BACKGROUND: The study aims to illustrate the clinical characteristics and development of septic shock in intensive care unit (ICU) patients confirmed with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, and to perform a comprehensive analysis of the association between septic shock and clinical outcomes in critically ill patients with coronavirus disease (COVID-19).
METHODS: Patients confirmed with SARS-CoV-2 infection, who were admitted to the ICU of the Third People’s Hospital of Shenzhen from January 1 to February 7, 2020, were enrolled. Clinical characteristics and outcomes were compared between patients with and without septic shock.
RESULTS: In this study, 35 critically ill patients with COVID-19 were included. Among them, the median age was 64 years (interquartile range [IQR] 59-67 years), and 10 (28.4%) patients were female. The median ICU length of stay was 16 days (IQR 8-23 days). Three (8.6%) patients died during hospitalization. Nine (25.7%) patients developed septic shock in the ICU, and these patients had a significantly higher incidence of organ dysfunction and a worse prognosis than patients without septic shock.
CONCLUSIONS: Septic shock is associated with a poor outcome in critically ill COVID-19 patients and is one of the hallmarks of the severity of patients receiving ICU care. A dysregulated immune response, uncontrolled inflammation, and coagulation disorders are strongly associated with the development and progression of COVID-19-related septic shock.

BACKGROUND: This study aims to compare the epidemiological, clinical and laboratory characteristics between patients with coronavirus disease (COVID-19) and influenza A (H1N1), and to develop a differentiating model and a simple scoring system.
METHODS: We retrospectively analyzed the data from patients with COVID-19 and H1N1. The logistic regression model based on clinical and laboratory characteristics was constructed to distinguish COVID-19 from H1N1. Scores were assigned to each of independent discrimination factors based on their odds ratios. The performance of the prediction model and scoring system was assessed.
RESULTS: A total of 236 patients were recruited, including 20 COVID-19 patients and 216 H1N1 patients. Logistic regression revealed that age >34 years, temperature ≤37.5 °C, no sputum or myalgia, lymphocyte ratio ≥20% and creatine kinase-myocardial band isoenzyme (CK-MB) >9.7 U/L were independent differentiating factors for COVID-19. The area under curves (AUCs) of the prediction model and scoring system in differentiating COVID-19 from H1N1 were 0.988 and 0.962, respectively.
CONCLUSIONS: There are certain differences in clinical and laboratory features between patients with COVID-19 and H1N1. The simple scoring system may be a useful tool for the early identification of COVID-19 patients from H1N1 patients.

BACKGROUND: A pandemic of coronavirus disease (COVID-19) has been declared by the World Health Organization (WHO) and caring for critically ill patients is expected to be at the core of battling this disease. However, little is known regarding an early detection of patients at high risk of fatality.

METHODS: This retrospective cohort study recruited consecutive adult patients admitted between February 8 and February 29, 2020, to the three intensive care units (ICUs) in a designated hospital for treating COVID-19 in Wuhan. The detailed clinical information and laboratory results for each patient were obtained. The primary outcome was in-hospital mortality. Potential predictors were analyzed for possible association with outcomes, and the predictive performance of indicators was assessed from the receiver operating characteristic (ROC) curve.

RESULTS: A total of 121 critically ill patients were included in the study, and 28.9% (35/121) of them died in the hospital. The non-survivors were older and more likely to develop acute organ dysfunction, and had higher Sequential Organ Failure Assessment (SOFA) and quick SOFA (qSOFA) scores. Among the laboratory variables on admission, we identified 12 useful biomarkers for the prediction of in-hospital mortality, as suggested by area under the curve (AUC) above 0.80. The AUCs for three markers neutrophil-to-lymphocyte ratio (NLR), thyroid hormones free triiodothyronine (FT3), and ferritin were 0.857, 0.863, and 0.827, respectively. The combination of two easily accessed variables NLR and ferritin had comparable AUC with SOFA score for the prediction of in-hospital mortality (0.901 vs. 0.955, P=0.085).

CONCLUSIONS: Acute organ dysfunction combined with older age is associated with fatal outcomes in COVID-19 patients. Circulating biomarkers could be used as powerful predictors for the in-hospital mortality.

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 R²=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.