LDL-C/HDL-C is associated with ischaemic stroke in patients with non-valvular atrial fibrillation: a case-control study

Background This study explored the relationships between the low−/high-density lipoprotein cholesterol ratio (LDL-C/HDL-C) and other clinical indicators and ischaemic stroke (IS) in patients with non-valvular atrial fibrillation (NVAF) in Xinjiang. The findings could provide a theoretical and therapeutic basis for NVAF patients. Methods NVAF patients who were admitted to 10 medical centres across Xinjiang were divided into stroke (798 patients) and control (2671 patients) groups according to the occurrence of first acute IS. Univariate and multivariate logistic regression analysis were used to examine the independent risk factors for IS in NVAF patients. Factor analysis and principal component regression analysis were used to analyse the main factors influencing IS. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminatory ability of LDL-C/HDL-C for predicting the occurrence of IS. Results The stroke group had an average age of 71.64 ± 9.96 years and included 305 females (38.22%). The control group had a mean age of 67.30 ± 12.01 years and included 825 females (30.89%). Multivariate logistic regression showed that the risk of IS in the highest LDL-C/HDL-C quartile (≥2.73) was 16.23-fold that of the lowest quartile (< 1.22); IS risk was 2.27-fold higher in obese patients than in normal-weight subjects; IS risk was 3.15-fold higher in smoking patients than in non-smoking patients. The area under the ROC curve of LDL-C/HDL-C was 0.76, the optimal critical value was 2.33, the sensitivity was 63.53%, and the specificity was 76.34%. Principal component regression analysis showed that LDL-C/HDL-C, age, smoking, drinking, LDL-C and hypertension were risk factors for IS in NVAF patients. Conclusions LDL-C/HDL-C > 1.22, smoking, BMI ≥24 kg/m2 and CHA2DS2-VASc score were independent risk factors for IS in NVAF patients; LDL-C/HDL-C was the main risk factor.

LDL-C/HDL-C is associated with ischaemic stroke in patients with non-valvular atrial fibrillation: a case-control study

Background: This study explored the relationships between the low-/high-density lipoprotein cholesterol ratio (LDL-C/HDL-C) and other clinical indicators and ischaemic stroke (IS) in patients with non-valvular atrial fibrillation (NVAF) in Xinjiang. The findings could provide a theoretical and therapeutic basis for NVAF patients.Methods: NVAF patients who were admitted to 10 medical centres across Xinjiang were divided into stroke (798 patients) and control (2671 patients) groups according to the occurrence of first acute IS. Univariate and multivariate logistic regression analysis were used to examine the independent risk factors for IS in NVAF patients. Factor analysis and principal component regression analysis were used to analyse the main factors influencing IS. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminatory ability of LDL-C/HDL-C for predicting the occurrence of IS.Results: The stroke group had an average age of 71.64 ± 9.96 years and included 305 females (38.22%). The control group had a mean age of 67.30 ± 12.01 years and included 825 females (30.89%). Multivariate logistic regression showed that the risk of IS in the highest LDL-C/HDL-C quartile ( ≥2.73) was 16.23-fold that of the lowest quartile ( < 1.22); IS risk was 2.27-fold higher in obese patients than in normal-weight subjects; IS risk was 3.15-fold higher in smoking patients than in non-smoking patients. The area under the ROC curve of LDL-C/HDL-C was 0.76, the optimal critical value was 2.33, the sensitivity was 63.53%, and the specificity was 76.34%. Principal component regression analysis showed that LDL-C/HDL-C, age, smoking, drinking, LDL-C and hypertension were risk factors for IS in NVAF patients.Conclusions: LDL-C/HDL-C >1.22, smoking, BMI ≥24 kg/m2 and CHA2DS2-VASc score were independent risk factors for IS in NVAF patients; LDL-C/HDL-C was the main risk factor.

LDL-C/HDL-C is associated with ischaemic stroke in patients with non-valvular atrial fibrillation: a case-control study

Background: This study explored the relationships between the low-/high-density lipoprotein cholesterol ratio (LDL-C/HDL-C) and other clinical indicators and ischaemic stroke (IS) in patients with non-valvular atrial fibrillation (NVAF) in Xinjiang. The findings could provide a theoretical and therapeutic basis for NVAF patients.Methods: NVAF patients who were admitted to 10 medical centres across Xinjiang were divided into stroke (798 patients) and control (2671 patients) groups according to the occurrence of first acute IS occurred. Univariate and multivariate logistic regression analysis were used to examine the independent risk factors for IS in NVAF patients. Factor analysis and principal component regression analysis were used to analyse the main factors influencing IS. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminatory ability of LDL-C/HDL-C for predicting the occurrence of IS.Results: The stroke group had an average age of 71.64 ± 9.96 years and included 305 females (38.22%). The control group had a mean age of 67.30 ± 12.01 years and included 825 females (30.89%). Multivariate logistic regression showed that the risk of IS in the highest LDL-C/HDL-C quartile ( ≥2.73) was 16.23-fold that of the lowest quartile ( < 1.22); IS risk was 2.27-fold higher in obese patients than in normal-weight subjects; IS risk was 3.15-fold higher in smoking patients than in non-smoking patients. The area under the ROC curve of LDL-C/HDL-C was 0.76, the optimal critical value was 2.33, the sensitivity was 63.53%, and the specificity was 76.34%. Principal component regression analysis showed that LDL-C/HDL-C, age, smoking, drinking, LDL-C and hypertension were risk factors for IS in NVAF patients.Conclusions: LDL-C/HDL-C >1.22, smoking, BMI ≥24 kg/m2 and CHA2DS2-VASc score were independent risk factors for IS in NVAF patients; LDL-C/HDL-C was the main risk factor.

Rainfall Model Using Principal Component Regression Analysis with R Software in Sulawesi

Indonesia is a tropical country that has two seasons, rainy and dry. Nowadays, the earth is experiencing the climate change phenomenon which causes erratic rainfall. The rainfall is influenced by several factors, one of which is the local scale factor. This research was aimed to build a rainfall model in Sulawesi to find out how the rainfall relationship with local scale factor in Sulawesi. In this research, the data used were secondary data which consisted of 15 samples with 6 variables from Badan Pusat Statistik (BPS). The limitation of the sample size in this study was due to the limited secondary data available in the field. The data was processed using Principal Component Regression Analysis. The first step was reducing local scale factor variables so that the principal component variable could be obtained that can explain variability from the original data which then that variable was analyzed using principal regression analysis. The data were analyzed by utilizing R Studio software. The results show that two principal component variables can explain 75.2% of the variability of original data and only one principal component variable that was significant to the rainfall variable. The regression model explained that the relationship between rainfall, humidity, air temperature, air pressure, and solar radiation was in the same direction while the relationship between rainfall and wind velocity was not in the same direction. Overall, the results of the study provided an overview of the application of the Principal Component Regression analysis to model the rainfall phenomenon in the Sulawesi region using the R program.

LDL-C/HDL-C is Associated With Ischaemic Stroke in Patients With Non-Valvular Atrial Fibrillation: A Case-Control Study

Background This study explored relationships between low-/high-density lipoprotein cholesterol ratio (LDL-C/HDL-C) and other clinical indicators and ischaemic stroke (IS) in non-valvular atrial fibrillation (NVAF) patients in Xinjiang, which could provide a theoretical and therapeutic basis for patients with NVAF. Methods NVAF patients who were admitted to 10 medical centres across Xinjiang were divided into the stroke (798 patients) and control (2671 patients) groups according to whether acute IS occurred. Univariate and multivariate logistic regression analysis was used to examine the independent risk factors for IS in NVAF patients. We used factor analysis and principal component regression analysis to analyse the main influencing factors of IS. Receiver operating characteristic (ROC) curve analysis was used to evaluate the optimal cut-off value of LDL-C/HDL-C in predicting IS. Results Multivariate logistic regression showed that the risk of IS in the highest quartile of LDL-C/HDL-C (≥ 2.73) was 16.23-fold that in the lowest quartile (< 1.22); IS risk was 2.27-fold higher in obese patients (BMI ≥ 28 kg/m2) than in normal-weight subjects; IS risk was 3.15-fold higher in smoking than in non-smoking patients. The area under the ROC curve of LDL-C/HDL-C was 0.76, optimal critical value was 2.33, sensitivity was 63.53%, and specificity was 76.34%. Principal component regression analysis showed that LDL-C/HDL-C, age, smoking, drinking, LDL-C and hypertension were risk factors for IS in NVAF patients. Conclusions LDL-C/HDL-C > 1.22, smoking and BMI ≥ 24 kg/m2 were independent risk factors for IS in NVAF patients, of which LDL-C/HDL-C was the main risk factor.

Optimal batching plan of deoxidation alloying based on principal component analysis and linear programming

As the market competition of steel mills is severe, deoxidization alloying is an important link in the metallurgical process. To solve this problem, principal component regression analysis is adopted to reduce the dimension of influencing factors, and a reasonable and reliable prediction model of element yield is established. Based on the constraint conditions such as target cost function constraint, yield constraint and non-negative constraint, linear programming is adopted to design the lowest cost batting scheme that meets the national standards and production requirements. The research results provide a reliable optimization model for the deoxidization and alloying process of steel mills, which is of positive significance for improving the market competitiveness of steel mills, reducing waste discharge and protecting the environment.