A Delphi process to address medication appropriateness for older persons with multiple chronic conditions

BACKGROUND: Frameworks exist to evaluate the appropriateness of medication regimens for older patients with multiple medical conditions (MCCs). Less is known about how to translate the concepts of the frameworks into specific strategies to identify and remediate inappropriate regimens. METHODS: Modified Delphi method involving iterative rounds of input from panel members. Panelists (n = 9) represented the disciplines of nursing, medicine and pharmacy. Included among the physicians were two geriatricians, one general internist, one family practitioner, one cardiologist and two nephrologists. They participated in 3 rounds of web-based anonymous surveys. RESULTS: The panel reached consensus on a set of markers to identify problems with medication regimens, including patient/caregiver report of non-adherence, medication complexity, cognitive impairment, medications identified by expert opinion as inappropriate for older persons, excessively tight blood sugar and blood pressure control among persons with diabetes mellitus, patient/caregiver report of adverse medication effects or medications not achieving desired outcomes, and total number of medications. The panel also reached consensus on approaches to address these problems, including endorsement of strategies to discontinue medications with known benefit if necessary because of problems with feasibility or lack of alignment with patient goals. CONCLUSIONS: The results of the Delphi process provide the basis for an algorithm to improve medication regimens among older persons with MCCs. The algorithm will require assessment not only of medications and diagnoses but also cognition and social support, and it will support discontinuation of medications both when risks outweigh benefits and when regimens are not feasible or do not align with goals

Integrating Emerging Areas of Nursing Science into PhD Programs

The Council for the Advancement of Nursing Science aims to “facilitate and recognize life-long nursing science career development” as an important part of its mission. In light of fast-paced advances in science and technology that are inspiring new questions and methods of investigation in the health sciences, the Council for the Advancement of Nursing Science convened the Idea Festival for Nursing Science Education and appointed the Idea Festival Advisory Committee to stimulate dialogue about linking PhD education with a renewed vision for preparation of the next generation of nursing scientists. Building on the 2010 American Association of Colleges of Nursing Position Statement “The Research-Focused Doctoral Program in Nursing: Pathways to Excellence,” Idea Festival Advisory Committee members focused on emerging areas of science and technology that impact the ability of research-focused doctoral programs to prepare graduates for competitive and sustained programs of nursing research using scientific advances in emerging areas of science and technology. The purpose of this article is to describe the educational and scientific contexts for the Idea Festival, which will serve as the foundation for recommendations for incorporating emerging areas of science and technology into research-focused doctoral programs in nursing

Emerging Areas of Science: Recommendations for Nursing Science Education from the Council for the Advancement of Nursing Science Idea Festival

The Council for the Advancement of Nursing Science aims to “facilitate and recognize life-long nursing science career development” as an important part of its mission. In light of fast-paced advances in science and technology that are inspiring new questions and methods of investigation in the health sciences, the Council for the Advancement of Nursing Science convened the Idea Festival for Nursing Science Education and appointed the Idea Festival Advisory Committee (IFAC) to stimulate dialogue about linking PhD education with a renewed vision for preparation of the next generation of nursing scientists. Building on the 2005 National Research Council report Advancing The Nation\u27s Health Needs and the 2010 American Association of Colleges of Nursing Position Statement on the Research-Focused Doctorate Pathways to Excellence, the IFAC specifically addressed the capacity of PhD programs to prepare nursing scientists to conduct cutting-edge research in the following key emerging and priority areas of health sciences research: omics and the microbiome; health behavior, behavior change, and biobehavioral science; patient-reported outcomes; big data, e-science, and informatics; quantitative sciences; translation science; and health economics. The purpose of this article is to (a) describe IFAC activities, (b) summarize 2014 discussions hosted as part of the Idea Festival, and (c) present IFAC recommendations for incorporating these emerging areas of science and technology into research-focused doctoral programs committed to preparing graduates for lifelong, competitive careers in nursing science. The recommendations address clearer articulation of program focus areas; inclusion of foundational knowledge in emerging areas of science in core courses on nursing science and research methods; faculty composition; prerequisite student knowledge and skills; and in-depth, interdisciplinary training in supporting area of science content and methods

Emerging Areas of Nursing Science and PhD Education for The 21\u3csup\u3est\u3c/sup\u3e Century: Response to Commentaries

We respond to commentaries from the American Academy of Nursing, the American Association of Colleges of Nursing, and the National Institute of Nursing Research on our thoughts about integrating emerging areas of science into nursing PhD programs. We identify areas of agreement and focus our response on cross-cutting issues arising from cautions about the unique focus of nursing science and how best to proceed with incorporation of emerging areas of science into nursing PhD programs

The Determining Risk of Vascular Events by Apnea Monitoring (DREAM) Study: Design, Rationale and Methods

Purpose The goal of the Determining Risk of Vascular Events by Apnea Monitoring (DREAM) study is to develop a prognostic model for cardiovascular outcomes, based on physiologic variables—related to breathing, sleep architecture, and oxygenation—measured during polysomnography in US veterans. Methods The DREAM study is a multi-site, retrospective observational cohort study conducted at three Veterans Affairs (VA) centers (West Haven, CT; Indianapolis, IN; Cleveland, OH). Veterans undergoing polysomnography between January 1, 2000 and December 31, 2004 were included based on referral for evaluation of sleep-disordered breathing, documented history and physical prior to sleep testing, and ≥2-h sleep monitoring. Demographic, anthropomorphic, medical, medication, and social history factors were recorded. Measures to determine sleep apnea, sleep architecture, and oxygenation were recorded from polysomnography. VA Patient Treatment File, VA–Medicare Data, Vista Computerized Patient Record System, and VA Vital Status File were reviewed on dates subsequent to polysomnography, ranging from 0.06 to 8.8 years (5.5 ± 1.3 years; mean ± SD). Results The study population includes 1840 predominantly male, middle-aged veterans. As designed, the main primary outcome is the composite endpoint of acute coronary syndrome, stroke, transient ischemic attack, or death. Secondary outcomes include incidents of neoplasm, congestive heart failure, cardiac arrhythmia, diabetes, depression, and post-traumatic stress disorder. Laboratory outcomes include measures of glycemic control, cholesterol, and kidney function. (Actual results are pending.) Conclusions This manuscript provides the rationale for the inclusion of veterans in a study to determine the association between physiologic sleep measures and cardiovascular outcomes and specifically the development of a corresponding outcome-based prognostic model

Recommendations of Common Data Elements to Advance the Science of Selfâ Management of Chronic Conditions

PurposeCommon data elements (CDEs) are increasingly being used by researchers to promote data sharing across studies. The purposes of this article are to (a) describe the theoretical, conceptual, and definition issues in the development of a set of CDEs for research addressing selfâ management of chronic conditions; (b) propose an initial set of CDEs and their measures to advance the science of selfâ management; and (c) recommend implications for future research and dissemination.Design and MethodsBetween July 2014 and December 2015 the directors of the National Institute of Nursing Research (NINR)â funded P20 and P30 centers of excellence and NINR staff met in a series of telephone calls and a faceâ toâ face NINRâ sponsored meeting to select a set of recommended CDEs to be used in selfâ management research. A list of potential CDEs was developed from examination of common constructs in current selfâ management frameworks, as well as identification of variables frequently used in studies conducted in the centers of excellence.FindingsThe recommended CDEs include measures of three selfâ management processes: activation, selfâ regulation, and selfâ efficacy for managing chronic conditions, and one measure of a selfâ management outcome, global health.ConclusionsThe selfâ management of chronic conditions, which encompasses a considerable number of processes, behaviors, and outcomes across a broad range of chronic conditions, presents several challenges in the identification of a parsimonious set of CDEs. This initial list of recommended CDEs for use in selfâ management research is provisional in that it is expected that over time it will be refined. Comment and recommended revisions are sought from the research and practice communities.Clinical RelevanceThe use of CDEs can facilitate generalizability of research findings across diverse population and interventions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134268/1/jnu12233_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134268/2/jnu12233.pd

The Use of Technology to Support Precision Health in Nursing Science

PurposeThis article outlines how current nursing research can utilize technology to advance symptom and self‐management science for precision health and provides a roadmap for the development and use of technologies designed for this purpose.ApproachAt the 2018 annual conference of the National Institute of Nursing Research (NINR) Research Centers, nursing and interdisciplinary scientists discussed the use of technology to support precision health in nursing research projects and programs of study. Key themes derived from the presentations and discussion were summarized to create a proposed roadmap for advancement of technologies to support health and well‐being.ConclusionsTechnology to support precision health must be centered on the user and designed to be desirable, feasible, and viable. The proposed roadmap is composed of five iterative steps for the development, testing, and implementation of technology‐based/enhanced self‐management interventions. These steps are (a) contextual inquiry, focused on the relationships among humans, and the tools and equipment used in day‐to‐day life; (b) value specification, translating end‐user values into end‐user requirements; (c) design, verifying that the technology/device can be created and developing the prototype(s); (d) operationalization, testing the intervention in a real‐world setting; and (e) summative evaluation, collecting and analyzing viability metrics, including process data, to evaluate whether the technology and the intervention have the desired effect.Clinical RelevanceInterventions using technology are increasingly popular in precision health. Use of a standard multistep process for the development and testing of technology is essential.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151985/1/jnu12518.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151985/2/jnu12518_am.pd

Biomarkers as Common Data Elements for Symptom and Selfâ Management Science

PurposeBiomarkers as common data elements (CDEs) are important for the characterization of biobehavioral symptoms given that once a biologic moderator or mediator is identified, biologically based strategies can be investigated for treatment efforts. Just as a symptom inventory reflects a symptom experience, a biomarker is an indicator of the symptom, though not the symptom per se. The purposes of this position paper are to (a) identify a â minimum setâ of biomarkers for consideration as CDEs in symptom and selfâ management science, specifically biochemical biomarkers; (b) evaluate the benefits and limitations of such a limited array of biomarkers with implications for symptom science; (c) propose a strategy for the collection of the endorsed minimum set of biologic samples to be employed as CDEs for symptom science; and (d) conceptualize this minimum set of biomarkers consistent with National Institute of Nursing Research (NINR) symptoms of fatigue, depression, cognition, pain, and sleep disturbance.Design and MethodsFrom May 2016 through January 2017, a working group consisting of a subset of the Directors of the NINR Centers of Excellence funded by P20 or P30 mechanisms and NINR staff met bimonthly via telephone to develop this position paper suggesting the addition of biomarkers as CDEs. The full group of Directors reviewed drafts, provided critiques and suggestions, recommended the minimum set of biomarkers, and approved the completed document. Best practices for selecting, identifying, and using biological CDEs as well as challenges to the use of biological CDEs for symptom and selfâ management science are described. Current platforms for sample outcome sharing are presented. Finally, biological CDEs for symptom and selfâ management science are proposed along with implications for future research and use of CDEs in these areas.FindingsThe recommended minimum set of biomarker CDEs include proâ and antiâ inflammatory cytokines, a hypothalamicâ pituitaryâ adrenal axis marker, cortisol, the neuropeptide brainâ derived neurotrophic factor, and DNA polymorphisms.ConclusionsIt is anticipated that this minimum set of biomarker CDEs will be refined as knowledge regarding biologic mechanisms underlying symptom and selfâ management science further develop. The incorporation of biological CDEs may provide insights into mechanisms of symptoms, effectiveness of proposed interventions, and applicability of chosen theoretical frameworks. Similarly, as for the previously suggested NINR CDEs for behavioral symptoms and selfâ management of chronic conditions, biological CDEs offer the potential for collaborative efforts that will strengthen symptom and selfâ management science.Clinical RelevanceThe use of biomarker CDEs in biobehavioral symptoms research will facilitate the reproducibility and generalizability of research findings and benefit symptom and selfâ management science.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143764/1/jnu12378.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143764/2/jnu12378_am.pd

Advancing Symptom Science Through Use of Common Data Elements

BACKGROUND: Use of common data elements (CDEs), conceptually defined as variables that are operationalized and measured in identical ways across studies, enables comparison of data across studies in ways that would otherwise be impossible. Although healthcare researchers are increasingly using CDEs, there has been little systematic use of CDEs for symptom science. CDEs are especially important in symptom science because people experience common symptoms across a broad range of health and developmental states, and symptom management interventions may have common outcomes across populations. PURPOSES: The purposes of this article are to (a) recommend best practices for the use of CDEs for symptom science within and across centers; (b) evaluate the benefits and challenges associated with the use of CDEs for symptom science; (c) propose CDEs to be used in symptom science to serve as the basis for this emerging science; and (d) suggest implications and recommendations for future research and dissemination of CDEs for symptom science. DESIGN: The National Institute of Nursing Research (NINR)-supported P20 and P30 Center directors applied published best practices, expert advice, and the literature to identify CDEs to be used across the centers to measure pain, sleep, fatigue, and affective and cognitive symptoms. FINDINGS: We generated a minimum set of CDEs to measure symptoms. CONCLUSIONS: The CDEs identified through this process will be used across the NINR Centers and will facilitate comparison of symptoms across studies. We expect that additional symptom CDEs will be added and the list will be refined in future work. CLINICAL RELEVANCE: Symptoms are an important focus of nursing care. Use of CDEs will facilitate research that will lead to better ways to assist people to manage their symptoms