ABSTRACT
Vaccination is crucial for avoiding infection-associated morbidity and mortality among immunocompromised patients. However, immunocompromised patients respond less well to vaccinations compared to healthy people, and little is known about the relative efficacy of various vaccines among different immunocompromised states. A total of 54 systematic reviews (22 COVID-19; 32 non-COVID-19) published within the last 5 years in Pubmed® were reviewed. They demonstrated similar patterns within three seroconversion response categories: good (about >60% when compared to healthy controls), intermediate (~40-60%), and poor (about <40%). Good vaccine responses would be expected for patients with chronic kidney disease, human immunodeficiency virus infection (normal CD4 counts), immune-mediated inflammatory diseases, post-splenectomy states, and solid tumors. Intermediate vaccine responses would be expected for patients with anti-cytotoxic T-lymphocyte antigen-4 therapy, hematologic cancer, and human immunodeficiency virus infection (low CD4 counts). Poor vaccine responses would be expected for patients with B-cell-depleting agents (e.g., anti-CD20 therapy), hematopoietic stem-cell transplant, solid organ transplant, and liver cirrhosis. For all vaccine response categories, vaccination should be timed when patients are least immunosuppressed. For the intermediate and poor vaccine response categories, high-dose vaccine, revaccination when patients are less immunosuppressed, checking for seroconversion, additional booster doses, and long-acting monoclonal antibodies may be considered, supplemented by shielding measures.
ABSTRACT
BACKGROUND: The recent focus on the critical setting, especially with the COVID-19 pandemic, has highlighted the need for minimizing contact-based care and increasing robotic use. Robotics is a rising field in the context of health care, and we sought to evaluate the use of robots in critical care settings. OBJECTIVE: Although robotic presence is prevalent in the surgical setting, its role in critical care has not been well established. We aimed to examine the uses and limitations of robots for patients who are critically ill. METHODS: This systematic review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. MEDLINE, Embase, IEEE Xplore, and ACM Library were searched from their inception to December 23, 2021. Included studies involved patients requiring critical care, both in intensive care units or high-dependency units, or settings that required critical care procedures (eg, intubation and cardiopulmonary resuscitation). Randomized trials and observational studies were included. RESULTS: A total of 33 studies were included. The greatest application of robots in the intensive care unit was in the field of telepresence, whereby robots proved advantageous in providing a reduced response time, earlier intervention, and lower mortality rates. Challenges of telepresence included regulatory and financial barriers. In therapy and stroke rehabilitation, robots achieved superior clinical outcomes safely. Robotic use in patient evaluation and assessment was mainly through ultrasound evaluation, obtaining satisfactory to superior results with the added benefits of remote assessment, time savings, and increased efficiency. Robots in drug dispensing and delivery increased efficiency and generated cost savings. All the robots had technological limitations and hidden costs. CONCLUSIONS: Overall, our results show that robotic use in critical care settings is a beneficial, effective, and well-received intervention that delivers significant benefits to patients, staff, and hospitals. Looking ahead, it is necessary to form strong ethical and legislative frameworks and overcome various regulatory and financial barriers. TRIAL REGISTRATION: PROSPERO International Prospective Register of Systematic Reviews CRD42021234162; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=234162.
Subject(s)
COVID-19 , Robotics , Critical Care , Critical Illness , Humans , PandemicsABSTRACT
With the rapid development of various coronavirus disease 2019 (COVID-19) vaccines in a bid to counter and contain the COVID-19 pandemic, unusual and uncommon side effects of COVID-19 vaccination have been increasingly reported in the literature. Ipsilateral lymphadenopathy is a fairly common side effect of vaccination of any kind, with its etiology most commonly related to reactive lymphadenopathy. However, Kikuchi-Fujimoto Disease (KFD) or necrotizing histiocytic lymphadenitis is rarely observed post-vaccination, with only one other case of KFD post COVID-19 vaccination reported to date. We report two more cases of KFD post COVID-19 vaccination in the Asian population, highlighting the clinical course and salient clinical, radiological and histologic findings. In addition, we provide a literature review of the existing cases of lymphadenopathy post COVID-19 vaccination with cytologic and/or histologic correlation.
ABSTRACT
BACKGROUND: COVID-19 was first discovered in December 2019 and has since evolved into a pandemic. OBJECTIVE: To address this global health crisis, artificial intelligence (AI) has been deployed at various levels of the health care system. However, AI has both potential benefits and limitations. We therefore conducted a review of AI applications for COVID-19. METHODS: We performed an extensive search of the PubMed and EMBASE databases for COVID-19-related English-language studies published between December 1, 2019, and March 31, 2020. We supplemented the database search with reference list checks. A thematic analysis and narrative review of AI applications for COVID-19 was conducted. RESULTS: In total, 11 papers were included for review. AI was applied to COVID-19 in four areas: diagnosis, public health, clinical decision making, and therapeutics. We identified several limitations including insufficient data, omission of multimodal methods of AI-based assessment, delay in realization of benefits, poor internal/external validation, inability to be used by laypersons, inability to be used in resource-poor settings, presence of ethical pitfalls, and presence of legal barriers. AI could potentially be explored in four other areas: surveillance, combination with big data, operation of other core clinical services, and management of patients with COVID-19. CONCLUSIONS: In view of the continuing increase in the number of cases, and given that multiple waves of infections may occur, there is a need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by high patient numbers.