The relationship between platelet to albumin ratio and disease activity in axial spondyloarthritis patients

2021 ◽  
Author(s):  
Yukai Huang ◽  
Weiming Deng ◽  
Xia Pan ◽  
Meng Liu ◽  
Zheng Zhong ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Disease Activity ◽  
Logistic Regression Analysis ◽  
Axial Spondyloarthritis ◽  
Diagnostic Value ◽  
Active Group ◽  
Youden Index ◽  
Binary Logistic Regression Analysis ◽  
Albumin Ratio

ABSTRACT Objectives The study aims to investigate the clinical significance of platelet to albumin ratio (PAR), neutrophil to albumin ratio (NAR), and monocyte to albumin ratio (MAR) in axial spondyloarthritis (axSpA). Methods Two hundred and ninety-seven axSpA patients and 71 healthy volunteers were recruited. AxSpA patients were divided into inactive group and active group. Spearman’s correlation, receiver operating characteristic (ROC) curves, and binary logistic regression analysis were conducted. Results Albumin was lower in axSpA group, while neutrophil, platelet, monocyte, NAR, PAR, and MAR were higher (p < .05). Albumin was negatively correlated with BASDAI and BASFI (p < .05). Platelet, NAR, PAR, MAR, ESR, and CRP were all positively correlated with BASDAI and BASFI (p < .05). Albumin was lower in axSpA of active group, while platelet, NAR, PAR, MAR, ESR, and CRP were higher (p < .05). ROC curve indicated that the AUC of PAR for axSpA of active group was higher than that of other variables. The optimal cut-off value of PAR was 6.354, with Youden index of 0.337, specificity of 55.4%, and sensitivity of 78.4%. Logistic regression analysis result suggested that PAR was an independent indicator for axSpA disease activity. Conclusions PAR had a high diagnostic value for axSpA of active group. PAR was a novel and reliable indicator for axSpA disease activity.

scholarly journals Predicting Burn Mortality Using a Simple Novel Prediction Model

2021 ◽  
Author(s):  
Sneha Sharma ◽  
Raman Tandon
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Receiver Operating Characteristic ◽  
Logistic Regression Analysis ◽  
Operating Characteristic ◽  
Binary Logistic Regression ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Binary Logistic Regression Analysis

Abstract Background Prediction of outcome for burn patients allows appropriate allocation of resources and prognostication. There is a paucity of simple to use burn-specific mortality prediction models which consider both endogenous and exogenous factors. Our objective was to create such a model. Methods A prospective observational study was performed on consecutive eligible consenting burns patients. Demographic data, total burn surface area (TBSA), results of complete blood count, kidney function test, and arterial blood gas analysis were collected. The quantitative variables were compared using the unpaired student t-test/nonparametric Mann Whitney U-test. Qualitative variables were compared using the ⊠2-test/Fischer exact test. Binary logistic regression analysis was done and a logit score was derived and simplified. The discrimination of these models was tested using the receiver operating characteristic curve; calibration was checked using the Hosmer—Lemeshow goodness of fit statistic, and the probability of death calculated. Validation was done using the bootstrapping technique in 5,000 samples. A p-value of <0.05 was considered significant. Results On univariate analysis TBSA (p <0.001) and Acute Physiology and Chronic Health Evaluation II (APACHE II) score (p = 0.004) were found to be independent predictors of mortality. TBSA (odds ratio [OR] 1.094, 95% confidence interval [CI] 1.037–1.155, p = 0.001) and APACHE II (OR 1.166, 95% CI 1.034–1.313, p = 0.012) retained significance on binary logistic regression analysis. The prediction model devised performed well (area under the receiver operating characteristic 0.778, 95% CI 0.681–0.875). Conclusion The prediction of mortality can be done accurately at the bedside using TBSA and APACHE II score.

scholarly journals POS0455 EFFECT OF ANTI-Ro/SSA ANTIBODIES FOR TREATMENT RESPONSE TO METHOTREXATE IN RHEUMATOID ARTHRITIS: A RETROSPECTIVE MULTICENTER OBSERVATIONAL STUDY

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 458.1-458
Author(s):  
R. Yokochi ◽  
H. Tamai ◽  
T. Kido ◽  
Y. Yagyu ◽  
D. Waki ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Disease Activity ◽  
Clinical Response ◽  
Logistic Regression Analysis ◽  
Age At Onset ◽  
Treat To Target ◽  
Low Disease Activity ◽  
Target Strategy

Background:Several previous observational studies have suggested that patients with anti-Ro/SSA antibody-positive rheumatoid arthritis (RA) may respond poorly to treatment, including tumor necrosis factor inhibitors1. However, its influence on methotrexate (MTX) treatment, which is the anchor drug of treat-to-target strategy in RA treatment, remains unclear.Objectives:We compared the clinical response to MTX in both anti-Ro/SSA antibody-positive and -negative patients with MTX-naiive RA and investigated the reasons for the difference in response.Methods:We recruited 210 consecutive patients with RA who were newly started on MTX in this retrospective cohort study. The effect of the presence of anti-Ro/SSA antibodies on achieving low disease activity (LDA) of DAS28-CRP at six months after initiating MTX was investigated by using logistic regression analysis. CDAI, SDAI, concomitant using DMARDs and painkillers, patient’s and evaluator’s VAS, tender joint counts, and swollen joint counts at six months were also compared between the anti-Ro/SSA-positive patients and -negative patients. Missing data were imputed by using multiple imputations before multivariate analysis.Results:32 anti-Ro/SSA antibody-positive patients and 178 anti-Ro/SSA antibody-negative patients were included. The rate of achieving DAS28-LDA at six months was significantly lower in the anti-Ro/SSA antibody-positive patients than those in the anti-Ro/SSA antibody-negative patients (56.2% versus 75.8%, P=0.03). in the logistic regression analysis, the presence of anti-Ro/SSA antibodies was an independent negative predictor for achieving DAS-28-LDA at six months (OR:0.431, 95%CI: 0.190-0.978, P=0.044) (Table1). Anti-Ro/SSA antibody-positive patients had significantly higher patient’s VAS at six months (median [IQR]: 22 [15-41] vs 19 [5-30], P=0.038), and prescribed NSAIDs (37.5% vs 18.0%, P=0.018). CDAI and SDAI after six months were not significantly different between the group.Conclusion:The presence of anti-Ro/SSA antibodies might be one of the predictive factors for the insufficient response to treat to target strategy in RA treatment. Residual pain was suspected as one of the mechanisms contributing to the lesser clinical response of MTX in anti-Ro antibody-positive RA.References:[1]Ran Matsudaira wt al. J Rheumatol 2011;38(11):2346-54Table 1.Logistic regression analysis for the rate of achieving DAS28 low disease activity at six months.Risk factor Odds ratio95%CIP valueAge at onset0.9930.968-1.0180.586Sex (woman)0.6430.300-1.3840.258RF-positive1.9620.853-4.5110.112ACPA-positive0.5520.225-1.3510.192Anti-Ro/SSA antibody-positive0.4310.190-0.9780.044Disclosure of Interests:None declared

scholarly journals Predicting organizational performance from motivation in Oromia Seed Enterprise Bale branch

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Askalech Feyisa Jobira ◽  
Abdulnasir Abdulmelike Mohammed
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Intrinsic Motivation ◽  
Organizational Performance ◽  
Logistic Regression Analysis ◽  
Extrinsic Motivation ◽  
Binary Logistic Regression ◽  
Moderate Level ◽  
Binary Logistic Regression Analysis ◽  
Study Results

AbstractMotivation is one of the most researched yet crucial topics in academia from various perspectives. Despite this, researches show mixed results about the effect of extrinsic motivation on intrinsic motivation and organizational performance. Studies in Ethiopia also lack causal analysis and theoretical underpinning that made contributions from academia very little. Hence, this research is important to assess the effect of extrinsic motivation on intrinsic motivation and organizational performance from a cognitive evaluation theory perspective. The researchers adopted an explanatory research design with a quantitative approach. The entire 119 employees of the Oromia Seed Enterprise, Bale branch were included in the study to collect primary data through a close-ended questionnaire. The collected data was processed by SPSS software version 20. The relationship analysis was addressed by correlation and binary logistic regression analysis. Seen from extrinsic and intrinsic motivation aspects, the findings of the study showed that Oromia Seed Enterprise had a moderate level of organizational performance and a moderate level of employees’ motivation. The correlation analysis result indicated that employees’ extrinsic and intrinsic motivation had a positive relationship with organizational performance. The binary logistic regression analysis also indicated that extrinsic and intrinsic motivation had a positive and significant influence on organizational performance. However, the interaction effect of intrinsic and extrinsic motivation on organizational performance was not significant, implying the absence of influence when both intrinsic and extrinsic motivations happen at the same time. Finally, the study results have a theoretical contribution for compensating the lack of actual experience in the Ethiopian organization’s context. Equally, the understanding of the moderated relationship among the study variables may encourage Oromia Seed Enterprise and its managers to develop a practical motivation system, which entertains the complex interaction of motivation variables to improve organizational performance. In addition, studies of this nature can inform policymakers to strengthen an incentive system as well as other motivation veins in the Ethiopian public organizations.

scholarly journals Binary Logistic Regression Analysis of Variables That Influence Poverty in Central Java

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Sendi Nugraha Nurdiansah ◽  
Laelatul Khikmah
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Binary Logistic Regression ◽  
Equation Model ◽  
Binary Logistic Regression Analysis ◽  
Poor People ◽  
Regional Government ◽  
Central Java ◽  
The Government

The phenomenon of poverty is a serious problem faced by almost every country in the world. This is because poverty can affect various aspects of people's lives. One of the causes of poverty is due to lack of income and assets to meet basic needs such as food, clothing, housing, health level and acceptable education. In addition, poverty occurs because of the powerlessness of society to get out of the problems it faces. The Central Java regional government incorporated poverty issues into the Regional Medium-Term Development Plan (RPJMD) because Central Java has a high number of poor people. This was done as an effort by the Central Java government to reduce poverty. Therefore, research is needed to find out the variables that most influence poverty in order to assist the government in developing the RPJMD. To find out what factors influence poverty in Central Java with the dichotomous categorical response variable, binary logistic regression analysis was used. The results showed that based on the analysis conducted did not obtain a logistic regression equation model because there were no significant parameters because there were no variables that had a sig value <0.05. Existing variables are Number of Population, Female Head of Household, Number of Children not in School, Number of Disabled Individuals, Number of Chronic Disease Individuals, Unemployment, Non-Electricity Lighting Sources, Unprotected Drinking Water Sources, Kerosene and Wood Cooking Fuels, Location Facilities Defecation (BAB) Not Available, so there are no variables that affect the level of poverty in Central Java Province.

scholarly journals Factors affecting internet use among university students in Sarawak, Malaysia: an empirical study

2020 ◽  
Vol 7 (3) ◽  
pp. 848
Author(s):  
M. Mizanur Rahman ◽  
M. Taha Arif ◽  
Fready Luke ◽  
Santha Letchumi ◽  
Fatin Nabila ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
University Students ◽  
Logistic Regression Analysis ◽  
Internet Use ◽  
Binary Logistic Regression ◽  
Problematic Internet Use ◽  
The Internet ◽  
Binary Logistic Regression Analysis ◽  
Factors Affecting

Background: The internet has become an indispensable tool for communication, academic research, information and entertainment. However, heavy users of the internet lead to less confidence in social skills and the tendency to be isolated. The study aimed to assess the pattern of internet use and factors affecting problematic internet use among university students.Methods: This cross-sectional study conducted among the students of a university in Sarawak, Malaysia. A multistage cluster sampling technique was adapted to select the participants. Data were collected from 463 students by self-administered questionnaire. Hierarchical binary logistic regression analysis was done to determine the potential factors for problematic internet use.Results: The mean age of the students was 22 years, with a standard deviation of 1.6 years. Two-fifths (61.8%) of the students had no problematic internet use. However, 35.4% had moderate and 2.8% had severe problematic internet use. Hierarchical binary logistic regression analysis found that age of the students, year of study, duration of daily internet use and use of social networking like Skype appeared to be potential predictors of problematic internet use (p<0.05).Conclusions: This study was conducted in only one university, thus did not depict the overall scenarios of the country. The implications of the findings are still worth noting in the process of designing internet addiction studies among university students. Overall, this study has unearthed some useful insights which can serve as a guide to more elaborate studies.

scholarly journals Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies

2018 ◽  
Vol 46 (9) ◽  
pp. 3656-3664 ◽  
Author(s):  
Wenbo Xu ◽  
Yang Zhao ◽  
Shiyan Nian ◽  
Lei Feng ◽  
Xuejing Bai ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Density Lipoprotein ◽  
Binary Logistic Regression ◽  
Lipoprotein Cholesterol ◽  
Binary Logistic Regression Analysis ◽  
Confounding Factors ◽  
Analysis Strategies ◽  
Glutamyl Transferase

Objective To investigate the importance of controlling confounding factors during binary logistic regression analysis. Methods Male coronary heart disease (CHD) patients (n = 664) and healthy control subjects (n = 400) were enrolled. Fourteen indexes were collected: age, uric acid, cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, apolipoprotein A1, apolipoprotein B100, lipoprotein a, homocysteine, total bilirubin, direct bilirubin, indirect bilirubin, and γ-glutamyl transferase. Associations between these indexes and CHD were assessed by logistic regression, and results were compared by using different analysis strategies. Results 1) Without controlling for confounding factors, 14 indexes were directly inputted in the analysis process, and 11 indexes were finally retained. A model was obtained with conflicting results. 2) According to the application conditions for logistic regression analysis, all 14 indexes were weighed according to their variances and the results of correlation analysis. Seven indexes were finally included in the model. The model was verified by receiver operating characteristic curve, with an area under the curve of 0.927. Conclusions When binary logistic regression analysis is used to evaluate the complex relationships between risk factors and CHD, strict control of confounding factors can improve the reliability and validity of the analysis.

scholarly journals Performance of cytokine models in predicting SLE activity

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Nopparat Ruchakorn ◽  
Pintip Ngamjanyaporn ◽  
Thanitta Suangtamai ◽  
Thanuchporn Kafaksom ◽  
Charin Polpanumas ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Disease Activity ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Lupus Erythematosus ◽  
Activity Index ◽  
Multiple Logistic Regression Analysis ◽  
Negative Relationship ◽  
Complement C3

Abstract Background Identification of universal biomarkers to predict systemic lupus erythematosus (SLE) flares is challenging due to the heterogeneity of the disease. Several biomarkers have been reported. However, the data of validated biomarkers to use as a predictor for lupus flares show variation. This study aimed to identify the biomarkers that are sensitive and specific to predict lupus flares. Methods One hundred and twenty-four SLE patients enrolled in this study and were prospectively followed up. The evaluation of disease activity achieved by the SLE disease activity index (SLEDAI-2K) and clinical SLEDAI (modified SLEDAI). Patients with active SLE were categorized into renal or non-renal flares. Serum cytokines were measured by multiplex bead-based flow cytometry. The correlation and logistic regression analysis were performed. Results Levels of IFN-α, MCP-1, IL-6, IL-8, and IL-18 significantly increased in active SLE and correlated with clinical SLEDAI. Complement C3 showed a weakly negative relationship with IFN-α and IL-18. IL-18 showed the highest positive likelihood ratios for active SLE. Multiple logistic regression analysis showed that IL-6, IL-8, and IL-18 significantly increased odds ratio (OR) for active SLE at baseline while complement C3 and IL-18 increased OR for active SLE at 12 weeks. IL-18 and IL-6 yielded higher sensitivity and specificity than anti-dsDNA and C3 to predict active renal and active non-renal, respectively. Conclusion The heterogeneity of SLE pathogenesis leads to different signaling mechanisms and mediates through several cytokines. The monitoring of cytokines increases the sensitivity and specificity to determine SLE disease activity. IL-18 predicts the risk of active renal SLE while IL-6 and IL-8 predict the risk of active non-renal. The sensitivity and specificity of these cytokines are higher than the anti-dsDNA or C3. We propose to use the serum level of IL-18, IL-6, and IL-8 to monitor SLE disease activity in clinical practice.

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