Investigation and emergency management of a nosocomial infection outbreak of Omicron BA.5.1.3 strain of SARS-CoV-2

Objective: To investigate a nosocomial infection outbreak of Omicron BA.5.1.3 strain of SARS-CoV-2, and to analyze the transmission mode of Omicron BA.5.1.3 strain in hospitals, in order to evaluate the effect of nosocomial infection control and provide a basis for the epidemic prevention and control of this strain in hospitals. Methods: The onsite epidemiological methods were used to investigate the confirmed cases and their close contacts, and the prevention and control measures of nosocomial infection caused by this outbreak were studied. Results: The outbreak time of nosocomial infection was from August 4 to August 8, and the incubation period was 1-5 days, with an average incubation period of 3.0 days. The first confirmed case was a 53-year-old woman who received three doses of COVID-19 vaccine and accompanied the family of the patient in the hospital. She had traveled to a high-risk area three days before onset of the disease, and the virus type was Omicron BA.5.1.3 strain. The outbreak area was two adjacent wards of the hospital, and the incidence rates of inpatients in the two wards were 66.67% (2/3) and 33.33% (1/3), respectively. A total of 967 people were affected, including 1 imported case, 4 hospitalized cases (3 hospitalized patients and 1 nurse), 537 close contacts and 425 secondary close contacts. On August 5, the city's disease control and prevention telephone notified the first confirmed COVID-19 case. Within 0.5 hours, the ward where the case was located was sealed and static management was carried out. Measures such as district grid management, nucleic acid test in the whole hospital and in-hospital flow control were initiated. Environmental sampling, whole environment disinfection and telephone flow adjustment of case 1 were completed within 4 hours. Close contacts, secondary close contacts sampling and control were completed within 24 hours. We paid attention to the dynamics of close contacts and secondary close contacts, as if whose nucleic acid was positive, further measures could be taken to eliminate the risks. The hospital returned to normal management on August 13. Conclusion: The novel coronavirus BA.5.1.3 strain shows strong pathogenicity, short incubation period, causing overall mild disease. Timely and comprehensive prevention and control measures were the key meathods to nosocomial infection control.

AI detection of mild COVID-19 pneumonia from chest CT scans.

OBJECTIVES: An artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated. METHODS: In this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model. RESULTS: The proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI: 89.2-93.9%), the overall specificity was 90.5% (304/336; p < 0.001, 95% CI: 88.0-92.9%) and the general AUC value was 0.955 (p < 0.001). CONCLUSIONS: A deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription-polymerase chain reaction (RT-PCR) test. KEY POINTS: • The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible. • The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified. • This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations.

Highlighted Prospects of an IgM/IgG Antibodies Test in Identifying Individuals With Asymptomatic Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection.

CONTEXT.­: Covert severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections could be seeding new outbreaks. How to identify asymptomatic SARS-CoV-2 infections early has become a global focus. OBJECTIVE.­: To explore the roles of immunoglobulin M (IgM) and immunoglobulin G (IgG) antibodies detection, nucleic acid tests, and computed tomography (CT) scanning to identify asymptomatic SARS-CoV-2 infection. DESIGN.­: The clinical data of 389 individuals with close contacts, including in general characteristics, SARS-CoV-2 etiology, serum-specific IgM and IgG antibody detection and CT imaging results, were systematically analyzed. RESULTS.­: The present study showed that only 89 of 389 individuals with close contacts were positive after the first nucleic acid test, while 300 individuals were still negative after 2 nucleic acid tests. Among the 300 individuals, 75 did not have pneumonia, and the other 225 individuals had pulmonary imaging changes. A total of 143 individuals were eventually diagnosed as having asymptomatic infection through IgM antibody and IgG antibody detection. The sensitivity, specificity, and false-negative rate of IgM and IgG antibody detection were approximately 97.1% (347 of 357), 95.3% (204 of 214), and 4.67% (10 of 214), respectively. It also indicated that during approximately 2 weeks, most individuals were both IgM positive and IgG positive, accounting for 68.57% (72 of 105). During approximately 3 weeks, the proportion of IgM-positive and IgG-positive individuals decreased to 8.57% (9 of 105), and the proportion of IgM-negative and IgG-positive individuals increased to 76.19% (80 of 105). CONCLUSIONS.­: There are highlighted prospects of IgM/IgG antibody detection as a preferred method in identifying the individuals with asymptomatic SARS-CoV-2 infection, especially combined with nucleic acid tests and pulmonary CT scanning.

Pilot Study of Robot-Assisted Teleultrasound Based on 5G Network: A New Feasible Strategy for Early Imaging Assessment During COVID-19 Pandemic.

Early diagnosis is critical for the prevention and control of the coronavirus disease 2019 (COVID-19). We attempted to apply a protocol using teleultrasound, which is supported by the 5G network, to explore the feasibility of solving the problem of early imaging assessment of COVID-19. Four male patients with confirmed or suspected COVID-19 were hospitalized in isolation wards in two different cities. Ultrasound specialists, located in two other different cities, carried out the robot-assisted teleultrasound and remote consultation in order to settle the problem of early cardiopulmonary evaluation. Lung ultrasound, brief echocardiography, and blood volume assessment were performed. Whenever difficulties of remote manipulation and diagnosis occurred, the alternative examination was repeated by a specialist from another city, and in sequence, remote consultation was conducted immediately to meet the consensus. The ultrasound specialists successfully completed the telerobotic ultrasound. Lung ultrasound indicated signs of pneumonia with varying degrees in all cases and mild pleural effusion in one case. No abnormalities of cardiac structure and function and blood volume were detected. Remote consultation on the issue of manipulation practice, and the diagnosis in one case was conducted. The cardiopulmonary information was delivered to the frontline clinicians immediately for further treatment. The practice of teleultrasound protocol makes early diagnosis and repeated assessment available in the isolation ward. Ultrasound specialists can be protected from infection, and personal protective equipment can be spared. Quality control can be ensured by remote consultations among doctors. This protocol is worth consideration as a feasible strategy for early imaging assessment in the COVID-19 pandemic.