Lung Recruitment Assessed by Electrical Impedance Tomography (RECRUIT): A Multicenter Study of COVID-19 Acute Respiratory Distress Syndrome.

Rationale: Defining lung recruitability is needed for safe positive end-expiratory pressure (PEEP) selection in mechanically ventilated patients. However, there is no simple bedside method including both assessment of recruitability and risks of overdistension as well as personalized PEEP titration. Objectives: To describe the range of recruitability using electrical impedance tomography (EIT), effects of PEEP on recruitability, respiratory mechanics and gas exchange, and a method to select optimal EIT-based PEEP. Methods: This is the analysis of patients with coronavirus disease (COVID-19) from an ongoing multicenter prospective physiological study including patients with moderate-severe acute respiratory distress syndrome of different causes. EIT, ventilator data, hemodynamics, and arterial blood gases were obtained during PEEP titration maneuvers. EIT-based optimal PEEP was defined as the crossing point of the overdistension and collapse curves during a decremental PEEP trial. Recruitability was defined as the amount of modifiable collapse when increasing PEEP from 6 to 24 cm H2O (ΔCollapse24-6). Patients were classified as low, medium, or high recruiters on the basis of tertiles of ΔCollapse24-6. Measurements and Main Results: In 108 patients with COVID-19, recruitability varied from 0.3% to 66.9% and was unrelated to acute respiratory distress syndrome severity. Median EIT-based PEEP differed between groups: 10 versus 13.5 versus 15.5 cm H2O for low versus medium versus high recruitability (P < 0.05). This approach assigned a different PEEP level from the highest compliance approach in 81% of patients. The protocol was well tolerated; in four patients, the PEEP level did not reach 24 cm H2O because of hemodynamic instability. Conclusions: Recruitability varies widely among patients with COVID-19. EIT allows personalizing PEEP setting as a compromise between recruitability and overdistension. Clinical trial registered with www.clinicaltrials.gov (NCT04460859).

Supporting function and value orientation of science and technology in public health emergency

The COVID-19 is the major public health event that has the fastest speed of transmission, the most extensive infection and the most difficult control and prevention since the founding of new China. Summarizing this public health incident, we can find that technology has played a supporting role in many areas but it also has shown its weaknesses and shortcomings. During the "14th Five-Year Plan" period, the innovation-driven national strategy and the construction of the national public health system will provide greater opportunities and new higher requirements for the development of our country's science and technology. How to further leverage the support of science and technology in major public health emergencies and how to carry forward scientific spirit are of great significance. Specifically, we should adhere to the value orientation of "people-centered" and promote the scientific literacy of the whole people in the popularization of science as well as increase reform and innovation in the scientific and technological system and mechanism.

Sigh in Patients With Acute Hypoxemic Respiratory Failure and ARDS: The PROTECTION Pilot Randomized Clinical Trial.

BACKGROUND: Sigh is a cyclic brief recruitment maneuver: previous physiologic studies showed that its use could be an interesting addition to pressure support ventilation to improve lung elastance, decrease regional heterogeneity, and increase release of surfactant. RESEARCH QUESTION: Is the clinical application of sigh during pressure support ventilation (PSV) feasible? STUDY DESIGN AND METHODS: We conducted a multicenter noninferiority randomized clinical trial on adult intubated patients with acute hypoxemic respiratory failure or ARDS undergoing PSV. Patients were randomized to the no-sigh group and treated by PSV alone, or to the sigh group, treated by PSV plus sigh (increase in airway pressure to 30 cm H2O for 3 s once per minute) until day 28 or death or successful spontaneous breathing trial. The primary end point of the study was feasibility, assessed as noninferiority (5% tolerance) in the proportion of patients failing assisted ventilation. Secondary outcomes included safety, physiologic parameters in the first week from randomization, 28-day mortality, and ventilator-free days. RESULTS: Two-hundred and fifty-eight patients (31% women; median age, 65 [54-75] years) were enrolled. In the sigh group, 23% of patients failed to remain on assisted ventilation vs 30% in the no-sigh group (absolute difference, -7%; 95% CI, -18% to 4%; P = .015 for noninferiority). Adverse events occurred in 12% vs 13% in the sigh vs no-sigh group (P = .852). Oxygenation was improved whereas tidal volume, respiratory rate, and corrected minute ventilation were lower over the first 7 days from randomization in the sigh vs no-sigh group. There was no significant difference in terms of mortality (16% vs 21%; P = .337) and ventilator-free days (22 [7-26] vs 22 [3-25] days; P = .300) for the sigh vs no-sigh group. INTERPRETATION: Among hypoxemic intubated ICU patients, application of sigh was feasible and without increased risk. TRIAL REGISTRY: ClinicalTrials.gov; No.: NCT03201263; URL: www.clinicaltrials.gov.

Assisting scalable diagnosis automatically via CT images in the combat against COVID-19.

The pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.