COVID-19 in intensive care medicine.

The coronavirus disease 2019 (COVID-19) pandemic can lead to a severe course of disease in immunosuppressed patients requiring intensive care unit treatment even though a number of new vaccines and new antiviral drugs exist. One of the main reasons for this is the generally poorer immune response under immunosuppression. Therefore, it is all the more important to know the stages of the disease and to select the currently available therapeutic options accordingly.Copyright © 2023, The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.

MMFC: MULTI-MODAL FUSION CASCADE FRAMEWORK FOR COVID-19 DISEASE COURSE CLASSIFICATION

Many deep learning methods have been proposed for the diagnosis of COVID-19 since the global pandemic. However, few studies have focused on the disease course classification of COVID-19, which is crucial for radiologists to determine treatment plans. This paper proposes a Multi-Modal Fusion Cascade (MMFC) framework for this task, which can make the most of multi-modal information, including CT image and bio-information (laboratory examination, clinical characterization, etc.). The proposed framework consists of two parts: Bio-Visual Feature Learning Module (BFL) and Joint Decision Module (JD). Firstly, BFL learns the discriminative visual features from the mediastinal window with the assistance of bio-information. According to the official Treatment Protocol of China, the bio-information is chosen and helps the BFL better extract the images’ bio-visual features and then obtained a disease course classification result based on CT images. Secondly, JD uses bio-information again and fuses the confidence of BFL’s result to make the joint decision. Experimental results show that our framework significantly improves accuracy and sensitivity compared to the baseline. © 2021 IEEE

[Blood Group Distribution Characteristics of COVID-19 Patients in Xinjiang].

OBJECTIVE: To analyze and summarize ABO and Rh(D) blood group distribution and related indicators of COVID-19 patients, and understand the relationship between blood group and disease course of COVID-19 patients in Xinjiang. METHODS: A total of 831 patients with confirmed or asymptomatic COVID-19 infection treated in People's Hospital of Xinjiang Uygur Autonomous Region from July 2020 to August 2020 were enrolled as study group, and 2 778 healthy people in a third Grade A hospital in the region during the same period were selected as control group. ABO and Rh(D) blood group antigens were identified, and relevant medical data were collected for statistical analysis. RESULTS: The proportion of O-type population and Rh(D) positive population in the study group was 24.79% and 96.27%, which were lower than those in the normal control group (29.73% and 97.73%) (P<0.05). The proportion of AB type and Rh(D) negative population was 14.20% and 3.73%, which was higher than that in control group (10.62% and 2.27%) (P<0.05). The proportion of female patients in Type O group was lower than that in control group. The proportion of female patients in AB group was higher than that in control group (P<0.01), while the proportion of type O patients in the age group less than or equal to 45 years old and greater than 60 years old was lower. Different blood groups of Uygur population showed their own characteristics in different sex, but there was no statistical significance due to the limited sample (P>0.05). Moreover, the course of disease and clinical diagnosis of COVID-19 patients were different among different blood groups (P<0.05). CONCLUSION: This study found that the blood type distribution of COVID-19 patients in Xinjiang has its own characteristics, and the blood type is related to the course and clinical diagnosis of COVID-19. In the future, the data can be widely included in people from different ethnic groups and different regions to improve relevant studies.

[Mild COVID-19 disease in healthcare workers at a German university clinic : The "first wave" at the University Medical Center Hamburg-Eppendorf]. / Milde COVID-19-Verläufe bei Mitarbeitenden einer Universitätsklinik : Die "erste Welle" am Universitätsklinikum Hamburg-Eppendorf.

BACKGROUND: Healthcare workers are among the most exposed and potentially most threatened populations of the ongoing COVID-19 pandemic. Despite some reports on numbers of infections with SARS-CoV­2 in German healthcare workers, the courses of their clinical presentation when affected by COVID-19 are not well described. OBJECTIVE: In this contribution, characteristics and progressions of infected cases among healthcare workers at the University Medical Center Hamburg-Eppendorf during the first wave of the COVID-19 pandemic will be presented. METHODS: Between 1 July and 28 July 2020, 67 healthcare workers, who previously tested positive for SARS-CoV­2 via PCR, were invited via E­mail to participate in an anonymous online questionnaire; 39 persons participated. RESULTS: Participants (58%) were mostly ≤ 39 years old (64%) and female (70%). Most healthcare workers were involved in direct patient management (85%), including contact with SARS-CoV­2 positive patients (62%). All participants reported acute symptoms with a median duration of 19 days. The most frequent symptoms were fatigue (85%), anosmia (67%), cough (64%), headache (62%), and shortness of breath (51%). The disease course was mostly mild with low admission rates (5%). Ongoing symptoms lasting more than four weeks post-symptom-onset, particularly anosmia, fatigue, and shortness of breath, were reported by 38%. This group more frequently had pre-existing conditions (53% vs. 12%, p = 0.010), specifically hypertension (27% vs. 4%, p = 0.062). DISCUSSION: Healthcare workers reported mostly mild courses of COVID-19 despite increased contact with SARS-CoV-2 patients. However, some reported persistent symptoms months after infection.

Dynamical characteristics of the COVID-19 epidemic: Estimation from cases in Colombia.

OBJECTIVE: To characterize the dynamics of the coronavirus disease 2019 (COVID-19) epidemic, for modeling purposes. METHODS: Data from Colombian official case information were collated for a period of 5 months. Dynamical parameters of the disease spread were then estimated from the data. Probability distribution models were identified, representing the time from symptom onset to hospitalization, to intensive care unit (ICU) admission, and to death. Kaplan-Meier estimates were also computed for the probability of eventually requiring hospitalization, needing ICU attention, and dying from the disease (the case fatality ratio). RESULTS: Probability distributions of the times and probabilities were computed for the population and for groups based on age and sex. The results showed that for the times that characterize the course of the disease for a given patient (time to hospitalization, ICU admission, or death), the variation from one age group to another was very small (around 10% of the fixed effect intercept) and the effect of sex was even smaller (around 1%). The course of the disease appeared to be very similar for all patients. On the other hand, the probability that a patient would advance from one stage of the disease to another (to hospitalization, ICU admission, or death) was heavily influenced by sex and age. The relative risk of death for male individuals was 1.7 times that of female individuals (based on 22 924 deaths). CONCLUSIONS: The times from one stage of the disease to another were almost independent of the major patient variables (sex, age). This was in stark contrast to the probabilities of progressing from one stage to another, which showed a strong dependence on age and sex. Data also showed that the length of hospital and ICU stays were almost independent of sex and age. The only factor that affected this length was the eventual outcome of the disease (survival or death); the time was significantly longer for surviving patients.