scholarly journals Logistic Regression Model to Investigate the Risk Factors for Glaucoma

2021 ◽  
Vol 8 (6) ◽  
pp. 881-887
Author(s):  
Amaal Sadeq Hamoodi
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Climatic Factors ◽  
Geographical Location ◽  
Cross Sectional ◽  
Factors Affecting ◽  
Observational Design ◽  
The Relationship

Many studies have found that age, race, gender, past family history, and intraocular pressure (IOP) of the eyes are key risk factors for glaucoma disease. The current study aims to evaluate the relationship between glaucoma and various glaucoma risk factors in the Arabian ethnicity using a cohort cross-sectional observational design. Therefore, the current study is targeted at building a regression model to estimate the probability of injury to glaucoma disease, which is one of the serious diseases that affect the eyes. It uses the logistic regression model, which is one of the modern non parametric methods, and a cohort cross-sectional observational design. The study included a total of 136 glaucoma patients. The findings show that there is no link between gender and glaucoma (p = 0.202), while there is a link between age and glaucoma (p = 0.004). Furthermore, the findings demonstrate that there is no association between diabetic mellitus (DM) and glaucoma (p = 0.273), although there is a relationship between hypertension and healing degree (p = 0.035) and diabetes and healing degree (p = 0.001). The findings also show that the factors affecting injury are: age, gender, pressure, and geographical location, and that diabetes and climatic factors are not influential. Current findings may aid in the development of intervention strategies that will raise glaucoma awareness in the future.

The relationship between disability and suicide: prospective evidence from the Ten to Men cohort

2018 ◽  
Vol 41 (4) ◽  
pp. 707-713 ◽  
Author(s):  
Allison Milner ◽  
Anne-Marie Bollier ◽  
Eric Emerson ◽  
Anne Kavanagh
Keyword(s):  
Mental Health ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
People With Disabilities ◽  
Cross Sectional ◽  
Longitudinal Cohort ◽  
The Past ◽  
The Relationship

Abstract Background People with disabilities often face a range of social and economic adversities. Evidence suggests that these disadvantages result in poorer mental health. Some research also indicates that people with disabilities are more likely experience thoughts about suicide than people without disability, although most of this research is based on small cross-sectional samples. Methods We explored the relationship between self-reported disability (measured at baseline) and likelihood of reporting thoughts of suicide (measured at follow up) using a large longitudinal cohort of Australian males. A logistic regression model was conducted with thoughts of suicide within the past 12 months (yes or no) as the outcome and disability as the exposure. The models adjusted for relevant confounders, including mental health using the SF-12 MCS, and excluded males who reported thoughts of suicide at baseline. Results After adjustment, there was a 1.48 (95% CI: 0.98–2.23, P = 0.063) increase in the odds of thoughts of suicide among men who also reported a disability. The size of association was similar to that of being unemployed. Conclusions Males reporting disability may also suffer from thoughts of suicide. We speculate that discrimination may be one explanation for the observed association. More research on this topic is needed.

scholarly journals Prediction of risk factors of periodontal disease by logistic regression: a study done in Karnataka, India

2018 ◽  
Vol 5 (12) ◽  
pp. 5301
Author(s):  
Shivalingappa Basavantappa Javali ◽  
Mohan Anantarao Sunkad ◽  
Appasaheb Saheb Wantamutte
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Periodontal Disease ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Cross Sectional Study ◽  
Full Model ◽  
Treatment Needs ◽  
Cross Sectional ◽  
Periodontal Index

Background: The purpose of the study was to analyze the dependence of oral health diseases i.e. periodontal disease by Community Periodontal Index of Treatment Needs (CPITN) by considering the number of risk factors through the applications of logistic regression model.Methods: This cross sectional study involves a systematic random sample of 600 permanent dentition aged between 18-40 years in Karnataka, India. The mean age was 34.26±7.28. The risk factors of periodontal disease were established by multiple logistic regression models using SPSS 21.0 statistical software.Results: The factors like frequency of brushing, timings of cleaning teeth and type of toothpastes are significant persistent predictors of periodontal disease. The log likelihood value of full model is –1085.7876 and AIC is 1.2577 followed by reduced regression model are -1019.8106 and 1.1748 respectively for periodontal disease. The area under receiver operating characteristic (ROC) curve for the periodontal disease is 0.6128 (full model) and 0.5821 (reduced model).Conclusions: The logistic regression model is useful in predicting risk factors like-frequency of brushing, timings of cleaning teeth and type of toothpastes for periodontal disease. The fitting performance of reduced logistic regression model is slightly a better fit as compared to full logistic regression model in identifying the these risk factors for both dichotomous periodontal disease. 

Application of Principal Component Analysis and Logistic Regression Model in Lupus Nephritis Patients with Clinical Hypothyroidism

2020 ◽  
Author(s):  
Ting Huang ◽  
Jiarong Li ◽  
Weiru Zhang
Keyword(s):  
Risk Factors ◽  
Principal Component Analysis ◽  
Logistic Regression ◽  
Lupus Nephritis ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Principal Component ◽  
Component Analysis ◽  
Cross Sectional

Abstract Background : Previous studies indicate that the prevalence of hypothyroidism is much higher in patients with lupus nephritis (LN) than in the general population, and is associated with LN’s activity. Principal component analysis (PCA) and logistic regression can help determine relevant risk factors and identify LN patients at high risk of hypothyroidism; as such, these tools may prove useful in managing this disease. Methods: We carried out a cross-sectional study of 143 LN patients diagnosed by renal biopsy, all of whom had been admitted to Xiangya Hospital of Central South University in Changsha, China, between June 2012 and December 2016. The PCA–logistic regression model was used to determine the influential principal components for LN patients who have hypothyroidism. Results : Our PCA–logistic regression analysis results demonstrated that serum creatinine, blood urea nitrogen, blood uric acid, total protein, albumin, and anti-ribonucleoprotein antibody were important clinical variables for LN patients with hypothyroidism. The area under the curve of this model was 0.855. Conclusion : The PCA–logistic regression model performed well in identifying important risk factors for certain clinical outcomes, and promoting clinical research on other diseases will be beneficial. Using this model, clinicians can identify at-risk subjects and either implement preventative strategies or manage current treatments.

scholarly journals Which Factors Affect Probability of Women Using Prenatal Care? The Case of Bangladesh

2014 ◽  
Vol 2 (2) ◽  
pp. 39-46
Author(s):  
Sharjil Muktafi Haque ◽  
A. K Enamul Haque
Keyword(s):  
Logistic Regression ◽  
Prenatal Care ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Cross Sectional ◽  
Policy Makers ◽  
Factors Affecting ◽  
Health Related ◽  
Insight Into

This paper determines the probability of women in Bangladesh taking prenatal care based on changes in socioeconomic and health-related variables. Insight into factors affecting prenatal care usage will help policy-makers redirect health-related strategies and policies in more equitable directions. We used a total of 1,099 cross-sectional observations from Bangladesh Bureau of Statistics (2000) to estimate a logistic regression model. Our results show that education and income is positively associated with odds of women taking prenatal care while increase in age reduces odds of taking this service. We end by making several policy-relevant recommendations.

scholarly journals A comprehensive, contemporary assessment of the association between hepatosteatosis and coronary artery calcium scoring

2021 ◽  
Vol 22 (Supplement_1) ◽  
Author(s):  
T Heseltine ◽  
SW Murray ◽  
RL Jones ◽  
M Fisher ◽  
B Ruzsics
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Coronary Artery ◽  
Regression Model ◽  
Coronary Artery Calcium ◽  
Logistic Regression Model ◽  
Prediction Models ◽  
Multinomial Logistic Regression ◽  
Cvd Risk ◽  
Male Sex

Abstract Funding Acknowledgements Type of funding sources: None. onbehalf Liverpool Multiparametric Imaging Collaboration Background Coronary artery calcium (CAC) score is a well-established technique for stratifying an individual’s cardiovascular disease (CVD) risk. Several well-established registries have incorporated CAC scoring into CVD risk prediction models to enhance accuracy. Hepatosteatosis (HS) has been shown to be an independent predictor of CVD events and can be measured on non-contrast computed tomography (CT). We sought to undertake a contemporary, comprehensive assessment of the influence of HS on CAC score alongside traditional CVD risk factors. In patients with HS it may be beneficial to offer routine CAC screening to evaluate CVD risk to enhance opportunities for earlier primary prevention strategies. Methods We performed a retrospective, observational analysis at a high-volume cardiac CT centre analysing consecutive CT coronary angiography (CTCA) studies. All patients referred for investigation of chest pain over a 28-month period (June 2014 to November 2016) were included. Patients with established CVD were excluded. The cardiac findings were reported by a cardiologist and retrospectively analysed by two independent radiologists for the presence of HS. Those with CAC of zero and those with CAC greater than zero were compared for demographic and cardiac risks. A multivariate analysis comparing the risk factors was performed to adjust for the presence of established risk factors. A binomial logistic regression model was developed to assess the association between the presence of HS and increasing strata of CAC. Results In total there were 1499 patients referred for CTCA without prior evidence of CVD. The assessment of HS was completed in 1195 (79.7%) and CAC score was performed in 1103 (92.3%). There were 466 with CVD and 637 without CVD. The prevalence of HS was significantly higher in those with CVD versus those without CVD on CTCA (51.3% versus 39.9%, p = 0.007). Male sex (50.7% versus 36.1% p= <0.001), age (59.4 ± 13.7 versus 48.1 ± 13.6, p= <0.001) and diabetes (12.4% versus 6.9%, p = 0.04) were also significantly higher in the CAC group compared to the CAC score of zero. HS was associated with increasing strata of CAC score compared with CAC of zero (CAC score 1-100 OR1.47, p = 0.01, CAC score 101-400 OR:1.68, p = 0.02, CAC score >400 OR 1.42, p = 0.14). This association became non-significant in the highest strata of CAC score. Conclusion We found a significant association between the increasing age, male sex, diabetes and HS with the presence of CAC. HS was also associated with a more severe phenotype of CVD based on the multinomial logistic regression model. Although the association reduced for the highest strata of CAC (CAC score >400) this likely reflects the overall low numbers of patients within this group and is likely a type II error. Based on these findings it may be appropriate to offer routine CVD risk stratification techniques in all those diagnosed with HS.

scholarly journals Risk factors on admission associated with hospital length of stay in patients with COVID-19: a retrospective cohort study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Anping Guo ◽  
Jin Lu ◽  
Haizhu Tan ◽  
Zejian Kuang ◽  
Ying Luo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Liver Disease ◽  
Length Of Stay ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Hospital Length ◽  
Clinical Treatment ◽  
Hospital Length Of Stay ◽  
Female Sex

AbstractTreating patients with COVID-19 is expensive, thus it is essential to identify factors on admission associated with hospital length of stay (LOS) and provide a risk assessment for clinical treatment. To address this, we conduct a retrospective study, which involved patients with laboratory-confirmed COVID-19 infection in Hefei, China and being discharged between January 20 2020 and March 16 2020. Demographic information, clinical treatment, and laboratory data for the participants were extracted from medical records. A prolonged LOS was defined as equal to or greater than the median length of hospitable stay. The median LOS for the 75 patients was 17 days (IQR 13–22). We used univariable and multivariable logistic regressions to explore the risk factors associated with a prolonged hospital LOS. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated. The median age of the 75 patients was 47 years. Approximately 75% of the patients had mild or general disease. The univariate logistic regression model showed that female sex and having a fever on admission were significantly associated with longer duration of hospitalization. The multivariate logistic regression model enhances these associations. Odds of a prolonged LOS were associated with male sex (aOR 0.19, 95% CI 0.05–0.63, p = 0.01), having fever on admission (aOR 8.27, 95% CI 1.47–72.16, p = 0.028) and pre-existing chronic kidney or liver disease (aOR 13.73 95% CI 1.95–145.4, p = 0.015) as well as each 1-unit increase in creatinine level (aOR 0.94, 95% CI 0.9–0.98, p = 0.007). We also found that a prolonged LOS was associated with increased creatinine levels in patients with chronic kidney or liver disease (p < 0.001). In conclusion, female sex, fever, chronic kidney or liver disease before admission and increasing creatinine levels were associated with prolonged LOS in patients with COVID-19.

scholarly journals Quality of Care in Long-Term Hemodialysis Patients in Mexico

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Torres-Díaz JA ◽  
◽  
Gonzalez-Gonzalez JG ◽  
Zúniga-Hernández JA ◽  
Olivo-Gutiérrez MC ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Quality Of Care ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
World Literature ◽  
Logistic Regression Model ◽  
Hospital Admissions ◽  
Cross Sectional

Introduction: The End Stage Renal Disease (ESRD) is one of the leading causes of mortality in Mexico. The quality of care these patients receive remains uncertain. Methods: This is a descriptive, single-center and cross-sectional cohort study. The KDOQI performance measures, hemoglobin level >11 g/dL, blood pressure <140/90 mmHg, serum albumin >4 g/dL and use of arteriovenous fistula of patients with ESRD on hemodialysis were analyzed in a period of a year. The association between mortality and the KDOQI objectives was evaluated with a logistic regression model. A linear regression model was also performed with the number of readmissions. Results: A total of 124 participants were included. Participants were categorized by the number of measures completed. Fourteen (11.3%) of the participants did not meet any of the goals, 51 (41.1%) met one, 43 (34.7%) met two, 11 (8.9%) met three, and 5 (4%) met the four clinical goals analyzed. A mortality of 11.2% was registered. In the logistic regression model, the number of goals met had an OR for mortality of 1.1 (95% CI 0.5-2.8). In the linear regression model, for the number of readmissions, a beta correlation with the number of KDOQI goals met was 0.246 (95% CI -0.872-1.365). Conclusion: The attainment of clinical goals and the mortality rate in our center is similar to that reported in the world literature. Our study did not find a significant association between compliance with clinical guidelines and mortality or the number of hospital admissions in CKD patients on hemodialysis.

scholarly journals Screening Risk Factors and Interaction Analysis of Hypertension in Overweight and Obesity Population based on Three Statistical Models

2021 ◽  
Author(s):  
Li Lu Wei ◽  
Yu jian
Keyword(s):  
Neural Network ◽  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Bp Neural Network ◽  
Logistic Regression Model ◽  
Classification Tree ◽  
Overweight And Obesity ◽  
Tree Model ◽  
Classification Tree Model

Abstract Background Hypertension is a common chronic disease in the world, and it is also a common basic disease of cardiovascular and brain complications. Overweight and obesity are the high risk factors of hypertension. In this study, three statistical methods, classification tree model, logistic regression model and BP neural network, were used to screen the risk factors of hypertension in overweight and obese population, and the interaction of risk factors was conducted Analysis, for the early detection of hypertension, early diagnosis and treatment, reduce the risk of hypertension complications, have a certain clinical significance.Methods The classification tree model, logistic regression model and BP neural network model were used to screen the risk factors of hypertension in overweight and obese people.The specificity, sensitivity and accuracy of the three models were evaluated by receiver operating characteristic curve (ROC). Finally, the classification tree CRT model was used to screen the related risk factors of overweight and obesity hypertension, and the non conditional logistic regression multiplication model was used to quantitatively analyze the interaction.Results The Youden index of ROC curve of classification tree model, logistic regression model and BP neural network model were 39.20%,37.02% ,34.85%, the sensitivity was 61.63%, 76.59%, 82.85%, the specificity was 77.58%, 60.44%, 52.00%, and the area under curve (AUC) was 0.721, 0.734,0.733, respectively. There was no significant difference in AUC between the three models (P>0.05). Classification tree CRT model and logistic regression multiplication model suggested that the interaction between NAFLD and FPG was closely related to the prevalence of overweight and obese hypertension.Conclusion NAFLD,FPG,age,TG,UA, LDL-C were the risk factors of hypertension in overweight and obese people. The interaction between NAFLD and FPG increased the risk of hypertension.

Sign in / Sign up

Export Citation Format

Share Document