How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

Biomarkers in outpatient heart failure management; Are they correlated to and do they influence clinical judgment?

AIMS: Heart failure (HF) management is complicated by difficulties in clinical assessment. Biomarkers may help guide HF management, but the correspondence between clinical evaluation and biomarker serum levels has hardly been studied. We investigated the correlation between biomarkers and clinical signs and symptoms, the influence of patient characteristics and comorbidities on New York Heart Association (NYHA) classification and the effect of using biomarkers on clinical evaluation. METHODS AND RESULTS: This post-hoc analysis comprised 622 patients (77 ± 8 years, 76 % NYHA class ≥3, 80 % LVEF ≤45 %) participating in TIME-CHF, randomising patients to either NT-proBNP-guided or symptom-guided therapy. Biomarker measurements and clinical evaluation were performed at baseline and after 1, 3, 6, 12 and 18 months. NT-proBNP, GDF-15, hs-TnT and to a lesser extent hs-CRP and cystatin-C were weakly correlated to NYHA, oedema, jugular vein distension and orthopnoea (ρ-range: 0.12-0.33; p < 0.01). NT-proBNP correlated more strongly to NYHA class in the NT-proBNP-guided group compared with the symptom-guided group. NYHA class was significantly influenced by age, body mass index, anaemia, and the presence of two or more comorbidities. CONCLUSION: In HF, biomarkers correlate only weakly with clinical signs and symptoms. NYHA classification is influenced by several comorbidities and patient characteristics. Clinical judgement seems to be influenced by a clinician's awareness of NT-proBNP concentrations