Categorisation of continuous variables in a logistic regression model using the R package CatPredi

2015 ◽  
Author(s):  
Irantzu Barrio ◽  
María Xosé Rodríguez-Álvarez ◽  
Inmaculada Arostegui
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
R Package ◽  
Continuous Variables

scholarly journals Long Noncoding RNA THAP9-AS1 and TSPOAP1-AS1 Provide Potential Diagnostic Signatures for Pediatric Septic Shock

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Yong Wu ◽  
Qigai Yin ◽  
Xiaobao Zhang ◽  
Pin Zhu ◽  
Hengfei Luan ◽  
...  
Keyword(s):  
Septic Shock ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Pediatric Patients ◽  
R Package ◽  
Differentially Expressed ◽  
Blood Samples ◽  
Health Control ◽  
Control Samples

Background. Sepsis is a systemic inflammatory syndrome caused by infection with a high incidence and mortality. Although long noncoding RNAs have been identified to be closely involved in many inflammatory diseases, little is known about the role of lncRNAs in pediatric septic shock. Methods. We downloaded the mRNA profiles GSE13904 and GSE4607, of which GSE13904 includes 106 blood samples of pediatric patients with septic shock and 18 health control samples; GSE4607 includes 69 blood samples of pediatric patients with septic shock and 15 health control samples. The differentially expressed lncRNAs were identified through the limma R package; meanwhile, GO terms and KEGG pathway enrichment analysis was performed via the clusterProfiler R package. The protein-protein interaction (PPI) network was constructed based on the STRING database using the targets of differently expressed lncRNAs. The MCODE plug-in of Cytoscape was used to screen significant clustering modules composed of key genes. Finally, stepwise regression analysis was performed to screen the optimal lncRNAs and construct the logistic regression model, and the ROC curve was applied to evaluate the accuracy of the model. Results. A total of 13 lncRNAs which simultaneously exhibited significant differences in the septic shock group compared with the control group from two sets were identified. According to the 18 targets of differentially expressed lncRNAs, we identified some inflammatory and immune response-related pathways. In addition, several target mRNAs were predicted to be potentially involved in the occurrence of septic shock. The logistic regression model constructed based on two optimal lncRNAs THAP9-AS1 and TSPOAP1-AS1 could efficiently separate samples with septic shock from normal controls. Conclusion. In summary, a predictive model based on the lncRNAs THAP9-AS1 and TSPOAP1-AS1 provided novel lightings on diagnostic research of septic shock.

scholarly journals COVID-19 associated hospitalization in 571 patients with fibromyalgia—A population-based study

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261772
Author(s):  
Mor Amital ◽  
Niv Ben-Shabat ◽  
Howard Amital ◽  
Dan Buskila ◽  
Arnon D. Cohen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Multivariable Analysis ◽  
Population Based ◽  
Female Dominance ◽  
Categorical Variables ◽  
Continuous Variables ◽  
Multivariable Logistic Regression Model ◽  
Population Based Study

Objective To identify predicators of patients with fibromyalgia (FM) that are associated with a severe COVID-19 disease course. Methods We utilized the data base of the Clalit Health Services (CHS); the largest public organization in Israel, and extracted data concerning patients with FM. We matched two subjects without FM to each subject with FM by sex and age and geographic location. Baseline characteristics were evaluated by t-test for continuous variables and chi-square for categorical variables. Predictors of COVID-19 associated hospitalization were identified using univariable logistic regression model, significant variables were selected and analyzed by a multivariable logistic regression model. Results The initial cohort comprised 18,598 patients with FM and 36,985 matched controls. The mean age was 57.5± 14.5(SD), with a female dominance of 91%. Out of this cohort we extracted the study population, which included all patients contracted with COVID-19, and consisted of 571 patients with FM and 1008 controls. By multivariable analysis, the following variables were found to predict COVID-19 associated hospitalization in patients with FM: older age (OR, 1.25; CI, 1.13–1.39; p<0.001), male sex (OR, 2.63; CI, 1.18–5.88; p<0.05) and hypertension (OR, 1.75; CI, 1.04–2.95; p<0.05). Conclusion The current population-based study revealed that FM per se was not directly associated with COVID-19 hospitalization or related mortality. Yet classical risk factors endangering the general population were also relevant among patients with FM.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p &lt; 0,0001), education (p &lt; 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

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