Cardiovascular risk among patients recovered from COVID-19 in the short and medium term: what does the current evidence conclude?.

Cardiovascular risk and diseases among patients recovered from COVID-19 is a recent field of study in the world medical literature and is also of vital importance because a large number of patients develop complications once the acute phase of the disease is over. The broad spectrum of myocardial injury in cardiovascular diseases can range from the asymptomatic elevation of cardiac troponin levels to the development of fulminant myocarditis and/or circulatory shock, which can leave significant sequelae. Despite the fact that there is no clear strategy to treat cardiac events that occur during COVID-19 infection and taking into account that treatment is mainly aimed at relieving patients' symptoms as they arise, the objective of this work was to find out and collect current evidence on this subject, so that readers can be offered a reference guide in Spanish that contributes to the development of their health profession. The methodology used was a literature search in databases including Medline, Scopus and ScienceDirect within a time window between 2019 and 2022. The main results revealed that the molecular and pathophysiological mechanisms involved in post-COVID-19 syndrome include the renin-angiotensin-aldosterone system since SARS-CoV-2 tropism is linked to angiotensin-converting enzyme 2. This causes an alteration of the neurohumoral response of the cardiovascular, renal and digestive systems, generating deficits in the signaling pathways and causing direct damage to the heart, lungs and other organs. Post-COVID-19 syndrome, in general, is defined as the occurrence or persistence of symptoms three or four weeks after the acute phase of the disease. This could then be considered as a time window of risk and strict follow-up to assess in a personalized way the risk among the different groups of patients, especially those with a past history of cardiovascular disease. The main results revealed disorders such as heart failure, arrhythmias, pericarditis and myocarditis, which require early detection and occur days or even weeks after the acute phase of COVID-19.Copyright © La revista. Publicado por la Universidad de San Martin de Porres, Peru.

Rapid review : a research method to respond urgently to a problem

The COVID 19 pandemic demonstrated the importance of rapidly sharing evidence-based scientific findings to help guide healthcare decision making. This article aimed to propose a summary to facilitate understanding of the rapid review (RR) research method in general medicine. RR is a form of knowledge synthesis with a simplified methodology which produces results with a short turnaround time, specifically meant to answer research questions where the issue appears urgent, in collaboration with interested parties. The analysis of the articles is carried out in a way that limits the means used while still obtaining reliable results. Differents steps are necessary for this to be achieved. However, due to the non-exhaustive nature of this form of literature review, there are methodological biases associated with it. RR is not designed to replace a systematic review when one is possible, but it can provide rapid results when an issue related to healthcare is a priority.

Underrepresentation of Respiratory Therapists as Experts in Delphi Studies on Respiratory Practices and Research Priorities.

Delphi survey techniques are a common consensus method used to collect feedback from an expert panel to inform practices, establish guidelines, and identify research priorities. Collecting respiratory therapists' (RT) expertise and experiences as part of consensus-building methodologies is one way to ensure that they align with RT practices and to better influence respiratory care practice. This narrative review aimed to report the RT representation in expert panels of Delphi studies focused on respiratory therapy practices and research priorities. The research question that guided this review is: to what extent are RTs included as expert participants among published Delphi studies relate to respiratory therapy and research topics? We conducted a structured search of the literature and identified 23 papers that reported Delphi studies related to respiratory care practices and 15 that reported on respiratory-related research priorities. Delphi studies that focused on reporting consensus on respiratory care practices included the following: (1) mechanical ventilation, (2) high-flow nasal cannula therapy, (3) COVID-19 respiratory management, (4) home oxygen therapy, (5) cardiopulmonary monitoring, and (6) disease-specific guidelines. Delphi studies that focused on establishing respiratory research priorities included the following: (1) theory and practice-orientated knowledge gaps, and (2) priority research topics for empirical investigation. The results of this review suggest that RTs were rarely included as expert participants and, when involved, were minimally represented (5% to 33%). Given RTs' diverse and relevant experience in respiratory care, incorporating their perspectives to inform future education, respiratory care practices, and research priorities would allow evidence to better align with knowledge gaps deemed important for the respiratory therapy profession.

Place of distancing measures in containing epidemics: a scoping review.

Distancing is one of the barrier measures in mitigating epidemics. We aimed to investigate the typology, effectiveness, and side effects of distancing rules during epidemics. Electronic searches were conducted on MEDLINE, PubMed in April 2020, using Mesh-Terms representing various forms of distancing ('social isolation', 'social distancing', 'quarantine') combining with 'epidemics'. PRISMA-ScR statement was consulted to report this review. A total of 314 titles were identified and 93 were finally included. 2009 influenza A and SARS-CoV-2 epidemics were the most studied. Distancing measures were mostly classified as case-based and community-based interventions. The combination of distancing rules, like school closure, home working, isolation and quarantine, has proven to be effective in reducing R0 and flattening the epidemic curve, also when initiated early at a high rate and combined with other non-pharmaceutical interventions. Epidemiological and modeling studies showed that Isolation and quarantine in the 2009 Influenza pandemic were effective measures to decrease attack rate also with high level of compliance but there was an increased risk of household transmission. lockdown was also effective to reduce R0 from 2.6 to 0.6 and to increase doubling time from 2 to 4 days in the covid-19 pandemic. The evidence for school closure and workplace distancing was moderate as single intervention. Psychological disorder, unhealthy behaviors, disruption of economic activities, social discrimination, and stigmatization were the main side effects of distancing measures. Earlier implementation of combined distancing measures leads to greater effectiveness in containing outbreaks. Their indication must be relevant and based on evidence to avoid adverse effects on the community. These results would help decision-makers to develop response plans based on the required experience and strengthen the capacity of countries to fight against future epidemics. Mesh words: Physical Distancing, Quarantine, Epidemics, Public Health, Scoping Review.

Emerging Artificial Intelligence-Empowered mHealth: Scoping Review.

BACKGROUND: Artificial intelligence (AI) has revolutionized health care delivery in recent years. There is an increase in research for advanced AI techniques, such as deep learning, to build predictive models for the early detection of diseases. Such predictive models leverage mobile health (mHealth) data from wearable sensors and smartphones to discover novel ways for detecting and managing chronic diseases and mental health conditions. OBJECTIVE: Currently, little is known about the use of AI-powered mHealth (AIM) settings. Therefore, this scoping review aims to map current research on the emerging use of AIM for managing diseases and promoting health. Our objective is to synthesize research in AIM models that have increasingly been used for health care delivery in the last 2 years. METHODS: Using Arksey and O'Malley's 5-point framework for conducting scoping reviews, we reviewed AIM literature from the past 2 years in the fields of biomedical technology, AI, and information systems. We searched 3 databases, PubsOnline at INFORMS, e-journal archive at MIS Quarterly, and Association for Computing Machinery (ACM) Digital Library using keywords such as "mobile healthcare," "wearable medical sensors," "smartphones", and "AI." We included AIM articles and excluded technical articles focused only on AI models. We also used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) technique for identifying articles that represent a comprehensive view of current research in the AIM domain. RESULTS: We screened 108 articles focusing on developing AIM models for ensuring better health care delivery, detecting diseases early, and diagnosing chronic health conditions, and 37 articles were eligible for inclusion, with 31 of the 37 articles being published last year (76%). Of the included articles, 9 studied AI models to detect serious mental health issues, such as depression and suicidal tendencies, and chronic health conditions, such as sleep apnea and diabetes. Several articles discussed the application of AIM models for remote patient monitoring and disease management. The considered primary health concerns belonged to 3 categories: mental health, physical health, and health promotion and wellness. Moreover, 14 of the 37 articles used AIM applications to research physical health, representing 38% of the total studies. Finally, 28 out of the 37 (76%) studies used proprietary data sets rather than public data sets. We found a lack of research in addressing chronic mental health issues and a lack of publicly available data sets for AIM research. CONCLUSIONS: The application of AIM models for disease detection and management is a growing research domain. These models provide accurate predictions for enabling preventive care on a broader scale in the health care domain. Given the ever-increasing need for remote disease management during the pandemic, recent AI techniques, such as federated learning and explainable AI, can act as a catalyst for increasing the adoption of AIM and enabling secure data sharing across the health care industry.

Alerts in Clinical Decision Support Systems (CDSS): A Bibliometric Review and Content Analysis.

A clinical decision support system (CDSS) informs or generates medical recommendations for healthcare practitioners. An alert is the most common way for a CDSS to interact with practitioners. Research about alerts in CDSS has proliferated over the past ten years. The research trend is ongoing with new emerging terms and focus. Bibliometric analysis is ideal for researchers to understand the research trend and future directions. Influential articles, institutes, countries, authors, and commonly used keywords were analyzed to grasp a comprehensive view on our topic, alerts in CDSS. Articles published between 2011 and 2021 were extracted from the Web of Science database. There were 728 articles included for bibliometric analysis, among which 24 papers were selected for content analysis. Our analysis shows that the research direction has shifted from patient safety to system utility, implying the importance of alert usability to be clinically impactful. Finally, we conclude with future research directions such as the optimization of alert mechanisms and comprehensiveness to enhance alert appropriateness and to reduce alert fatigue.

The Fast Literature Assessment and Review (FLARE) Initiative. A Collaborative Effort for Timely Literature Appraisal during the Coronavirus Disease Pandemic.

The emergence and worldwide spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused major disruptions to the healthcare system and medical education. In response, the scientific community has been acquiring, releasing, and publishing data at a remarkable pace. At the same time, medical practitioners are taxed with greater professional duties than ever before, making it challenging to stay current with the influx of medical literature.To address the above mismatch between data release and provider capacity and to support our colleagues, physicians at the Massachusetts General Hospital have engaged in an electronic collaborative effort focused on rapid literature appraisal and dissemination regarding SARS-CoV-2 with a focus on critical care.Members of the Division of Pulmonary and Critical Care, the Division of Cardiology, and the Department of Medicine at Massachusetts General Hospital established the Fast Literature Assessment and Review (FLARE) team. This group rapidly compiles, appraises, and synthesizes literature regarding SARS-CoV-2 as it pertains to critical care, relevant clinical questions, and anecdotal reports. Daily, FLARE produces and disseminates highly curated scientific reviews and opinion pieces, which are distributed to readers using an online newsletter platform.Interest in our work has escalated rapidly. FLARE was quickly shared with colleagues outside our division, and, in a short time, our audience has grown to include more than 4,000 readers across the globe.Creating a collaborative group with a variety of expertise represents a feasible and acceptable way of rapidly appraising, synthesizing, and communicating scientific evidence directly to frontline clinicians in this time of great need.