scholarly journals Preoperative QRS Duration Predicts the Responsiveness of Chronic Heart Failure Patients with Pacemaker Indications To Left Bundle Branch Area Pacing Treatment

2021 ◽  
Author(s):  
Tianping Chen ◽  
Xu Geng ◽  
Yaxing Fang ◽  
Yuchun Yin ◽  
Naiju Zhang
Keyword(s):  
Heart Failure ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Chronic Heart Failure ◽  
Logistic Regression Analysis ◽  
Binary Logistic Regression ◽  
Left Ventricular ◽  
Binary Logistic Regression Analysis ◽  
Peak Time ◽  
Qrs Duration

Abstract Background This study investigated the predictive value of preoperative QRS duration (ORSd) in responsiveness of chronic heart failure (CHF) patients with pacemaker indications to the left bundle branch area pacing (LBBAP). Methods Thirty-one CHF patients with cardiac function categorized as NYHA class II or above and indications for pacemaker therapy who successfully underwent LBBAP treatment were enrolled in this study. Based on the 12-month postoperative responsiveness to treatment, patients were divided into a responsiveness group (n=18) and a no-responsiveness group (n=13). Data from all patients were collected for analysis. Multivariate binary logistic regression analysis was used to determine the independent factors associated with the responsiveness to LBBAP treatment. Results Among the 31 patients with LBBAP, 16 patients (51.6%) responded to the treatment, and 15 patients (48.4%) had no response. There were significant differences between these two groups with regard to complete left bundle branch block (CLBBB), preoperative QRSd, and preoperative left ventricular peak time (LVAT). Univariate logistic regression analysis showed that CLBBB, preoperative QRSd, and preoperative LVAT were all significantly correlated with responsiveness to LBBAP. Multivariate binary logistic regression analysis showed that ORSd was an independent predictor of responsiveness to LBBAP. The maximum area under the ROC curve for QRSd was 0.827, the maximum Youden index was 0.679, with the optimal cutoff point of QRSd ≥ 153 ms, a sensitivity of 81.3%, and a specificity of 86.7%. Conclusion Preoperative ORSd predicts the responsiveness of CHF patients with pacemaker indications to LBBAP.

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 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 Association Between Face Masks Use and Occupation in Cameroon: Perceived Susceptibility to COVID-19 and Physician’s Roles

2020 ◽  
Vol 2 (4) ◽  
pp. 279-284
Author(s):  
Armel Fosso Setubi ◽  
◽  
Mersha Wubie ◽  
Alain Pitti Djida ◽  
Xavier Emmanuel Fosoumo ◽  
...  
Keyword(s):  
Social Media ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Risk Perception ◽  
Logistic Regression Analysis ◽  
Health Professionals ◽  
Binary Logistic Regression ◽  
Face Mask ◽  
Binary Logistic Regression Analysis ◽  
Perceived Susceptibility

Introduction: Face masks use among Cameroonians is a new behavior. Many citizens have adopted face masks as a step to prevent the spread of COVID-19. Individuals might use face masks because of their profession, risk perception, and social status. This study aims at evaluating the relation between occupation and face masks use in COVID-19 setting in Cameroon. Our study hypothesizes that face masks use is not associated with occupation in Cameroon. Methods: A survey was conducted from May 13 to June 3, 2020, among Cameroonians ranging from ages 15 to 66+. Participants answered questions regarding face mask use, occupation, perceived susceptibility to COVID-19, and physicians' guidance. A binary logistic regression analysis was used to examine the association between face masks use and occupation. Results: A total of 1,525 persons responded to the survey among 837 males and 688 females. After adjustment, face masks use was not associated with the occupation. Guidance provided by physicians were positively associated with face masks use (OR=2.06, 95%CI:1.48-2.85). Face masks use was also associated with reported answers on whether: face masks protect against COVID-19 (OR=10.48, 95%CI: 7.20-15.25), ability to easily access physician’s guidance about COVID-19 on social media (OR=2.70, 95%CI: 1.94-3.75), and the belief that Africans are naturally resistant to COVID-19 (OR=1.73, 95% CI: 1.13-2.65). Conclusions: Our study provides evidence that face masks use in COVID-19 setting in Cameroon is not associated with occupation. However, physicians’ role has an impact on people’s behavior. The association between face masks use and the belief that Africans are naturally resistant to COVID-19 points to low perceived susceptibility that needs to be addressed by health professionals and competent civil authorities.

scholarly journals FACTORS AFFECTING ACCESS TO FORMAL CREDIT BY SMALLHOLDER FARMERS IN AN GIANG PROVINCE

2015 ◽  
Vol 18 (1) ◽  
pp. 28-39
Author(s):  
Ngoc Nhan Nhu Nguyen ◽  
Chinh Duc Pham
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Smallholder Farmers ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis ◽  
Factors Affecting ◽  
Five Factors ◽  
Household Assets ◽  
Loan Guarantees

This study was conducted to determinefactors that affect the access to formal credit by smallholder farmers in An Giang province. Applying binary logistic regression analysis on a sample of 210 households, we found that the access to formal credit by these households are affected by five factors, namely total value of household assets, participation in organizations, demand for loans from credit institutions, loan guarantees and accumulated income, in which the demand for loans has the greatest impact. From the regression results, we built a model to forecast the access to formal credit by households with 93.8%. precise forecastprobability.

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