scholarly journals Clinical characteristics of COVID-19 and establishment of a disease risk prediction model

2020 ◽  
Author(s):  
Tao Fan ◽  
Bo Hao ◽  
Shuo Yang ◽  
Bo Shen ◽  
Zhixin Huang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Risk Prediction ◽  
Clinical Characteristics ◽  
Disease Risk ◽  
Clinical Manifestations ◽  
Risk Prediction Model ◽  
Common Symptom ◽  
Severe Group ◽  
Independent Risk Factors

Abstract Background: COVID-19 is spreading worldwide. No specific medicine has been used for the treatment of coronavirus infections. The aim of this study is to establish a new risk predictive model to screen potential critical patients for early intervention.Methods: In this study, Clinical characteristics were collected and analyzed from 317 confirmed cases of COVID-19. A total of 175 of the 317 cases with detailed examination results were included to establish models for predicting the risk of disease progression. Major independent risk factors were incorporated into MuLBSTA model to establish new models for predicting critical risk. We further tracked 25 mild or moderate patients with COVID-19 to research dynamic changes of the major independent risk factors in COVID-19 progression.Results: The average age of all of the 317 patients was 47.76 (SD 17.22). A total of 48 (15.14%) were diagnosed with mild disease with a median age of 34(39.29±13.04), 116(36.59%) were diagnosed with moderate disease with a median age of 34(38.78±12.32), 38(11.99%) were diagnosed as severe with a median age of 56(58.24±15.12), and 115(36.28) were diagnosed as critical with a median age of 59(56.89+17.09). The most common symptom at onset of illness were fever(211[66.56%] patients). Age>50, CK>64, CD4≤461, and CD8≤241 were predicted to be major independent risk factors that could promote COVID-19 progression. Compared with the MuLBSTA model, the predictive ability of the CD4-CD8-MuLBSTA model and the CD4-MuLBSTA model were improved by 11.87% and 11.79%, respectively. In the prospective study, CK value began to show significant differences from day13. The average CD4 in Severe Group began to decline significantly on the fourth day, and the CD8 maintained a relatively low level in the Severe Group after day13.Conclusions: Severe COVID-19 patients were significantly older than non-severe patients. Immune systems of severe COVID-19 patients were significantly suppressed, and advanced age(>50 years), low levels of CD4(≤461) or CD8(≤241) was important clinical manifestations of rapid deterioration. CK values in severe COVID-19 patients were significantly higher than in no severe patients. CD4 and CD8 were incorporated into the MuLBSTA to establish a new model, which is an ideal risk prediction model for COVID-19 patients.

A Risk Prediction Model of Readmission after coronary artery bypass grafting (CABG) in China

2020 ◽  
Author(s):  
Guozhen Liu ◽  
Yinghong Zhang ◽  
Wen Zhang ◽  
Liu Hu ◽  
Tiao Lv ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Artery ◽  
Coronary Artery Bypass Grafting ◽  
Prediction Model ◽  
Risk Prediction ◽  
Artery Bypass ◽  
Risk Prediction Model ◽  
Bypass Grafting ◽  
Modeling Group ◽  
Independent Risk Factors

Abstract Background At present, there is no risk prediction model suitable for the Chinese population after coronary artery bypass grafting (CABG), this study aims to analyze the risk factors related to readmission after CABG and to construct a risk prediction model of readmission for patients with CABG in China. Methods A total of 1983 patients who had undergone CABG at Wuhan Asian Heart Hospital from 2017 to 2019 were selected to collect general patient information. Univariate analysis was performed on the data of 825 patients in the modeling group to determine potential risk factors, and then independent risk factors of readmission after CABG were determined by multivariate logistic regression. Hosmer-Lemeshow （H-L） test, calibration curve and the area under the receiver operating characteristic （ROC） curve are used to test the calibration and discrimination of the model. Results Six preoperative variables （age≥65, female, Private insurance, diabetes, hypertension level2,3, congenital heart disease）were independent risk factors of readmission after CABG. Our risk prediction model has high application value （the area under the ROC curve of the modeling group is 0.876, and of the validation group is 0.865, H-L test: P=0.561〉0.05）. Conclusion The risk prediction model in our study can be used to predict the risk of readmission in CABG patients in clinical work, providing a basis for more effective perioperative treatment and care to prevent patients from being readmitted to hospital.

scholarly journals A Model for Risk Prediction of Cerebrovascular Disease Prevalence—Based on Community Residents Aged 40 and above in a City in China

2021 ◽  
Vol 18 (12) ◽  
pp. 6584
Author(s):  
Qin Zhu ◽  
Die Luo ◽  
Xiaojun Zhou ◽  
Xianxu Cai ◽  
Qi Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Prediction Model ◽  
Cerebrovascular Disease ◽  
Risk Prediction ◽  
Disease Risk ◽  
Risk Index ◽  
Risk Prediction Model ◽  
Jiangxi Province ◽  
Community Residents ◽  
Local Residents

Cerebrovascular disease (CVD) is the leading cause of death in many countries including China. Early diagnosis and risk assessment represent one of effective approaches to reduce the CVD-related mortality. The purpose of this study was to understand the prevalence and influencing factors of cerebrovascular disease among community residents in Qingyunpu District, Nanchang City, Jiangxi Province, and to construct a model of cerebrovascular disease risk index suitable for local community residents. A stratified cluster sampling method was used to sample 2147 community residents aged 40 and above, and the prevalence of cerebrovascular diseases and possible risk factors were investigated. It was found that the prevalence of cerebrovascular disease among local residents was 4.5%. Poisson regression analysis found that old age, lack of exercise, hypertension, diabetes, smoking, and family history of cerebrovascular disease are the main risk factors for local cerebrovascular disease. The relative risk ORs were 3.284, 2.306, 2.510, 3.194, 1.949, 2.315, respectively. For these six selected risk factors, a cerebrovascular disease risk prediction model was established using the Harvard Cancer Index method. The R value of the risk prediction model was 1.80 (sensitivity 81.8%, specificity 47.0%), which was able to well predict the risk of cerebrovascular disease among local residents. This provides a scientific basis for the further development of local cerebrovascular disease prevention and control work.

scholarly journals P161 Crohn's disease risk prediction model appropriately stratifies patients' risk for developing disease related complications

2017 ◽  
Vol 11 (suppl_1) ◽  
pp. S159-S159 ◽  
Author(s):  
K.D. Thompson ◽  
L.S. Siegel ◽  
T. MacKenzie ◽  
M.C. Dubinsky ◽  
C.A. Siegel
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Prediction Model ◽  
Risk Prediction ◽  
Disease Risk ◽  
Risk Prediction Model

scholarly journals A Cox-Based Risk Prediction Model for Early Detection of Cardiovascular Disease: Identification of Key Risk Factors for the Development of a 10-Year CVD Risk Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaona Jia ◽  
Mirza Mansoor Baig ◽  
Farhaan Mirza ◽  
Hamid GholamHosseini
Keyword(s):  
Risk Factors ◽  
Cardiovascular Disease ◽  
Heart Rate ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Model ◽  
Clinical Laboratory ◽  
Risk Prediction Model ◽  
Validation Dataset ◽  
Cvd Risk

Background and Objective. Current cardiovascular disease (CVD) risk models are typically based on traditional laboratory-based predictors. The objective of this research was to identify key risk factors that affect the CVD risk prediction and to develop a 10-year CVD risk prediction model using the identified risk factors. Methods. A Cox proportional hazard regression method was applied to generate the proposed risk model. We used the dataset from Framingham Original Cohort of 5079 men and women aged 30-62 years, who had no overt symptoms of CVD at the baseline; among the selected cohort 3189 had a CVD event. Results. A 10-year CVD risk model based on multiple risk factors (such as age, sex, body mass index (BMI), hypertension, systolic blood pressure (SBP), cigarettes per day, pulse rate, and diabetes) was developed in which heart rate was identified as one of the novel risk factors. The proposed model achieved a good discrimination and calibration ability with C-index (receiver operating characteristic (ROC)) being 0.71 in the validation dataset. We validated the model via statistical and empirical validation. Conclusion. The proposed CVD risk prediction model is based on standard risk factors, which could help reduce the cost and time required for conducting the clinical/laboratory tests. Healthcare providers, clinicians, and patients can use this tool to see the 10-year risk of CVD for an individual. Heart rate was incorporated as a novel predictor, which extends the predictive ability of the past existing risk equations.

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