC)

Feasibility and challenges of addressing this CQ or CC :

NIH has begun to develop its own capacity for such data collection and analysis, a very positive step. In addition, NIH may wish to consider modest research grant funding for research on the biomedical workforce by academic labor economists.

Name of idea submitter and other team members who worked on this idea : Michael S. Teitelbaum

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15 up votes
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