Association of Nonacute Opioid Use and Cardiovascular Diseases: A Scoping Review of the Literature.
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ABSTRACT:
SUBMITTER: Singleton JH
PROVIDER: S-EPMC8403306 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background: Pharmacists are on the frontline caring for patients at risk of an opioid overdose and for patients with an opioid use disorder (OUD). Dispensing naloxone and medications for OUD and counseling patients about these medications are ways pharmacists can provide care. Key to pharmacists' involvement is their willingness to take on these practice responsibilities. Methods: The purpose of this scoping review is to identify, evaluate, and summarize published literature describing pharmacists' attitudes toward naloxone and medications for OUD, i.e., methadone, buprenorphine, and naltrexone. All searches were performed on December 7, 2018, in 5 databases: Embase.com, PubMed.gov, Cumulative Index to Nursing and Allied Health Literature (CINAHL) via EBSCOhost, Cochrane Central Register of Controlled Trials via Wiley, and Clarivate Web of Science. Articles included original research conducted in the United States, described attitude-related language toward naloxone and medications for OUD, and pharmacists. Results: A total of 1323 articles were retrieved, 7 were included. Five studies reported on pharmacists' attitudes toward naloxone dispensing, 1 study reported on attitudes toward naloxone, buprenorphine, and buprenorphine/naloxone, and 1 reported on attitudes toward buprenorphine/naloxone. Respondents were diverse, including pharmacists from different practice specialties. Studies found that pharmacists agreed with a naloxone standing order, believed that naloxone should be dispensed to individuals at risk of an opioid overdose, and were supportive of dispensing buprenorphine. A minority of pharmacists expressed negative attitudes. Barriers cited to implementation included education and training, workflow, and management support. Conclusions: Pharmacists were positive in their attitudes toward increased practice responsibilities for patients at risk of an opioid overdose or with an OUD. Pharmacists must receive education and training to be current in their understanding of OUD medications, and they must be supported in order to provide effective care to this patient population.
| S-EPMC6920531 | biostudies-literature
Project description:ObjectiveTo summarize the current literature to identify what research has been conducted, examine the approaches used, and determine what is presently known about prescription and nonprescription opioid receipts and use among individuals with traumatic brain injury (TBI).Data sourcesThe search strategy included the following: opioid; opiate; analgesics, opioid; opiate alkaloids; or opioid-related disorders; AND brain injury; brain injuries; brain injuries, traumatic; head injury; head injuries; head injuries, closed; head injuries, penetrating; brain concussion; diffuse axonal injury; diffuse axonal injuries; brain trauma/s; head trauma/s; concussion; craniocerebral trauma/s; or TBI. Filters included English and Adults (19+ years). Study Selection: Inclusion: English language, adults with stable TBI, and prescription opioid receipt or use after TBI. Exclusion: Animal models, populations with other acquired brain injury, acute TBI management, and non-peer-reviewed articles, theses, or conference abstracts. Multiple reviewers screened abstracts and full-text articles for eligibility. In total, 771 abstracts were screened, 183 full texts were reviewed, and 21 met eligibility criteria. Data Extraction: Relevant content was independently extracted by multiple observers, including authors, design, sample identification and data source/s, TBI severity, TBI assessment, opioid assessment, study population (demographics, N), military affiliation, comparison groups, date of data collection, and summary of findings.ResultsStudies were published between 1987 and 2019; most data were collected prior to 2015. The majority utilized administrative and electronic medical record data from the Department of Veterans Affairs and retrospective cohort designs, and most focused on prescription opioids. There were no studies evaluating interventions to reduce use of opioids in TBI populations. Preliminary findings suggest that prescription opioid receipt is strongly related to psychological symptoms, including comorbid depression, anxiety, and posttraumatic stress disorder.ConclusionsDespite increased awareness of opioid receipt and use following TBI, there is limited investigation on the examination of this issue. Future studies should include more varied patient populations as well as evaluate interventions to reduce opioid use following TBI.
| S-EPMC8428300 | biostudies-literature
Project description:BackgroundAlthough many quality indicator (QI) sets have been developed for acute cardiovascular diseases, a comprehensive summary is lacking. In this scoping review we aimed to summarize the available evidence on the QI sets for acute cardiovascular diseases, and assess the QI set development process. We followed the Joanna Briggs Institute framework and the PRISMA extension for scoping reviews.MethodsWe conducted a systematic search of MEDLINE, EMBASE, and major international guidelines on QIs for acute major cardiovascular diseases. The study included articles published after 2000.ResultsAmong the 3112 articles screened, 18 were included in this scoping review. Among the 18 articles included, 12 were on acute coronary syndrome (ACS), five on acute heart failure (AHF), and two on acute aortic dissection (AAD); one article included QIs for both ACS and AHF. Only four of these studies conducted a systematic search with a search strategy. From the 18 articles, 268 QIs containing duplication between articles were identified (191 QIs were for ACS, 57 were for AHF, and 20 were for AAD) and QI measurements varied across articles.ConclusionsThis scoping review provides a comprehensive list of QIs for acute cardiovascular diseases. Our results may be helpful to clinicians and organizations seeking to develop QIs for acute cardiovascular care in the future.
| S-EPMC9254543 | biostudies-literature
Project description:Purpose of reviewIn this study, we planned and carried out a scoping review of the literature to learn how machine learning (ML) has been investigated in cardiovascular imaging (CVI).Recent findingsDuring our search, we found numerous studies that developed or utilized existing ML models for segmentation, classification, object detection, generation, and regression applications involving cardiovascular imaging data. We first quantitatively investigated the different aspects of study characteristics, data handling, model development, and performance evaluation in all studies that were included in our review. We then supplemented these findings with a qualitative synthesis to highlight the common themes in the studied literature and provided recommendations to pave the way for upcoming research.SummaryML is a subfield of artificial intelligence (AI) that enables computers to learn human-like decision-making from data. Due to its novel applications, ML is gaining more and more attention from researchers in the healthcare industry. Cardiovascular imaging is an active area of research in medical imaging with lots of room for incorporating new technologies, like ML.Supplementary informationThe online version contains supplementary material available at 10.1007/s40134-022-00407-8.
| S-EPMC9742664 | biostudies-literature
Project description:Greater integration of medication-assisted treatment (MAT) for opioid use disorder (OUD) in U.S. primary care settings would expand access to treatment for this condition. Models for integrating MAT into primary care vary in structure. This article summarizes findings of a technical report for the Agency for Healthcare Research and Quality describing MAT models of care for OUD, based on a literature review and interviews with key informants in the field. The report describes 12 representative models of care for integrating MAT into primary care settings that could be considered for adaptation across diverse health care settings. Common components of existing care models include pharmacotherapy with buprenorphine or naltrexone, provider and community education, coordination and integration of OUD treatment with other medical and psychological needs, and psychosocial services and interventions. Models vary in how each component is implemented. Decisions about adopting MAT models of care should be individualized to address the unique milieu of each implementation setting.
| S-EPMC5504692 | biostudies-other
Project description:BackgroundThe opioid-related overdose epidemic remains a persistent public health problem in the United States and has been accelerated by the 2019 coronavirus disease pandemic. Existing, evidence-based treatment options for opioid use disorder (OUD) are broadly underutilized, particularly by people experiencing homelessness (PEH). PEH are also more likely to misuse and overdose on opioids. To better understand current gaps and disparities in OUD treatment experienced by PEH and efforts to address them, we synthesized the literature reporting on the intersection of housing status and OUD treatment.MethodsWe conducted a scoping review of the literature from the electronic databases MEDLINE, Embase, PsycINFO, and Web of Science Core Collection. We included studies describing treatment-related outcomes specific to PEH and articles assessing OUD treatment interventions tailored to this population. Relevant findings were compiled via thematic analysis and narratively synthesized.Results60 articles met our inclusion criteria, including 43 descriptive and 17 intervention-focused studies. These studies demonstrated that PEH experience more barriers to OUD treatment than their housed counterparts and access inpatient and detoxification treatment more commonly than pharmacotherapy. However, the reviewed literature indicated that PEH have similar outcomes once engaged in pharmacotherapy. Efficacious interventions for PEH were low-barrier and targeted, with housing interventions also demonstrating benefit.ConclusionsPEH have diminished access to evidence-based OUD treatment, particularly medications, and require targeted approaches to improve engagement and retention. To mitigate the disproportionate opioid-related morbidity and mortality PEH experience, innovative, flexible, and interdisciplinary OUD treatment models are necessary, with housing support playing an important role.
| S-EPMC9758007 | biostudies-literature
Project description:Background: Antimicrobial resistance (AMR) is a global public health threat with the potential to cause millions of deaths. There has been a tremendous increase in the use of antimicrobials, stemming from preventive chemotherapy elimination and control programs addressing neglected tropical diseases (NTDs). This study aims to identify the frequency of drug resistance for 11 major NTDs and 20 treatment drugs within a specific period by systematically analyzing the study design, socio-demographic factors, resistance, and countries of relevant studies. Methods: Adhering to PRISMA guidelines, we performed systematic reviews of the major 11 NTDs to identify publications on drug resistance between 2000 and 2016. A quality assessment tool adapted for evaluating observational and experimental studies was applied to assess the quality of eligible studies. Results: One of the major findings is that six NTDs have information on drug resistance, namely human African trypanosomiasis, leishmaniasis, onchocerciasis, schistosomiasis, soil-transmitted helminths, and trachoma. Many studies recorded resistance due to diagnostic tests, and few studies indicated clinical resistance. Although most studies were performed in Africa where there is the occurrence of several NTDs, there was no link between disease burden and locations of study. Conclusions: Based on this study we deduce that monitoring and surveillance systems need to be strengthened to enable the early detection of AMR and the mitigation of its global spread.
| S-EPMC6603949 | biostudies-literature
Project description:BackgroundScreening for problematic opioid use is increasingly recommended in patients receiving palliative care.AimTo identify tools used to assess for the presence or risk of problematic opioid use in palliative care.DesignScoping review.Data sourcesBibliographic databases (inception to January 31, 2020), reference lists, and grey literature were searched to find primary studies reporting on adults receiving palliative care and prescription opioids to manage symptoms from advanced cancer, neurodegenerative diseases, or end-stage organ diseases; and included tools to assess for problematic opioid use. There were no restrictions based on study design, location, or language.ResultsWe identified 42 observational studies (total 14,431 participants) published between 2009 and 2020 that used questionnaires (n = 32) and urine drug tests (n = 21) to assess for problematic opioid use in palliative care, primarily in US (n = 38) and outpatient palliative care settings (n = 36). The questionnaires were Cut down, Annoyed, Guilty, and Eye-opener (CAGE, n = 8), CAGE-Adapted to Include Drugs (CAGE-AID, n = 6), Opioid Risk Tool (n = 9), Screener and Opioid Assessment for Patients with Pain (SOAPP; n = 3), SOAPP-Revised (n = 2), and SOAPP-Short Form (n = 5). Only two studies' primary objectives were to evaluate a questionnaire's psychometric properties in patients receiving palliative care. There was wide variation in how urine drug tests were incorporated into palliative care; frequency of abnormal urine drug test results ranged from 8.6% to 70%.ConclusionGiven the dearth of studies using tools developed or validated specifically for patients receiving palliative care, further research is needed to inform clinical practice and policy regarding problematic opioid use in palliative care.
| S-EPMC8267087 | biostudies-literature
Project description:BACKGROUND:Emerging machine learning technologies are beginning to transform medicine and healthcare and could also improve the diagnosis and treatment of rare diseases. Currently, there are no systematic reviews that investigate, from a general perspective, how machine learning is used in a rare disease context. This scoping review aims to address this gap and explores the use of machine learning in rare diseases, investigating, for example, in which rare diseases machine learning is applied, which types of algorithms and input data are used or which medical applications (e.g., diagnosis, prognosis or treatment) are studied. METHODS:Using a complex search string including generic search terms and 381 individual disease names, studies from the past 10 years (2010-2019) that applied machine learning in a rare disease context were identified on PubMed. To systematically map the research activity, eligible studies were categorized along different dimensions (e.g., rare disease group, type of algorithm, input data), and the number of studies within these categories was analyzed. RESULTS:Two hundred eleven studies from 32 countries investigating 74 different rare diseases were identified. Diseases with a higher prevalence appeared more often in the studies than diseases with a lower prevalence. Moreover, some rare disease groups were investigated more frequently than to be expected (e.g., rare neurologic diseases and rare systemic or rheumatologic diseases), others less frequently (e.g., rare inborn errors of metabolism and rare skin diseases). Ensemble methods (36.0%), support vector machines (32.2%) and artificial neural networks (31.8%) were the algorithms most commonly applied in the studies. Only a small proportion of studies evaluated their algorithms on an external data set (11.8%) or against a human expert (2.4%). As input data, images (32.2%), demographic data (27.0%) and "omics" data (26.5%) were used most frequently. Most studies used machine learning for diagnosis (40.8%) or prognosis (38.4%) whereas studies aiming to improve treatment were relatively scarce (4.7%). Patient numbers in the studies were small, typically ranging from 20 to 99 (35.5%). CONCLUSION:Our review provides an overview of the use of machine learning in rare diseases. Mapping the current research activity, it can guide future work and help to facilitate the successful application of machine learning in rare diseases.
| S-EPMC7285453 | biostudies-literature
Project description:Atherosclerotic cardiovascular diseases (ASCVD), including coronary artery disease, cerebrovascular disease, and peripheral arterial disease, represent a significant cause of premature death worldwide. Biomarkers, the evaluation of which would allow the detection of ASCVD at the earliest stage of development, are intensively sought. Moreover, from a clinical point of view, a valuable biomarker should also enable the assessment of the patient's prognosis. It has been known for many years that the concentration of fibrinogen in plasma increases, inter alia, in patients with ASCVD. On the one hand, an increased plasma fibrinogen concentration may be the cause of the development of atherosclerotic lesions (increased risk of atherothrombosis); on the other hand, it may be a biomarker of ASCVD, as it is an acute phase protein. In addition, a number of genetic polymorphisms and post-translational modifications of fibrinogen were demonstrated that may contribute to the risk of ASCVD. This review summarizes the current data on the importance of fibrinogen as a biomarker of ASCVD, and also presents the relationship between molecular modifications of this protein in the context of ASCVD.
| S-EPMC8745133 | biostudies-literature