Optimal Respiratory Assistance Strategy for Patients with COVID-19.
Ontology highlight
ABSTRACT:
SUBMITTER: Hatipoglu U
PROVIDER: S-EPMC8086547 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description: Not available
| S-EPMC8086545 | biostudies-literature
Project description: Not available
| S-BSST955 | biostudies-other
Project description:Timely diagnostic testing for active SARS-CoV-2 viral infections is key to controlling the spread of the virus and preventing severe disease. A central public health challenge is defining test allocation strategies with limited resources. In this paper, we provide a mathematical framework for defining an optimal strategy for allocating viral diagnostic tests. The framework accounts for imperfect test results, selective testing in certain high-risk patient populations, practical constraints in terms of budget and/or total number of available tests, and the purpose of testing. Our method is not only useful for detecting infections, but can also be used for long-time surveillance to detect new outbreaks. In our proposed approach, tests can be allocated across population strata defined by symptom severity and other patient characteristics, allowing the test allocation plan to prioritize higher risk patient populations. We illustrate our framework using historical data from the initial wave of the COVID-19 outbreak in New York City. We extend our proposed method to address the challenge of allocating two different types of diagnostic tests with different costs and accuracy, for example, the RT-PCR and the rapid antigen test (RAT), under budget constraints. We show how this latter framework can be useful to reopening of college campuses where university administrators are challenged with finite resources for community surveillance. We provide a R Shiny web application allowing users to explore test allocation strategies across a variety of pandemic scenarios. This work can serve as a useful tool for guiding public health decision-making at a community level and adapting testing plans to different stages of an epidemic. The conceptual framework has broader relevance beyond the current COVID-19 pandemic.
| S-EPMC8661762 | biostudies-literature
Project description: Not available
| S-BSST416 | biostudies-other
Project description:Understanding the pathology of COVID-19 is a global research priority. Early evidence suggests that the microbiome may be playing a role in disease progression, yet current studies report contradictory results. Here, we examine potential confounders in COVID-19 microbiome studies by analyzing the upper respiratory tract microbiome in well-phenotyped COVID-19 patients and controls combining microbiome sequencing, viral load determination, and immunoprofiling. We found that time in the intensive care unit and the type of oxygen support explained the most variation within the upper respiratory tract microbiome, dwarfing (non-significant) effects from viral load, disease severity, and immune status. Specifically, mechanical ventilation was linked to altered community structure, lower species- and higher strain-level diversity, and significant shifts in oral taxa previously associated with COVID-19.
| EGAS00001004951 | EGA
Project description:Since the beginning of March 2020, the coronavirus disease 2019 (COVID-19) pandemic has caused more than 13,000 deaths in Europe, almost 54% of which has occurred in Italy. The Italian healthcare system is experiencing a stressful burden, especially in terms of intensive care assistance. In fact, the main clinical manifestation of COVID-19 patients is represented by an acute hypoxic respiratory failure secondary to bilateral pulmonary infiltrates, that in many cases, results in an acute respiratory distress syndrome and requires an invasive ventilator support. A precocious respiratory support with non-invasive ventilation or high flow oxygen should be avoided to limit the droplets' air-dispersion and the healthcare workers' contamination. The application of a continuous positive airway pressure (CPAP) by means of a helmet can represent an effective alternative to recruit diseased alveolar units and improve hypoxemia. It can also limit the room contamination, improve comfort for the patients, and allow for better clinical assistance with long-term tolerability. However, the initiation of a CPAP is not free from pitfalls. It requires a careful titration and monitoring to avoid a delayed intubation. Here, we discuss the rationale and some important considerations about timing, criteria, and monitoring requirements for patients with COVID-19 respiratory failure requiring a CPAP treatment.
| S-EPMC7230457 | biostudies-literature
Project description:BACKGROUND AND OBJECTIVE:For decades, mathematical models have been used to predict the behavior of physical and biological systems, as well as to define strategies aiming at the minimization of the effects regarding different types of diseases. In the present days, the development of mathematical models to simulate the dynamic behavior of the novel coronavirus disease (COVID-19) is considered an important theme due to the quantity of infected people worldwide. In this work, the objective is to determine an optimal control strategy for vaccine administration in COVID-19 pandemic treatment considering real data from China. Two optimal control problems (mono- and multi-objective) to determine a strategy for vaccine administration in COVID-19 pandemic treatment are proposed. The first consists of minimizing the quantity of infected individuals during the treatment. The second considers minimizing together the quantity of infected individuals and the prescribed vaccine concentration during the treatment. METHODS:An inverse problem is formulated and solved in order to determine the parameters of the compartmental Susceptible-Infectious-Removed model. The solutions for both optimal control problems proposed are obtained by using Differential Evolution and Multi-objective Optimization Differential Evolution algorithms. RESULTS:A comparative analysis on the influence related to the inclusion of a control strategy in the population subject to the epidemic is carried out, in terms of the compartmental model and its control parameters. The results regarding the proposed optimal control problems provide information from which an optimal strategy for vaccine administration can be defined. CONCLUSIONS:The solution of the optimal control problem can provide information about the effect of vaccination of a population in the face of an epidemic, as well as essential elements for decision making in the economic and governmental spheres.
| S-EPMC7368913 | biostudies-literature
Project description:BackgroundIn the face of the current COVID-19 pandemic, the timely prediction of upcoming medical needs for infected individuals enables better and quicker care provision when necessary and management decisions within health care systems.ObjectiveThis work aims to predict the medical needs (hospitalizations, intensive care unit admissions, and respiratory assistance) and survivability of individuals testing positive for SARS-CoV-2 infection in Portugal.MethodsA retrospective cohort of 38,545 infected individuals during 2020 was used. Predictions of medical needs were performed using state-of-the-art machine learning approaches at various stages of a patient's cycle, namely, at testing (prehospitalization), at posthospitalization, and during postintensive care. A thorough optimization of state-of-the-art predictors was undertaken to assess the ability to anticipate medical needs and infection outcomes using demographic and comorbidity variables, as well as dates associated with symptom onset, testing, and hospitalization.ResultsFor the target cohort, 75% of hospitalization needs could be identified at the time of testing for SARS-CoV-2 infection. Over 60% of respiratory needs could be identified at the time of hospitalization. Both predictions had >50% precision.ConclusionsThe conducted study pinpoints the relevance of the proposed predictive models as good candidates to support medical decisions in the Portuguese population, including both monitoring and in-hospital care decisions. A clinical decision support system is further provided to this end.
| S-EPMC8080965 | biostudies-literature
Project description: Not available
| S-BSST1269 | biostudies-other
Project description:BACKGROUND:There are no clear expert consensus or guidelines on how to treat 2019 coronavirus disease (COVID-19). The objective of this study is to investigate the short-term effect of risk-adapted treatment strategy on patients with COVID-19. METHODS:We collected the medical records of 55 COVID-19 patients for analysis. We divided these patients into mild, moderate and severe groups, and risk-adapted treatment approaches were given according to the illness severity. RESULTS:Twelve patients were in mild group and 22 were in moderate group (non-severe group, n=34), and 21 patients were in severe group. At the end of the first two weeks after admission, clinical manifestations had completely despeared in 31(91.2%)patients in non-severe group, and 18(85.7%) patients in severe group (p=0.85). Both groups had a satisfied chest CT imaging recovery, which includes 22(64.7%) patients in non-severe group and 12(57.1%) patients in severe group recovered at least 50% of the whole leisions in the first week, and 28(82.4%) and 16(76.2%) recovered at least 75% in the second week, respectively. There were no significant differences in SARS-CoV-2 nucleic acid negativity (p=0.92). There were also no significant differences in the levels of SARS-CoV-2-IgM and IgG antibody production between the two groups (p=0.13, 0.62). There were 45 cases were discharged from the hospital, and no patients died at the time of this clinical analysis. CONCLUSIONS:Risk-adapted treatment strategy was associated with significant clinical manifestations alleviation and clinical imaging recovery. In severe COVID-19 patients, early and short-term use of low-dose methylprednisolone was beneficial and did not delay SARS-CoV-2 nucleic acid clearance and influence IgG antibody production.
| S-EPMC7270846 | biostudies-literature