Exploiting an early warning Nomogram for predicting the risk of ICU admission in patients with COVID-19: a multi-center study in China.

BACKGROUND: Novel coronavirus disease 2019 (COVID-19) is a global public health emergency. Here, we developed and validated a practical model based on the data from a multi-center cohort in China for early identification and prediction of which patients will be admitted to the intensive care unit (ICU). METHODS: Data of 1087 patients with laboratory-confirmed COVID-19 were collected from 49 sites between January 2 and February 28, 2020, in Sichuan and Wuhan. Patients were randomly categorized into the training and validation cohorts (7:3). The least absolute shrinkage and selection operator and logistic regression analyzes were used to develop the nomogram. The performance of the nomogram was evaluated for the C-index, calibration, discrimination, and clinical usefulness. Further, the nomogram was externally validated in a different cohort. RESULTS: The individualized prediction nomogram included 6 predictors: age, respiratory rate, systolic blood pressure, smoking status, fever, and chronic kidney disease. The model demonstrated a high discriminative ability in the training cohort (C-index = 0.829), which was confirmed in the external validation cohort (C-index = 0.776). In addition, the calibration plots confirmed good concordance for predicting the risk of ICU admission. Decision curve analysis revealed that the prediction nomogram was clinically useful. CONCLUSION: We established an early prediction model incorporating clinical characteristics that could be quickly obtained on hospital admission, even in community health centers. This model can be conveniently used to predict the individual risk for ICU admission of patients with COVID-19 and optimize the use of limited resources.

A prediction model for major adverse cardiovascular events (MACE) in patients with coronavirus disease 2019 (COVID-19).

BACKGROUND: Emerging evidence shows that cardiovascular injuries and events in coronavirus disease 2019 (COVID-19) should be considered. The current study was conducted to develop an early prediction model for major adverse cardiovascular events (MACE) during hospitalizations of COVID-19 patients. METHODS: This was a retrospective, multicenter, observational study. Hospitalized COVID-19 patients from Wuhan city, Hubei Province and Sichuan Province, China, between January 14 and March 9, 2020, were randomly divided into a training set (70% of patients) and a testing set (30%). All baseline data were recorded at admission or within 24 h after admission to hospitals. The primary outcome was MACE during hospitalization, including nonfatal myocardial infarction, nonfatal stroke and cardiovascular death. The risk factors were selected by LASSO regression and multivariate logistic regression analysis. The nomogram was assessed by calibration curve and decision curve analysis (DCA). RESULTS: Ultimately, 1206 adult COVID-19 patients were included. In the training set, 48 (5.7%) patients eventually developed MACE. Six factors associated with MACE were included in the nomogram: age, PaO2/FiO2 under 300, unconsciousness, lymphocyte counts, neutrophil counts and blood urea nitrogen. The C indices were 0.93 (95% CI 0.90, 0.97) in the training set and 0.81 (95% CI 0.70, 0.93) in the testing set. The calibration curve and DCA demonstrated the good performance of the nomogram. CONCLUSIONS: We developed and validated a nomogram to predict the development of MACE in hospitalized COVID-19 patients. More prospective multicenter studies are needed to confirm our results.

Application of neural-network hybrid models in estimating the infection functions of nonlinear epidemic models (preprint)

Hybrid neural network models combine the advantages of a neural network's fitting functionality with differential equation models to reflect actual physical processes and are widely used in analyzing time-series data. Most related studies have focused on linear hybrid models, but only a few have examined nonlinear problems. In this work, we use a hybrid nonlinear epidemic neural network as the entry point to study its power in predicting the correct infection function of an epidemic model. To achieve this goal, we combine the bifurcation theory of the nonlinear differential model with the mean-squared error loss and design a novel loss function to ensure model trainability. Furthermore, we find the unique existence conditions supporting ordinary differential equations to estimate the correct infection function. Using the Runge Kutta method, we perform numerical experiments on our proposed model and verify its soundness. We also apply it to real COVID-19 data to accurately discover the change law of its infectivity.

Blood Urea Nitrogen to Serum Albumin Ratio (BAR) Predicts Critical Illness in Patients with Coronavirus Disease 2019 (COVID-19).

PURPOSE: We sought to explore the prognostic value of blood urea nitrogen (BUN) to serum albumin ratio (BAR) and further develop a prediction model for critical illness in COVID-19 patients. PATIENTS AND METHODS: This was a retrospective, multicenter, observational study on adult hospitalized COVID-19 patients from three provinces in China between January 14 and March 9, 2020. Primary outcome was critical illness, including admission to the intensive care unit (ICU), need for invasive mechanical ventilation (IMV), or death. Clinical data were collected within 24 hours after admission to hospitals. The predictive performance of BAR was tested by multivariate logistic regression analysis and receiver operating characteristic (ROC) curve and then a nomogram was developed. RESULTS: A total of 1370 patients with COVID-19 were included and 113 (8.2%) patients eventually developed critical illness in the study. Baseline age (OR: 1.031, 95% CI: 1.014, 1.049), respiratory rate (OR: 1.063, 95% CI: 1.009, 1.120), unconsciousness (OR: 40.078, 95% CI: 5.992, 268.061), lymphocyte counts (OR: 0.352, 95% CI: 0.204, 0.607), total bilirubin (OR: 1.030, 95% CI: 1.001, 1.060) and BAR (OR: 1.319, 95% CI: 1.183, 1.471) were independent risk factors for critical illness. The predictive AUC of BAR was 0.821 (95% CI: 0.784, 0.858; P<0.01) and the optimal cut-off value of BAR was 3.7887 mg/g (sensitivity: 0.690, specificity: 0.786; positive predictive value: 0.225, negative predictive value: 0.966; positive likelihood ratio: 3.226, negative likelihood ratio: 0.394). The C index of nomogram including above six predictors was 0.9031125 (95% CI: 0.8720542, 0.9341708). CONCLUSION: Elevated BAR at admission is an independent risk factor for critical illness of COVID-19. The novel predictive nomogram including BAR has superior predictive performance.

Prognostic value of three rapid scoring scales and combined predictors for the assessment of patients with coronavirus disease 2019.

AIM: To explore the factors affecting mortality in patients with COVID-19 and to verify the predictive value of the three rapid scoring scales MEWS, RAPS and REMS. DESIGN: Cross-sectional observational study. METHODS: Kaplan-Meier and Cox survival analyses were performed to identify the risk factors associated with COVID-19-related death. A ROC curve analysis was used to evaluate the abilities of the three scoring scales to predict the prognosis of COVID-19 patients. RESULTS: Age, low blood oxygen saturation level and decreased lymphocyte count were the high risk factors for COVID-19-related mortality. The analysis of the abilities of the three scales to predict the prognosis of COVID-19 patients: The AUC of 0.641 for the RAPS (p = .065). The MEWS (AUC = 0.705, p = .007), compared with RAPS, the NRI was 0.371(p = .03), and the IDI = 0.092 (p = .046); The REMS (AUC = 0.841, p < .001), compared with MEWS, the NRI was 0.227(p = .12), and the IDI=0.09(p = .047); The Combining Predictor (AUC = 0.878, p < .001), compared with REMS, the NRI was 0.25(p = .113), and the IDI=0.02(p = .598). CONCLUSION: Patients with an old age, low blood oxygen saturation level and decreased lymphocyte count were at a high risk of COVID-19-related mortality. Moreover, our analysis revealed that the REMS had a better prognostic ability than the MEWS and RAPS when applied to COVID-19 patients. Our findings suggest that the REMS can be used as a rapid scoring tool for the early assessment of COVID-19 severity.

Efficacy and safety of umifenovir for coronavirus disease 2019 (COVID-19): A systematic review and meta-analysis.

We conducted this systemic review and meta-analysis in an attempt to evaluate the efficacy and safety of umifenovir in coronavirus disease 2019 (COVID-19). We searched PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure, and medRxiv database. We included both retrospective and prospective studies. The mean difference (MD) and risk ratio (RR) with 95% confidence intervals (CI) were applied to assess the effectiveness of umifenovir for COVID-19. A total of 12 studies with 1052 patients were included in our final studies. Compared with control group, umifenovir was associated with higher negative rate of PCR on day 14 (RR:1.27; 95% CI: 1.04 to 1.55). However, umifenovir is not related to nucleus acid negative conversion time (MD: 0.09; 95% CI: -1.48 to 1.65), negative rate on day 7 (RR:1.09; 95% CI: 0.91 to 1.31), incidence of composite endpoint (RR:1.20; 95% CI: 0.61 to 2.37), rate of fever alleviation on day 7 (RR:1.00; 95% CI: 0.91 to 1.10), rate of cough alleviation on day 7 (RR:1.00; 95% CI: 0.85 to 1.18), or hospital length of stay (MD: 1.34; 95% CI: -2.08 to 4.76). Additionally, umifenovir was safe in COVID-19 patients (RR for incidence of adverse events: 1.29; 95% CI: 0.57 to 2.92). The results of sensitivity analysis and subgroup analysis were similar to pooled results. There is no evidence to support the use of umifenovir for improving patient-important outcomes in patients with COVID-19.

Diagnostic Value of Hematological and Biochemical Parameters Combinations for Predicting Coronavirus Disease 2019 (COVID-19) in Suspected Patients.

BACKGROUND: The severe epidemiologic situation of COVID-19 due to the limited capacity of healthcare systems makes it necessary to improve the hospital management and early identification and stratification of patients. The aim of the study was to explore hematological and biochemical parameters at admission to the hospital as novel early predictors for diagnosis with coronavirus disease 2019 (COVID-19) among all suspected patients. METHODS: This was a retrospective, multicenter, observational study. The clinical data of all suspected patients were analyzed. The suspected patients with negative RT-PCR results were included as the control group, and compared with confirmed patients. Receiver- operating characteristic (ROC) curves and logistic regression analyses were used to evaluate the hematological indexes. RESULTS: In total, 326 confirmed COVID-19 patients and 116 control patients were included. The predictive ability of combinations of the hematological and biochemical parameters was significantly superior to that of a single parameter. The area under the ROC curve (AUC) of the aspartate aminotransferase (AST) to neutrophil ratio index (ANRI) and the AST to monocyte ratio index (AMRI) were 0.791 and 0.812, respectively. In the multivariate analysis, an ANRI ≥ 6.03(OR: 3.26, 95% CI: 1.02-10.40, P=0.046) and an AMRI ≥ 36.32(OR: 3.64. 95% CI: 1.24-10.68, P=0.02) at admission were independent risk factors related to the occurrence of COVID-19. CONCLUSIONS: We found two novel predictors with promising predictive capacities for COVID-19 among all suspected patients: ANRI and AMRI. Our findings need to be confirmed in further studies.

A novel risk score to predict diagnosis with coronavirus disease 2019 (COVID-19) in suspected patients: A retrospective, multicenter, and observational study.

The aim of the study was to explore a novel risk score to predict diagnosis with COVID-19 among all suspected patients at admission. This was a retrospective, multicenter, and observational study. The clinical data of all suspected patients were analyzed. Independent risk factors were identified via multivariate logistic regression analysis. Finally, 336 confirmed COVID-19 patients and 139 control patients were included. We found nine independent risk factors for diagnosis with COVID-19 at admission to hospital: epidemiological exposure histories (OR:13.32; 95%CI, 6.39-27.75), weakness/fatigue (OR:4.51, 95%CI, 1.70-11.96), heart rate less than 100 beat/minutes (OR:3.80, 95%CI, 2.00-7.22), bilateral pneumonia (OR:3.60, 95%CI, 1.83-7.10), neutrophil count less than equal to 6.3 × 109 /L (OR: 6.77, 95%CI, 2.52-18.19), eosinophil count less than equal to 0.02 × 109 /L (OR:3.14, 95%CI, 1.58-6.22), glucose more than equal to 6 mmol/L (OR:2.43, 95%CI, 1.04-5.66), D-dimer ≥ 0.5 mg/L (OR:3.49, 95%CI, 1.22-9.96), and C-reactive protein less than 5 mg/L (OR:3.83, 95%CI, 1.86-7.92). As for the performance of this risk score, a cut-off value of 20 (specificity: 0.866; sensitivity: 0.813) was identified to predict COVID-19 according to reciever operator characteristic curve and the area under the curve was 0.921 (95%CI: 0.896-0.945; P < .01). We designed a novel risk score which might have a promising predictive capacity for diagnosis with COVID-19 among suspected patients.

A novel risk score to predict cardiovascular complications in patients with coronavirus disease 2019 (COVID-19): A retrospective, multicenter, observational study.

BACKGROUND: We conducted this study to explore a novel risk score to predict cardiovascular complications in patients with coronavirus disease 2019 (COVID-19). METHODS: The current study was a retrospective, multicenter, observational study. The clinical data of COVID-19 patients at admission were collected. Patients were randomly divided into training set and testing set (70% vs. 30% of patients). Independent risk factors were identified via logistic regression analysis. RESULTS: Finally, 1207 patients were included. Ten independent risk factors associated with cardiovascular complications were identified in training set: male (odds ratio [OR]: 1.84; 95% confidence interval [CI]: 1.18, 2.85), age ≥ 60 years old (OR: 2.01; 95% CI: 1.3, 3.2), cough (OR: 1.86; 95% CI: 1.16, 3), chronic heart disease (OR: 2.3; 95% CI: 1.19, 4.46), lymphocyte count ≤1.1 × 109 /L at admission (OR: 1.60; 95% CI: 1.03, 2.47), blood urea nitrogen ≥7 mmol/L at admission (OR: 2.14; 95% CI: 1.27, 3.62), estimated glomerular filtration rate ≤90 ml/min/1.73 m2 at admission (OR: 2.08; 95% CI: 1.13, 3.83), activated partial thromboplastin time ≥37 s (OR: 3.07; 95% CI: 1.37, 6.86), D-dimer ≥ 0.5 mg/L (OR: 2.12; 95% CI: 1.33, 3.36) and procalcitonin ≥0.5 µg/L (OR: 3.58; 95% CI: 1.40, 9.14). The area under curve of ROC curve was 0.773 (95% CI: 0.723, 0.822; p < .01). The risk score had robustness and generalizability after validation. Cardiovascular complications were significantly associated with poorer survivals (log-rank test: p < .001). CONCLUSIONS: We developed and validated a novel risk score, which has a promising predictive capacity for cardiovascular complications in COVID-19 patients.

Prevention and treatment system of novel coronavirus infection in medical and health institutions: experience in West China Hospital of Sichuan University.

The novel coronavirus infection broke out in Wuhan, China, in December 2019, and progressed to a global pandemic. We describe the measures taken by West China Hospital of Sichuan University to address the diagnosis, prevention and treatment of the infection.

Humanistic care and psychological counseling on psychological disorders in medical students after COVID-19 outbreak: A protocol of systematic review.

BACKGROUND: The objective of this study is to investigate the effects of humanistic care and psychological counseling (HCPC) on psychological disorders (PD) in medical students after coronavirus disease 2019 (COVID-19) outbreak. METHODS: We will search randomized controlled trials or case-controlled studies of HCPC on PD in medical students after COVID-19 outbreak in the following electronic databases: PUBMED/MEDLINE, EMBASE, Cochrane Library, CINAHL, AMED, WANGFANG, and CNKI. The time is restricted from the construction of each database to the present. All process of study selection, data collection, and study quality evaluation will be carried out by two independent authors. Any different opinions will be solved by a third author through discussion. We will employ RevMan 5.3 software to conduct statistical analysis. RESULTS: This study will provide a better understanding of HCPC on PD in medical students after COVID-19 outbreak. CONCLUSIONS: This study may offer strong evidence for clinical practice to treat PD in medical students after COVID-19 outbreak. STUDY REGISTRATION: CRD42020193199.