Search

Complementary and integrative health encompasses diverse medical and health-care systems, practices, and products that are not generally considered part of conventional medicine. Conventional medicine (also called Western or allopathic medicine) is medicine as practiced by holders of MD (medical doctor) and DO (doctor of osteopathy) degrees and by allied health professionals, such as physical therapists, psychologists, nurse practitioners, and registered nurses. The boundaries between complementary health approaches and conventional medicine are not absolute, and specific complementary practices may, over time, become widely accepted. Despite the fact that rigorous, well-designed clinical trials for many complementary health approaches are often lacking and, therefore, the safety and effectiveness of many complementary health approaches are uncertain, complementary health approach use is common, particularly among individuals with chronic pain and advanced illness. This chapter presents evidence regarding selected complementary health modalities in the settings of chronic pain and palliative care. The presentation of the evidence is organized according to the following categories: (1) mind–body practices, (2) manipulative and body-based practices, (3) manipulation of energy fields (biofield therapies), and (4) biologically based therapies. Where sufficient data are available, the evidence is presented in table format. For some modalities, little evidence is available. For these modalities, the existing evidence is described in the text only. Despite many years of complementary health practice and common usage, rigorous scientific research on complementary health approaches has occurred only relatively recently. Complementary health approach research is limited by methodologic and ethical issues. Gaps in research are thus the norm, and the current evidence base is insufficient.

To obtain insight into the ontogeny of mantle cell lymphoma (MCL), we assessed 206 patients from a morphological, immunohistochemical, and immunogenetic perspective. Our series included nodal (n = 151), extranodal. (n = 28), and primary splenic (n = 27) MCL cases. Skewing of the immunoglobulin heavy variable (IGHV) gene repertoire was noted, with only four IGHV genes accounting for 46% of cases and approximately 70% of cases (107/154) bearing an imprint of somatic hypermutation (SHM) ranging from minimalto pronounced. Interestingly, a distinctive immunophenotypic and immunogenetic profile was identified for primary splenic MCL, which was enriched for DBA.44-positive cases (P < 0.001) and used the IGHV1-8 gene more frequently (P = 0.02) compared to nodal or extranodal cases, alluding to distinct immunopathogenetic and antigen selection processes. Expression of CD27 (considered a marker of activated B cells) was generally dissociated from SHM and was more prevalent in cases with no or minimal/borderline SHM. These findings support the idea that antigen drive is relevant for most MCL cases, although the specific antigens and the precise location of affinity maturation remain to be elucidated. Moreover, they raise the intriguing hypothesis of multiple cellular origins for MCL.

Objectives Understand the utility of the REVEAL PAH risk calculator vs LCDs in prognostication of PAH. Understand how prognostication tools can enhance hospice certification and recertification narratives. Original Research Background: Pulmonary Arterial Hypertension is a progressive and fatal disease with no cure. A recent small study suggests that a majority of patients with PAH die in the hospital (67%) and most in the ICU (83%). Research suggests that patients benefit most from receiving hospice care from 2-3 months. Local Coverage Determinations (LCDs) were created to assess whether patients are likely to be in their last 6 months of life. However, these LCDs have not been updated with recent disease-specific prognostication research. We asked whether there were newer disease-specific prognostication tools that could be compared to or used with the LCDs to better predict PAH survival time. Research Objectives: To review the literature for PAH prognostication tools, then evaluate and compare prognostication tools for PAH as relevant to hospice referral, certification, and recertification narratives. Methods: Phase 1: A review of the PAH-specific prognostication literature to identify target tools. Phase 2: A retrospective analysis of 100% sample of hospice patients with a diagnosis of PAH who received hospice services at one large nonprofit hospice and palliative care provider in the Mid-Atlantic region since 2010. Results: Phase 1: 12 articles on prognostication of PAH were identified, of which one tool, the REVEAL, was the most highly regarded. Phase 2: A 100% sample (n=21) of charts from deceased hospice patients seen between 2010-2013 were selected for a retrospective analysis. The analysis will compare the LCDs against the REVEAL prognostication tool in predicting actual date of death and length of stay in hospice. Conclusions: The REVEAL risk calculator is a prognostication tool that could aid hospice and palliative care physicians in more accurately predicting survival time as well as appropriate and timely referrals to hospice. Implications for Research, Policy, or Practice: As therapies and treatment options progress, it is critical to continue to research