Mortality risk analysis for patients with severe coronavirus disease 2019 pneumonia

BACKGROUND: Coronavirus Disease 2019 (COVID-19) is currently a global pandemic. Information about predicting mortality in severe COVID-19 remains unclear. METHODS: A total of 151 COVID-19 in-patients from January 23 to March 8, 2020, were divided into severe and critically severe groups and survival and mortality groups. Differences in the clinical and imaging data between the groups were analyzed. Factors associated with COVID-19 mortality were analyzed by logistic regression, and a mortality prediction model was developed. RESULTS: Many clinical and imaging indices were significantly different between groups, including age, epidemic history, medical history, duration of symptoms before admission, routine blood parameters, inflammatory-related factors, Na+, myocardial zymogram, liver and renal function, coagulation function, fraction of inspired oxygen and complications. The proportions of patients with imaging Stage III and a comprehensive computed tomography score were significantly increased in the mortality group. Factors in the prediction model included patient age, cardiac injury, acute kidney injury, and acute respiratory distress syndrome. The area under the receiver operating characteristic curve of the prediction model was 0.9593. CONCLUSIONS: The clinical and imaging data reflected the severity of COVID-19 pneumonia. The mortality prediction model might be a promising method to help clinicians quickly identify COVID-19 patients who are at high risk of death.

Comparison of acute respiratory distress syndrome in patients with COVID-19 and influenza A (H7N9) virus infection.

OBJECTIVES: We aimed to compared the clinical features of acute respiratory distress syndrome (ARDS) induced by COVID-19 and H7N9 virus infections. METHODS: Clinical data of 100 patients with COVID-19 and 46 patients with H7N9 were retrospectively analyzed. RESULTS: Elevated inflammatory indices and coagulation disorders were more common in COVID-19-ARDS group than in the H7N9-ARDS group. The median interval from illness onset to ARDS development was shorter in H7N9-ARDS. The PaO2/FiO2 level was lower in H7N9-ARDS, whereas the Sepsis-related Organ Failure Assessment score was higher in COVID-19-ARDS. The proportion of patients with disseminated intravascular coagulation and liver injury in COVID-19-ARDS and H7N9-ARDS was 45.5% versus 3.1% and 28.8% versus 50%, respectively (P <0.05). The mean interval from illness onset to death was shorter in H7N9-ARDS. A total of 59.1% patients with H7N9-ARDS died of refractory hypoxemia compared with 28.9% with COVID-19-ARDS (P = 0.014). Patients with COVID-19-ARDS were more likely to die of septic shock and multiple organ dysfunction compared with H7N9-ARDS (71.2% vs 36.4%, P = 0.005). CONCLUSION: Patients with H7N9 were more susceptible to develop severe ARDS and showed a more acute disease course. COVID-19-ARDS was associated with severe inflammatory response and coagulation dysfunction, whereas liver injury was more common in H7N9-ARDS. The main causes of death between patients with the two diseases were different.

Identification and Prediction of Novel Clinical Phenotypes for Intensive Care Patients With SARS-CoV-2 Pneumonia: An Observational Cohort Study.

Background: Phenotypes have been identified within heterogeneous disease, such as acute respiratory distress syndrome and sepsis, which are associated with important prognostic and therapeutic implications. The present study sought to assess whether phenotypes can be derived from intensive care patients with coronavirus disease 2019 (COVID-19), to assess the correlation with prognosis, and to develop a parsimonious model for phenotype identification. Methods: Adult patients with COVID-19 from Tongji hospital between January 2020 and March 2020 were included. The consensus k means clustering and latent class analysis (LCA) were applied to identify phenotypes using 26 clinical variables. We then employed machine learning algorithms to select a maximum of five important classifier variables, which were further used to establish a nested logistic regression model for phenotype identification. Results: Both consensus k means clustering and LCA showed that a two-phenotype model was the best fit for the present cohort (N = 504). A total of 182 patients (36.1%) were classified as hyperactive phenotype, who exhibited a higher 28-day mortality and higher rates of organ dysfunction than did those in hypoactive phenotype. The top five variables used to assign phenotypes were neutrophil-to-lymphocyte ratio (NLR), ratio of pulse oxygen saturation to the fractional concentration of oxygen in inspired air (Spo2/Fio2) ratio, lactate dehydrogenase (LDH), tumor necrosis factor α (TNF-α), and urea nitrogen. From the nested logistic models, three-variable (NLR, Spo2/Fio2 ratio, and LDH) and four-variable (three-variable plus TNF-α) models were adjudicated to be the best performing, with the area under the curve of 0.95 [95% confidence interval (CI) = 0.94-0.97] and 0.97 (95% CI = 0.96-0.98), respectively. Conclusion: We identified two phenotypes within COVID-19, with different host responses and outcomes. The phenotypes can be accurately identified with parsimonious classifier models using three or four variables.

Specific cytokines in the inflammatory cytokine storm of patients with COVID-19-associated acute respiratory distress syndrome and extrapulmonary multiple-organ dysfunction.

BACKGROUND: To date, specific cytokines associated with development of acute respiratory distress syndrome (ARDS) and extrapulmonary multiple organ dysfunction (MOD) in COVID-19 patients have not been systematically described. We determined the levels of inflammatory cytokines in patients with COVID-19 and their relationships with ARDS and extrapulmonary MOD. METHODS: The clinical and laboratory data of 94 COVID-19 patients with and without ARDS were analyzed. The levels of inflammatory cytokines (interleukin 6 [IL-6], IL-8, IL-10, and tumor necrosis factor α [TNF-α]) were measured on days 1, 3, and 5 following admission. Seventeen healthy volunteers were recruited as controls. Correlations in the levels of inflammatory cytokines with clinical and laboratory variables were analyzed, furthermore, we also explored the relationships of different cytokines with ARDS and extrapulmonary MOD. RESULTS: The ARDS group had higher serum levels of all 4 inflammatory cytokines than the controls, and these levels steadily increased after admission. The ARDS group also had higher levels of IL-6, IL-8, and IL-10 than the non-ARDS group, and the levels of these cytokines correlated significantly with coagulation parameters and disseminated intravascular coagulation (DIC). The levels of IL-6 and TNF-α correlated with the levels of creatinine and urea nitrogen, and were also higher in ARDS patients with acute kidney injury (AKI). All 4 inflammatory cytokines had negative correlations with PaO2/FiO2. IL-6, IL-8, and TNF-α had positive correlations with the APACHE-II score. Relative to survivors, non-survivors had higher levels of IL-6 and IL-10 at admission, and increasing levels over time. CONCLUSIONS: The cytokine storm apparently contributed to the development of ARDS and extrapulmonary MOD in COVID-19 patients. The levels of IL-6, IL-8, and IL-10 correlated with DIC, and the levels of IL-6 and TNF-α were associated with AKI. Relative to survivors, patients who died within 28 days had increased levels of IL-6 and IL-10.

Early risk factors for extrapulmonary organ injury in adult COVID-19 patients.

BACKGROUND: The novel 2019 coronavirus (COVID-19) has caused a global pandemic, and often leads to extrapulmonary organ injury. However, the risk factors for extrapulmonary organ injury are still unclear. We aim to explore the risk factors for extrapulmonary organ injury and the association between extrapulmonary organ injury and the prognosis in COVID-19 patients. METHODS: We implemented a single-center, retrospective, observational study, in which a total of 349 confirmed COVID-19 patients admitted to Tongji Hospital from January 25, 2020, to February 25, 2020, were enrolled. We collected demographic, clinical, laboratory, and treatment data from electronic medical records. Potential risk factors for extrapulmonary organ injury of COVID-19 patients were analyzed by a multivariable binary logistic model, and multivariable Cox proportional hazards regression model was used for survival analysis in the patients with extrapulmonary organ injury. RESULTS: The average age of the included patients was 61.73±14.64 years. In the final logistic model, variables including aged 60 or older [odds ratio (OR) 1.826, 95% confidence interval (CI): 1.060-3.142], acute respiratory distress syndrome (ARDS) (OR 2.748, 95% CI: 1.051-7.185), lymphocytes count lower than 1.1×109/L (OR 0.478, 95% CI: 0.240-0.949), level of interleukin-6 (IL-6) greater than 7 pg/mL (OR 1.664, 95% CI: 1.005-2.751) and D-Dimer greater than 0.5 µg/mL (OR 2.190, 95% CI: 1.176-4.084) were significantly associated with the extrapulmonary organ injury. Kaplan-Meier curve and log-rank test showed that the probabilities of survival for patients with extrapulmonary organ injury were significantly lower than those without extrapulmonary organ injury. Multivariate Cox proportional hazards model showed that only myocardial injury (P=0.000, HR: 5.068, 95% CI: 2.728-9.417) and circulatory system injury (P=0.000, HR: 4.076, 95% CI: 2.216-7.498) were the independent factors associated with COVID-19 patients' poor prognosis. CONCLUSIONS: Older age, lymphocytopenia, high level of D-Dimer and IL-6, and the severity of lung injury were the high-risk factors of extrapulmonary organ injury in COVID-19 patients. Myocardial and circulatory system injury were the most important risk factors related to poor outcomes of COVID-19 patients. It may help clinicians to identify extrapulmonary organ injury early and initiate appropriate treatment.

Corticosteroid Therapy Is Associated With Improved Outcome in Critically Ill Patients With COVID-19 With Hyperinflammatory Phenotype.

BACKGROUND: Corticosteroid therapy is used commonly in patients with COVID-19, although its impact on outcomes and which patients could benefit from corticosteroid therapy are uncertain. RESEARCH QUESTION: Are clinical phenotypes of COVID-19 associated with differential response to corticosteroid therapy? STUDY DESIGN AND METHODS: Critically ill patients with COVID-19 from Tongji Hospital treated between January and February 2020 were included, and the main exposure of interest was the administration of IV corticosteroids. The primary outcome was 28-day mortality. Marginal structural modeling was used to account for baseline and time-dependent confounders. An unsupervised machine learning approach was carried out to identify phenotypes of COVID-19. RESULTS: A total of 428 patients were included; 280 of 428 patients (65.4%) received corticosteroid therapy. The 28-day mortality was significantly higher in patients who received corticosteroid therapy than in those who did not (53.9% vs 19.6%; P < .0001). After marginal structural modeling, corticosteroid therapy was not associated significantly with 28-day mortality (hazard ratio [HR], 0.80; 95% CI, 0.54-1.18; P = .26). Our analysis identified two phenotypes of COVID-19, and compared with the hypoinflammatory phenotype, the hyperinflammatory phenotype was characterized by elevated levels of proinflammatory cytokines, higher Sequential Organ Failure Assessment scores, and higher rates of complications. Corticosteroid therapy was associated with a reduced 28-day mortality (HR, 0.45; 95% CI, 0.25-0.80; P = .0062) in patients with the hyperinflammatory phenotype. INTERPRETATION: For critically ill patients with COVID-19, corticosteroid therapy was not associated with 28-day mortality, but the use of corticosteroids showed significant survival benefits in patients with the hyperinflammatory phenotype.