Logistic Regression Analysis of the Influencing Factors of Cryptogenic Stroke with Positive c-TCD

Objective: To explore the influencing factors and logistic regression characteristics of cryptogenic stroke in patients with positive transcranial doppler bubble test (c-TCD). Methods: A total of 134 cases of cryptogenic stroke that were diagnosed by Tianshui First People’s Hospital from November 2018 to April 2020 were selected according to the TOAST (Trial of ORG 10172 in Acute Stroke Treatment) classification criteria. According to c-TCD results, there were 70 cases of right to left shunt that were included in the positive group and 64 cases without right to left shunt in the negative group. Gender, age, smoking, diabetes, hypertension, and factors affecting the positive rate of foam were analyzed. According to the abnormal embolism scale scores, logistic regression equation was used to analyze the independent influencing factors. Results: The influencing factors of cryptogenic stroke in patients with positive c-TCD were correlated with age, gender, and abnormal embolism scale scores (p < 0.05). For each grade increase in age, the proportion of positive foam test was calculated to be 3.21 times, and the proportion of female to male was calculated to be 2.25 times. For each grade increase in the scores, the proportion of positive foam test was calculated to be 2.55 times. Conclusion: Female, older age, and higher scores in the abnormal embolism scale are the influencing factors for cryptogenic stroke in patients with positive c-TCD.

Download Full-text

Analysis of cognitive characteristics and influencing factors of mass sports rehabilitation in Taiyuan

10.21203/rs.2.15690/v1 ◽

2019 ◽

Author(s):

Yaye He ◽

Jiangong Wang ◽

Liangyuan Zhao

Keyword(s):

Logistic Regression ◽

Regression Analysis ◽

Health Status ◽

Influencing Factors ◽

Logistic Regression Analysis ◽

Education Level ◽

Factors Affecting ◽

Exercise Rehabilitation ◽

And Gender ◽

Socio Economic Factors

Abstract Background: To assess the awareness regarding sports rehabilitation among residents of Taiyuan. Method: From September 27, 2018 to March 29, 2019, 1200 residents who met the inclusion/ criteria were selected using convenient sampling method. The population was surveyed by self-designed questionnaires, and single factor and two-category logistic regression analysis (stepwise forward method) was used to identify the factors influencing awareness of mass sports rehabilitation in Taiyuan. Results: A total of 1200 questionnaires were issued, of which 1167 were collected and 1101 were valid. The corresponding recovery and effective recovery rates were 97.25% and 94.34% respectively. The overall rate of awareness of exercise rehabilitation was 80.7%, and education level, occupation, income and health status were significant influencing factors (R<0.05). The results of two-class logistic regression analysis showed that age, occupation, education level, income level and health status were the influencing factors affecting the public's perception of the sports rehabilitation concept (R<0.05), whereas gender, occupation, education level and health status influenced understanding of the establishment of the rehabilitation department in Taiyuan (R<0.05), and gender, age, education level and health status affected understanding of the types of patients receiving rehabilitation (R<0.05). Conclusion: There is a high general awareness regarding sports rehabilitation, and is influenced by various socio-economic factors.

Download Full-text

Evaluation of Prognostic Factors for Clinical Pregnancy Rate Following Artificial Insemination by Husband in the Chinese Population

Frontiers in Medicine ◽

10.3389/fmed.2021.638560 ◽

2021 ◽

Vol 8 ◽

Author(s):

Yumei Luo ◽

Shunhong Wu ◽

Jingru Yuan ◽

Hua Zhou ◽

Yufang Zhong ◽

...

Keyword(s):

Logistic Regression ◽

Prognostic Factors ◽

Normal Form ◽

Pregnancy Rate ◽

Influencing Factors ◽

Artificial Insemination ◽

Regression Equation ◽

Multivariate Logistic Regression ◽

Successful Pregnancy ◽

Logistic Regression Equation

Background: To determine the independent prognostic factors and develop a multivariate logistic regression model for predicting successful pregnancy following artificial insemination by husband (AIH) in infertile Chinese couples.Methods: A total of 3,015 AIH cycles with superovulation from 1,853 infertile Chinese couples were retrospectively analyzed. The clinical characteristics and sperm parameters were compared between the pregnant and non-pregnant groups. Multivariate logistic regression analysis was performed to remove the confounding factors and create an equation to predict the successful pregnancy. Receiver operating characteristic (ROC) curves were constructed for evaluating the abilities for prognostic classification of the independent predictors and the equation.Results: The overall pregnancy rate was 13.0%. The pregnancy rate of double intrauterine insemination (IUI) (18.9%) was significantly higher than that of single IUI (11.4%). The pregnancy rate of the stimulated cycle (14.4%) was significantly higher than that of the natural cycle (9.0%). The pregnancy rates of the age groups <40 years are ~3 times higher than that of the ≥40 years age group. Among sperm parameters, the influencing factors included straight-line velocity (VSL), sperm deformity index (SDI), and normal form rate (all P < 0.05). A multivariate logistic regression equation was created based on the above influencing factors. ROC analysis showed that the prognostic power of the equation is better than those of individual predictors.Conclusion: Cycle treatment options, single/double IUI, female age, sperm VSL, SDI, and normal form rate could predict successful pregnancy following AIH in China. The multivariate logistic regression equation exhibited a greater value for prognostic classification than single predictors.

Download Full-text

Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

Methods of Information in Medicine ◽

10.1055/s-0038-1634910 ◽

1993 ◽

Vol 32 (02) ◽

pp. 175-179 ◽

Cited By ~ 7

Author(s):

B. Brambati ◽

T. Chard ◽

J. G. Grudzinskas ◽

M. C. M. Macintosh

Keyword(s):

Logistic Regression ◽

General Population ◽

Likelihood Ratio ◽

False Positive ◽

False Positive Rate ◽

Ratio Method ◽

Detection Rates ◽

Gaussian Distributions ◽

Positive Rate ◽

Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

Download Full-text

Aphid–Plant–Phytovirus Pathosystems: Influencing Factors from Vector Behaviour to Virus Spread

Agriculture ◽

10.3390/agriculture11060502 ◽

2021 ◽

Vol 11 (6) ◽

pp. 502

Author(s):

Junior Corneille Fingu-Mabola ◽

Frédéric Francis

Keyword(s):

Influencing Factors ◽

Virus Transmission ◽

Physical Factors ◽

Factors Affecting ◽

Vector Borne Diseases ◽

Vector Behaviour ◽

Crop Fields ◽

Host Seeking ◽

The Impact ◽

Vector Efficiency

Aphids are responsible for the spread of more than half of the known phytovirus species. Virus transmission within the plant–aphid–phytovirus pathosystem depends on vector mobility which allows the aphid to reach its host plant and on vector efficiency in terms of ability to transmit phytoviruses. However, several other factors can influence the phytoviruses transmission process and have significant epidemiological consequences. In this review, we aimed to analyse the aphid behaviours and influencing factors affecting phytovirus spread. We discussed the impact of vector host-seeking and dispersal behaviours mostly involved in aphid-born phytovirus spread but also the effect of feeding behaviours and life history traits involved in plant–aphid–phytovirus relationships on vector performances. We also noted that these behaviours are influenced by factors inherent to the interactions between pathosystem components (mode of transmission of phytoviruses, vector efficiency, plant resistance, …) and several biological, biochemical, chemical or physical factors related to the environment of these pathosystem components, most of them being manipulated as means to control vector-borne diseases in the crop fields.

Download Full-text

The role of serum β-trophin and endostatin in patients with polycystic ovary syndrome: Are they correlated?

BMC Women s Health ◽

10.1186/s12905-021-01198-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Wei Gong ◽

Aikmu Bilixzi ◽

Xinmei Wang ◽

Yanli Lu ◽

Li Wan ◽

...

Keyword(s):

Polycystic Ovary Syndrome ◽

Influencing Factors ◽

Polycystic Ovary ◽

Pearson Correlation ◽

Healthy Controls ◽

Newly Diagnosed ◽

Ovary Syndrome ◽

Factors Affecting ◽

Main Factors

Abstract Background It’s necessary to investigate the serum β-trophin and endostatin (ES) level and its influencing factors in patients with newly diagnosed polycystic ovary syndrome (PCOS). Methods Newly diagnosed PCOS patients treated in our hospital were selected, and healthy women who took physical examination during the same period as healthy controls. We detected and compared the related serum indicators between two groups, Pearson correlation were conducted to identify the factors associated with β-trophin and ES, and the influencing factors of β-trophin and ES were analyzed by logistic regression. Results A total of 62 PCOS patients and 65 healthy controls were included. The BMI, WHI, LH, FSH, TT, FAI, FBG, FINS, HOMA-IR, TC, TG, LDL, ES in PCOS patients were significantly higher than that of healthy controls, while the SHBG and HDL in PCOS patients were significantly lower than that of healthy controls (all p < 0.05). β-trophin was closely associated with BMI (r = 0.427), WHR (r = 0.504), FBG (r = 0.385), TG (r = 0.405) and LDL (r = 0.302, all p < 0.05), and ES was closely associated with BMI (r = 0.358), WHR (r = 0.421), FBG (r = 0.343), TC (r = 0.319), TG (r = 0.404, all p < 0.05). TG, BMI, WHR and FBG were the main factors affecting the serum β-trophin levels (all p < 0.05). FBG, TC and BMI were the main factors affecting the serum ES levels (all p < 0.05). The TG, β-trophin, ES level in PCOS patients with insulin resistance (IR) were significantly higher than that of those without IR (all p < 0.05). Conclusion Increased β-trophin is closely associated with increased ES in patients with PCOS, which may be the useful indicators for the management of PCOS.

Download Full-text

Knowledge, attitudes, practices, and influencing factors of anxiety among pregnant women in Wuhan during the outbreak of COVID-19: a cross-sectional study

BMC Pregnancy and Childbirth ◽

10.1186/s12884-021-03561-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Wenping Ding ◽

Jianmei Lu ◽

Yan Zhou ◽

Weizhong Wei ◽

Zhihong Zhou ◽

...

Keyword(s):

Logistic Regression ◽

Pregnant Women ◽

Influencing Factors ◽

Information Source ◽

Influential Factors ◽

Public Health Issue ◽

Cross Sectional ◽

Factors Associated ◽

Attitudes And Practices ◽

Prenatal Anxiety

Abstract Background Prenatal anxiety has been a significant public health issue globally, leading to adverse health outcomes for mothers and children. The study aimed to evaluate the sociodemographic characteristics, knowledge, attitudes, and practices (KAP), and anxiety level of pregnant women during the coronavirus disease 2019 (COVID-19) epidemic in Wuhan and investigate the influencing factors for prenatal anxiety in this specific context. Methods Pregnant subjects’ KAP towards COVID-19 and their sociodemographics and pregnancy information were collected using questionnaires. The Zung Self-Rating Anxiety Scale (SAS) was used to assess anxiety status. Factors associated with the level of prenatal anxiety were analyzed by Pearson’s chi-square test and multivariable logistic regression analyses. Results The prenatal anxiety prevalence in this population was 20.8%. The mean score of knowledge was 13.2 ± 1.1 on a 0 ~ 14 scale. The attitudes and practices data showed that 580/ 817 (71.0%) were very concerned about the news of COVID-19, 455/817 (55.7%) considered the official media to be the most reliable information source for COVID-19, and 681/817 (83.4%) were anxious about the possibility of being infected by COVID-19. However, only 83/817 (10.2%) worried about contracting COVID-19 infection through the ultrasound transducer during a routing morphology scan. About two-thirds 528/817 (64.6%) delayed or canceled the antenatal visits. Approximately half of them 410/817 (50.2%) used two kinds of personal protection equipments (PPEs) during hospital visits. Logistic regression analysis revealed that the influential factors for prenatal anxiety included previous children in the family, knowledge score, media trust, worry of contracting the COVID-19 infection and worry about getting infected with COVID-19 from the ultrasound probe antenatal care (ANC) schedule. Conclusion Prenatal anxiety was prevalent among pregnant women in Wuhan during the outbreak of COVID-19. The current findings identified factors associated with the level of prenatal anxiety that could be targeted for psychological care.

Download Full-text

Analysis on application level based on ordinal logistic regression and best of advanced manufacturing technologies (AMT) selection based on fuzzy-TOPSIS integration approach

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189663 ◽

2021 ◽

pp. 1-11

Author(s):

Guilian Wang ◽

Liyan Zhang ◽

Jing Guo

Keyword(s):

Logistic Regression ◽

Organizational Factors ◽

Fuzzy Topsis ◽

Ordinal Logistic Regression ◽

Industrial Robots ◽

Policy Implications ◽

Market Pricing ◽

Factors Affecting ◽

Pricing Power ◽

The Core Competence

This paper try to fully reveal the key factors affecting the the level of AMT application in micro- and small enterprises (MSEs) from its organizational factors by ordinal logistic regression. The results show that MSEs have a relatively high level of AMT application as a whole due to the maturity and cost reduction of basic technologies such as artificial intelligence, digital manufacturing and industrial robots. In this paper we propose manufacturing world analysis at Application using Logistic Regression and best AMT selection using Fuzzy-TOPSIS Integration approach.Considering the influence mechanism of each factor, the important factors that affect the application level of AMT are the enterprise’s market pricing power, the main production types, technical, market and management capabilities, organization development incentives and the interaction with external stakeholders. Based on the results above, the following policy implications are proposed: further expanding the customized production in MSEs to gradually improve the market pricing power, expanding the core competence of enterprises, enhancing the employee autonomy, and strengthening the interaction with industry organizations.

Download Full-text

Experimental study on the factors affecting squeak noise occurrence in automotive suspension bushings

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/09544070211024109 ◽

2021 ◽

pp. 095440702110241

Author(s):

Byunghyun Kang ◽

Cheol Choi ◽

Daeun Sung ◽

Seongho Yoon ◽

Byoung-Ho Choi

Keyword(s):

Neural Network ◽

Logistic Regression ◽

Influencing Factors ◽

Frictional Force ◽

Prediction Models ◽

Statistical Tests ◽

Network Models ◽

Multiple Logistic Regression ◽

Neural Network Models ◽

Automotive Suspension

In this study, friction tests are performed, via a custom-built friction tester, on specimens of natural rubber used in automotive suspension bushings. By analyzing the problematic suspension bushings, the eleven candidate factors that influence squeak noise are selected: surface lubrication, hardness, vulcanization condition, surface texture, additive content, sample thickness, thermal aging, temperature, surface moisture, friction speed, and normal force. Through friction tests, the changes are investigated in frictional force and squeak noise occurrence according to various levels of the influencing factors. The degree of correlation between frictional force and squeak noise occurrence with the factors is determined through statistical tests, and the relationship between frictional force and squeak noise occurrence based on the test results is discussed. Squeak noise prediction models are constructed by considering the interactions among the influencing factors through both multiple logistic regression and neural network analysis. The accuracies of the two prediction models are evaluated by comparing predicted and measured results. The accuracies of the multiple logistic regression and neural network models in predicting the occurrence of squeak noise are 88.2% and 87.2%, respectively.

Download Full-text

Factors Affecting Salt Reduction Measure Adoption among Chinese Residents

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18020445 ◽

2021 ◽

Vol 18 (2) ◽

pp. 445

Author(s):

Zeying Huang ◽

Di Zeng

Keyword(s):

Logistic Regression ◽

Food Intake ◽

Nutrition Education ◽

Online Survey ◽

Adaptive Lasso ◽

Stepwise Logistic Regression ◽

Salt Reduction ◽

Salt Restriction ◽

Salt Diet ◽

Factors Affecting

China has the highest mortality rate caused by diseases and conditions associated with its high-salt diet. Since 2016, China has initiated a national salt reduction campaign that aims at promoting the usage of salt information on food labels and salt-restriction spoons and reducing condiment and pickled food intake. However, factors affecting individuals’ decisions to adopt these salt reduction measures remain largely unknown. By comparing the performances of logistic regression, stepwise logistic regression, lasso logistic regression and adaptive lasso logistic regression, this study aims to fill this gap by analyzing the adoption behaviour of 1610 individuals from a nationally representative online survey. It was found that the practices were far from adopted and only 26.40%, 22.98%, 33.54% and 37.20% reported the adoption of labelled salt information, salt-restriction spoons, reduced condiment use in home cooking and reduced pickled food intake, respectively. Knowledge on salt, the perceived benefits of salt reduction, participation in nutrition education and training programs on sodium reduction were positively associated with using salt information labels. Adoption of the other measures was largely explained by people’s awareness of hypertension risks and taste preferences. It is therefore recommended that policy interventions should enhance Chinese individuals’ knowledge of salt, raise the awareness of the benefits associated with a low-salt diet and the risks associated with consuming excessive salt and reshape their taste choices.

Download Full-text

Grey Relational Grade Based Quantitative Analysis of the Factors Influencing the Load Characteristics of A Power Grid

E3S Web of Conferences ◽

10.1051/e3sconf/20185301012 ◽

2018 ◽

Vol 53 ◽

pp. 01012 ◽

Cited By ~ 1

Author(s):

Wei Pan ◽

Caijia Lei ◽

Wei Jia ◽

Hui Gao ◽

Binghua Fang

Keyword(s):

Quantitative Analysis ◽

Influencing Factors ◽

Power Grid ◽

Grey System Theory ◽

Grey System ◽

Factors Affecting ◽

Grey Relational Grade ◽

Load Characteristics ◽

Grey Relational ◽

Main Factor

Regarding analysis of load characteristics of a power grid, there are multiple factors that influence the variation of load characteristics. Among these factors, the influence of different ones on the change of load characteristic is somewhat different, thus the degree of influence of various factors needs to be quantified to distinguish the main and minor factors of load characteristics. Based on this, the grey relational analysis in the grey system theory is employed as the basis of mathematical model in this paper. Firstly, the main factors affecting the load characteristics of a power grid are analysed. Then, the principle of quantitative analysis of the influencing factors by using grey relational grade is introduced. Lastly, the load of Guangzhou power grid is selected as the research object, thereby the main factor of temperature affecting the load characteristics is quantitatively analysed, such that the correlation between temperature and load is established. In this paper, by investigating the influencing factors and the degree of influence of load characteristics, the law of load characteristics changes can be effectively revealed, which is of great significance for power system planning and dispatching operation.

Download Full-text