Search

This study aimed at investigating the mechanisms underlying the oxygenation response to proning and recruitment maneuvers in coronavirus disease 2019 (COVID-19) pneumonia. Twenty-five patients with COVID-19 pneumonia, at variable times since admission (from 1 to 3 weeks), underwent computed tomography (CT) lung scans, gas-exchange and lung-mechanics measurement in supine and prone positions at 5 cmH2O and during recruiting maneuver (supine, 35 cmH2O). Within the non-aerated tissue, we differentiated the atelectatic and consolidated tissue (recruitable and non-recruitable at 35 cmH2O of airway pressure). Positive/negative response to proning/recruitment was defined as increase/decrease of PaO2/FiO2. Apparent perfusion ratio was computed as venous admixture/non aerated tissue fraction. The average values of venous admixture and PaO2/FiO2 ratio were similar in supine-5 and prone-5. However, the PaO2/FiO2 changes (increasing in 65% of the patients and decreasing in 35%, from supine to prone) correlated with the balance between resolution of dorsal atelectasis and formation of ventral atelectasis (p = 0.002). Dorsal consolidated tissue determined this balance, being inversely related with dorsal recruitment (p = 0.012). From supine-5 to supine-35, the apparent perfusion ratio increased from 1.38 ± 0.71 to 2.15 ± 1.15 (p = 0.004) while PaO2/FiO2 ratio increased in 52% and decreased in 48% of patients. Non-responders had consolidated tissue fraction of 0.27 ± 0.1 vs. 0.18 ± 0.1 in the responding cohort (p = 0.04). Consolidated tissue, PaCO2 and respiratory system elastance were higher in patients assessed late (all p

Background To assess diagnostic performance of lung ultrasound (LUS) in identifying ARDS morphology (focal vs non-focal), compared with the gold standard computed tomography. Methods Mechanically ventilated ARDS patients undergoing lung computed tomography and ultrasound were enrolled. Twelve fields, were evaluated. LUS score was graded from 0 (normal) to 3 (consolidation) according to B-lines extent. Total and regional LUS score as the sum of the four ventral (LUSV), intermediate (LUSI) or dorsal (LUSD) fields, were calculated. Based on lung CT, ARDS morphology was defined as (1) focal (loss of aeration with lobar distribution); (2) non-focal (widespread loss of aeration or segmental loss of aeration distribution associated with uneven lung attenuation areas), and diagnostic accuracy of LUS in discriminating ARDS morphology was determined by AU-ROC in training and validation set of patients. Results Forty-seven patients with ARDS (25 training set and 22 validation set) were enrolled. LUSTOT, LUSV and LUSI but not LUSD score were significantly lower in focal than in non-focal ARDS morphologies (p TOT, LUSV, LUSI and LUSD for identification of non-focal ARDS morphology were 0.890, 0.958, 0.884 and 0.421, respectively. LUSV value ≥ 3 had the best predictive value (sensitivity = 0.95, specificity = 1.00) in identifying non-focal ARDS morphology. In the validation set, an LUSV score ≥ 3 confirmed to be highly predictive of non-focal ARDS morphology, with a sensitivity and a specificity of 94% and 100%. Conclusions LUS had a valuable performance in distinguishing ARDS morphology.

COVID-19 infection may lead to acute respiratory distress syndrome (CARDS) where severe gas exchange derangements may be associated, at least in the early stages, only with minor pulmonary infiltrates. This may suggest that the shunt associated to the gasless lung parenchyma is not sufficient to explain CARDS hypoxemia. We designed an algorithm (VentriQlar), based on the same conceptual grounds described by J.B. West in 1969. We set 498 ventilation–perfusion (VA/Q) compartments and, after calculating their blood composition (PO2, PCO2, and pH), we randomly chose 106 combinations of five parameters controlling a bimodal distribution of blood flow. The solutions were accepted if the predicted PaO2 and PaCO2 were within 10% of the patient’s values. We assumed that the shunt fraction equaled the fraction of non-aerated lung tissue at the CT quantitative analysis. Five critically-ill patients later deceased were studied. The PaO2/FiO2 was 91.1 ± 18.6 mmHg and PaCO2 69.0 ± 16.1 mmHg. Cardiac output was 9.58 ± 0.99 L/min. The fraction of non-aerated tissue was 0.33 ± 0.06. The model showed that a large fraction of the blood flow was likely distributed in regions with very low VA/Q (Qmean = 0.06 ± 0.02) and a smaller fraction in regions with moderately high VA/Q. Overall LogSD, Q was 1.66 ± 0.14, suggestive of high VA/Q inequality. Our data suggest that shunt alone cannot completely account for the observed hypoxemia and a significant VA/Q inequality must be present in COVID-19. The high cardiac output and the extensive microthrombosis later found in the autopsy further support the hypothesis of a pathological perfusion of non/poorly ventilated lung tissue. NEW & NOTEWORTHY Hypothesizing that the non-aerated lung fraction as evaluated by the quantitative analysis of the lung computed tomography (CT) equals shunt (VA/Q = 0), we used a computational approach to estimate the magnitude of the ventilation–perfusion inequality in severe COVID-19. The results show that a severe hyperperfusion of poorly ventilated lung region is likely the cause of the observed hypoxemia. The extensive microthrombosis or abnormal vasodilation of the pulmonary circulation may represent the pathophysiological mechanism of such VA/Q distribution.

Lung damage during mechanical ventilation involves lung volume and alveolar water content, and lung ultrasound (LUS) and electrical impedance tomography changes are related to these variables. We investigated whether these techniques may detect any signal modification during the development of ventilator-induced lung injury (VILI).Experimental animal study.Experimental Department of a University Hospital.Forty-two female pigs (24.2 ± 2.0 kg).The animals were randomized into three groups (n = 14): high tidal volume (TV) (mean TV, 803.0 ± 121.7 mL), high respiratory rate (RR) (mean RR, 40.3 ± 1.1 beats/min), and high positive-end-expiratory pressure (PEEP) (mean PEEP, 24.0 ± 1.1 cm H2O). The study lasted 48 hours. At baseline and at 30 minutes, and subsequently every 6 hours, we recorded extravascular lung water, end-expiratory lung volume, lung strain, respiratory mechanics, hemodynamics, and gas exchange. At the same time-point, end-expiratory impedance was recorded relatively to the baseline. LUS was assessed every 12 hours in 12 fields, each scoring from 0 (presence of A-lines) to 3 (consolidation).In a multiple regression model, the ratio between extravascular lung water and end-expiratory lung volume was significantly associated with the LUS total score (p0.002; adjusted R2, 0.21). The variables independently associated with the end-expiratory difference in lung impedance were lung strain (p0.001; adjusted R2, 0.18) and extravascular lung water (p0.001; adjusted R2, 0.11).Data suggest as follows. First, what determines the LUS score is the ratio between water and gas and not water alone. Therefore, caution is needed when an improvement of LUS score follows a variation of the lung gas content, as after a PEEP increase. Second, what determines the end-expiratory difference in lung impedance is the strain level that may disrupt the intercellular junction, therefore altering lung impedance. In addition, the increase in extravascular lung water during VILI development contributed to the observed decrease in impedance.

Purpose We investigated if the stress applied to the lung during non-invasive respiratory support may contribute to the coronavirus disease 2019 (COVID-19) progression. Methods Single-center, prospective, cohort study of 140 consecutive COVID-19 pneumonia patients treated in high-dependency unit with continuous positive airway pressure (n = 131) or non-invasive ventilation (n = 9). We measured quantitative lung computed tomography, esophageal pressure swings and total lung stress. Results Patients were divided in five subgroups based on their baseline PaO2/FiO2 (day 1): non-CARDS (median PaO2/FiO2 361 mmHg, IQR [323–379]), mild (224 mmHg [211–249]), mild-moderate (173 mmHg [164–185]), moderate-severe (126 mmHg [114–138]) and severe (88 mmHg [86–99], p

Coronavirus disease 2019 (COVID-19) pneumonia is an evolving disease. We will focus on the development of its pathophysiologic characteristics over time, and how these time-related changes determine modifications in treatment. In the emergency department: the peculiar characteristic is the coexistence, in a significant fraction of patients, of severe hypoxaemia, near-normal lung computed tomography imaging, lung gas volume and respiratory mechanics. Despite high respiratory drive, dyspnoea and respiratory rate are often normal. The underlying mechanism is primarily altered lung perfusion. The anatomical prerequisites for PEEP (positive end-expiratory pressure) to work (lung oedema, atelectasis, and therefore recruitability) are lacking. In the high-dependency unit: the disease starts to worsen either because of its natural evolution or additional patient self-inflicted lung injury (P-SILI). Oedema and atelectasis may develop, increasing recruitability. Noninvasive supports are indicated if they result in a reversal of hypoxaemia and a decreased inspiratory effort. Otherwise, mechanical ventilation should be considered to avert P-SILI. In the intensive care unit: the primary characteristic of the advance of unresolved COVID-19 disease is a progressive shift from oedema or atelectasis to less reversible structural lung alterations to lung fibrosis. These later characteristics are associated with notable impairment of respiratory mechanics, increased arterial carbon dioxide tension (PaCO2), decreased recruitability and lack of response to PEEP and prone positioning.

Introduction Mechanical ventilation (MV) plays a crucial role during the early post-operative period of lung transplantation (LTx). An accurate setting of MV is important in order to prevent ventilation-induced lung injury, which may lead to Primary Graft Dysfunction (PDG), and to set a protective ventilation strategy whenever this complication occurs. Unfortunately, respiratory mechanics may be unpredictable: these patients receive lungs which may not perfectly match their chest walls, causing a patient-donor size-mismatch, is conventionally defined by the ratio between recipient and donor lungs predicted-Total Lung Capacity (r-pTLC and d-pTLC, respectively) and may modify the distribution of pressures inside the respiratory system between lung and chest wall. In order to improve our understanding of the LTx respiratory mechanics and to evaluate whether ventilation setting was dangerous for the transplanted lungs, we placed an Esophageal Balloon Catheter (EBC) and analysed the esophageal pressure (Pes) in the early post-operative period of patients receiving bilateral sequential single LTx (BSSLTx). Methods We observed the respiratory mechanics of patients receiving BSSLTx over 1-year period at Molinette Hospital (Ospedale Molinette, Citta della Salute e della Scienza, Turin, Italy). For each patient respiratory mechanics was evaluated at least two times in the post-transplant period: intraoperatively (T0), just before the chest closure, and post-operatively (T1), in ICU. Patients and donors data, recipient-donor size-matching and outcomes were compared with respiratory mechanics. Results Between November 2018 and October 2019, we included 22 patients receiving LTx. Among them, 9 were excluded: 6 were single LTx, 1 was a paediatric patient and 2 patients had incomplete data. Among the 13 patients included, 3 (23%) developed PGD: 5 patients were best matched (d/pTLC 0,9 – 1,1; 5/13, 38,4%), 6 were undersized (d/pTLC 1,1; 2/13, 15,3%) but no correlation was found between size-matching and incidence of high-grade PGD (PGD grade 2 or 3) (Fig.1). We found no statistically significant difference in transpulmonary pressure (PTP i, PTP e, PTP drive), transpulmonary compliance (CTP) or chest elastance (ECW) at T0 or T1 in patients who developed PGD compared to those without PGD. However, we noticed an early raise of PTP i in patients who were going to develop PGD (no PGD δ% t0-t1 PTP i – 14,6% vs PGD δ% t0-t1 PTP i + 14,0%; p=0,1402) (Fig. 2). Moreover, we found that this early raising in PTP i (δ% t0-t1 PTP i) was statistically related with the ICU-length-of-stay (ICU-LOS) (r2=0,57; p=0,050) (Fig. 3) Discussion The overall incidence of PGD in our centre was 23%, however no correlation with patient-donor size-matching. An early raise of transpulmonary pressure was observed among patients who developed PGD and it was statistically correlated with higher ICU-LOS.

Objective The negative effect of sepsis on the myocardium affects its electric functionality. This study aims to evaluate the incidence of atrial fibrillation (AF) in patients with septic shock, and the mortality rate of patients with AF versus patients that maintained sinus rhythm (SR). Methods This is a one-year observational prospective pilot study. It was conducted at the Department of Anaesthesia and Intensive Care of Pisa University. Patients with septic shock were enrolled in this study. They were divided in two groups based on the occurrence of AF while in the ICU. Data were collected at admission and after 72 hours, and the data consisted of anamnesis, vital parameters, blood results and severity score. Results Out of 27 patients, 9 developed AF during the first 72 hours. At admission and at 72 hours, SOFA was statistically higher in the patients with AF (p=0.012 and p=0.002, respectively). In the AF group, the overall mortality was 66.7%, whereas, it was 11.1% (p=0.006) in the patients with SR. Age, rhythm and noradrenaline dosage were univariate predictors of total mortality. Conclusion In patients with septic shock, AF has a high incidence, and it correlated with a worse outcome. Patients with higher SOFA score are at a greater risk of developing arrhythmia.