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Objective To investigate the characteristics of post-traumatic stress disorder (PTSD) in college students during the outbreak of COVID-19, and to explore the mediating role of psychological resilience between social support and PTSD. Methods By using direct selection method, 572 college students in Anhui and Shanghai were selected and administered with General Characteristics Questionnaire, the PTSD Checklist-Civilian Version(PCL-C), Psychological Resilience Scale(PRS) and Social Support Rating Scale(SSRS). Results Among the participants, 25.0% had moderate PTSD symptoms, 11.7% had obvious PTSD symptoms, and the positive rate of PTSD was 36.7%. The prevalence of PTSD in college students was higher in males than in females (X2=4.31, P < 0.05). The junior students were higher than other students (X2=16.81, P < 0.01). The scores of social support, psychological resilience and PTSD were (33.79+or-4.83), (92.17+or-13.39) and (35.50+or-11.39), respectively. The correlations of all variables were statistically significant(r=-0.49-0.76, P < 0.05);The mediation test showed that social support could not only negatively predict PTSD directly(direct effect was -0.35), but also indirectly affect PTSD through psychological resilience(indirect effect was -0.32). Conclusion More than one third of college students have PTSD symptoms, and psychological resilience plays a partial mediating role in the relationship between social support and PTSD, social support can both directly and negatively predict PTSD and indirectly affect PTSD by increasing an individual's psychological resilience.
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COVID-19 is a raging pandemic that has created havoc with its impact ranging from loss of millions of human lives to social and economic disruptions of the entire world. Therefore, error-free prediction, quick diagnosis, disease identification, isolation and treatment of a COVID patient have become extremely important. Nowadays, mining knowledge and providing scientific decision making for diagnosis of diseases from clinical datasets has found wide-ranging applications in healthcare sector. In this direction, among different data mining tools, association rule mining has already emerged out as a popular technique to extract invaluable information and develop important knowledge-base to help in intelligent diagnosis of distinct diseases quickly and automatically. In this paper, based on 5434 records of COVID cases collected from a popular data science community and using Rapid Miner Studio software, an attempt is put forward to develop a predictive model based on frequent pattern growth algorithm of association rule mining to determine the likelihood of COVID-19 in a patient. It identifies breathing problem, fever, dry cough, sore throat, abroad travel and attended large gathering as the main indicators of COVID-19. Employing the same clinical dataset, a linear regression model is also proposed having a moderately high coefficient of determination of 0.739 in accurately predicting the occurrence of COVID-19. A decision support system can also be developed using the association rules to ease out and automate early detection of other diseases. © 2023 by the authors.
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The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. Note to Practitioners—In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate. IEEE
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Purpose: In December 2019, the Covid-19 pandemic began in the world. To reduce mortality, in addiction to mass vaccination, it is necessary to massify and accelerate clinical diagnosis, as well as creating new ways of monitoring patients that can help in the construction of specific treatments for the disease. Objective: In this work, we propose rapid protocols for clinical diagnosis of COVID-19 through the automatic analysis of hematological parameters using evolutionary computing and machine learning. These hematological parameters are obtained from blood tests common in clinical practice. Method: We investigated the best classifier architectures. Then, we applied the particle swarm optimization algorithm (PSO) to select the most relevant attributes: serum glucose, troponin, partial thromboplastin time, ferritin, D-dimer, lactic dehydrogenase, and indirect bilirubin. Then, we assessed again the best classifier architectures, but now using the reduced set of features. Finally, we used decision trees to build four rapid protocols for Covid-19 clinical diagnosis by assessing the impact of each selected feature. The proposed system was used to support clinical diagnosis and assessment of disease severity in patients admitted to intensive and semi-intensive care units as a case study in the city of Paudalho, Brazil. Results: We developed a web system for Covid-19 diagnosis support. Using a 100-tree random forest, we obtained results for accuracy, sensitivity, and specificity superior to 99%. After feature selection, results were similar. The four empirical clinical protocols returned accuracies, sensitivities and specificities superior to 98%. Conclusion: By using a reduced set of hematological parameters common in clinical practice, it was possible to achieve results of accuracy, sensitivity, and specificity comparable to those obtained with RT-PCR. It was also possible to automatically generate clinical decision protocols, allowing relatively accurate clinical diagnosis even without the aid of the web decision support system. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Engineering.
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Background: Students worldwide belong to a vulnerable group with an above-average tendency towards depressive disorders. Empirical studies also show that depressive moods among students have increased significantly during the coronavirus disease 2019 (COVID-19) pandemic. Objectives: The aim of the article is to examine whether the stress experiences caused by the pandemic are related to the depressed mood of the students. In addition, it is analyzed whether resilience, coping and social support as resources are associated with the depressive moods of students. The aim is to clarify which resources can explain the connection between the stress experiences caused by the pandemic and the depressive mood of students as mediators. Methods: The study is based on an online survey of students at Magdeburg-Stendal University of Applied Sciences for the 2020/21 winter semester. The data of 621 students were evaluated. A mediation analysis was carried out. Results: Pandemic-related stress experiences are significantly related to the depressive moods of students. Resilience is also a significant factor influencing depressive moods and partially mediates the influence of pandemic-related stressful experiences on the depressed mood of the students. Coping and social support show no significant association with the depressed mood of the students. Conclusion: Starting points for reducing the depressive mood lie in reducing the stress caused by the pandemic and in strengthening the resilience of the students.
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During the COVID-19 pandemic it became evident that outcome prediction of patients is crucial for triaging, when resources are limited and enable early start or increase of available therapeutic support. COVID-19 demographic risk factors for severe disease and death were rapidly established, including age and sex. Common Clinical Decision Support Systems (CDSS) and Early Warning Systems (EWS) have been used to triage based on demographics, vital signs and laboratory results. However, all of these have limitations, such as dependency of laboratory investigations or set threshold values, were derived from more or less specific cohort studies. Instead, individual illness dynamics and patterns of recovery might be essential characteristics in understanding the critical course of illness.The pandemic has been a game changer for data, and the concept of real-time massive health data has emerged as one of the important tools in battling the pandemic. We here describe the advantages and limitations of established risk scoring systems and show how artificial intelligence applied on dynamic vital parameter changes, may help to predict critical illness, adverse events and death in patients hospitalized with COVID-19.Machine learning assisted dynamic analysis can improve and give patient-specific prediction in Clinical Decision Support systems that have the potential of reducing both morbidity and mortality. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Background: The coronavirus disease 2019 (COVID-19) pandemic has had an immense impact on psychic health. Children and adolescents are considered especially vulnerable which is why health promotion and prevention programs are important and should be designed in a motivating way to be attractive to this age group. The aim of this work is to investigate whether young people can be reached with the help of innovative digital formats for health promotion. Methods: An app-based training to promote self-regulation was offered to adolescents in lower secondary education in autumn 2020. Data were collected using an app. The types of motivation to participate in the training were analyzed. In addition, the students were able to rate the attractiveness of the training with the help of a self-designed evaluation sheet. Results: Of the 91 registered participants, 39.56% completed the study. In all, 40.91% of the students stated that the training was "very" helpful and 36.36% rated it as "quite" helpful. Although 50% of the respondents found the app-based training "modern and motivating", the other half would have preferred more personal support. Conclusion: The results confirm previous study results with health apps in children and adolescents. This age group is interested in digital formats, but health apps are not used continuously in this age group unless obligatory.
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Background: The onset of the coronavirus pandemic created diverse stressors for families with small children such as isolation, limited public and private childcare options, and balancing work and childcare. Fear of the future, feelings of uncertainty and loneliness led to a rise in mental health problems. Public family support services also faced significant challenges: while families felt more need for support, established means of reaching families and providing services were severely curtailed. Objectives: The current study aimed at capturing pandemic-related changes in family needs and at documenting experiences in the use of newly developed analog and digital services provided by public family support institutions in the city of Hamburg, Germany. Materials and methods: We conducted focus groups with staff members of different types of public family support services and parents who were using these services. Central topics of discussion were pandemic-related experiences and ideas for the future of public family support services. Results: Results confirm an increase in family pressures. Parents and staff members missed low-threshold accessibility of services and personal contact and dialogue. Creative approaches to complement services digitally were developed. Several of these approaches were considered beneficial, especially for reaching new target groups and strengthening interprofessional cooperation in the field. Conclusions: In-person support services need to be upheld. However, digital services can effectively complement analog formats. A successful combination requires effective resource distribution and staff member qualification measures.
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PurposeThis paper aims to assess the feasibility of a hybrid manufacturing and remanufacturing system (HMRS) for essential commodities in the context of COVID-19. Specifically, it emphasises using HMRS based on costs associated with various manufacturing activities.Design/methodology/approachThe combination of mathematical model and system dynamics is used to model the HMRS system. The model was tried on sanitiser bottle manufacturing to generalise the result.FindingsThe remanufacturing cost is higher because of reverse logistics, inspection and holding costs. Ultimately remanufacturing costs turn out to be lesser than the original manufacturing the moment system attains stability.Practical implicationsThe study put forth the reason to encourage remanufacturing towards sustainability through government incentives.Originality/valueThe study put forth the feasibility of the HMRS system for an essential commodity in the context of a covid pandemic. The research implemented system dynamics for modelling and validation.
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Background and Objectives: Faster than expected, the COVID-19 disease changed people's lives on an unprecedented scale. The present research aimed to shed light on the economic challenges of the pandemic and the efforts made concerning economic resilience. Thus, this study delved into the experience of families residing in a suburban town. Methods: The present study was qualitative in type. It used a qualitative content analysis with a guided approach conducted through 17 in-depth semi-structured individual interviews with subjects over 15 years of age living in Tawheed Gonabad town. These subjects had lived in the area for at least three years. The interviews were held and audio-recorded in a purposive sampling method after gaining informed consent from the participants in the spring of 2021. In order to estimate the validity of the data, Lincoln and Goba's criteria were used. Results: The economic resilience of families during the pandemic was marked by three main categories and nine sub-categories. The categories were: (1) changes to the economic dimension of the family (the sub-categories: employment, income, consumption and socioeconomic status), (2) solutions to the economic changes of the family (sub-categories: reliance on internal resources, family and receiving support from outside of the family), and (3) the effectiveness of economic resilience of families at higher levels (sub-categories: macroeconomics, family social capital and regional resilience). As more detailed results showed, the pandemic has caused a decrease in the income and consumption of essential items in quantity and quality and imposed excessive costs on the target community. The dominant solution to economic problems has been changing consumer's behavior and income diversification. The lack of supportive plans, poor social networks and the identity of the neighborhood are the significant barriers to the increase of economic resilience. Conclusion: The families investigated in the present study were vulnerable in many ways and had low economic resilience. In order to improve the families' level of economic resilience, it is necessary to know the context and carry out interventions and support plans based on the families' internal and external capacities, including the neighborhood's empowering conditions.
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This proceedings contains 112 s that cover a wide range of topics related to microbiology. The s cover a wide range of topics related to microbiology, including new paradigms in a microbe-threatened world, the human-animal spillover of SARS-CoV-2 and its implications for public health, preparing for the next pandemic, antimicrobial resistance and the fight against it. Furthermore, tuberculosis, monkeypox, and their potential threat on a global scale are also discussed. The presentations also cover a variety of other topics, such as vaccines and vaccinations, COVID-19 vaccines, addressing vaccine hesitancy, key issues related to the COVID-19 healthcare system, regional support for outbreak preparedness, enhancing regional health security in Asia through genomic surveillance, the role of molecular diagnostic capacity in COVID-19 control, antimicrobial resistance in COVID-19 times, paediatric nosocomial infections, prescription ethics from a primary care perspective, the BCG vaccine and its relevance in the prevention of tuberculosis and beyond, tuberculosis as a forgotten pandemic, vector-borne diseases during COVID-19, the role of media advocacy in vector-borne diseases control and management, engaging communities in tackling vector-borne diseases, the way forward in managing mental health in the COVID-19 endemic phase, the spread of zoonotic diseases, and whole genome sequencing of SARS-CoV-2: clinical applications and experience.
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BACKGROUND: The COVID-19 pandemic posed new challenges for cognitive aging since it brought interruptions in family relations for older adults in immigrant communities. This study examines the consequences of COVID-19 for the familial and social support systems of aging Middle Eastern/Arab immigrants in Michigan, the largest concentration in the United States. We conducted six focus groups with 45 participants aged 60 and older to explore participant descriptions of changes and difficulties faced during the pandemic relating to their cognitive health, familial and social support systems, and medical care. The findings indicate challenges around social distancing for older Middle Eastern/Arab American immigrants, which generated three overarching themes: fear, mental health, and social relationships. These themes provide unique insights into the lived experiences of older Middle Eastern/Arab American adults during the pandemic and bring to light culturally embedded risks to cognitive health and well-being. A focus on the well-being of older Middle Eastern/Arab American immigrants during COVID-19 advances understanding of how environmental contexts inform immigrant health disparities and the sociocultural factors that shape minority aging.
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COVID-19 , Cognitive Aging , Emigrants and Immigrants , Humans , United States/epidemiology , Middle Aged , Aged , Arabs/psychology , Pandemics , Self Report , COVID-19/epidemiology , Michigan/epidemiologyABSTRACT
During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.
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Over the last three COVID-19 effective years, it was evident that healthcare has been the most sensitive sector to electricity failures. Therefore, if well developed and implemented, a microgrid system with an integrated energy storage system (ESS) installed in hospitals has great potential to provide an uninterrupted and low-energy cost solution. In this article, we target to show the importance of the installed ESS against the problems that will arise from power outages and energy quality problems in hospitals. Besides, it aims to construct an energy management system (EMS) based on the scheduling model to meet the lowest cost of a system containing solar panels, microturbine, gas boiler, and energy storage units that are repurposed lithium-ion batteries from electric vehicles and thermal storage tank. EMS is a mixed-integer linear program to meet the hospital's electricity, heating, and cooling demands with the lowest cost for every hour. The established scheduling model is run for a hospital in Antioch, Turkiye, with 197 beds, 4 operating rooms, 2 resuscitation units, and 9 intensive care units for every hour based on the data in 2019. With the EMS, approximately 25% savings were achieved compared to the previous energy cost. Furthermore, as the result of the net present value calculation, the payback period of the proposed system is estimated to be approximately seven years.
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Diagnostic testing is a fundamental component in effective outbreak containment during every phase of a pandemic. Test samples are collected at testing facilities and subsequently analyzed at specialized laboratories. In high-income countries where health care providers are often privately owned, the assignments of samples from testing facilities to laboratories are determined by individual stakeholders. While this decentralized system effectively matches supply and demand during normal times, dispersed outbreaks, e.g., as encountered during the COVID-19 pandemic, lead to imbalanced requests for diagnostic capacity. With no coordinating entity in place to match demands at testing facilities to laboratory capacities, local backlogs build up rapidly thus increasing waiting times for test results and thus impeding subsequent containment efforts. To ease the impact of erratic regional outbreaks through improved logistics activities, we develop a rolling horizon framework which repeatedly solves a mathematical programming snapshot problem based on the current number of test samples. The procedure dynamically adapts to requirements resulting from the pandemic activity and supports rather than replaces decentralized operations in order to match testing requests with available laboratory capacities. We present problem-specific performance indicators and assess the quality of our procedure in a case study based on the COVID-19 outbreak in 2020 in Germany. Experimental results demonstrate the potential of coordinating mechanisms to support the logistics related to diagnostic testing and hence to reduce waiting times for PCR test results. Significant improvements are achieved even when interventions in the decentralized assignment process only occur in response to increased pandemic activity.
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Background & Aims: In early January 2020, a new corona virus called corona was identified as an infectious agent by the World Health Organization and caused a viral pneumonia outbreak, the first of which was reported in Wuhan, China in December 2019. The virus has so far infected most countries in the world and has become a global problem. By this time in December 2021, about 265 million people in the world have been infected with this virus and 5 million 270 thousand people have died from this disease. According to the World Health Organization, the incidence of this disease is still increasing and will become the third leading cause of death in the world by 2030. This disease has a special complexity and has multiple dimensions and consequences that have caused many problems in the field of health, social and economic as well as psychological for people. The emergence of this disease is now a public health crisis. According to this research, exposure to news and restrictions caused by this disease can lead to many mental health problems. In fact, one of the situations that puts a lot of stress on people during the outbreak of covid 19 disease is the inability to predict and uncertainty about the control and end of the disease. Mental health is defined as a harmonious and harmonious behavior with society, recognizing and accepting social realities, the power to adapt to them and meeting one's balanced needs and is an important factor for the health of society. The prevalence of the disease can also increase feelings of loneliness, decrease social support, feelings of fear and anxiety to clinical stress and anxiety, obsessive-compulsive disorder associated with the disease, and decreased life expectancy. One of the hopeful factors is health and the disease can cause despair, fear and even despair of the patient. The outbreak of a disease has a much deeper and wider impact and affects not only the affected community and relatives, but the entire community. Because everyone finds themselves at risk, and therefore people's feel of safe and healthy changes, and this situation causes people to despair. Hope is the capacity to imagine the ability to create paths to desirable goals and to imagine the motivation to move in those paths. Hope predicts physical and mental health such as positive response to medical interventions, mental health, effective getting along, and health-promoting behaviors. Covid 19 disease can also lead to psychological problems due to its infectious nature and unpredictable nature. In this regard, various researchers consider the implementation of public health policies, including areas related to individual and collective mental health in accordance with the different stages of the epidemic of this disease is very necessary. Mindfulness can be an effective tool for achieving peace of mind and body that helps people become aware of their current feelings. Mindfulness-based interventions are considered as one of the third generation or third wave cognitive-behavioral therapies. Mindfulness is a form of meditation rooted in Eastern religious teachings and rituals, especially Buddhism. Segal has defined mindfulness as paying attention to specific and purposeful ways, in the present time, without judgment or prejudice. Linhan stressed for the first time the need to pay attention to mindfulness as one of the essential components of psychological therapy. Mindfulness requires the development of three components: judgment avoidance, purposeful awareness, and focus on the present moment. Focusing on the present and processing all aspects of the above experience makes one aware of the daily activities and automatic functioning of the mind in the past and future world and he controls emotions, thoughts, and physical states through moment-to-moment awareness of thoughts. As a result, it is released from the everyday and automatic mind focused on the past and the future. Although general vaccination has reduced the virus in some countries, including Iran, and reduced the number of infected people, a large num
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The healthcare sector is very interested in machine learning (ML) and artificial intelligence (AI). Nevertheless, applying AI applications in scientific contexts is difficult due to explainability issues. Explainable AI (XAI) has been studied as a potential remedy for the problems with current AI methods. The usage of ML with XAI may be capable of both explaining models and making judgments, in contrast to AI techniques like deep learning. Computer applications called medical decision support systems (MDSS) affect the decisions doctors make regarding certain patients at a specific moment. MDSS has played a crucial role in systems' attempts to improve patient safety and the standard of care, particularly for non-communicable illnesses. They have moreover been a crucial prerequisite for effectively utilizing electronic healthcare (EHRs) data. This chapter offers a broad overview of the application of XAI in MDSS toward various infectious diseases, summarizes recent research on the use and effects of MDSS in healthcare with regard to non-communicable diseases, and offers suggestions for users to keep in mind as these systems are incorporated into healthcare systems and utilized outside of contexts for research and development.
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Relevance: The global consequences of the COVID-19 pandemic emphasize today the importance of the concept of "One Health" for the health system, which provides for the use of a coordinated, joint, interdisciplinary and intersectoral approach to eliminate potential or existing risks arising at the interface of the "environment-animal-human-ecosystem". The aim of the work is to study the experience of countries in implementing the concept of "One Health". Search strategy: comparative analysis of publications on the research topic, sources indexed in the databases of the electronic library e-Library, Google Academy, Pubmed, Web of Science, Scopus. 26 countries from the European Union, South America and Africa were subject to analysis. The criteria defined are: institutional framework;mechanisms of intersectoral interaction and programs/tools for the implementation of the concept of "One Health". Results: The literature review provides a comparative analysis of the experience of implementing the concept of "One Health" in 26 countries. Realizing the importance of "One Health" in the general concept of public safety, countries have launched an active policy to promote it in the last decade. Characteristic features of country policies are the intersectoral approach with appropriate support from the government of the country, the activity of all participants in promoting the initiative and their investment.
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PurposeThis study aims to reveal the topic structure and evolutionary trends of health informatics research in library and information science.Design/methodology/approachUsing publications in Web of Science core collection, this study combines informetrics and content analysis to reveal the topic structure and evolutionary trends of health informatics research in library and information science. The analyses are conducted by Pajek, VOSviewer and Gephi.FindingsThe health informatics research in library and information science can be divided into five subcommunities: health information needs and seeking behavior, application of bibliometrics in medicine, health information literacy, health information in social media and electronic health records. Research on health information literacy and health information in social media is the core of research. Most topics had a clear and continuous evolutionary venation. In the future, health information literacy and health information in social media will tend to be the mainstream. There is room for systematic development of research on health information needs and seeking behavior.Originality/valueTo the best of the authors' knowledge, this is the first study to analyze the topic structure and evolutionary trends of health informatics research based on the perspective of library and information science. This study helps identify the concerns and contributions of library and information science to health informatics research and provides compelling evidence for researchers to understand the current state of research.
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When COVID-19 ARDS abolishes pulmonary function, VV-ECMO can provide gas exchange. If oxygenation remains insufficient despite maximal VV-ECMO support, the addition of esmolol has been proposed. Conflict exists, however, as to the oxygenation level which should trigger beta-blocker initiation. We evaluated the effect of esmolol therapy on oxygenation and oxygen delivery in patients with negligible native lung function and various degrees of hypoxemia despite maximal VV-ECMO support. We found that, in COVID-19 patients with negligible pulmonary gas exchange, the generalized use of esmolol administration to raise arterial oxygenation by slowing heart rate and thereby match native cardiac output to maximal attainable VV ECMO flows actually reduces systemic oxygen delivery in many cases.