Understanding clinical characteristics influencing adverse outcomes of Omicron infection: a retrospective study with propensity score matching from a Fangcang hospital.

Objectives: The epidemic of coronavirus disease 2019 (COVID-19) is causing global health concerns. The aim of this study was to evaluate influence of clinical characteristics on outcomes during the Omicron outbreak. Methods: A total of 25182 hospitalized patients were enrolled, including 39 severe patients and 25143 non-severe patients. Propensity score matching (PSM) was applied to balance the baseline characteristics. Logistic regression analysis was used to assess the risk of severe disease, as well as the risk of prolonged viral shedding time (VST) and increased length of hospital stay (LOS). Results: Before PSM, patients in the severe group were older, had higher symptom scores, and had a higher proportion of comorbidities (p<0.001). After PSM, there were no significant differences in age, gender, symptom score and comorbidities between severe (n=39) and non-severe (n=156) patients. Symptoms of fever (OR=6.358, 95%CI 1.748-23.119, p=0.005) and diarrhea (OR=6.523, 95%CI 1.061-40.110, p=0.043) were independent risk factors for development of severe disease. In non-severe patients, higher symptom score was associated with prolonged VST (OR=1.056, 95% CI 1.000-1.115, p=0.049) and LOS (OR=1.128, 95% CI 1.039-1.225, p=0.004); older age was associated with longer LOS (OR=1.045, 95% CI 1.007-1.084, p=0.020). Conclusion: The overall condition of the Shanghai Omicron epidemic was relatively mild. Potential risk factors for fever, diarrhea, and higher symptom score can help clinicians to predict clinical outcomes in COVID-19 patients.

Artificial intelligence computed tomography helps evaluate the severity of COVID-19 patients: A retrospective study.

BACKGROUND: Computed tomography (CT) is a noninvasive imaging approach to assist the early diagnosis of pneumonia. However, coronavirus disease 2019 (COVID-19) shares similar imaging features with other types of pneumonia, which makes differential diagnosis problematic. Artificial intelligence (AI) has been proven successful in the medical imaging field, which has helped disease identification. However, whether AI can be used to identify the severity of COVID-19 is still underdetermined. METHODS: Data were extracted from 140 patients with confirmed COVID-19. The severity of COVID-19 patients (severe vs. non-severe) was defined at admission, according to American Thoracic Society (ATS) guidelines for community-acquired pneumonia (CAP). The AI-CT rating system constructed by Hangzhou YITU Healthcare Technology Co., Ltd. was used as the analysis tool to analyze chest CT images. RESULTS: A total of 117 diagnosed cases were enrolled, with 40 severe cases and 77 non-severe cases. Severe patients had more dyspnea symptoms on admission (12 vs. 3), higher acute physiology and chronic health evaluation (APACHE) II (9 vs. 4) and sequential organ failure assessment (SOFA) (3 vs. 1) scores, as well as higher CT semiquantitative rating scores (4 vs. 1) and AI-CT rating scores than non-severe patients (P<0.001). The AI-CT score was more predictive of the severity of COVID-19 (AUC=0.929), and ground-glass opacity (GGO) was more predictive of further intubation and mechanical ventilation (AUC=0.836). Furthermore, the CT semiquantitative score was linearly associated with the AI-CT rating system (Adj R 2=75.5%, P<0.001). CONCLUSIONS: AI technology could be used to evaluate disease severity in COVID-19 patients. Although it could not be considered an independent factor, there was no doubt that GGOs displayed more predictive value for further mechanical ventilation.

Lactate dehydrogenase, an independent risk factor of severe COVID-19 patients: a retrospective and observational study.

BACKGROUND: The World Health Organization has declared coronavirus disease 2019 (COVID-19) a public health emergency of global concern. Updated analysis of cases might help identify the risk factors of illness severity. RESULTS: The median age was 63 years, and 44.9% were severe cases. Severe patients had higher APACHE II (8.5 vs. 4.0) and SOFA (2 vs. 1) scores on admission. Among all univariable parameters, lymphocytes, CRP, and LDH were significantly independent risk factors of COVID-19 severity. LDH was positively related both with APACHE II and SOFA scores, as well as P/F ratio and CT scores. LDH (AUC = 0.878) also had a maximum specificity (96.9%), with the cutoff value of 344.5. In addition, LDH was positively correlated with CRP, AST, BNP and cTnI, while negatively correlated with lymphocytes and its subsets. CONCLUSIONS: This study showed that LDH could be identified as a powerful predictive factor for early recognition of lung injury and severe COVID-19 cases. METHODS: We extracted data regarding 107 patients with confirmed COVID-19 from Renmin Hospital of Wuhan University. The degree of severity of COVID-19 patients (severe vs. non-severe) was defined at the time of admission according to American Thoracic Society guidelines for community acquired pneumonia.