Objective:To explore the correlation between early inflammation indicators and the severity of coronavirus disease 2019 (COVID-19).Methods:A retrospective study was conducted. Patients with COVID-19 admitted to Wenzhou Central Hospital from January 17 to February 14, 2020 were enrolled. The general information, chest CT before admission, the first laboratory parameters and chest CT within 24 hours after admission were collected. Patients were followed up for 30 days after the first onset of dyspnea or pulmonary imaging showed that the lesions progressed more than 50% within 24 to 48 hours (according to the criteria for severe cases) as the study endpoint. According to the endpoint, the patients were divided into two groups: mild type/common type group and severe/critical group, and the differences in general information and inflammation index of the two groups were compared. Logistic regression was used to analyze the inflammation index and the severity of COVID-19. Receiver operating characteristic (ROC) curve was draw to evaluate the predictive value of early inflammation indicators for severe/critical in patients with COVID-19.Results:A total of 140 patients with COVID-19 were included, 74 males and 66 females; the average age was (45±14) years old; 6 cases (4.3%) of mild type, 107 cases (76.4%) of common type, and 22 cases (15.7%) of severe type, 5 cases (3.6%) were critical. There were significantly differences in ages (years old: 43±13 vs. 57±13), the proportion of patients with one chronic disease (17.7% vs. 55.6%), C-reactive protein [CRP (mg/L): 7.3 (2.3, 21.0) vs. 40.1 (18.8, 62.6)], lymphocyte count [LYM (×10 9/L): 1.3 (1.0, 1.8) vs. 0.8 (0.7, 1.1)], the neutrophil/lymphocyte ratio [NLR: 2.1 (1.6, 3.0) vs. 3.1 (2.2, 8.8)] and multilobularinltration, hypo-lymphocytosis, bacterial coinfection, smoking history, hyper-tension and age [MuLBSTA score: 5.0 (3.0, 5.0) vs. 5.0 (5.0, 7.0)] between mild/common group and severe/critical group (all P < 0.05). Univariate Logistic regression analysis showed that CRP, NLR, MuLBSTA score, age, and whether chronic diseases were associated with the severity of COVID-19 [odds ratio ( OR) and 95% confidence interval (95% CI) were 1.037 (1.020-1.055), 1.374 (1.123-1.680), 1.574 (1.296-1.911), 1.082 (1.042-1.125), 6.393 (2.551-16.023), respectively, all P < 0.01]. Further multivariate Logistic regression analysis showed that CRP and MuLBSTA score were risk factors for the development of COVID-19 to severe/critical cases [OR and 95% CI were 1.024 (1.002-1.048) and 1.321 (1.027-1.699) respectively, both P < 0.05]. ROC curve analysis showed that the area under the curve for CRP and MuLBSTA score to predict severe/critical cases were both 0.818, and the best cut-off points were 27.4 mg/L and 6.0 points, respectively. Conclusion:CRP and MuLBSTA score are related to the severity of COVID-19, and may have good independent predictive ability for the development of severe/critical illness.

Purpose: This study aimed to identify the risk factors and sonographic variables that could be integrated into a predictive model for endometrial cancer (EC) and atypical endometrial hyperplasia (AEH) in women with abnormal uterine bleeding (AUB). Materials and Methods: This retrospective study included 1837 patients who presented with AUB and underwent endometrial sampling. Multivariable logistic regression was developed based on clinical and sonographic covariates [endometrial thickness (ET), resistance index (RI) of the endometrial vasculature] assessed for their association with EC/AEH in the development group (n=1369), and a predictive nomogram was proposed. The model was validated in 468 patients. Results: Histological examination revealed 167 patients (12.2%) with EC or AEH in the development group. Using multivariable logistic regression, the following variables were incorporated in the prediction of endometrial malignancy: metabolic diseases [odds ratio (OR)=7.764, 95% confidence intervals (CI) 5.042–11.955], family history (OR=3.555, 95% CI 1.055–11.971), age ≥40 years (OR=3.195, 95% CI 1.878–5.435), RI ≤0.5 (OR=8.733, 95% CI 4.311–17.692), and ET ≥10 mm (OR=8.479, 95% CI 5.440–13.216). :A nomogram was created using these five variables with an area under the curve of 0.837 (95% CI 0.800–0.874). The calibration curve showed good agreement between the observed and predicted occurrences. For the validation group, the model provided acceptable discrimination and calibration. Conclusion The proposed nomogram model showed moderate prediction accuracy in the differentiation between benign and malignant endometrial lesions among women with AUB.

Objective: To establish a visualized nomogram with early predictive value for the severity of first-onset acute pancreatitis (AP). Methods: 706 cases of first-onset AP patients admitted to the First Affiliated Hospital of Wenzhou Medical University within 72 hours from January 2013 to January 2016 were collected. According to the revised Atlanta classification of AP in 2012, AP patients was divided into non-severe pancreatitis (NSAP, also called mild acute pancreatitis and moderately severe acute pancreatitis) group and severe acute pancreatitis (SAP) group. The demographic data (age, body mass index and admission time, etc) and laboratory tests (serum amylase, blood sugar, albumin, white blood cells, creatinine, urea nitrogen) were collected and statistically analyzed. Logistic univariate and multivariant regression analysis were performed based on the relevant clinical indicators. The statistically significant indicators were used to obtain regression equations. The R-language software was used to obtain the visualized nomogram via LR model, which was further validated by ROC curve analysis. Results: In univariate logistic regression analysis, the OR (95% CI) values of blood glucose, creatinine at admission and 24 h after admission, urea nitrogen at admission and 24 h after admission, white blood cell, albumin in NSAP group and SAP group were 1.132(1.080-1.186), 1.019(1.013-1.025), 1.026(1.020-1.033), 1.066(1.035-1.099), 1.333(1.241-1.432), 1.083(1.032-1.136), and 0.853(0.811-0.889), and all the differences were statistically significant (all P values <0.05). Multivariate logistic regression analysis showed that the regression equation of the LR model was Y=-2.657-0.116×albumin(g/L)+ 0.082×white blood cell(×10⁹/L)+ 0.118× glycemia(mmol/L)+ 0.022× 24 h after admission creatinine (μmol/L). A total score of more than 60 points on the nomogram predicted the possibility of SAP. If the total score exceeded 130, the possibility of SAP may be up to 14% or more. Furthermore, the ROC curve analysis confirmed that the sensitivity and specificity of LR model established in this study for predicting of SAP were superior to those of urea nitrogen, creatinine and BISAP score alone by AUC, respectively. Conclusions This nomogram may be a useful clinical tool for predicting the severity of the first-onset acute pancreatitis.