Inspiratory effort and lung mechanics in spontaneously breathing patients with acute respiratory failure due to COVID-19. A matched control study.

Several physical and biological mechanisms can drive progression between the different phases of lung injury due to SARS-CoV-2 infection, thus modifying the mechanical properties and behavior of COVID-19 over time. In this research letter we have presented the findings of a registered clinical trial aimed at describing and comparing the inspiratory effort (primary outcome) and the breathing pattern of spontaneously breathing patients with ARF in COVID-19 and historically matched non-COVID-19 patients, either candidate to NIV. Moreover, we reported the response to a 2 hours NIV trial in the two groups. Spontaneously breathing COVID-19 at their early onset of acute respiratory failure with indication for NIV showed different mechanical characteristics and breathing pattern when compared with non-COVID-19

Fibrotic idiopathic interstitial lung disease: the molecular and cellular key players.

Interstitial lung disease (ILDs) that are known as diffuse parenchymal lung diseases (DPLDs) lead to the damage of alveolar epithelium and lung parenchyma culminating into inflammation and widespread fibrosis. ILDs that account for more than 200 different pathologies, can be di-vided into two groups: ILDs that have a known cause and those where the cause is unknown clas-sified as Idiopathic Interstitial Pneumonia (IIPs). IIPs include idiopathic pulmonary fibrosis (IPF), non-specific interstitial pneumonia (NSIP), cryptogenic organizing pneumonia (COP) known also as bronchiolitis obliterans organizing pneumonia (BOOP), Acute interstitial pneumonia (AIP), Desquamative Interstitial Pneumonia (DIP), Respiratory bronchiolitis-associated interstitial lung disease (RB-ILD), and lymphocytic interstitial pneumonia (LIP). In this review our aim is to de-scribe the pathogenic mechanisms that lead to the onset and progression of the different IIPs, starting from IPF as the most studied, in order to find both common and standalone molecular and cellular key players among them. Finally, a deeper molecular and cellular characterization of different interstitial lung disease without known cause, would contribute to give a more accurate diagnosis to the patients, that would translate in a more effective treatment decision

Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia - challenges, strengths, and opportunities in a global health emergency.

Aims- The aim of this study was to estimate a 48 hour prediction of moderate to severe respiratory failure, requiring mechanical ventilation, in hospitalized patients with COVID-19 pneumonia. Methods- This was an observational study that comprised consecutive patients with COVID-19 pneumonia admitted to hospital from 21 February to 6 April 2020. The patients\u2019 medical history, demographic, epidemiologic and clinical data were collected in an electronic patient chart. The dataset was used to train predictive models using an established machine learning framework leveraging a hybrid approach where clinical expertise is applied alongside a data-driven analysis. The study outcome was the onset of moderate to severe respiratory failure defined as PaO 2 /FiO 2 ratio <150 mmHg in at least one of two consecutive arterial blood gas analyses in the following 48 hours. Shapley Additive exPlanations values were used to quantify the positive or negative impact of each variable included in each model on the predicted outcome. Results- A total of 198 patients contributed to generate 1068 usable observations which allowed to build 3 predictive models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth \u201cboosted mixed model\u201d included 20 variables was selected from the model 3, achieved the best predictive performance (AUC=0.84) without worsening the FN rate. Its clinical performance was applied in a narrative case report as an example. Conclusion- This study developed a machine model with 84% prediction accuracy, which is able to assist clinicians in decision making process and contribute to develop new analytics to improve care at high technology readiness levels

Continuous Metabolic Monitoring in Infant Cardiac Surgery: Toward an Individualized Cardiopulmonary Bypass Strategy

Cardiopulmonary bypass (CPB) in infants is associated with morbidity due to systemic inflammatory response syndrome (SIRS). Strategies to mitigate SIRS include management of perfusion temperature, hemodilution, circuit miniaturization, and biocompatibility. Traditionally, perfusion parameters have been based on body weight. However, intraoperative monitoring of systemic and cerebral metabolic parameters suggest that often, nominal CPB flows may be overestimated. The aim of the study was to assess the safety and efficacy of continuous metabolic monitoring to manage CPB in infants during open-heart repair. Between December 2013 and October 2014, 31 consecutive neonates, infants, and young children undergoing surgery using normothermic CPB were enrolled. There were 18 male and 13 female infants, aged 1.4\u2009\ub1\u20091.7 years, with a mean body weight of 7.8\u2009\ub1\u20093.8\u2009kg and body surface area of 0.39\u2009m2 . The study was divided into two phases: (i) safety assessment; the first 20 patients were managed according to conventional CPB flows (150\u2009mL/min/kg), except for a 20-min test during which CPB was adjusted to the minimum flow to maintain MVO2 >70% and rSO2 >45% (group A); (ii) efficacy assessment; the following 11 patients were exclusively managed adjusting flows to maintain MVO2 >70% and rSO2 >45% for the entire duration of CPB (group B). Hemodynamic, metabolic, and clinical variables were compared within and between patient groups. Demographic variables were comparable in the two groups. In group A, the 20-min test allowed reduction of CPB flows greater than 10%, with no impact on pH, blood gas exchange, and lactate. In group B, metabolic monitoring resulted in no significant variation of endpoint parameters, when compared with group A patients (standard CPB), except for a 10% reduction of nominal flows. There was no mortality and no neurologic morbidity in either group. Morbidity was comparable in the two groups, including: inotropic and/or mechanical circulatory support (8 vs. 1, group A vs. B, P\u2009=\u20090.07), reexploration for bleeding (1 vs. none, P\u2009=\u2009not significant [NS]), renal failure requiring dialysis (none vs. 1, P\u2009=\u2009NS), prolonged ventilation (9 vs. 4, P\u2009=\u2009NS), and sepsis (2 vs. 1, P\u2009=\u2009NS). The present study shows that normothermic CPB in neonates, infants, and young children can be safely managed exclusively by systemic and cerebral metabolic monitoring. This strategy allows reduction of at least 10% of predicted CPB flows under normothermia and may lay the ground for further tailoring of CPB parameters to individual patient needs