Development and validation of a nomogram for predicting in-hospital survival rates of patients with COVID-19.

Objective: Our aim was to develop and validate a nomogram for predicting the in-hospital 14-day (14 d) and 28-day (28 d) survival rates of patients with coronavirus disease 2019 (COVID-19). Methods: Clinical data of patients with COVID-19 admitted to the Renmin Hospital of Wuhan University from December 2022 to February 2023 and the north campus of Shanghai Ninth People's Hospital from April 2022 to June 2022 were collected. A total of 408 patients from Renmin Hospital of Wuhan University were selected as the training cohort, and 151 patients from Shanghai Ninth People's Hospital were selected as the verification cohort. Independent variables were screened using Cox regression analysis, and a nomogram was constructed using R software. The prediction accuracy of the nomogram was evaluated using the receiver operating characteristic (ROC) curve, C-index, and calibration curve. Decision curve analysis was used to evaluate the clinical application value of the model. The nomogram was externally validated using a validation cohort. Result: In total, 559 patients with severe/critical COVID-19 were included in this study, of whom 179 (32.02 %) died. Multivariate Cox regression analysis showed that age >80 years [hazard ratio (HR) = 1.539, 95 % confidence interval (CI): 1.027-2.306, P = 0.037], history of diabetes (HR = 1.741, 95 % CI: 1.253-2.420, P = 0.001), high APACHE II score (HR = 1.083, 95 % CI: 1.042-1.126, P < 0.001), sepsis (HR = 2.387, 95 % CI: 1.707-3.338, P < 0.001), high neutrophil-to-lymphocyte ratio (NLR) (HR = 1.010, 95 % CI: 1.003-1.017, P = 0.007), and high D-dimer level (HR = 1.005, 95 % CI: 1.001-1.009, P = 0.028) were independent risk factors for 14 d and 28 d survival rates, whereas COVID-19 vaccination (HR = 0.625, 95 % CI: 0.440-0.886, P = 0.008) was a protective factor affecting prognosis. ROC curve analysis showed that the area under the curve (AUC) of the 14 d and 28 d hospital survival rates in the training cohort was 0.765 (95 % CI: 0.641-0.923) and 0.814 (95 % CI: 0.702-0.938), respectively, and the AUC of the 14 d and 28 d hospital survival rates in the verification cohort was 0.898 (95 % CI: 0.765-0.962) and 0.875 (95 % CI: 0.741-0.945), respectively. The calibration curves of 14 d and 28 d hospital survival showed that the predicted probability of the model agreed well with the actual probability. Decision curve analysis (DCA) showed that the nomogram has high clinical application value. Conclusion: In-hospital survival rates of patients with COVID-19 were predicted using a nomogram, which will help clinicians in make appropriate clinical decisions.

A Nomogram Model for Prediction of Mortality Risk of Patients with Dangerous Upper Gastrointestinal Bleeding: A Two-center Retrospective Study.

OBJECTIVE: This study aimed to establish a nomogram model to predict the mortality risk of patients with dangerous upper gastrointestinal bleeding (DUGIB), and identify high-risk patients who require emergent therapy. METHODS: From January 2020 to April 2022, the clinical data of 256 DUGIB patients who received treatments in the intensive care unit (ICU) were retrospectively collected from Renmin Hospital of Wuhan University (n=179) and the Eastern Campus of Renmin Hospital of Wuhan University (n=77). The 179 patients were treated as the training cohort, and 77 patients as the validation cohort. Logistic regression analysis was used to calculate the independent risk factors, and R packages were used to construct the nomogram model. The prediction accuracy and identification ability were evaluated by the receiver operating characteristic (ROC) curve, C index and calibration curve. The nomogram model was also simultaneously externally validated. Decision curve analysis (DCA) was then used to demonstrate the clinical value of the model. RESULTS: Logistic regression analysis showed that hematemesis, urea nitrogen level, emergency endoscopy, AIMS65, Glasgow Blatchford score and Rockall score were all independent risk factors for DUGIB. The ROC curve analysis indicated the area under curve (AUC) of the training cohort was 0.980 (95%CI: 0.962-0.997), while the AUC of the validation cohort was 0.790 (95%CI:0.685-0.895). The calibration curves were tested for Hosmer-Lemeshow goodness of fit for both training and validation cohorts (P=0.778, P=0.516). CONCLUSION: The developed nomogram is an effective tool for risk stratification, early identification and intervention for DUGIB patients.

Construction and Validation of Mortality Risk Nomograph Model for Severe/Critical Patients with COVID-19.

Objective: A nomograph model of mortality risk for patients with coronavirus disease 2019 (COVID-19) was established and validated. Methods: We collected the clinical medical records of patients with severe/critical COVID-19 admitted to the eastern campus of Renmin Hospital of Wuhan University from January 2020 to May 2020 and to the north campus of Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, from April 2022 to June 2022. We assigned 254 patients to the former group, which served as the training set, and 113 patients were assigned to the latter group, which served as the validation set. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression were used to select the variables and build the mortality risk prediction model. Results: The nomogram model was constructed with four risk factors for patient mortality following severe/critical COVID-19 (≥3 basic diseases, APACHE II score, urea nitrogen (Urea), and lactic acid (Lac)) and two protective factors (percentage of lymphocyte (L%) and neutrophil-to-platelets ratio (NPR)). The area under the curve (AUC) of the training set was 0.880 (95% confidence interval (95%CI), 0.837~0.923) and the AUC of the validation set was 0.814 (95%CI, 0.705~0.923). The decision curve analysis (DCA) showed that the nomogram model had high clinical value. Conclusion: The nomogram model for predicting the death risk of patients with severe/critical COVID-19 showed good prediction performance, and may be helpful in making appropriate clinical decisions for high-risk patients.

Prognostic value of serum amyloid A in patients with COVID-19.

OBJECTIVE: To investigate the prognostic value of serum amyloid A (SAA) in the patients with Corona Virus Disease 2019 (COVID-19). METHODS: The medical data of 89 COVID-19 patients admitted to Renmin Hospital of Wuhan University from January 3, 2020 to February 26, 2020 were collected. Eighty-nine cases were divided into survival group (53 cases) and non-survival group (36 cases) according to the results of 28-day follow-up. The SAA levels of all patients were recorded and compared on 1 day after admission (before treatment) and 3 days, 5 days, and 7 days after treatment. The ROC curve was drawn to analyze the prognosis of patients with COVID-19 by SAA. RESULTS: The difference of comparison of SAA between survival group and non-survival group before treatment was not statistically significant, Z1 = - 1.426, P = 0.154. The Z1 values (Z1 is the Z value of the rank sum test) of the two groups of patients at 3 days, 5 days, and 7 days after treatment were - 5.569, - 6.967, and - 7.542, respectively. The P values were all less than 0.001, and the difference was statistically significant. The ROC curve results showed that SAA has higher sensitivity to the prognostic value of 1 day (before treatment), 3 days, 5 days, and 7 days after treatment, with values of 0.806, 0.972, 0.861, and 0.961, respectively. Compared with SAA on the 7th day and C-reactive protein, leukocyte count, neutrophil count, lymphocyte count, and hemoglobin on the 7th day, the sensitivities were: 96.1%, 83.3%, 88.3%, 83.3%, 67.9%, and 83.0%, respectively, of which SAA has the highest sensitivity. CONCLUSION: SAA can be used as a predictor of the prognosis in patients with COVID-19.

Lentivirus-Mediated siRNA Targeting ER-α Inhibits Tumorigenesis and Induces Apoptosis in Hepatocarcinoma Cells.

BACKGROUND AND OBJECTIVES: Estrogen receptor-α (ER-α) plays important roles in hepatocarcinogenesis. Recent studies have shown that ER-α could lead to cell cycle progression or inhibition of apoptosis. To better understand the role of ER-α, RNA interference (RNAi) was used to inhibit ER-α expression in the human hepatocellular carcinoma (HCC) cells. METHODS: Lentivirus-mediated ER-α small interfering RNA (siRNA) was transfected into HCC cells Hep3B. ER-α expression was monitored by real-time polymerase chain reaction (PCR) and western blot. Cell proliferation, apoptosis, and invasion were examined by methyl thiazol tetrazolium (MTT), flow cytometry (FCM), and invasion assay, respectively. RESULTS: ER-α siRNA efficiently downregulated the expression of ER-α in Hep3B cells at both mRNA and protein levels in a time-dependent manner. ER-α siRNA also inhibited cell proliferation and reduced cell invasion (compared with other groups, P < 0.05, resp.). Furthermore, knockdown of ER-α slowed down the cell population at S phase and increased the rate of apoptosis (P < 0.05, resp.). CONCLUSION: ER-α knockdown suppressed the growth of HCC cells. Thus, ER-α may play a very important role in carcinogenesis of HCC and its knockdown may offer a new potential gene therapy approach for human liver cancer in the future.