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National Institutes of Health Research Plan on Rehabilitation.

ABSTRACT: One in 5 Americans experiences disability that affects daily function because of impairments in mobility, impairments in cognitive function, sensory impairment, or communication impairment. The need for rehabilitation strategies to optimize function and reduce disability is a clear priority for research to address this public health challenge. The National Institutes of Health (NIH) recently published a Research Plan on Rehabilitation that provides a set of priorities to guide the field over the next 5 years. The plan was developed with input from multiple institutes and centers within the NIH, the National Advisory Board for Medical Rehabilitation Research, and the public. This article provides an overview of the need for this research plan, an outline of its development, and a listing of six priority areas for research.

SUBMITTER: National Institutes of Health Medical Rehabilitation Coordinating Committee

PROVIDER: S-EPMC5397097 | biostudies-literature | 2017 May/Jun

REPOSITORIES: biostudies-literature

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National Institutes of Health Research Plan on Rehabilitation.

The American journal of occupational therapy : official publication of the American Occupational Therapy Association 20170501 3

One in 5 Americans experiences disability that affects daily function because of impairments in mobility, impairments in cognitive function, sensory impairment, or communication impairment. The need for rehabilitation strategies to optimize function and reduce disability is a clear priority for research to address this public health challenge. The National Institutes of Health (NIH) recently published a Research Plan on Rehabilitation that provides a set of priorities to guide the field over the ...[more]

PMID: 28422640

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Project description:BackgroundReadmissions are a common problem in cardiac surgery. The goal of this study was to examine the frequency, timing, and associated risk factors for readmission after cardiac operations.MethodsA 10-center cohort study prospectively enrolled 5,158 adult cardiac surgical patients (5,059 included in analysis) to assess risk factors for infection after cardiac operations. Data were also collected on all-cause readmissions occurring within 65 days after the operation. Major outcomes included the readmission rate stratified by procedure type, cause of readmission, length of readmission stay, and discharge disposition after readmission. Multivariable Cox regression was used to determine risk factors for time to first readmission.ResultsThe overall rate of readmission was 18.7% (number of readmissions, 945). When stratified by the most common procedure type, readmission rates were isolated coronary artery bypass grafting, 14.9% (n = 248); isolated valve, 18.3% (n = 337); and coronary artery bypass grafting plus valve, 25.0% (n = 169). The three most common causes of first readmission within 30 days were infection (17.1% [n = 115]), arrhythmia (17.1% [n = 115]), and volume overload (13.5% [n = 91]). More first readmissions occurred within 30 days (80.6% [n = 672]) than after 30 days (19.4% [n = 162]), and 50% of patients were readmitted within 22 days from the index operation. The median length of stay during the first readmission was 5 days. Discharge in 15.8% of readmitted patients (n = 128) was to a location other than home. Baseline patient characteristics associated with readmission included female gender, diabetes mellitus on medication, chronic obstructive pulmonary disease, elevated creatinine, lower hemoglobin, and longer operation time. More complex surgical procedures were associated with an increased risk of readmission compared with the coronary artery bypass grafting group.ConclusionsNearly 1 of 5 patients who undergo cardiac operations require readmission, an outcome with significant health and economic implications. Management practices to avert in-hospital infections, reduce postoperative arrhythmias, and avoid volume overload offer important targets for quality improvement.

Project description:ImportanceThe US has historically resettled more refugees than any other country, with over 3.5 million refugees since 1980. The National Institutes of Health (NIH) is the largest public funder of biomedical research and development, but its role in mitigating many health disparities refugees experience through its funded research remains unknown.ObjectiveTo examine the NIH's research funding patterns on refugee health research over the last 2 decades.Design, setting, and participantsSecondary analysis of NIH-funded grants between 2000 and 2020 using a cross-sectional study design. The NIH Research Portfolio Online Reporting Tools database was used to find relevant grants. Data were analyzed from November 2021 to September 2022.Main outcomes and measuresNIH grants awarded by year, state, grant type, research area, funding institute, grant duration, and amount funded.ResultsOf 1.7 million NIH grants funded over the 20-year study period, only 78 addressed refugee health. Funded grants were mostly training grants (23 grants [29%]), followed by hypothesis-driven research (R01 grants; 22 grants [28%]), pilot or preliminary investigation proposals (13 grants [17%]), and other types of grants (20 grants [26%]). The most studied research domain was mental health (36 grants [46%]), followed by refugee family dynamics and women's and children's health (14 grants [18%]). A total of 26 grants (33%) were funded by the National Institute of Mental Health and 15 (19%) were funded by the National Institute of Child Health and Human Development. Most grants were US-based (60 grants [76%]) and the state of Massachusetts received the greatest amount of funding ($14 825 852 [18%]). In 2020, the NIH allocated about $2.3 million to refugee health research, or less than 0.01% of its $42 billion budget that year. The number of grants funded in each time period did not always reflect changes in the number of refugees resettled in the US over the years.Conclusions and relevanceThis cross-sectional study found that there remain significant gaps in the understanding of and interventions in the health research needs of refugees locally and along the migratory route. To close these gaps, the NIH should increase its investments in comprehensive studies assessing the physical, mental, and social well-being of this expanding population. This can be achieved by ensuring that all NIH institutes allocate budgets specifically for refugee health research and extend support for the training of refugee researchers.

Project description:The National Institutes of Health (NIH) Health Care Systems Research Collaboratory (NIH Collaboratory) seeks to produce generalizable knowledge about the conduct of pragmatic research in health systems. This analysis applied the PRECIS-2 pragmatic trial criteria to five NIH Collaboratory pragmatic trials to better understand 1) the pragmatic aspects of the design and implementation of treatments delivered in real world settings and 2) the usability of the PRECIS-2 criteria for assessing pragmatic features across studies and across time.Using the PRECIS-2 criteria, five pragmatic trials were each rated by eight raters. For each trial, we reviewed the original grant application and a required progress report written at the end of a 1-year planning period that included changes to the protocol or implementation approach. We calculated median scores and interrater reliability for each PRECIS domain and for the overall trial at both time points, as well as the differences in scores between the two time points. We also reviewed the rater comments associated with the scores.All five trials were rated to be more pragmatic than explanatory, with comments indicating that raters generally perceived them to closely mirror routine clinical care across multiple domains. The PRECIS-2 domains for which the trials were, on average, rated as most pragmatic on the 1 to 5 scale at the conclusion of the planning period included primary analysis (mean?=?4.7 (range?=?4.5 to 4.9)), recruitment (4.3 (3.6 to 4.8)), eligibility (4.1 (3.4 to 4.8)), setting (4.1 (4.0 to 4.4)), follow-up (4.1 (3.4 to 4.9)), and primary outcome (4.1 (3.5 to 4.9)). On average, the less pragmatic domains were organization (3.3 (2.6 to 4.4)), flexibility of intervention delivery (3.5 (2.1-4.5)), and flexibility of intervention adherence (3.8 (2.8-4.5)). Interrater agreement was modest but statistically significant for four trials (Gwet's AC1 statistic range 0.23 to 0.40) and the intraclass correlation coefficient ranged from 0.05 to 0.31. Rating challenges included assigning a single score for domains that may relate to both patients and care settings (that is, eligibility or recruitment) and determining to what extent aspects of complex research interventions differ from usual care.These five trials in diverse healthcare settings were rated as highly pragmatic using the PRECIS-2 criteria. Applying the tool generated insightful discussion about real-world design decisions but also highlighted challenges using the tool. PRECIS-2 raters would benefit from additional guidance about how to rate the interwoven patient and practice-level considerations that arise in pragmatic trials.Clinicaltrials.gov trial registrations: NCT02019225 , NCT01742065 , NCT02015455 , NCT02113592 , NCT02063867 .

Project description:ImportanceDespite the rapid growth of interest and diversity in applications of artificial intelligence (AI) to biomedical research, there are limited objective ways to characterize the potential for use of AI in clinical practice.ObjectiveTo examine what types of medical AI have the greatest estimated translational impact (ie, ability to lead to development that has measurable value for human health) potential.Design, setting, and participantsIn this cohort study, research grants related to AI awarded between January 1, 1985, and December 31, 2020, were identified from a National Institutes of Health (NIH) award database. The text content for each award was entered into a Natural Language Processing (NLP) clustering algorithm. An NIH database was also used to extract citation data, including the number of citations and approximate potential to translate (APT) score for published articles associated with the granted awards to create proxies for translatability.ExposuresUnsupervised assignment of AI-related research awards to application topics using NLP.Main outcomes and measuresAnnualized citations per $1 million funding (ACOF) and average APT score for award-associated articles, grouped by application topic. The APT score is a machine-learning based metric created by the NIH Office of Portfolio Analysis that quantifies the likelihood of future citation by a clinical article.ResultsA total of 16 629 NIH awards related to AI were included in the analysis, and 75 applications of AI were identified. Total annual funding for AI grew from $17.4 million in 1985 to $1.43 billion in 2020. By average APT, interpersonal communication technologies (0.488; 95% CI, 0.472-0.504) and population genetics (0.463; 95% CI, 0.453-0.472) had the highest translatability; environmental health (ACOF, 1038) and applications focused on the electronic health record (ACOF, 489) also had high translatability. The category of applications related to biochemical analysis was found to have low translatability by both metrics (average APT, 0.393; 95% CI, 0.388-0.398; ACOF, 246).Conclusions and relevanceBased on this study's findings, data on grants from the NIH can apparently be used to identify and characterize medical applications of AI to understand changes in academic productivity, funding support, and potential for translational impact. This method may be extended to characterize other research domains.

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National Institutes of Health Research Plan on Rehabilitation.

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National Institutes of Health Research Plan on Rehabilitation.

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