To describe the epidemiological and clinical characteristics of patients with Corona Virus Disease 2019 (COVID-19) in Beijing. To analyze the application of corticosteroids in patients with severe pneumonia. We collected information on demographic characteristics, exposure history, clinical characteristics, corticosteroids use, and outcomes of the 65 confirmed cases of COVID-19 at Fifth Medical Center of PLA General Hospital from Jan 20 to Feb 23, 2020. The final follow-up date observed was April 15th, 2020. The number of patients with mild, general, severe, and critical type were 10 (15.38%), 32 (49.23%), 8 (12.31%), and 15 (23.08%), respectively. The median incubation period was 6 days. Notable outliers were 1 patient at 16 days and 1 patient at 21 days. In lymphocyte subgroup analysis, decreases in total, T, CD4, and CD8 lymphocytes were more common as the disease worsened (All P < 0.05). Methylprednisolone (mPSL) was applied to 31 (47.69%) patients with pneumonia, including 10 (31.25%) general, 8 (100%) severe, and 13 (86.67%) critical patients, respectively. Corticosteroids inhibited Interleukin-6(IL-6) production (P = 0.0215) but did not affect T lymphocyte (P = 0.0796). There was no significant difference between patients using lower dose (≤ 2 mg/kg day) and higher dose (> 2 mg/kg day) mPSL in inhibiting IL-6 production (P = 0.5856). Thirty of 31 patients (96.77%) had stopped mPSL due to improvement of pneumonia. Virus RNA clearance time lengthened with disease progression (P = 0.0001). In general type, there was no significant difference in virus clearance time between patients with (15, 12-19 days) and without (14.5, 11-18 days) (P = 0.7372) mPSL use. Lymphocyte, especially T lymphocyte, in severe and critical patients showed a dramatic decrease. Application of lower dose corticosteroids (≤ 2 mg/kg day) could inhibit IL-6 production (a representative of cytokines) as effectively as a higher dose. Proper use corticosteroids in general type patients did not delay virus clearance. In December 2019, cases of acute respiratory disease (ARD), now known as a Corona Virus Disease 2019 (COVID-19) occurred in Wuhan, Hubei Province, China 1-3. Presently, the laboratory-confirmed cases and recorded deaths in the world are still increasing at an alarming rate 4-10. COVID-19 clinical types were defined according to the Diagnosis and Treatment of Pneumonia caused by Novel Coronavirus (Version 6 Trial) published on the website of the Central Government of the People's Republic of China 11. There are four distinct clinical types based on the severity of the disease. However, the differences in clinical characteristics, corticosteroids application, and outcomes among different clinical types have not been reported.

To clarify the characteristics and distribution of hospital environmental microbiome associated with confirmed COVID-19 patients. Environmental samples with varying degrees of contamination which were associated with confirmed COVID-19 patients were collected, including 13 aerosol samples collected near eight patients in different wards, five swabs from one patient’s skin and his personal belongings, and two swabs from the surface of positive pressure respiratory protective hood and the face shield from a physician who had close contact with one patient. Metagenomic next-generation sequencing (mNGS) was used to analyze the composition of the microbiome. One of the aerosol samples (near patient 4) was detected positive for COVID-19, and others were all negative. The environmental samples collected in different wards possessed protean compositions and community structures, the dominant genera including Pseudomonas, Corynebacterium, Neisseria, Staphylococcus, Acinetobacter, and Cutibacterium. Top 10 of genera accounted for more than 76.72%. Genera abundance and proportion of human microbes and pathogens radiated outward from the patient, while the percentage of environmental microbes increased. The abundance of the pathogenic microorganism of medical supplies is significantly higher than other surface samples. The microbial compositions of the aerosol collected samples nearby the patients were mostly similar to those from the surfaces of the patient's skin and personal belongings, but the abundance varied greatly. The positive rate of COVID-19 RNA detected from aerosol around patients in general wards was quite low. The ward environment was predominantly inhabited by species closely related to admitted patients. The spread of hospital microorganisms via aerosol was influenced by the patients’ activity.

This study was performed to visualize the hemodynamic effects of pulmonary microcirculation and ventilation/perfusion (V/Q) matching after mechanical ventilation under different cardiac outputs and positive end-expiratory pressures (PEEPs). Ten experimental pigs were randomly divided into high and low tidal volume groups, and ventilation/perfusion were measured by electrical impedance tomography (EIT) at different PEEPs. Then, all the pigs were redivided into high cardiac output (CO) and low CO groups and measured by EIT at different PEEP levels with a low tidal volume. Additionally, sidestream dark field (SDF) was used to measure pulmonary microcirculation. Hemodynamic parameters and respiratory mechanics parameters were recorded. As PEEP increased at high tidal volume, blood flow was impaired at a higher PEEP (20 cmH2O) compared with low tidal volume (shunt: 30.01 ± 0.69% vs. 17.95 ± 0.72%; V/Q ratio: 65.12 ± 1.97% vs. 76.57 ± 1.25%, p < 0.01). Low tidal volume combined with an appropriate PEEP is the best option from the match between ventilation and pulmonary blood flow. Increasing PEEP can solve the problem of excessive shunt at high CO, and the V/Q ratio tends to match. At low CO, the increased dead space can reach as high as 64.64 ± 7.13% when PEEP = 20 cmH2O. With increasing PEEP, the microcirculation index deteriorates, including total vessel density (TVD), proportion of perfused vessel (PPV), perfused vessel density (PVD), and microcirculatory flow index (MFI). The periodic collapse of pulmonary capillaries or interruption of blood flow obviously occurred with high PEEP. The hemodynamic parameters indicated that the transpulmonary capillary wall pressure (Pcap) of the low CO group was negative at PEEP = 5 cmH2O, which determines the opening and closing of the pulmonary microcirculation and controls lung perfusion and the production of extravascular lung water. Therefore, it is essential to couple macrocirculation and pulmonary microcirculation during mechanical ventilation by improving shunting and optimizing Pcap.

Ventilators in the intensive care units (ICU) are life-support devices that help physicians to gain additional time to cure the patients. The aim of the study was to establish a scoring system to evaluate the ventilator performance in the context of COVID-19. The scoring system was established by weighting the ventilator performance on five different aspects: the stability of pressurization, response to leaks alteration, performance of reaction, volume delivery, and accuracy in oxygen delivery. The weighting factors were determined with analytic hierarchy process (AHP). Survey was sent out to 66 clinical and mechanical experts. The scoring system was built based on 54 valid replies. A total of 12 commercially available ICU ventilators providing non-invasive ventilation were evaluated using the novel scoring system. A total of eight ICU ventilators with non-invasive ventilation mode and four dedicated non-invasive ventilators were tested according to the scoring system. Four COVID-19 phenotypes were simulated using the ASL5000 lung simulator, namely (1) increased airway resistance (IR) (10 cm H2O/L/s), (2) low compliance (LC) (compliance of 20 ml/cmH2O), (3) low compliance plus increased respiratory effort (LCIE) (respiratory rate of 40 and inspiratory effort of 10 cmH2O), (4) high compliance (HC) (compliance of 50 ml/cmH2O). All of the ventilators were set to three combinations of pressure support and positive end-expiratory pressure levels. The data were collected at baseline and at three customized leak levels. Significant inaccuracies and variations in performance between different non-invasive ventilators were observed, especially in the aspect of leaks alteration, oxygen and volume delivery. Some ventilators have stable performance in different simulated phenotypes whereas the others have over 10% scoring differences. It is feasible to use the proposed scoring system to evaluate the ventilator performance. In the COVID-19 pandemic, clinicians should be aware of possible strengths and weaknesses of ventilators.

ObjectiveIn order to facilitate education for clinical users, performance aspects of the high-flow nasal cannula (HFNC) devices were evaluated in the present study. A multidimensional HFNC clinical evaluation system was established accordingly.Materials and MethodsClinical staff from Chinese hospitals were invited to participate in an online questionnaire survey. The questionnaire was mainly about the accuracy of temperature, flow rate, and oxygen concentration of HFNC, as well as its humidification capacity. We also investigated how the clinical staff of different professions made decisions on HFNC evaluation indicators. Based on the results of the questionnaire survey of clinicians with rich experience in using HFNC, the relative weights of temperature accuracy, flow velocity accuracy, oxygen concentration accuracy, and humidification ability of HFNC equipment were calculated by the AHP to establish a clinical evaluation system. Four kinds of common HFNC devices were tested and evaluated, and the clinical performance of the four kinds of HFNC devices was evaluated by the new scoring system.ResultsA total of 356 clinicians participated in and completed the questionnaire survey. To ensure the reliability of the HFNC evaluation system, we only adopted the questionnaire results of clinicians with rich experience in using HFNCs. Data from 247 questionnaires (80 doctors, 105 nurses, and 62 respiratory therapists [RTs]) were analyzed. A total of 174 participants used HFNC more than once a week; 88.71% of RTs used HFNC ≥ 1 score daily, 62.86% of nurses used HFNC ≥ 1 score daily, and 66.25% of doctors used HFNC ≥ 1 daily. There was no significant difference in the frequency of use between doctors and nurses. Finally, the relative weights of temperature accuracy (0.088), humidification capacity (0.206), flow velocity accuracy (0.311), and oxygen concentration accuracy (0.395) in the HFNC clinical evaluation system were obtained. The relative weights of clinicians with different occupations and the frequency of HFNC use were obtained. After testing four kinds of HFNC devices through the evaluation system, it was found that the four kinds of HFNC devices have different advantages in different clinical performances, and AiRVO2 has excellent performance with regard to temperature accuracy and humidification ability. HF-75A and NeoHiF-i7 are good at ensuring the stability of oxygen concentration and the accuracy of the flow velocity of the transported gas, while OH-80S is relatively stable in all aspects.ConclusionThe clinical evaluation system of HFNC is based on the weight of the experience of clinical personnel with different medical backgrounds. Although the existing practitioners have different educational backgrounds (academic qualifications, majors), our evaluation system can enhance clinical staff’s awareness of HFNC and further optimize the clinical use of HFNC.