scholarly journals Elevated lipoprotein(a) and lipoprotein-associated phospholipase A2 are associated with unfavorable functional outcomes in patients with ischemic stroke

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Xue Jiang ◽  
Jie Xu ◽  
Xiwa Hao ◽  
Jing Xue ◽  
Ke Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Ischemic Stroke ◽  
Regression Model ◽  
Functional Outcomes ◽  
Logistic Regression Model ◽  
Enzyme Linked Immunosorbent Assay ◽  
Women Patients ◽  
Lipoprotein A ◽  
Stroke Registry ◽  
Different Levels

Abstract Background The association of lipoprotein(a) [Lp(a)] and stroke functional outcomes was conflicting. The aim of the study was to clarify whether high Lp(a) is associated with unfavorable functional outcomes in patients with ischemic stroke. Methods A total of 9709 individuals from the third China National Stroke Registry cohort were recruited. Plasma level of Lp(a) at admission was measured with enzyme-linked immunosorbent assay. The cut-off was set at the median for Lp(a). Functional outcome was assessed using the modified Rankin scale (mRS) at 3 months and 1 year after ischemic stroke. The association between Lp(a) and functional outcomes was evaluated using a logistic regression model. Results The median age was 63.0 years, and 31.1% participants were women. Patients in higher Lp(a) group had higher incidences of unfavorable functional outcomes at 3 months. In logistic regression model, elevated Lp(a) levels were associated with unfavorable functional outcomes at 3 months (Q4 vs. Q1: odds ratio 1.33, 95% confidence interval 1.11–1.61). Subgroup analysis showed that in the lower Lp-PLA2 group, Lp(a) level was not associated with functional outcomes, but in the higher Lp-PLA2 group, Lp(a) level was significantly associated with functional outcomes. After grouped by different levels of Lp(a) and Lp-PLA2, the Lp(a) high/ Lp-PLA2 high group showed the highest incidence of unfavorable functional outcomes at 3 months and 1 year. Conclusions Elevated Lp(a) level is associated with unfavorable functional outcomes in patients with ischemic stroke. The increment in both Lp(a) and Lp-PLA2 are associated with unfavorable functional outcomes at 3 months and 1 year after ischemic stroke.

scholarly journals Synergetic Effect of Lipoprotein(a) and Lipoprotein-Associated Phospholipase A2 on Functional Outcomes in Patients with Ischemic Stroke

2021 ◽  
Author(s):  
Lemin Zheng ◽  
Xue Jiang ◽  
Jie Xu ◽  
Xiwa Hao ◽  
Jing Xue ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Regression Model ◽  
Functional Outcome ◽  
Functional Outcomes ◽  
Cox Regression ◽  
Synergetic Effect ◽  
Cox Regression Model ◽  
Lipoprotein A ◽  
Relationship Of ◽  
The Relationship

Abstract Background:The relationship of lipoprotein(a) [Lp(a)] and stroke functional outcomes was conflicting. The relationship of Lp(a) and Lp-PLA2 levels to functional outcomes is unclear. The aim was to clarify whether high Lp(a) is associated with poor functional outcomes and examine the relationship of Lp(a) and Lp-PLA2 to functional outcomes in patients with ischemic stroke.Methods：A total of 10,422 individuals from the third China National Stroke Registry cohort were recruited. Plasma level of Lp(a) at admission was measured with enzyme-linked immunosorbent assay. The cut-off was set at the median for Lp(a). Functional outcome was assessed using the modified Rankin scale (mRS) at 3 months after stroke. The association between Lp(a) and stroke functional outcomes was evaluated using a multivariate Cox regression model.Results：The median age was 63.0 years, and 31.6% participants were women. Patients in higher Lp(a) group had higher incidences of poor functional outcome at 3 months (P<0.0001). In multivariate cox regression model, elevated Lp(a) levels were associated with poor functional outcomes at 3 months (Q4 vs. Q1: hazard ratio 1.39, 95% confidence interval 1.11-1.75). Subgroup analysis showed the significant effect of interaction of Lp-PLA2 level with Lp(a) level on functional outcomes (p=0.008). After stratification by Lp(a) and Lp-PLA2, the Lp(a) high/ Lp-PLA2 high group showed the highest incidence of poor functional outcomes at 3 months.Conclusions:Elevated Lp(a) level is associated with poor functional outcomes in patients with ischemic stroke. Lp(a) has a synergetic effect with Lp-PLA2 on functional outcomes after ischemic stroke.

scholarly journals Work absence and multimorbidity in Portugal: results from the 1st National Health Examination Survey

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
J Matos ◽  
C Matias Dias ◽  
A Félix
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Chronic Diseases ◽  
National Health ◽  
Logistic Regression Model ◽  
Health Examination ◽  
Work Absence ◽  
Health Examination Survey ◽  
Absence From Work ◽  
The Impact

Abstract Background Studies on the impact of patients with multimorbidity in the absence of work indicate that the number and type of chronic diseases may increase absenteeism and that the risk of absence from work is higher in people with two or more chronic diseases. This study analyzed the association between multimorbidity and greater frequency and duration of work absence in the portuguese population between the ages of 25 and 65 during 2015. Methods This is an epidemiological, observational, cross-sectional study with an analytical component that has its source of information from the 1st National Health Examination Survey. The study analyzed univariate, bivariate and multivariate variables under study. A multivariate logistic regression model was constructed. Results The prevalence of absenteeism was 55,1%. Education showed an association with absence of work (p = 0,0157), as well as professional activity (p = 0,0086). It wasn't possible to verify association between the presence of chronic diseases (p = 0,9358) or the presence of multimorbidity (p = 0,4309) with absence of work. The prevalence of multimorbidity was 31,8%. There was association between age (p &lt; 0,0001), education (p &lt; 0,001) and yield (p = 0,0009) and multimorbidity. There is no increase in the number of days of absence from work due to the increase in the number of chronic diseases. In the optimized logistic regression model the only variables that demonstrated association with the variable labor absence were age (p = 0,0391) and education (0,0089). Conclusions The scientific evidence generated will contribute to the current discussion on the need for the health and social security system to develop policies to patients with multimorbidity. Key messages The prevalence of absenteeism and multimorbidity in Portugal was respectively 55,1% and 31,8%. In the optimized model age and education demonstrated association with the variable labor absence.

scholarly journals Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes

2021 ◽  
Vol 11 (14) ◽  
pp. 6594
Author(s):  
Yu-Chia Hsu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Pattern Recognition ◽  
Logistic Regression ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Logistic Regression Model ◽  
National Football League ◽  
Performance Metrics ◽  
Betting Market

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction.

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