Identification and Validation of a Novel Clinical Signature to Predict the Prognosis in Confirmed Coronavirus Disease 2019 Patients. Wu Shangrong,Du Zhiguo,Shen Sanying,Zhang Bo,Yang Hong,Li Xia,Cui Wei,Cheng Fangxiong,Huang Jin Clinical infectious diseases : an official publication of the Infectious Diseases Society of America BACKGROUND:Our aim in this study was to identify a prognostic biomarker to predict the disease prognosis and reduce the mortality rate of coronavirus disease 2019 (COVID-19), which has caused a worldwide pandemic. METHODS:COVID-19 patients were randomly divided into training and test groups. Univariate and multivariate Cox regression analyses were performed to identify the disease prognosis signature, which was selected to establish a risk model in the training group. The disease prognosis signature of COVID-19 was validated in the test group. RESULTS:The signature of COVID-19 was combined with the following 5 indicators: neutrophil count, lymphocyte count, procalcitonin, age, and C-reactive protein. The signature stratified patients into high- and low-risk groups with significantly relevant disease prognosis (log-rank test, P < .001) in the training group. The survival analysis indicated that the high-risk group displayed substantially lower survival probability than the low-risk group (log-rank test, P < .001). The area under the receiver operating characteristic (ROC) curve showed that the signature of COVID-19 displayed the highest predictive accuracy regarding disease prognosis, which was 0.955 in the training group and 0.945 in the test group. The ROC analysis of both groups demonstrated that the predictive ability of the signature surpassed the use of each of the 5 indicators alone. CONCLUSIONS:The signature of COVID-19 presents a novel predictor and prognostic biomarker for closely monitoring patients and providing timely treatment for those who are severely or critically ill. 10.1093/cid/ciaa793

Prediction for Progression Risk in Patients With COVID-19 Pneumonia: The CALL Score. Ji Dong,Zhang Dawei,Xu Jing,Chen Zhu,Yang Tieniu,Zhao Peng,Chen Guofeng,Cheng Gregory,Wang Yudong,Bi Jingfeng,Tan Lin,Lau George,Qin Enqiang Clinical infectious diseases : an official publication of the Infectious Diseases Society of America BACKGROUND:We aimed to clarify high-risk factors for coronavirus disease 2019 (COVID-19) with multivariate analysis and establish a predictive model of disease progression to help clinicians better choose a therapeutic strategy. METHODS:All consecutive patients with COVID-19 admitted to Fuyang Second People's Hospital or the Fifth Medical Center of Chinese PLA General Hospital between 20 January and 22 February 2020 were enrolled and their clinical data were retrospectively collected. Multivariate Cox regression was used to identify risk factors associated with progression, which were then were incorporated into a nomogram to establish a novel prediction scoring model. ROC was used to assess the performance of the model. RESULTS:Overall, 208 patients were divided into a stable group (n = 168, 80.8%) and a progressive group (n = 40,19.2%) based on whether their conditions worsened during hospitalization. Univariate and multivariate analyses showed that comorbidity, older age, lower lymphocyte count, and higher lactate dehydrogenase at presentation were independent high-risk factors for COVID-19 progression. Incorporating these 4 factors, the nomogram achieved good concordance indexes of .86 (95% confidence interval [CI], .81-.91) and well-fitted calibration curves. A novel scoring model, named as CALL, was established; its area under the ROC was .91 (95% CI, .86-.94). Using a cutoff of 6 points, the positive and negative predictive values were 50.7% (38.9-62.4%) and 98.5% (94.7-99.8%), respectively. CONCLUSIONS:Using the CALL score model, clinicians can improve the therapeutic effect and reduce the mortality of COVID-19 with more accurate and efficient use of medical resources. 10.1093/cid/ciaa414

A Tool for Early Prediction of Severe Coronavirus Disease 2019 (COVID-19): A Multicenter Study Using the Risk Nomogram in Wuhan and Guangdong, China. Gong Jiao,Ou Jingyi,Qiu Xueping,Jie Yusheng,Chen Yaqiong,Yuan Lianxiong,Cao Jing,Tan Mingkai,Xu Wenxiong,Zheng Fang,Shi Yaling,Hu Bo Clinical infectious diseases : an official publication of the Infectious Diseases Society of America BACKGROUND:Because there is no reliable risk stratification tool for severe coronavirus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identification of cases at high risk of progression to severe COVID-19. METHODS:In this retrospective multicenter study, 372 hospitalized patients with nonsevere COVID-19 were followed for > 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and those who maintained a nonsevere state were assigned to the severe and nonsevere groups, respectively. Based on baseline data of the 2 groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluated its performance. RESULTS:The training cohort consisted of 189 patients, and the 2 independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.4%) patients developed severe COVID-19. Older age; higher serum lactate dehydrogenase, C-reactive protein, coefficient of variation of red blood cell distribution width, blood urea nitrogen, and direct bilirubin; and lower albumin were associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the training cohort (area under the curve [AUC], 0.912 [95% confidence interval {CI}, .846-.978]; sensitivity 85.7%, specificity 87.6%) and the validation cohort (AUC, 0.853 [95% CI, .790-.916]; sensitivity 77.5%, specificity 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analyses indicated that nomogram conferred high clinical net benefit. CONCLUSIONS:Our nomogram could help clinicians with early identification of patients who will progress to severe COVID-19, which will enable better centralized management and early treatment of severe disease. 10.1093/cid/ciaa443

Clinical and pathological investigation of patients with severe COVID-19. Li Shaohua,Jiang Lina,Li Xi,Lin Fang,Wang Yijin,Li Boan,Jiang Tianjun,An Weimin,Liu Shuhong,Liu Hongyang,Xu Pengfei,Zhao Lihua,Zhang Lixin,Mu Jinsong,Wang Hongwei,Kang Jiarui,Li Yan,Huang Lei,Zhu Caizhong,Zhao Shousong,Lu Jiangyang,Ji Junsheng,Zhao Jingmin JCI insight BACKGROUND:Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory coronavirus 2 (SARS-CoV-2), has become a pandemic. This study addresses the clinical and immunopathological characteristics of severe COVID-19. METHODS:Sixty-nine patients with COVID-19 were classified into severe and nonsevere groups to analyze their clinical and laboratory characteristics. A panel of blood cytokines was quantified over time. Biopsy specimens from 2 deceased cases were obtained for immunopathological, ultrastructural, and in situ hybridization examinations. RESULTS:Circulating cytokines, including IL-8, IL-6, TNF-α, IP10, MCP1, and RANTES, were significantly elevated in patients with severe COVID-19. Dynamic IL-6 and IL-8 were associated with disease progression. SARS-CoV-2 was demonstrated to infect type II and type I pneumocytes and endothelial cells, leading to severe lung damage through cell pyroptosis and apoptosis. In severe cases, lymphopenia, neutrophilia, depletion of CD4+ and CD8+ T lymphocytes, and massive macrophage and neutrophil infiltrates were observed in both blood and lung tissues. CONCLUSIONS:A panel of circulating cytokines could be used to predict disease deterioration and inform clinical interventions. Severe pulmonary damage was predominantly attributed to both cytopathy caused by SARS-CoV-2 and immunopathologic damage. Strategies that prohibit pulmonary recruitment and overactivation of inflammatory cells by suppressing cytokine storm might improve the outcomes of patients with severe COVID-19. 10.1172/jci.insight.138070

Predictors for Severe COVID-19 Infection. Bhargava Ashish,Fukushima Elisa Akagi,Levine Miriam,Zhao Wei,Tanveer Farah,Szpunar Susanna M,Saravolatz Louis Clinical infectious diseases : an official publication of the Infectious Diseases Society of America BACKGROUND:COVID-19 is a pandemic disease caused by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Predictors for severe COVID-19 infection have not been well defined. Determination of risk factors for severe infection would enable identifying patients who may benefit from aggressive supportive care and early intervention. METHODS:We conducted a retrospective observational study of 197 patients with confirmed COVID-19 admitted to a tertiary academic medical center. RESULTS:Of 197 hospitalized patients, the mean (SD) age of the cohort was 60.6 (16.2) years, 103 (52.3%) were male, and 156 (82.1%) were black. Severe COVID-19 infection was noted in 74 (37.6%) patients, requiring intubation. Patients aged above 60 were significantly more likely to have severe infection. Patients with severe infection were significantly more likely to have diabetes, renal disease, and chronic pulmonary disease and had significantly higher white blood cell counts, lower lymphocyte counts, and increased C-reactive protein (CRP) than patients with nonsevere infection. In multivariable logistic regression analysis, risk factors for severe infection included pre-existing renal disease (odds ratio [OR], 7.4; 95% CI, 2.5-22.0), oxygen requirement at hospitalization (OR, 2.9; 95% CI, 1.3-6.7), acute renal injury (OR, 2.7; 95% CI, 1.3-5.6), and CRP on admission (OR, 1.006; 95% CI, 1.001-1.01). Race, age, and socioeconomic status were not independent predictors. CONCLUSIONS:Acute or pre-existing renal disease, supplemental oxygen upon hospitalization, and admission CRP were independent predictors for the development of severe COVID-19. Every 1-unit increase in CRP increased the risk of severe disease by 0.06%. 10.1093/cid/ciaa674