Abstract Clinical experience supports a critical role for nutrition in patients with spinal muscular atrophy (SMA). Three-day dietary intake records were analyzed for 156 visits in 47 SMA type I ...patients, 25 males and 22 females, ages 1 month to 13 years (median 9.8 months) and compared to dietary reference intakes for gender and age along with anthropometric measures and dual-energy X-ray absorptiometry (DEXA) data. Using standardized growth curves, twelve patients met criteria for failure to thrive (FTT) with weight for age <3rd percentile; eight met criteria based on weight for height. Percentage of body fat mass was not correlated with weight for height and weight for age across percentile categories. DEXA analysis further demonstrated that SMA type I children have higher fat mass and lower fat free mass than healthy peers ( p < 0.001). DEXA and dietary analysis indicates a strong correlation with magnesium intake and bone mineral density ( r = 0.65, p < 0.001). Average caloric intake for 1–3 years old was 68.8 ± 15.8 kcal/kg – 67% of peers’ recommended intake. Children with SMA type I may have lower caloric requirements than healthy age-matched peers, increasing risk for over and undernourished states and deficiencies of critical nutrients. Standardized growth charts may overestimate FTT status in SMA type I.
OBJECTIVES/GOALS: Characterize formal informatics methods and approaches for enabling reproducible translational research. Education of reproducible methods to translational researchers and ...informaticians. METHODS/STUDY POPULATION: We performed a scoping review 1 of selected informatics literature (e.g. 2,3) from PubMed and Scopus. In addition we reviewed literature and documentation of translational research informatics projects 4–21 at the University of Utah. RESULTS/ANTICIPATED RESULTS: The example informatics projects we identified in our literature covered a broad spectrum of translational research. These include research recruitment, research data requisition, study design and statistical analysis, biomedical vocabularies and metadata for data integration, data provenance and quality, and uncertainty. Elements impacting reproducibility of research include (1) Research Data: its semantics, quality, metadata and provenance; and (2) Research Processes: study conduct including activities and interventions undertaken, collections of biospecimens and data, and data integration. The informatics methods and approaches we identified as enablers of reproducibility include the use of templates, management of workflows and processes, scalable methods for managing data, metadata and semantics, appropriate software architectures and containerization, convergence methods and uncertainty quantification. In addition these methods need to be open and shareable and should be quantifiable to measure their ability to achieve reproducibility. DISCUSSION/SIGNIFICANCE OF IMPACT: The ability to collect large volumes of data collection has ballooned in nearly every area of science, while the ability to capturing research processes hasn’t kept with this pace. Potential for problematic research practices and irreproducible results are concerns.
Reproducibility is a core essentially of translational research. Translational research informatics provides methods and means for enabling reproducibility and FAIRness 22 in translational research. In addition there is a need for translational informatics itself to be reproducible to make research reproducible so that methods developed for one study or biomedical domain can be applied elsewhere. Such informatics research and development requires a mindset for meta-research 23.
The informatics methods we identified covers the spectrum of reproducibility (computational, empirical and statistical) and across different levels of reproducibility (reviewable, replicable, confirmable, auditable, and open or complete) 24–29. While there are existing and ongoing efforts in developing informatics methods for translational research reproducibility in Utah and elsewhere, there is a need to further develop formal informatics methods and approaches: the Informatics of Research Reproducibility.
In this presentation, we summarize the studies and literature we identified and discuss our key findings and gaps in informatics methods for research reproducibility. We conclude by discussing how we are covering these topics in a translational research informatics course.
1.
Pham MT, Rajić A, Greig JD, Sargeant JM, Papadopoulos A, McEwen SA. A scoping review of scoping reviews: advancing the approach and enhancing the consistency. Res Synth Methods. 2014 Dec;5(4):371–85.
2.
McIntosh LD, Juehne A, Vitale CRH, Liu X, Alcoser R, Lukas JC, Evanoff B. Repeat: a framework to assess empirical reproducibility in biomedical research. BMC Med Res Methodol Internet. 2017 Sep 18 cited 2018 Nov 30;17. Available from:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5604503/
3.
Denaxas S, Direk K, Gonzalez-Izquierdo A, Pikoula M, Cakiroglu A, Moore J, Hemingway H, Smeeth L. Methods for enhancing the reproducibility of biomedical research findings using electronic health records. BioData Min. 2017;10:31.
4.
Burnett N, Gouripeddi R, Wen J, Mo P, Madsen R, Butcher R, Sward K, Facelli JC. Harmonization of Sensor Metadata and Measurements to Support Exposomic Research. In: 2016 International Society of Exposure Science Internet. Research Triangle Park, NC, USA; 2017 cited 2017 Jun 17. Available from:
http://www.intlexposurescience.org/ISES2017
5.
Butcher R, Gouripeddi RK, Madsen R, Mo P, LaSalle B. CCTS Biomedical Informatics Core Research Data Service. In Salt Lake City; 2016.
6.
Cummins M, Gouripeddi R, Facelli J. A low-cost, low-barrier clinical trials registry to support effective recruitment. In Salt Lake City, Utah, USA; 2016 cited 2018 Nov 30. Available from:
//campusguides.lib.utah.edu/UtahRR16/abstracts
7.
Gouripeddi R, Warner P, Madsen R, Mo P, Burnett N, Wen J, Lund A, Butcher R, Cummins MR, Facelli J, Sward K. An Infrastructure for Reproducibile Exposomic Research. In: Research Reproducibility 2016 Internet. Salt Lake City, Utah, USA; 2016 cited 2018 Nov 30. Available from:
//campusguides.lib.utah.edu/UtahRR16/abstracts
8.
Eilbeck K, Lewis SE, Mungall CJ, Yandell M, Stein L, Durbin R, Ashburner M. The Sequence Ontology: a tool for the unification of genome annotations. Genome Biol. 2005;6:R44.
9.
Gouripeddi R, Cummins M, Madsen R, LaSalle B, Redd AM, Presson AP, Ye X, Facelli JC, Green T, Harper S. Streamlining study design and statistical analysis for quality improvement and research reproducibility. J Clin Transl Sci. 2017 Sep;1(S1):18–9.
10.
Gouripeddi R, Eilbeck K, Cummins M, Sward K, LaSalle B, Peterson K, Madsen R, Warner P, Dere W, Facelli JC. A Conceptual Architecture for Reproducible On-demand Data Integration for Complex Diseases. In: Research Reproducibility 2016 (UtahRR16) Internet. Salt Lake City, Utah, USA; 2016 cited 2017 Apr 25. Available from:
https://zenodo.org/record/168067
11.
Gouripeddi R, Lane E, Madsen R, Butcher R, LaSalle B, Sward K, Fritz J, Facelli JC, Cummins M, Shao J, Singleton R. Towards a scalable informatics platform for enhancing accrual into clinical research studies. J Clin Transl Sci. 2017 Sep;1(S1):20–20.
12.
Gouripeddi R, Deka R, Reese T, Butcher R, Martin B, Talbert J, LaSalle B, Facelli J, Brixner D. Reproducibility of Electronic Health Record Research Data Requests. In Washington, DC, USA; 2018 cited 2018 Apr 21. Available from:
https://zenodo.org/record/1226602#.WtvvyZch270
13.
Gouripeddi R, Mo P, Madsen R, Warner P, Butcher R, Wen J, Shao J, Burnett N, Rajan NS, LaSalle B, Facelli JC. A Framework for Metadata Management and Automated Discovery for Heterogeneous Data Integration. In: 2016 BD2K All Hands Meeting Internet. Bethesda, MD; November 29-30 cited 2017 Apr 25. Available from:
https://zenodo.org/record/167885
14.
Groat D, Gouripeddi R, Lin YK, Dere W, Murray M, Madsen R, Gestaland P, Facelli J. Identification of High-Level Formalisms that Support Translational Research Reproducibility. In: Research Reproducibility 2018 Internet. Salt Lake City, Utah, USA; 2018 cited 2018 Oct 30. Available from:
//campusguides.lib.utah.edu/UtahRR18/abstracts
15.
Huser V, Kahn MG, Brown JS, Gouripeddi R. Methods for examining data quality in healthcare integrated data repositories. Pac Symp Biocomput Pac Symp Biocomput. 2018;23:628–33.
16.
Lund A, Gouripeddi R, Burnett N, Tran L-T, Mo P, Madsen R, Cummins M, Sward K, Facelli J. Enabling Reproducible Computational Modeling: The Utah PRISMS Ecosystem. In Salt Lake City, Utah, USA; 2018 cited 2018 Oct 30. Available from:
//campusguides.lib.utah.edu/UtahRR18/abstracts
17.
Pflieger LT, Mason CC, Facelli JC. Uncertainty quantification in breast cancer risk prediction models using self-reported family health history. J Clin Transl Sci. 2017 Feb;1(1):53–9.
18.
Shao J, Gouripeddi R, Facelli J. Improving Clinical Trial Research Reproducibility using Reproducible Informatics Methods. In Salt Lake City, Utah, USA; 2018 cited 2018 Oct 30. Available from:
//campusguides.lib.utah.edu/UtahRR18/abstracts
19.
Shao J, Gouripeddi R, Facelli JC. Semantic characterization of clinical trial descriptions from
ClincalTrials.gov
and patient notes from MIMIC-III. J Clin Transl Sci. 2017 Sep;1(S1):12–12.
20.
Tiase V, Gouripeddi R, Burnett N, Butcher R, Mo P, Cummins M, Sward K. Advancing Study Metadata Models to Support an Exposomic Informatics Infrastructure. In Ottawa, Canada; 2018 cited 2018 Oct 30. Available from: =
http://www.eiseverywhere.com/ehome/294696/638649/?&t=8c531cecd4bb0a5efc6a0045f5bec0c3
21.
Wen J, Gouripeddi R, Facelli JC. Metadata Discovery of Heterogeneous Biomedical Datasets Using Token-Based Features. In: IT Convergence and Security 2017 Internet. Springer, Singapore; 2017 cited 2017 Sep 6. p. 60–7. (Lecture Notes in Electrical Engineering). Available from:
https://link.springer.com/chapter/10.1007/978-981-10-6451-7_8
22.
Wilkinson MD, Dumontier M, Aalbersberg IjJ, Appleton G, Axton M, Baak A, Blomberg N, Boiten J-W, da Silva Santos LB, Bourne PE, Bouwman J, Brookes AJ, Clark T, Crosas M, Dillo I, Dumon O, Edmunds S, Evelo CT, Finkers R, Gonzalez-Beltran A, Gray AJG, Groth P, Goble C, Grethe JS, Heringa J, ’t Hoen PAC, Hooft R, Kuhn T, Kok R, Kok J, Lusher SJ, Martone ME, Mons A, Packer AL, Persson B, Rocca-Serra P, Roos M, van Schaik R, Sansone S-A, Schultes E, Sengstag T, Slater T, Strawn G, Swertz MA, Thompson M, van der Lei J, van Mulligen E, Velterop J, Waagmeester A, Wittenburg P, Wolstencroft K, Zhao J, Mons B. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016 Mar 15;3:160018.
23.
Ioannidis JPA. Meta-research: Why research on research matters. PLOS Biol. 2018 Mar 13;16(3):e2005468.
24.
Stodden V, Borwein J, Bailey DH. Setting the default to reproducible. Comput Sci Res SIAM News. 2013;46(5):4–6.
25.
Stodden V, McNutt M, Bailey DH, Deelman E, Gil Y, Hanson B, Heroux MA, Ioannidis JPA, Taufer M. Enhancing reproducibility for computational methods. Science. 2016 Dec 9;354(6317):1240–1.
26.
Stodden V, McNutt M, Bailey DH, Deelman E, Gil Y, Hanson B, Heroux MA, Ioannidis JPA, Taufer M. Enhancing reproducibility for computational methods. Science. 2016 Dec 9;
OBJECTIVES/SPECIFIC AIMS: Translational researchers often require the use of informatics methods in their work. Lack of an understanding of key informatics principles and methods limits the abilities ...of translational researchers to successfully implement Findable, Accessible, Interoperable, Reusable (FAIR) principles in grant proposal submissions and performed studies. In this study we describe our work in addressing this limitation in the workforce by developing a competency-based, modular course in informatics to meet the needs of diverse translational researchers. METHODS/STUDY POPULATION: We established a Translational Research Informatics Education Collaborative (TRIEC) consisting of faculty at the University of Utah (UU) with different primary expertise in informatics methods, and working in different tiers of the translational spectrum. The TRIEC, in collaboration with the Foundation of Workforce Development of the Utah Center for Clinical and Translational Science (CCTS), gathered informatics needs of early investigators by consolidating requests for informatics services, assistance provided in grant writing, and consultations. We then reviewed existing courses and literature for informatics courses that focused on clinical and translational researchers 3–9. Using the structure and content of the identified courses, we developed an initial draft of a syllabus for a Translational Research Informatics (TRI) course which included key informatics topics to be covered and learning activities, and iteratively refined it through discussions. The course was approved by the UU Department of Biomedical Informatics, UU Graduate School and the CCTS. RESULTS/ANTICIPATED RESULTS: The TRI course introduces informatics PhD students, clinicians, and public health practitioners who have a demonstrated interest in research, to fundamental principles and tools of informatics. At the completion of the course, students will be able to describe and identify informatics tools and methods relevant to translational research and demonstrate inter-professional collaboration in the development of a research proposal addressing a relevant translational science question that utilizes the state-of-the-art in informatics. TRI covers a diverse set of informatics content presented as modules: genomics and bioinformatics, electronic health records, exposomics, microbiomics, molecular methods, data integration and fusion, metadata management, semantics, software architectures, mobile computing, sensors, recruitment, community engagement, secure computing environments, data mining, machine learning, deep learning, artificial intelligence and data science, open source informatics tools and platforms, research reproducibility, and uncertainty quantification. The teaching methods for TRI include (1) modular didactic learning consisting of presentations and readings and face-to-face discussions of the content, (2) student presentations of informatics literature relevant to their final project, and (3) a final project consisting of the development, critique and chalk talk and formal presentations of informatics methods and/or aims of an National Institutes of Health style K or R grant proposal. For (3), the student presents their translational research proposal concept at the beginning of the course, and works with members of the TRIEC with corresponding expertise. The final course grade is a combination of the final project, paper presentations and class participation. We offered TRI to a first cohort of students in the Fall semester of 2018. DISCUSSION/SIGNIFICANCE OF IMPACT: Translational research informatics is a sub-domain of biomedical informatics that applies and develops informatics theory and methods for translational research. TRI covers a diverse set of informatics topics that are applicable across the translational spectrum. It covers both didactic material and hands-on experience in using the material in grant proposals and research studies. TRI’s course content, teaching methodology and learning activities enable students to initially learn factual informatics knowledge and skills for translational research correspond to the ‘Remember, Understand, and Apply’ levels of the Bloom’s taxonomy 10. The final project provides opportunity for applying these informatics concepts corresponding to the ‘Analyze, Evaluate, and Create’ levels of the Bloom’s taxonomy 10. This inter-professional, competency-based, modular course will develop an informatics-enabled workforce trained in using state-of-the-art informatics solutions, increasing the effectiveness of translational science and precision medicine, and promoting FAIR principles in research data management and processes. Future work includes opening the course to all Clinical and Translational Science Award hubs and publishing the course material as a reference book. While student evaluations for the first cohort will be available end of the semester, true evaluation of TRI will be the number of trainees taking the course and successful grant proposal submissions. References: 1. Wilkinson MD, Dumontier M, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016 Mar 15. 2. National Center for Advancing Translational Sciences. Translational Science Spectrum. National Center for Advancing Translational Sciences. 2015 cited 2018 Nov 15. Available from: https://ncats.nih.gov/translation/spectrum 3. Hu H, Mural RJ, Liebman MN. Biomedical Informatics in Translational Research. 1 edition. Boston: Artech House; 2008. 264 p. 4. Payne PRO, Embi PJ, Niland J. Foundational biomedical informatics research in the clinical and translational science era: a call to action. J Am Med Inform Assoc JAMIA. 2010;17(6):615–6. 5. Payne PRO, Embi PJ, editors. Translational Informatics: Realizing the Promise of Knowledge-Driven Healthcare. Softcover reprint of the original 1
st
ed. 2015 edition. Springer; 2016. 196 p. 6. Richesson R, Andrews J, editors. Clinical Research Informatics. 2
nd
ed. Springer International Publishing; 2019. (Health Informatics). 7. Robertson D, MD GHW, editors. Clinical and Translational Science: Principles of Human Research. 2 edition. Amsterdam: Academic Press; 2017. 808 p. 8. Shen B, Tang H, Jiang X, editors. Translational Biomedical Informatics: A Precision Medicine Perspective. Softcover reprint of the original 1
st
ed. 2016 edition. S.l.: Springer; 2018. 340 p. 9. Valenta AL, Meagher EA, Tachinardi U, Starren J. Core informatics competencies for clinical and translational scientists: what do our customers and collaborators need to know? J Am Med Inform Assoc. 2016 Jul 1;23(4):835–9. 10. Anderson LW, Krathwohl DR, Airasian PW, Cruikshank KA, Mayer RE, Pintrich PR, Raths J, Wittrock MC. A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives, Abridged Edition. 1 edition. New York: Pearson; 2000.
Abstract
Objective
The Recruitment Innovation Center (RIC), partnering with the Trial Innovation Network and institutions in the National Institutes of Health-sponsored Clinical and Translational ...Science Awards (CTSA) Program, aimed to develop a service line to retrieve study population estimates from electronic health record (EHR) systems for use in selecting enrollment sites for multicenter clinical trials. Our goal was to create and field-test a low burden, low tech, and high-yield method.
Materials and Methods
In building this service line, the RIC strove to complement, rather than replace, CTSA hubs’ existing cohort assessment tools. For each new EHR cohort request, we work with the investigator to develop a computable phenotype algorithm that targets the desired population. CTSA hubs run the phenotype query and return results using a standardized survey. We provide a comprehensive report to the investigator to assist in study site selection.
Results
From 2017 to 2020, the RIC developed and socialized 36 phenotype-dependent cohort requests on behalf of investigators. The average response rate to these requests was 73%.
Discussion
Achieving enrollment goals in a multicenter clinical trial requires that researchers identify study sites that will provide sufficient enrollment. The fast and flexible method the RIC has developed, with CTSA feedback, allows hubs to query their EHR using a generalizable, vetted phenotype algorithm to produce reliable counts of potentially eligible study participants.
Conclusion
The RIC’s EHR cohort assessment process for evaluating sites for multicenter trials has been shown to be efficient and helpful. The model may be replicated for use by other programs.
Although beta-blockers improve symptoms and survival in adults with heart failure, little is known about these medications in children and adolescents.
To prospectively evaluate the effects of ...carvedilol in children and adolescents with symptomatic systemic ventricular systolic dysfunction.
A multicenter, randomized, double-blind, placebo-controlled study of 161 children and adolescents with symptomatic systolic heart failure from 26 US centers. In addition to treatment with conventional heart failure medications, patients were assigned to receive placebo or carvedilol. Enrollment began in June 2000 and the last dose was given in May 2005 (each patient received medication for 8 months).
Patients were randomized in a 1:1:1 ratio to twice-daily dosing with placebo, low-dose carvedilol (0.2 mg/kg per dose if weight <62.5 kg or 12.5 mg per dose if weight > or =62.5 kg), or high-dose carvedilol (0.4 mg/kg per dose if weight <62.5 kg or 25 mg per dose if weight > or =62.5 kg) and were stratified according to whether each patient's systemic ventricle was a left ventricle or not.
The primary outcome was a composite measure of heart failure outcomes in patients receiving carvedilol (low- and high-dose combined) vs placebo. Secondary efficacy variables included individual components of this composite, echocardiographic measures, and plasma b-type natriuretic peptide levels.
There was no statistically significant difference between groups for the composite end point based on the percentage of patients who improved, worsened, or were unchanged. Among 54 patients assigned to placebo, 30 improved (56%), 16 worsened (30%), and 8 were unchanged (15%); among 103 patients assigned to carvedilol, 58 improved (56%), 25 worsened (24%), and 20 were unchanged (19%). The rates of worsening were lower than expected. The odds ratio for worsened outcome for patients in the combined carvedilol group vs the placebo group was 0.79 (95% CI, 0.36-1.59; P = .47). A prespecified subgroup analysis noted significant interaction between treatment and ventricular morphology (P = .02), indicating a possible differential effect of treatment between patients with a systemic left ventricle (beneficial trend) and those whose systemic ventricle was not a left ventricle (nonbeneficial trend).
These preliminary results suggest that carvedilol does not significantly improve clinical heart failure outcomes in children and adolescents with symptomatic systolic heart failure. However, given the lower than expected event rates, the trial may have been underpowered. There may be a differential effect of carvedilol in children and adolescents based on ventricular morphology.
clinicaltrials.gov Identifier: NCT00052026.
High-performance computing centers (HPC) traditionally have far less restrictive privacy management policies than those encountered in healthcare. We show how an HPC can be re-engineered to ...accommodate clinical data while retaining its utility in computationally intensive tasks such as data mining, machine learning, and statistics. We also discuss deploying protected virtual machines. A critical planning step was to engage the university's information security operations and the information security and privacy office. Access to the environment requires a double authentication mechanism. The first level of authentication requires access to the university's virtual private network and the second requires that the users be listed in the HPC network information service directory. The physical hardware resides in a data center with controlled room access. All employees of the HPC and its users take the university's local Health Insurance Portability and Accountability Act training series. In the first 3 years, researcher count has increased from 6 to 58.
Heart failure with recovered ejection fraction (HFrecEF) is a recently recognized phenotype of patients with a history of reduced left ventricular ejection fraction (LVEF) that has subsequently ...normalized. It is unknown whether such LVEF improvement is associated with improvements in health status.
To examine changes in health-related quality of life in patients with heart failure with reduced ejection fraction (HFrEF) whose LVEF normalized, compared with those whose LVEF remains reduced and those with HF with preserved EF (HFpEF).
This prospective cohort study was conducted at a tertiary care hospital from November 2016 to December 2018. Consecutive patients seen in a heart failure clinic who completed patient-reported outcome assessments were included. Clinical data were abstracted from the electronic health record. Data analysis was completed from February to December 2020.
Changes in Kansas City Cardiomyopathy Questionnaire overall summary score, Visual Analog Scale score, and Patient-Reported Outcomes Measurement Information System domain scores on physical function, fatigue, depression, and satisfaction with social roles over 1-year follow-up.
The study group included 319 patients (mean SD age, 60.4 15.5 years; 120 women 37.6%). At baseline, 212 patients (66.5%) had HFrEF and 107 (33.5%) had HFpEF. At a median follow-up of 366 (interquartile range, 310-421) days, LVEF had increased to 50% or more in 35 patients with HFrEF (16.5%). Recovery of systolic function was associated with heart failure-associated quality-of-life improvement, such that for each 10% increase in LVEF, the Kansas City Cardiomyopathy Questionnaire score improved by an mean (SD) of 4.8 (1.6) points (P = .003). Recovery of LVEF was also associated with improvement of physical function, satisfaction with social roles, and a reduction in fatigue.
Among patients with HFrEF in this study, normalization of left ventricular systolic function was associated with a significant improvement in health-related quality of life.
In a subset of patients with heart failure with reduced ejection fraction (HFrEF) disease modifying therapy results in improvement of left ventricular EF (HFiEF). Yet, it has been noted that HFiEF ...patients may experience clinical events exceeding that of a healthy population. Our aim was to examine health-related quality of life (hrQOL) in HFiEF and compare it to those with HFrEF and HF with preserved ejection fraction (HFpEF).
In a large HF clinic, hrQOL was routinely assessed using the disease-specific 12-question Kansas City Cardiomyopathy Questionnaire (KCCQ-12), the visual analogue scale (VAS) and the Patient-Reported Outcomes Measurement Information System (PROMIS) scale in the domains of physical function, fatigue, depression, and satisfaction with social roles and activities. HFrEF was defined as LVEF<50%, HFiEF as LVEF<50% at a point in time but ≥50% at the time of assessment and HFpEF as EF always ≥50%. Statistical difference among hrQOL scores was tested as mean difference of each score between comparison groups adjusted by age and gender.
The study population consisted of 1,337 patients, average age was 59±16.6 years and 65% were male. The demographics of patients in the three groups of interest - HFiEF (N=181), HFrEF (N=878) and HFpEF (n=318) are shown in Table 1. Compared to HFrEF and HFpEF, patients with HFiEF had better scores for KCCQ, PROMIS physical function and fatigue (p<0.05 for all comparisons) - Figure 1. HFiEF also had better score for VAS compared to HFrEF (p=0.002). There were no significant differences between scores for PROMIS depression and satisfaction with social roles among the three groups (p=NS). Despite the overall better hrQOL profile, HFiEF PROMIS scores remained below the population median.
Our results indicate that HFiEF patients have overall better hrQOL than patients with HFrEF and HFpEF. However, their hrQOL remains below that of the general population. More study is needed to determine the causes of these limitations and ways to further improve hrQOL in HFiEF patients.
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