scholarly journals A Multi-hospital Study in Wuhan, China: Protective Effects of Non-menopause and Female Hormones on SARS-CoV-2 infection

2020 ◽  
Author(s):  
Ting Ding ◽  
Jinjin Zhang ◽  
Tian Wang ◽  
Pengfei Cui ◽  
Zhe Chen ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Protective Factors ◽  
Regression Model ◽  
Disease Severity ◽  
Sex Hormones ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Protective Effects ◽  
Female Patients ◽  
Composite Endpoints

AbstractImportanceHow to explain the better prognosis of female coronavirus disease 2019 (COVID-19) patients than that of males?ObjectiveTo determine the correlation between menstruation status/sex hormones and prognosis of COVID-19, and to identify potential protective factors for female patients.Design, Setting, and ParticipantsA cross-sectional study of COVID-19 patients who were hospitalized at Tongji and Mobile Cabin Hospitals from Jan 28, 2020 to March 8, 2020.ExposuresConfirmed SARS-CoV-2 infection.Main Outcomes and MeasuresSex differences in severity and composite endpoints (admission to intensive care unit (ICU), use of mechanical ventilation, or death) of COVID-19 patients were compared. The correlation analysis and cox/logistic regression modeling of menstruation status/sex hormones and prognosis were conducted. Correlation between cytokines related to immunity and inflammation and disease severity or estradiol (E2) was revealed.ResultsChi square test indicated significant differences in distribution of composite endpoints (p<0.01) and disease severity (p=0.05) between male and female patients (n=1902). 435 female COVID-19 patients with menstruation records were recruited. By the end of Mar 8, 111 patients recovered and discharged (25.3%). Multivariate Cox regression model adjusted for age and severity indicated that post-menopausal patients show the greater risk of hospitalization time than non-menopausal patients (relative hazard [RH], 1.91; 95% confidence interval [CI], 1.06-3.46) Logistic regression model showed that higher anti-müllerian hormone (AMH) as a control for age increases the risk of severity of COVID-19 (HR=0.146,95%CI = (0.026-0.824) p=0.029). E2 showed protective effect against disease severity (HR= 0.335, 95%CI = (0.105-1.070), p= 0.046). In the Mann-Whitney U test, the higher levels of IL6 and IL8 were found in severe group (p= 0.040, 0.033). The higher levels of IL2R, IL6, IL8 and IL10 were also observed in patients with composite end points (p<0.001, <0.001, 0.009, 0.040). E2 levels were negatively correlated with IL2R, IL6, IL8 and TNFα in luteal phase (Pearson Correlation=−0.592, −0.558, −0.545, −0.623; p=0.033, 0.048, 0.054, 0.023) and with C3 in follicular phase (Pearson Correlation=-0.651; p=0.030).Conclusions and RelevanceMenopause is an independent risk factor for COVID-19. E2 and AMH are negatively correlated with COVID-19’s severity probably due to their regulation of cytokines related to immunity and inflammation.Key PointsQuestionAny differences in the outcomes between hospitalized female and male COVID-19 patients? If so, why?FindingsFemale patients display better prognosis than male patients. Non-menopausal women have shorter length of hospital stays, and AMH and E2 are negatively correlated with COVID-19’s severity. There is a negative correlation between E2 and the levels of IL6, IL8, IL2R and TNF-α, which are significantly correlated with disease severity or composite endpoint.MeaningNon-menopause and female sex hormones, especially E2 and AMH, are potential protective factors for females COVID-19 patients. E2 supplements could be potentially used for COVID-19 patients.

scholarly journals Analysis models for variables associated with breastfeeding duration

2013 ◽  
Vol 31 (3) ◽  
pp. 306-314 ◽  
Author(s):  
Edson Theodoro dos S. Neto ◽  
Eliana Zandonade ◽  
Adauto Oliveira Emmerich
Keyword(s):  
Logistic Regression ◽  
Protective Factors ◽  
Regression Model ◽  
Odds Ratio ◽  
Regression Models ◽  
Adjusted Odds Ratio ◽  
Cox Regression ◽  
Bottle Feeding ◽  
Breastfeeding Duration ◽  
Factors Associated

OBJECTIVE To analyze the factors associated with breastfeeding duration by two statistical models. METHODS A population-based cohort study was conducted with 86 mothers and newborns from two areas primary covered by the National Health System, with high rates of infant mortality in Vitória, Espírito Santo, Brazil. During 30 months, 67 (78%) children and mothers were visited seven times at home by trained interviewers, who filled out survey forms. Data on food and sucking habits, socioeconomic and maternal characteristics were collected. Variables were analyzed by Cox regression models, considering duration of breastfeeding as the dependent variable, and logistic regression (dependent variables, was the presence of a breastfeeding child in different post-natal ages). RESULTS In the logistic regression model, the pacifier sucking (adjusted Odds Ratio: 3.4; 95%CI 1.2-9.55) and bottle feeding (adjusted Odds Ratio: 4.4; 95%CI 1.6-12.1) increased the chance of weaning a child before one year of age. Variables associated to breastfeeding duration in the Cox regression model were: pacifier sucking (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.3) and bottle feeding (adjusted Hazard Ratio 2.0; 95%CI 1.2-3.5). However, protective factors (maternal age and family income) differed between both models. CONCLUSIONS Risk and protective factors associated with cessation of breastfeeding may be analyzed by different models of statistical regression. Cox Regression Models are adequate to analyze such factors in longitudinal studies.

scholarly journals The risk of flax versus straw bedding on ileal impaction in colic horses: Retrospective analysis of 2336 cases (2008-2017)

2018 ◽  
Vol 87 (5) ◽  
pp. 255-262 ◽  
Author(s):  
A. Dufourni ◽  
A. Decloedt ◽  
L. Lefère ◽  
D. De Clercq ◽  
P. Deprez ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Body Weight ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Cox Regression ◽  
Conditional Logistic Regression ◽  
Large Animal ◽  
Multivariable Logistic Regression Model ◽  
Multivariable Logistic Regression ◽  
Flax Shives

While mature coastal bermudagrass hay is strongly associated with ileal impaction in the Southeastern United States, stabling on flax bedding has anecdotally been associated with this condition in Europe. The aim of this retrospective study was to investigate the association between ileal impaction and the use of flax shives compared to straw as bedding in horses with colic. Medical records of 2336 referral cases evaluated for abdominal pain between January 2008 and May 2017 at the Department of Large Animal Internal Medicine, Ghent University were reviewed. Diagnosis, date of admission, age, breed, gender, body weight and stable bedding were recorded. Conditional logistic regression analysis was used to assess the association between ileal impaction and each individual variable. Odds ratios (OR) and 95% confidence intervals (CI) were determined. Predictors with a value of P < 0.2 were included in a multivariable Cox regression model and Wald’s test was used to assess parameter estimate significance. Further, the association between survival to discharge and type of bedding or type of treatment (medical versus surgical) was analyzed for horses with ileal impactions. The proportion of colic cases stabled on flax bedding at home was 11.3%. The overall prevalence of ileal impaction was 4.2%. In the flax group, the prevalence of ileal impaction was 9.4% as opposed to 3.6% within the straw group. The OR of 2.8 (95% CI 1.7-4.7; P < 0.001) in the multivariable logistic regression model indicated that horses stabled on flax shives were approximately three times more likely to have ileal impactions than horses stabled on straw. There was no significant association found between ileal impaction and the period of admission, age, gender or body weight in a multivariable logistic regression model. The odds for having ileal impaction is approximately six times (OR 6.3; 95% CI 2.4-16.4; P < 0.001) higher in draft horses than in warmbloods in the multivariable logistic regression model. No significant association was found between survival to discharge and type of bedding or treatment. These results suggest that horses with colic that were housed on flax bedding are more likely to present ileal impactions than horses housed on straw.

scholarly journals Association between small dense low-density lipoprotein cholesterol and neuroimaging markers of cerebral small vessel disease in middle-aged and elderly Chinese populations

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaorong Yu ◽  
Yanhua Yu ◽  
Cunsheng Wei ◽  
Lin Wang ◽  
Junying Jiang ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Pearson Correlation ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Lipoprotein Cholesterol ◽  
Low Density ◽  
Low Density Lipoprotein Cholesterol ◽  
Cutoff Value

Abstract Background Cerebral small vascular disease (CSVD) is one of the leading causes of death in the aged population and is closely related to abnormalities in low-density lipoprotein cholesterol (LDL-C). Our study aims to clarify the relationship between small and dense low-density lipoprotein cholesterol (sdLDL-C) (a subcomponent of LDL-C) and neuroimaging markers of CSVD. Methods In total, 1211 Chinese adults aged ≥45 years with cranial magnetic resonance imaging (MRI) were recruited in this retrospective study from January 2018 to May 2021. Serum lipids and other baseline characteristics were investigated in relation to the occurrence of CSVD. A logistic regression model was performed to analyze the relationships between LDL subtypes and CSVD risk, and the Pearson correlation coefficient was used to analyze the correlation between clinical characteristics and CSVD risk. ROC curves and AUCs were created and depicted to predict the best cutoff value of LDL-C subtypes for CSVD risk. Based on these data, we performed comprehensive analyses to investigate the risk factors for CSVD. Results Ultimately, 623 eligible patients were included in the present study. Of the 623 eligible patients, 487 were included in the CSVD group, and 136 were included in the group without CSVD (control group). We adjusted for confounders in the multivariate logistic regression model, and LDL-C3 was still higher in the CSVD patients than in the group of those without CSVD (OR (95% CI), 1.22(1.08–1.38), P < 0.05). Pearson correlation showed that there was a positive correlation between the levels of LDL-C3, LDL-C4, LDL-C5, glucose, age, hypertension, previous ischemic stroke and CSVD risk (r > 0.15, P < 0.01). Moreover, the best cutoff value of LDL-C3 to predict CSVD was 9.5 mg/dL with 68.4% sensitivity and 72.8% specificity, and the best cutoff value of LDL-C4 to predict CSVD was 5.5 mg/dL with 50.5% sensitivity and 90.4% specificity. Conclusion The results indicate that LDL-C3 is an independent risk factor for CSVD. A new prediction model based on LDL-C3 and LDL-C4 can help clinicians identify high-risk CSVD, even in people with normal LDL-C levels. The levels of sdLDL-C should be considered in the assessment and management of CSVD.

scholarly journals 0970 Longitudinal Study Of Infant Sleep Problems And Influencing Factors

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A368-A368
Author(s):  
Z Zhou ◽  
N Wang
Keyword(s):  
Logistic Regression ◽  
Risk Factor ◽  
Protective Factors ◽  
Regression Model ◽  
Body Length ◽  
Logistic Regression Model ◽  
Sleep Problems ◽  
Infant Sleep ◽  
Falling Asleep ◽  
Bedtime Routine

Abstract Introduction To investigate the sleep problems and influence factors in infants at the age of 1 month and 6 months through longitudinal questionnaire survey. Methods 600 healthy infants aged 1 month were selected from May to August 2014 from Chongqing health center for women and children. A self-made sleep questionnaire was used to investigate the infants’s sleep at the age of 1 month and 6 months respectively. Logistic regression model was used to analyze the correlation between sleep problems and factors. Results The incidence of nigt-waking, difficulty in falling asleep and night crying at 1 month of age were 30%, 17% and 14.1%, respectively, and at 6 months were 7.3%, 9.3% and 13.1%, respectively. Results of the Logistic regression analysis model of sleep problems of infants at 1 month old were: 1. body length grade, baby touch, sleep alone, putting the infants back into bed after being picked up and quiet, night feeding were the protective factors of night-waking, while diaper changing is the risk factor; 2.body length grade, listening to music, orderly bedtime routine, comforting without holding and putting the infants back into bed after being picked up and quiet were protective factors for difficulty in falling asleep, while diaper changing is the risk factor; 3. listening to music, orderly bedtime routine, watching and waiting, holding until sleep were all protective factors of night-crying. Logistic regression model analysis results of sleep problems of infants aged 6 months were: 1. The risk factors of night-waking include putting the infants back into bed after being picked up and quiet, holding the infants until falling asleep, diapers changing, and parent-children interaction before bedtime. 2. Listening to music and sleeping alone in a bed were protective factors for difficulty in falling asleep. 3. Touching, orderly bedtime routine and comforting without holding are protective factors of night-crying. Conclusion Sleep problems in infants are common and decrease with age. The factors of sleep problems at the age of 1 and 6 months are different, Targeted prevention are beneficial to the prevention of infant sleep problems. Support Chinese center for disease control and prevention grant

scholarly journals Association Between Sex Hormones and Visual Field Progression in Women With Primary Open Angle Glaucoma: A Cross-Sectional and Prospective Cohort Study

2021 ◽  
Vol 13 ◽  
Author(s):  
Yichao Qiu ◽  
Jian Yu ◽  
Li Tang ◽  
Jun Ren ◽  
Mingxi Shao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cohort Study ◽  
Sex Hormones ◽  
Cox Regression ◽  
Cross Sectional Study ◽  
Rank Test ◽  
Regression Analyses ◽  
Sectional Study ◽  
Cross Sectional ◽  
The Cross

Purpose: We evaluated the level of sex hormones in female patients with primary open angle glaucoma (POAG) to determine whether they are associated with the onset and/or progression of POAG.Methods: The cross-sectional study enrolled 63 women with POAG and 56 healthy women as normal control subjects. Furthermore, 57 women with POAG were included and followed-up for at least 2 years in the cohort study. All subjects were evaluated for serum concentration of sex hormones [prolactin (PRL), luteinizing hormone (LH), testosterone (TESTO), follicle-stimulating hormone (FSH), progesterone (PROG), and estrogen (E2)] and underwent visual field (VF) examination. In the cross-sectional study, Spearman analysis, linear regression analysis, and logistic regression analysis were performed to assess risk factors for POAG in women. In the cohort study, Cox regression analyses and Kaplan–Meier survival analysis were performed to identify factors associated with VF progression in women with POAG.Results: In the cross-sectional study, the level of E2 was significantly lower in the POAG group than in the normal group (p &lt; 0.05). Multiple logistic regression showed that the decreased level of E2 was a risk factor of POAG (OR = 0.27, 95% CI = 0.09–0.78, p &lt; 0.05), especially in premenopausal subjects. In the cohort study, there were 29 non-progression subjects and 28 progression subjects. Patients in the progression group had significantly lower levels of E2 than those in the no progression group (p &lt; 0.01). The decreased level of E2 at baseline was associated with POAG progression (HR = 0.08, 95% CI = 0.02–0.46, p &lt; 0.05), especially in premenopausal subjects. Patients with POAG and with lower baseline E2 levels had significantly lower VF non-progression rates than patients with higher E2 levels (log-rank test p &lt; 0.001), especially premenopausal subjects (log-rank test p &lt; 0.05). Additionally, logistic regression analyses, Cox regression analyses, and Kaplan–Meier survival analysis showed that PROG, LH, FSH, and TESTO were risk factors of POAG and/or significantly associated with POAG progression.Conclusion: A decreased E2 level is a POAG risk factor and is associated with VF progression in women with POAG, especially in premenopausal subjects. Additionally, other sex hormones (PROG, LH, FSH, and TESTO) might also play a role in POAG pathogenesis.

P6352The antimicrobial protein Azurocidin-1 is associated with prevalent diastolic dysfunction and incident congestive heart failure in a Swedish population cohort

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
J Molvin ◽  
M Pareek ◽  
O Melander ◽  
L Rastam ◽  
U Lindblad ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Logistic Regression ◽  
Linear Regression ◽  
Ejection Fraction ◽  
Regression Model ◽  
Diastolic Dysfunction ◽  
Cox Regression ◽  
Population Based ◽  
Left Ventricular

Abstract Background Although Azurocidin-1 (Azu-1), also known as heparin binding protein, has been associated with myocardial infarction, possible associations of Azu-1 with congestive heart failure (CHF) remains unknown. Here we tested the possible association of Azu-1 with prevalent diastolic dysfunction and/or incident CHF in a large Swedish prospective population based cohort. Methods Azu-1 was analyzed using the Proseek Multiplex CVD III panel in 1737 participants from a subsample of the population (mean age 67 years, 29% women) who underwent a complete echocardiographic examination. All biomarkers were logarithmized and standardized prior to statistical analysis. Logistic and linear regression were adjusted for age, sex, BMI, diabetes, systolic and diastolic blood, anti-hypertensive treatment and subjects with an ejection fraction below 50% were excluded for the analysis of prevalent diastolic dysfunction and Azu-1. For the linear regression model, we used E/é ratio as a key functional variable in assessing diastolic function according to ESC 2016 Guidelines for Acute and Chronic Heart Failure. Furthermore, we dichotomized the E/é ratio at >13 in another logistic regression model. Finally, in line with ESC Guidelines 2016, we combined the key functional (E/é >13) and key structural (left ventricular mass index (LVMI) ≥115 g/m2 for males and ≥95 g/m2 for females) alterations for diastolic dysfunction and used this variable in both logistic regression for association with Azu-1 and for Cox regression analysis of incident CHF. 1439 subjects (938 cases with some degree of diastolic dysfunction and 501 controls) remained for the analysis. For the analysis of incident CHF, Cox regression was used excluding subjects with ejection fraction below 50% and prevalent CHF and further adjusted for prevalent coronary disease on top of age, sex, BMI, diabetes, systolic and diastolic blood and anti-hypertensive treatment. 1,511 subjects (64 incident cases of CHF vs 1447 controls; median follow up time 8.9 years) remained. Results After adjustment for above mentioned risk factors, each 1 standard deviation (SD) of increase in Azu-1 was associated with any degree of prevalent diastolic dysfunction (odds ratio (OR) 1.13, p=0.048), E/é >13 OR 1.21, p=0.028 and for combined LVMI and E/é OR 1.17, p=0.015. In fully adjusted linear regression Azu-1 was associated with E/é with a β-coefficient of 0.056, p=0.018. In a fully adjusted Cox regression models Azu-1 was associated with incident CHF (hazard ratio (HR) 1.32, p=0.025). As expected and as proof of concept E/é >13 and combined LVMI with E/é were also associated with incident CHF; HR 2.84, p<0.001 and HR 2.12, p=0.006, respectively. Conclusion An inflammatory mediator, Azurocidin-1, is associated with prevalent diastolic dysfunction, E/é, E/é combined with LVMI as well as incident congestive heart failure in a population-based cohort.

scholarly journals Modeling Recidivism through Bayesian Regression Models and Deep Neural Networks

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 639
Author(s):  
Rolando de la Cruz ◽  
Oslando Padilla ◽  
Mauricio A. Valle ◽  
Gonzalo A. Ruz
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Cox Regression ◽  
Bayesian Regression ◽  
Rate Model ◽  
Cure Rate ◽  
Cox Regression Model ◽  
Cure Rate Model ◽  
Random Survival Forest

This study aims to analyze and explore criminal recidivism with different modeling strategies: one based on an explanation of the phenomenon and another based on a prediction task. We compared three common statistical approaches for modeling recidivism: the logistic regression model, the Cox regression model, and the cure rate model. The parameters of these models were estimated from a Bayesian point of view. Additionally, for prediction purposes, we compared the Cox proportional model, a random survival forest, and a deep neural network. To conduct this study, we used a real dataset that corresponds to a cohort of individuals which consisted of men convicted of sexual crimes against women in 1973 in England and Wales. The results show that the logistic regression model tends to give more precise estimations of the probabilities of recidivism both globally and with the subgroups considered, but at the expense of running a model for each moment of the time that is of interest. The cure rate model with a relatively simple distribution, such as Weibull, provides acceptable estimations, and these tend to be better with longer follow-up periods. The Cox regression model can provide the most biased estimations with certain subgroups. The prediction results show the deep neural network’s superiority compared to the Cox proportional model and the random survival forest.

scholarly journals Epidemiology and outcomes of bloodstream infections in severe burn patients: a six-year retrospective study

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Yangmin Hu ◽  
Danyang Li ◽  
Lingcheng Xu ◽  
Yuping Hu ◽  
Yiwen Sang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Cox Regression ◽  
Total Body Surface Area ◽  
Hospital Length Of Stay ◽  
Burn Patients ◽  
Severe Burn ◽  
Multivariate Logistic Regression ◽  
Cox Regression Model

Abstract Background Infection is the leading cause of morbidity and mortality among burn patients, and bloodstream infection (BSI) is the most serious. This study aimed to evaluate the epidemiology and clinical outcomes of BSI in severe burn patients. Methods Clinical variables of all patients admitted with severe burns (≥ 20% total body surface area, %TBSA) were analyzed retrospectively from January 2013 to December 2018 at a teaching hospital. The Kaplan–Meier method was utilized for plotting survival curves. Multivariate logistic regression and Cox regression model were also performed. Results A total of 495 patients were evaluated, of whom 136 (27.5%) had a BSI. The median time from the patients being burned to BSI was 8 days. For BSI onset in these patients, 47.8% (65/136) occurred in the first week. The most frequently isolated causative organism was A. baumannii (22.7%), followed by methicillin-resistant Staphylococcus aureus (18.7%) and K. pneumoniae (18.2%), in patients with BSI. Multivariate logistic regression analysis showed that %TBSA (p = 0.023), mechanical ventilation (p = 0.019), central venous catheter (CVC) (p < 0.001) and hospital length of stay (27d vs 50d, p < 0.001) were independent risk factors associated with BSI. Cox regression model showed that acute kidney injury (HR, 12.26; 95% CI 2.31–64.98; p = 0.003) and septic shock (HR, 4.36; 95% CI 1.16–16.34; p = 0.031) were identified as independent predictors of 30-day mortality of BSI in burn patients. Conclusions Multidrug resistant gram-negative bacteria were the main pathogens of BSI in severe burn patients. Accurate evaluation of risk factors for BSI and the mortality of BSI in severe burn patients may improve early appropriate management.

scholarly journals The Current Status of Health Promotion Lifestyle and Its Related Factors in Chinese Residents

2021 ◽  
Author(s):  
Qianqian Liu ◽  
Shusheng Huang ◽  
Xiaoyuan Qu ◽  
Aitian Yin
Keyword(s):  
Health Promotion ◽  
Logistic Regression ◽  
Regression Model ◽  
Behavioral Intention ◽  
Logistic Regression Model ◽  
Pearson Correlation ◽  
Current Status ◽  
Related Factors ◽  
Affective Factors ◽  
Factors Influencing

Abstract Objectives: This study aimed to explore the current status of Chinese residents' health promotion lifestyle and its influencing factors, especially explore how health attitudes affect health promotion lifestyle, thus can make targeted recommendations for health promotion in China and similar areas.Study design: This study was based on a household survey of 1769 adults aged 18 and over from Shandong Province of China conducted in 2018.Methods: A cross-sectional, face-to-face survey design was used. The between-group measured data were compared by One-way ANOVA or t-tests. The correlation between the health attitude and health promotion lifestyle was examined by Pearson correlation. Logistic regression model was used to examine the related factors influencing Chinese residents’ health promotion lifestyle.Results: The health promotion lifestyle of residents in Chinese residents was not good. Significant differences existed in health promotion lifestyle among different genders, education levels, income levels, marital status, and health attitude (Ps <0.001). Multivariable Logistic regression model found that gender, education level, annual family per capita income, affection and behavioral intention in health attitude were key factors influencing Chinese residents’ health promotion lifestyle (Ps<0.001). Conclusions: Individual health responsibilities need to be further strengthened. More affective factors and operable measures should be added to enhance health affection and health behavioral intention, so as to enhance health promotion lifestyle.

Sign in / Sign up

Export Citation Format

Share Document