scholarly journals Prediction of Lumbar Disc Bulging and Protrusion by Anthropometric Factors and Disc Morphology

2021 ◽  
Vol 18 (5) ◽  
pp. 2521
Author(s):  
Isabella Yu-Ju Hung ◽  
Tiffany Ting-Fang Shih ◽  
Bang-Bin Chen ◽  
Yue Leon Guo
Keyword(s):  
Logistic Regression ◽  
Body Weight ◽  
Regression Analysis ◽  
Lumbar Disc ◽  
Body Height ◽  
Area Under The Curve ◽  
Disc Height ◽  
Mri Scans ◽  
The Relationship ◽  
Better Than

The relationship between reduced disc height and disc bulging and/or protrusion has been controversial. The purposes of this study were to examine the relationship between disc morphology and disc bulging and protrusion and to establish a model for predicting disc bulging and protrusion. This is a retrospective study. A total of 452 MRI scans from a spine study were analysed, 210 (46.5%) were men. Logistic regression analysis was applied to identify the association between anthropometric factors, disc morphology factors, and outcome. Model 1 was constructed using anthropometric variables to investigate the capacity for predicting outcomes. Model 2 was constructed using anthropometric and disc morphology variables. Age, body weight, body height, disc height, and disc depth were significantly associated with outcome. The area under the curve (AUC) statistics of Model 2 were significantly better than those of Model 1 at the L3-L4 and L4-L5 levels but not at the L5-S1 level. The results showed an association between disc morphology and disc bulging and/or protrusion at the L3-L4, L4-L5, and L5-S1 levels. The model utilizing both anthropometric factors and disc morphology factors had a better capacity to predict disc bulging and/or protrusion compared with the model using only anthropometric factors.

PSVI-8 Meta-regression Analysis to Determine the Relationship Between Growing Pig Body Weight and Variation

2021 ◽  
Vol 99 (Supplement_1) ◽  
pp. 218-219
Author(s):  
Andres Fernando T Russi ◽  
Mike D Tokach ◽  
Jason C Woodworth ◽  
Joel M DeRouchey ◽  
Robert D Goodband ◽  
...  
Keyword(s):  
Body Weight ◽  
Regression Analysis ◽  
Sample Size ◽  
Polynomial Regression ◽  
Data Sets ◽  
Regression Equations ◽  
Prediction Equations ◽  
Data Set ◽  
Rate Of Increase ◽  
The Relationship

Abstract The swine industry has been constantly evolving to select animals with improved performance traits and to minimize variation in body weight (BW) in order to meet packer specifications. Therefore, understanding variation presents an opportunity for producers to find strategies that could help reduce, manage, or deal with variation of pigs in a barn. A systematic review and meta-analysis was conducted by collecting data from multiple studies and available data sets in order to develop prediction equations for coefficient of variation (CV) and standard deviation (SD) as a function of BW. Information regarding BW variation from 16 papers was recorded to provide approximately 204 data points. Together, these data included 117,268 individually weighed pigs with a sample size that ranged from 104 to 4,108 pigs. A random-effects model with study used as a random effect was developed. Observations were weighted using sample size as an estimate for precision on the analysis, where larger data sets accounted for increased accuracy in the model. Regression equations were developed using the nlme package of R to determine the relationship between BW and its variation. Polynomial regression analysis was conducted separately for each variation measurement. When CV was reported in the data set, SD was calculated and vice versa. The resulting prediction equations were: CV (%) = 20.04 – 0.135 × (BW) + 0.00043 × (BW)2, R2=0.79; SD = 0.41 + 0.150 × (BW) - 0.00041 × (BW)2, R2 = 0.95. These equations suggest that there is evidence for a decreasing quadratic relationship between mean CV of a population and BW of pigs whereby the rate of decrease is smaller as mean pig BW increases from birth to market. Conversely, the rate of increase of SD of a population of pigs is smaller as mean pig BW increases from birth to market.

scholarly journals Study on the relationship of depression, anxiety, lifestyle and eating habits with the severity of reflux esophagitis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rongxin Wang ◽  
Jing Wang ◽  
Shuiqing Hu
Keyword(s):  
Quality Of Life ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Reflux Esophagitis ◽  
Eating Habits ◽  
Positive Correlation ◽  
Relationship Of ◽  
The Relationship

Abstract Background The etiology of reflux esophagitis (RE) is multi-factorial. This study analyzed the relationship of depression, anxiety, lifestyle and eating habits with RE and its severity and further explored the impact of anxiety and depression on patients’ symptoms and quality of life. Methods From September 2016 to February 2018, a total of 689 subjects at Xuanwu Hospital Capital Medical University participated in this survey. They were divided into the RE group (patients diagnosed with RE on gastroscopy, n = 361) and the control group (healthy individuals without heartburn, regurgitation and other gastrointestinal symptoms, n = 328). The survey included general demographic information, lifestyle habits, eating habits, comorbidities, current medications, the gastroesophageal reflux disease (GERD) questionnaire (GerdQ), the Patient Health Questionnaire-9 depression scale and the General Anxiety Disorder-7 anxiety scale. Results The mean age and sex ratio of the two groups were similar. Multivariate logistic regression analysis identified the following factors as related to the onset of RE (p < 0.05): low education level; drinking strong tea; preferences for sweets, noodles and acidic foods; sleeping on a low pillow; overeating; a short interval between dinner and sleep; anxiety; depression; constipation; history of hypertension; and use of oral calcium channel blockers. Ordinal logistic regression analysis revealed a positive correlation between sleeping on a low pillow and RE severity (p = 0.025). Depression had a positive correlation with the severity of symptoms (rs = 0.375, p < 0.001) and patients’ quality of life (rs = 0.306, p < 0.001), whereas anxiety showed no such association. Conclusions Many lifestyle factors and eating habits were correlated with the onset of RE. Notably, sleeping on a low pillow was positively correlated with RE severity, and depression was positively related to the severity of symptoms and patients’ quality of life.

scholarly journals LDH, CRP and ALB predict nucleic acid turn negative within 14 days in symptomatic patients with COVID-19

2021 ◽  
pp. 003693302199424
Author(s):  
Gaoli Liu ◽  
Bicheng Zhang ◽  
Shaowen Zhang ◽  
Haifeng Hu ◽  
TingTing Liu
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Nucleic Acid ◽  
Hospital Stay ◽  
Prognostic Marker ◽  
Roc Curve ◽  
Prognostic Value ◽  
Statistical Significance ◽  
Biochemical Indicators ◽  
The Relationship

Aims To search for biochemical indicators that can identify symptomatic patients with COVID-19 whose nucleic acid could turn negative within 14 days, and assess the prognostic value of these biochemical indicators in patients with COVID-19. Patients and methods We collected the clinical data of patients with COVID-19 admitted to our hospital, by using logistic regression analysis and AUC curves, explored the relationship between biochemical indicators and nucleic acid positive duration, the severity of COVID-19, and hospital stay respectively. Results A total of two hundred and thirty-three patients with COVID-19 were enrolled in the study. We found patients whose nucleic acid turned negative within 14 days had lower LDH, CRP and higher ALB ( P < 0.05). ROC curve results indicated that lower LDH, TP, CRP and higher ALB predicted the nucleic acid of patients turned negative within 14 days with statistical significance( P < 0.05), AST, LDH, CRP and PCT predicted the severe COVID-19 with statistical significance, and CRP predicted hospital stay >31days with statistical significance ( P < 0.05). After verification, the probability of nucleic acid turning negative within 14 days in patients with low LDH (<256 U/L), CRP (<44.5 mg/L) and high ALB (>35.8 g/L) was about 4 times higher than that in patients with high LDH, CRP and low ALB ( P < 0.05). Conclusions LDH, CRP and ALB are useful prognostic marker for predicting nucleic acid turn negative within 14 days in symptomatic patients with COVID-19.

scholarly journals Repeated sprint ability (RSA) performance is not related to body height and body weight measurements among perak sukma 2018 male rugby players

2019 ◽  
Vol 10 (SPL1) ◽  
Author(s):  
Nor Fazila Abd Malek ◽  
Nur Ikhwan Mohamad ◽  
Mohansundar Sankaravel2 ◽  
Sakinah Mohd Hassan
Keyword(s):  
Body Weight ◽  
Pearson Correlation ◽  
Body Height ◽  
Anthropometric Measures ◽  
Actual Performance ◽  
Repeated Sprint Ability ◽  
Repeated Sprint ◽  
The Relationship ◽  
Rugby Players ◽  
Sprint Ability

Repeated sprint ability (RSA) is typically used for speed-endurance and agility-endurance training and assessment. RSA is preferred as it mimics actual performance during competition. RSA test involves straight sprint and quick turning movements. Thus, the aim of this study is to determine the relationship between height and body weight measurements on RSA performance, as height and weight may influence the mechanical properties of it. Ten male rugby players (body weight 72.99 ± 14.29 kg, height 169 ± 5.55 cm) selected for Perak SUKMA 2018 team were assessed during centralized training ~9 months prior to their competition.  Body weight and height were measured using standard laboratory weight and height scales prior to the RSA test. RSA were hand-timed for 5 repetitions x 2 sets x 60 seconds rest in between repetitions x 5 minutes rest in-between set. The sprints were performed in the evenings, on grass surface with participants wearing full playing attire. Standardized pre-game warm-up and cooling down were performed. Data recorded was analyzed statistically using a t-test comparison for set one versus set two performances. Pearson correlation coefficient was used to analyze the relationship between anthropometric measures and the RSA test. RSA performance has a negative correlation with the athletes’ height (r= -0.233 set 1, r = -0.435 set 2) and body weight (r= -0.556 set 1, r= -0.629 set 2). RSA performance was significantly different between set 1 and set 2 (p=0.001), with set 1 performance better than set 2. Body height and body weight did not influence RSA performance. Athletes involved did not have adequate speed-agility-endurance needed. In practical, selecting athletes based on their body height and body weight with the assumption that taller and heavier athletes may perform better may not be suitable for this kind of sport. On the other hand, using RSA to test and develop the speed-agility-endurance ability for rugby players is suggested.  

scholarly journals Association Between Triglyceride Glucose Index and Non-Small Cell Lung Cancer Risk in Chinese Population

2021 ◽  
Vol 11 ◽  
Author(s):  
Xin Yan ◽  
Yujuan Gao ◽  
Jingzhi Tong ◽  
Mi Tian ◽  
Jinghong Dai ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Cancer Risk ◽  
Small Cell Lung Cancer ◽  
Roc Curve ◽  
Small Cell ◽  
Small Cell Lung ◽  
Tyg Index ◽  
The Relationship

BackgroundNumerous studies showed that insulin resistance (IR) was associated with cancer risk. However, few studies investigated the relationship between IR and non-small cell lung cancer (NSCLC). The aim of this study is to explore the association of triglyceride glucose (TyG) index, a simple surrogate marker of IR, with NSCLC risk.Methods791 histologically confirmed NSCLC cases and 787 controls were enrolled in the present study. Fasting blood glucose and triglyceride were measured. The TyG index was calculated as ln [fasting triglycerides (mg/dl) ×fasting glucose (mg/dl)/2]. Logistic regression analysis was performed to estimate the relationship between NSCLC risk and the TyG index.ResultsThe TyG index was significantly higher in patients with NSCLC than that in controls (8.42 ± 0.55 vs 8.00 ± 0.45, P &lt; 0.01). Logistic regression analysis showed that the TyG index (OR = 3.651, 95%CI 2.461–5.417, P &lt; 0.001) was independently associated with NSCLC risk after adjusting for conventional risk factors. In addition, a continuous rise in the incidence of NSCLC was observed along the tertiles of the TyG index (29.4 vs 53.8 vs 67.2%, P &lt; 0.001). However, there were no differences of the TyG index in different pathological or TNM stages. In receiver operating characteristic (ROC) curve analysis, the optimal cut-off level for the TyG index to predict incident NSCLC was 8.18, and the area under the ROC curve (AUROC) was 0.713(95% CI 0.688–0.738).ConclusionsThe TyG index is significantly correlated with NSCLC risk, and it may be suitable as a predictor for NSCLC.

Abstract TMP81: Artificial Intelligence Beats Conventional Regression Analysis in Predicting Short and Long Term Risk of Stroke in Patients Undergoing Percutaneous Coronary Interventions

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Pradyumna Agasthi ◽  
Chieh-Ju Chao ◽  
Han Lun Wu ◽  
Farouk Mookadam ◽  
Nithin Venepally ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Area Under The Curve ◽  
Coronary Intervention ◽  
Test Sample ◽  
Training Sample ◽  
Logistic Regression Models ◽  
Percutaneous Coronary ◽  
Short And Long Term

Introduction: Ischemic stroke (IS) causes substantial morbidity and mortality in patients undergoing percutaneous coronary intervention (PCI) with a 5 yr incidence ~ 3%. We sought the test the accuracy of Machine learning (ML) algorithms in predicting IS in patients undergoing PCI. Methods: Mayo Clinic CathPCI registry data were retrospectively analyzed from Jan 2003 - June 2018 including 21,872 patients who underwent PCI. The cohort was randomly divided into a training sample (75%, n=16404) and a unique test sample (25%, n=5468) prior to model generation. The risk prediction model was generated utilizing a random forest algorithm (RF model) on 188 unique variables to predict the risk of IS at 6-month, 1, 2, and 5-year post PCI. Conventional risk factors for stroke were used for logistic regression. The receiver operating characteristic (ROC) curve and area under the curve for the RF and logistic regression models were compared for the test cohort. Results: The mean age was 66.9 ± 12.4 years, and 71% were male. Patient demographics and outcomes are shown in Table 1 . The ROC area under the curve for the RF model was superior compared to the logistic regression model in predicting IS at 6 months, 1,2 and 5 yrs for the test cohort ( Figure 1 .) Conclusions: The RF model accurately predicts short and long term risk of IS and outperforms logistic regression analysis in patients undergoing PCI.

Trust in police as an influencing factor on trust in government: 2SLS analysis using perception of safety

2020 ◽  
Vol 43 (4) ◽  
pp. 541-558
Author(s):  
Jisu Jeong ◽  
Seunghui Han
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Trust In Government ◽  
Content Type ◽  
Perception Of Safety ◽  
Ordered Logistic Regression ◽  
The Impact ◽  
The Relationship ◽  
Trust In Police

PurposeCitizen trust in police is important in terms of citizen consent to government policies and of police achieving their organizational goals. In the previous study, improvements in police policy, organizational operation and policing activities were developed to clarify which factors influence trust in police and how trust can be improved. This research raises the question, would changes in trust in police have an impact on trust in government? In this paper, this research question is discussed theoretically and the causal relationship analyzed empirically by applying OLS, ordered logistic, 2SLS and logistic regressions.Design/methodology/approachThe basic analysis methods are to apply the OLS and the ordered logistic regression. OLS regression analysis is an analytical method that minimizes an error range of a regression line. The assumptions for OLS are: linearity, independence, equilibrium, extrapolation and multicollinearity issues. These problems were statistically verified and analyzed, in order to confirm the robustness of the analysis results by comparing the results of the ordered logistic regression because of the sequence characteristic of the dependent variable. The data to be used in this study is the Asia Barometer Survey in 2013.FindingsTrust in police and citizen perception of safety are analyzed as important factors to increase trust in the government. The effects of trust in police are more significant than the effects of control variables, and the direction and strength of the results are stable. The effect of trust in police on trust in government is strengthened by the perception of safety (IV). In addition, OLS, ordered logistic regression analysis, which analyzed trust in central government and local government, and logistic regression analysis categorized by trust and distrust show the stability.Research limitations/implicationsThis paper has implications in terms of theoretical and empirical analysis of the relationship between trust in police and trust in government. In addition, the impact of perception of safety on trust in police can be provided to police officers, policymakers and governors who are seeking to increase trust in government. This paper is also meaningful in that it is the microscopic research based on the citizens' survey. One of the limitations of macroscopic research is that it does not consider the individual perceptions of citizens.Practical implicationsThe results of this paper can confirm the relationship of the virtuous cycle, which is perception of safety – trust in police – trust in government. The police will need to provide security services to improve citizens' perception of safety and make great efforts to create safer communities and society. Trust in police formed through this process can be an important component of trust in government. By making citizens feel safer and achieving trust in police, ultimately, trust in government will be improved.Originality/valueThe police perform one of the essential roles of government and are one of the major components of trust in government, but the police sector has been neglected compared to the roles of the economic and political sectors. These influences of macro factors are too abstract to allow specific policy directions to be suggested. If we consider trust in police, and factors that can improve trust in government, we can suggest practical policy alternatives.

scholarly journals Cigarette smoking and psychotic symptoms in bipolar affective disorder

2001 ◽  
Vol 179 (1) ◽  
pp. 35-38 ◽  
Author(s):  
Aiden Corvin ◽  
Ed O'Mahony ◽  
Myra O'Regan ◽  
Claire Comerford ◽  
Robert O'Connell ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Affective Disorder ◽  
Bipolar Affective Disorder ◽  
Psychotic Symptoms ◽  
Heavy Smoking ◽  
Ordinal Logistic Regression Analysis ◽  
History Of ◽  
Categorical Diagnosis ◽  
The Relationship

BackgroundAn association exists between smoking and schizophrenia, independent of other factors and related to psychotic symptomatology.AimsTo determine whether smoking is associated with psychosis in bipolar affective disorder.MethodSmoking data were collected from 92 unrelated patients with bipolar affective disorder. An ordinal logistic regression analysis tested the relationship between smoking severity and psychotic symptomatology, allowing for potential confounders.ResultsA significant relationship was detected between smoking/heavy smoking and history of psychosis (68.7%, n=44). Smoking was less prevalent in patients who were less symptomatic (56.5%, n=13) than in patients with a more severe psychosis (75.7%, n=31). Prevalence and severity of smoking predicted severity of psychotic symptoms (P=0.001), a relationship independent of other variables (P=0.0272).ConclusionA link between smoking and psychosis exists in bipolar affective disorder and may be independent of categorical diagnosis.

Current Status of Disaster Preparedness of Pharmacies and Differences in Disaster Awareness Based on Pharmacy Size

2019 ◽  
Vol 13 (4) ◽  
pp. 753-757 ◽  
Author(s):  
Kayoko Ozeki ◽  
Toshiyuki Ojima
Keyword(s):  
Public Health ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Disaster Preparedness ◽  
Disaster Mitigation ◽  
Mitigation Measures ◽  
Current Status ◽  
Disaster Victims ◽  
Subgroup Analyses ◽  
The Relationship

ABSTRACTObjectiveThe prevention of deaths caused indirectly by disasters is important, especially for evacuees requiring medication. Pharmacies play a major role in providing medication to disaster victims. Therefore, this study evaluated the current status of disaster preparedness among pharmacies, the extent of disaster awareness and knowledge of disaster mitigation measures, and any associations of these with the characteristics of pharmacies.MethodsQuestionnaires about disaster preparedness were sent to 337 pharmacies in Japan, in a region at high risk of major earthquakes. Tabulation analyses were carried out to examine the characteristics of pharmacies and then a logistic regression analysis was performed to examine the relationship between disaster awareness and the level of preparedness of pharmacies. Furthermore, to examine in detail any differences associated with pharmacy size, subgroup analyses were performed.ResultsHigh disaster awareness was significantly correlated with adequate disaster preparedness. However, in the subgroup analyses, no significant differences were observed among large pharmacies. In contrast, disaster awareness was significantly related to the disaster preparedness of small pharmacies.ConclusionThe findings suggest that the disaster preparedness of pharmacies is related to the level of disaster awareness, highlighting the importance of disaster awareness activities in ordinary times before a disaster. (Disaster Med Public Health Preparedness. 2019;13:753–757)

Association of Body Weight Change and Fluid Balance With Extubation Failure in Intensive Care Unit Patients: A Single-Center Observational Study

2019 ◽  
Vol 36 (2) ◽  
pp. 175-181
Author(s):  
Shota Maezawa ◽  
Daisuke Kudo ◽  
Noriko Miyagawa ◽  
Satoshi Yamanouchi ◽  
Shigeki Kushimoto
Keyword(s):  
Logistic Regression ◽  
Body Weight ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Fluid Balance ◽  
Weight Change ◽  
Invasive Mechanical Ventilation ◽  
Extubation Failure ◽  
Body Weight Change ◽  
Failure Group

Purpose: To elucidate whether fluid balance and body weight change are associated with failed planned extubation. Materials and Methods: Patients who received invasive mechanical ventilation for over 24 hours were enrolled and divided into extubation success and extubation failure groups. Fluid balance and body weight fluctuation within 24 and 48 hours before extubation and from admission to planned extubation were calculated. The primary outcome was extubation failure (ie, all-cause reintubation within 72 hours). The association of extubation failure with fluid balance and body weight change was assessed via logistic regression analysis. Results: Extubation failure occurred in 12(7.4%)/161 patients. The extubation success group had a significantly lower fluid balance within 24 hours before extubation than did the extubation failure group (−276 mL [−1111 to 456] vs 1217 mL [503 to 1875], P = .002). However, fluid balance within 48 hours before extubation, cumulative fluid balance, and body weight change were not significantly different between the 2 groups. The sensitivity and specificity of water balance +1000 mL within 24 hours before extubation for the extubation failure group were 0.54 and 0.84, respectively, based on the receiver operating characteristic curve. Logistic regression analysis showed that fluid balance within 24 hours before extubation was associated with extubation failure (odds ratio: 22.9, 95% confidence interval: 4.1-128.4). Conclusions: A larger fluid balance within 24 hours before extubation is associated with extubation failure. Thus, fluid balance may be a good indicator of extubation outcome.

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