Characterization of compliance phenotypes in COVID-19 acute respiratory distress syndrome.

BACKGROUND: Coronavirus disease 2019-associated acute respiratory distress syndrome (COVID-19 ARDS) seems to differ from the "classic ARDS", showing initial significant hypoxemia in the face of relatively preserved compliance and evolving later in a scenario of poorly compliant lungs. We tested the hypothesis that in patients with COVID-19 ARDS, the initial value of static compliance of respiratory system (Crs) (1) depends on the previous duration of the disease (i.e., the fewer days of illness, the higher the Crs and vice versa) and (2) identifies different lung patterns of time evolution and response to prone positioning. METHODS: This was a single-center prospective observational study. We enrolled consecutive mechanically ventilated patients with a diagnosis of COVID-19 who met ARDS criteria, admitted to intensive care unit (ICU). Patients were divided in four groups based on quartiles of initial Crs. Relationship between Crs and the previous duration of the disease was evaluated. Respiratory parameters collected once a day and during prone positioning were compared between groups. RESULTS: We evaluated 110 mechanically ventilated patients with a diagnosis of COVID-19 who met ARDS criteria admitted to our ICUs. Patients were divided in groups based on quartiles of initial Crs. The median initial Crs was 41 (32-47) ml/cmH2O. No association was found between the previous duration of the disease and the initial Crs. The Crs did not change significantly over time within each quartile. Positive end-expiratory pressure (PEEP) and driving pressure were respectively lower and greater in patients with lower Crs. Prone positioning significantly improved PaO2/FiO2 in the 4 groups, however it increased the Crs significantly only in patients in lower quartile of Crs. CONCLUSIONS: In our cohort, the initial Crs is not dependent on the previous duration of COVID-19 disease. Prone positioning improves oxygenation irrespective to initial Crs, but it ameliorates respiratory mechanics only in patients with lower Crs.

Chest X-ray at Emergency Admission and Potential Association with Barotrauma in Mechanically Ventilated Patients: Experience from the Italian Core of the First Pandemic Peak.

Barotrauma occurs in a significant number of patients with COVID-19 interstitial pneumonia undergoing mechanical ventilation. The aim of the current study was to investigate whether the Brixia score (BS) calculated on chest-X-rays acquired at the Emergency Room was associated with barotrauma. We retrospectively evaluated 117 SARS-CoV-2 patients presented to the Emergency Department (ED) and then admitted to the intensive care unit (ICU) for mechanical ventilation between February and April 2020. Subjects were divided into two groups according to the occurrence of barotrauma during their hospitalization. CXRs performed at ED admittance were assessed using the Brixia score. Distribution of barotrauma (pneumomediastinum, pneumothorax, subcutaneous emphysema) was identified in chest CT scans. Thirty-eight subjects (32.5%) developed barotrauma (25 pneumomediastinum, 24 pneumothorax, 24 subcutaneous emphysema). In the barotrauma group we observed higher Brixia score values compared to the non-barotrauma group (mean value 12.18 vs. 9.28), and logistic regression analysis confirmed that Brixia score is associated with the risk of barotrauma. In this work, we also evaluated the relationship between barotrauma and clinical and ventilatory parameters: SOFA score calculated at ICU admittance and number of days of non-invasive ventilation (NIV) prior to intubation emerged as other potential predictors of barotrauma.

An agent-based model to assess large-scale COVID-19 vaccination campaigns for the Italian territory: The case study of Lombardy region.

BACKGROUND: In Italy, the administration of COVID-19 vaccines began in late 2020. In the early stages, the number of available doses was limited. To maximize the effectiveness of the vaccine campaign, the national health agency assigned priority access to at-risk individuals, such as health care workers and the elderly. Current vaccination campaign strategies do not take full advantage of the latest mathematical models, which capture many subtle nuances, allowing different territorial situations to be analyzed aiming to make context-specific decisions. OBJECTIVES: The main objective is the definition of an agent-based model using open data and scientific literature to assess and optimize the impact of vaccine campaigns for an Italian region. Specifically, the aim is twofold: (i) estimate the reduction in the number of infections and deaths attributable to vaccines, and (ii) assess the performances of alternative vaccine allocation strategies. METHODS: The COVID-19 Agent-based simulator Covasim has been employed to build an agent-based model by considering the Lombardy region as case study. The model has been tailored by leveraging open data and knowledge from the scientific literature. Dynamic mobility restrictions and the presence of Variant of Concern have been explicitly represented. Free parameters have been calibrated using the grid search methodology. RESULTS: The model mimics the COVID-19 wave that hit Lombardy from September 2020 to April 2021. It suggests that 168,492 cumulative infections 2,990 cumulative deaths have been avoided due to the vaccination campaign in Lombardy from January 1 to April 30, 2021. Without vaccines, the number of deaths would have been 66% greater in the 80-89 age group and 114% greater for those over 90. The best vaccine allocation strategy depends on the goal. To minimize infections, the best policy is related to dose availability. If at least 1/3 of the population can be covered in 4 months, targeting at-risk individuals and the elderly first is recommended; otherwise, the youngest people should be vaccinated first. To minimize overall deaths, priority is best given to at-risk groups and the elderly in all scenarios. CONCLUSIONS: This work proposes a methodological approach that leverages open data and scientific literature to build a model of COVID-19 capable of assessing and optimizing the impact of vaccine campaigns. This methodology can help national institutions to design regional mathematical models that can support pandemic-related decision-making processes.

A SIAT3HE model of the COVID-19 pandemic in Bergamo, Italy.

The aim of this article is to give a better understanding of the dynamics of the SARS-CoV-2 pandemic in the Bergamo province (Italy), one of the most hit areas of the world, between February and April 2020. A new compartmental model, called SIAT3HE, was designed and fitted on accurate data about the pandemic provided by ATS Bergamo, the health protection agency of the Bergamo province. Our results show that SARS-CoV-2 reached Bergamo in January and infected 318,000 people, the 28.8% of the province population. The 43.1% of the infected individuals stayed asymptomatic. As 6,028 people died due to COVID-19 till April 30th, the infection fatality ratio of SARS-CoV-2 in the Bergamo province was 1.9%. These results are in very good agreement with available information: the number of infections is consistent with the results of recent serological surveys and the number of deaths due to COVID-19 is close to the excess mortality of the considered period.

Data on the first endurance activity of a Brushless DC motor for aerospace applications.

This article describes the data acquired during the first test activity carried out in the Reliable Electromechanical actuator for PRImary SurfacE with health monitoring (REPRISE) H2020 project. The data consist of a set of measures from an Electro-Mechanical Actuator (EMA) employed in small aircrafts, such as phase currents, positions, temperature and loads. A test bench was developed to perform endurance sessions in various loads and working conditions. Specifically, two datasets are provided: (i) measurements used to monitor the EMA degradation through time; (ii) measurements that characterize the EMA closed-loop dynamic behaviour in healthy condition. The data are helpful to develop and test system identification methods and condition monitoring approaches.

A discrete-time nonlinear Wiener model for the relaxation of soft biological tissues.

The present paper is devoted to introducing discrete-time models for the relaxation function of soft biological tissues. Discrete-time models are suitable for the analysis of sampled data and for digital simulations of continuous systems. Candidate models are searched for within both linear ARX structures and nonlinear Wiener models, consisting of an ARX element followed in cascade by a polynomial function. Both these discrete-time models correspond to sampling continuous-time exponential function series, thus preserving physical interpretation for the proposed relaxation model. The estimation data set consists of normalized stress relaxation curves drawn from experiments performed on samples of bovine pericardium. The normalized relaxation curves are found to be almost insensitive to both the magnitude of strain and the loading direction, and so a single model for the whole relaxation curves is assumed. In order to identify the parameters of the Wiener model an iterative algorithm is purposely designed. Over the ARX one, the nonlinear Wiener model exhibits higher capability of representing the experimental relaxation curves over the whole observation period. The stability of the solution for the iterative algorithm is assessed, and hence physical interpretation as material properties can be attached to the parameters of the nonlinear model. Suitable features of the Wiener model for computational application are also briefly presented.