Predictive Risk Factors at Admission and a "Burning Point" During Hospitalization Serve as Sequential Alerts for Critical Illness in Patients With COVID-19.

Background: We intended to establish a novel critical illness prediction system combining baseline risk factors with dynamic laboratory tests for patients with coronavirus disease 2019 (COVID-19). Methods: We evaluated patients with COVID-19 admitted to Wuhan West Union Hospital between 12 January and 25 February 2020. The data of patients were collected, and the illness severity was assessed. Results: Among 1,150 enrolled patients, 296 (25.7%) patients developed into critical illness. A baseline nomogram model consists of seven variables including age [odds ratio (OR), 1.028; 95% confidence interval (CI), 1.004-1.052], sequential organ failure assessment (SOFA) score (OR, 4.367; 95% CI, 3.230-5.903), neutrophil-to-lymphocyte ratio (NLR; OR, 1.094; 95% CI, 1.024-1.168), D-dimer (OR, 1.476; 95% CI, 1.107-1.968), lactate dehydrogenase (LDH; OR, 1.004; 95% CI, 1.001-1.006), international normalised ratio (INR; OR, 1.027; 95% CI, 0.999-1.055), and pneumonia area interpreted from computed tomography (CT) images (medium vs. small [OR, 4.358; 95% CI, 2.188-8.678], and large vs. small [OR, 9.567; 95% CI, 3.982-22.986]) were established to predict the risk for critical illness at admission. The differentiating power of this nomogram scoring system was perfect with an area under the curve (AUC) of 0.960 (95% CI, 0.941-0.972) in the training set and an AUC of 0.958 (95% CI, 0.936-0.980) in the testing set. In addition, a linear mixed model (LMM) based on dynamic change of seven variables consisting of SOFA score (value, 2; increase per day [I/d], +0.49), NLR (value, 10.61; I/d, +2.07), C-reactive protein (CRP; value, 46.9 mg/L; I/d, +4.95), glucose (value, 7.83 mmol/L; I/d, +0.2), D-dimer (value, 6.08 µg/L; I/d, +0.28), LDH (value, 461 U/L; I/d, +13.95), and blood urea nitrogen (BUN value, 6.51 mmol/L; I/d, +0.55) were established to assist in predicting occurrence time of critical illness onset during hospitalization. Conclusion: The two-checkpoint system could assist in accurately and dynamically predicting critical illness and timely adjusting the treatment regimen for patients with COVID-19.

Plasma Metabolomic Profiles in Recovered COVID-19 Patients without Previous Underlying Diseases 3 Months After Discharge.

BACKGROUND: It remains unclear whether discharged COVID-19 patients have fully recovered from severe complications, including the differences in the post-infection metabolomic profiles of patients with different disease severities. METHODS: COVID-19-recovered patients, who had no previous underlying diseases and were discharged from Wuhan Union Hospital for 3 months, and matched healthy controls (HCs) were recruited in this prospective cohort study. We examined the blood biochemical indicators, cytokines, lung computed tomography scans, including 39 HCs, 18 recovered asymptomatic (RAs), 34 recovered moderate (RMs), and 44 recovered severe/ critical patients (RCs). A liquid chromatography-mass spectrometry-based metabolomics approach was employed to profile the global metabolites of fasting plasma of these participants. RESULTS: Clinical data and metabolomic profiles suggested that RAs recovered well, but some clinical indicators and plasma metabolites in RMs and RCs were still abnormal as compared with HCs, such as decreased taurine, succinic acid, hippuric acid, some indoles, and lipid species. The disturbed metabolic pathway mainly involved the tricarboxylic cycle, purine, and glycerophospholipid metabolism. Moreover, metabolite alterations differ between RMs and RCs when compared with HCs. Correlation analysis revealed that many differential metabolites were closely associated with inflammation and the renal, pulmonary, heart, hepatic, and coagulation system functions. CONCLUSION: We uncovered metabolite clusters pathologically relevant to the recovery state in discharged COVID-19 patients which may provide new insights into the pathogenesis of potential organ damage in recovered patients.

Relationship between serum tumor markers and Anaplastic Lymphoma Kinase mutations in stage IV lung adenocarcinoma in Hubei province, Central China.

OBJECTIVE: The aim of this study was to explore the predictive value of carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCCAg), and neuron-specific enolase (NSE) in the prediction of anaplastic lymphoma kinase (ALK) mutations in advance stage non-small cell lung cancer (NSCLC). SUBJECTS AND METHODS: A total of 482 cases with untreated lung adenocarcinoma were retrospectively reviewed. Finally, 72 patients with stage IV were enrolled because of intact data of the detection of ALK rearrangement and serum tumor markers, as well they have not received any previous anticancer therapy. We used the one-way ANOVA analysis, correlation analysis, and multiple logistic regression analysis to evaluate the relationship between the level of serum tumor markers and ALK mutations. RESULTS: Fifteen cases with ALK mutations and 57 cases without mutations were identified. The result of the one-way ANOVA analysis showed only CEA was significantly associated with ALK mutations (95% CI:39.05-148.88; P = .001). The area under the ROC curve (AUC) of CEA was 0.705 (95%CI:0.567-0.843; P = .015). However, no significant association was observed between CEA and ALK mutations though the result of correlation analysis (P = .069) and multivariate logistic regression analysis (OR = 0.988, 95% CI: 0.972-1.003, P = .111). CONCLUSIONS: In our study, we performed on the patients with stage IV lung adenocarcinoma in our region and found preoperative serum levels of SCCAg, CYRF21-1, and NSE not suitable for the detection of ALK mutation. Although we observed a significant association between CEA and ALK mutations; however, it was not strong enough to distinguish ALK status for the patients in our region.

Value of serum tumor markers for predicting EGFR mutations and positive ALK expression in 1089 Chinese non-small-cell lung cancer patients: A retrospective analysis.

PURPOSE: The role of serum tumor markers (STMs) in the modern management of epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) mutations in lung cancer remains poorly described. In this study, we investigated whether STMs could be a valuable noninvasive tool to predict EGFR mutations and ALK positivity in non-small-cell lung cancer (NSCLC) patients. EXPERIMENTAL DESIGN: We retrospectively reviewed and included 1089 NSCLC patients who underwent EGFR or ALK mutation testing and STMs measurement prior to treatment. The differences in several clinical characteristics and STMs between the subgroups were analyzed. Multivariate logistic regression analysis was performed to identify predictors of EGFR mutations and ALK positivity. RESULTS: EGFR mutations were found more frequently in females (63.11%), never-smokers (59.69%), and those with lung adenocarcinoma (ADC) (53.87%). Negative carbohydrate antigen (CA) 125, ferritin (FERR), squamous cell carcinoma antigen (SCC), and soluble fragment of cytokeratin 19 (CYFRA 21-1) levels were significantly associated with EGFR mutations (p < 0.05). Multivariate analysis demonstrated that ADC, never-smoker status, and negative CA 125 and SCC results were predictors of EGFR mutations (p < 0.05). The receiver operating characteristic (ROC) curve yielded an area under the curve (AUC) of 0.715 (95% confidence interval [CI]: 0.673-0.758) for the combination of the four factors. Positive ALK expression was found more frequently in younger patients (median age: 49 years), females (8.40%), never-smokers (8.82%), and those negative for carcinoembryonic antigen (CEA) (8.02%). Multivariate analysis demonstrated that younger age and never-smoker status were the only independent predictors of ALK positivity (p < 0.05). The ROC curve yielded an AUC of 0.760 (95% CI: 0.677-0.844) for the combination of these two factors. CONCLUSION: STMs are associated with mutant EGFR status and could be integrated with other clinical factors to enhance the ability to distinguish EGFR mutation status among NSCLC patients. For ALK-positive patients, younger age and never-smoker status could predict the mutation status, whereas STMs could not.

Potential roles of IL-1 subfamily members in glycolysis in disease.

The interleukin-(IL)-1 subfamily consists of IL-1α, IL-1ß, IL-1 receptor antagonist IL-1Ra and IL-33. These cytokines are the main members of the IL-1 family and have been widely recognized as having significant roles in pro-inflammatory and immunomodulatory actions. Mounting evidence has revealed that these cytokines also play key roles in the regulation of glycolysis, which is an important metabolic pathway in most organisms that provides energy. Dysregulation of glycolysis is associated with various diseases, including type 2 diabetes, rheumatoid arthritis (RA) and cancer. We reviewed studies addressing the important roles of IL-1 subfamily cytokines, with particular focus on their ability to regulate glycolysis in disease states. In this review, we summarize the potential roles of IL-1 subfamily members in glycolysis in disease states and address the underlying mechanisms. Furthermore, we discuss the potential of these cytokines as therapeutic targets in clinical applications to provide insight into possible therapeutic strategies for treatment, especially for cancers.