Estimation of the LDL Subclasses in Ischemic Stroke as a Risk Factor in a Chinese population

Abstract Background: Acute ischemic stroke (AIS) is one of the leading causes of mortality and long-term disability worldwide. Our study aims to clarify the role of LDL subclasses in the occurrence of AIS and develop a risk prediction model based on these characteristics to identify high-risk people. Methods: Five hundred and sixty-six patients with AIS and 197 non-AIS controls were included in this study. Serum lipids and other baseline characteristics including fasting blood glucose (GLU), serum creatinine (Scr), and blood pressure were investigated in relation to occurrence of AIS. The LDL subfractions were classified and measured with the Lipoprint System by a polyacrylamide gel electrophoresis technique. Results: Levels of LDL-3, LDL-4 and LDL-5 subclasses were significantly higher in the AIS group compared to the non-AIS group and lower level of LDL-1 was prevalent in the AIS patients. Consistently, Spearman correlation coefficient demonstrated that sd‐demonevels, especially LDL-3 and LDL-4 levels, were significantly positively correlated with AIS. Furthermore, there is a significant positive correlation between small dense LDL (sd-LDL, that is LDL-3 to 7) levels and serum lipids including TC, LDL‐C, and TG. Increased LDL-3 and LDL-4 as well as decreased LDL-1 and LDL-2 were correlated to the occurrence of AIS, even in the people with normal LDL-C levels. A new prediction model including 12 variables can accurately predict the AIS risk in Chinese patients (AUC=0.82±0.04). Conclusions: Levels of LDL subclasses should be considered in addition to serum LDL-C in assessment and management of AIS. A new prediction model based on clinical variables including LDL subtractions can help clinicians identify high of AIS, even in the people with norm.

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Estimation of the LDL subclasses in ischemic stroke as a risk factor in a Chinese population

BMC Neurology ◽

10.1186/s12883-020-01989-6 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Ruisheng Duan ◽

Wenjun Xue ◽

Kunpeng Wang ◽

Nan Yin ◽

Hongyu Hao ◽

...

Keyword(s):

Ischemic Stroke ◽

Prediction Model ◽

Serum Lipids ◽

Polyacrylamide Gel Electrophoresis ◽

Fasting Blood Glucose ◽

Density Lipoprotein ◽

Chinese Patients ◽

Low Density ◽

Model Based ◽

The People

Abstract Background Acute ischemic stroke (AIS) is one of the leading causes of mortality and long-term disability worldwide. Our study aims to clarify the role of low-density lipoproteins (LDL) subclasses in the occurrence of AIS and develop a risk xprediction model based on these characteristics to identify high-risk people. Methods Five hundred and sixty-six patients with AIS and 197 non-AIS controls were included in this study. Serum lipids and other baseline characteristics including fasting blood glucose (GLU), serum creatinine (Scr), and blood pressure were investigated in relation to occurrence of AIS. The LDL subfractions were classified and measured with the Lipoprint System by a polyacrylamide gel electrophoresis technique. Results Levels of LDL-3, LDL-4 and LDL-5 subclasses were significantly higher in the AIS group compared to the non-AIS group and lower level of LDL-1 was prevalent in the AIS patients. Consistently, Spearman correlation coefficient demonstrated that sd-demonevels, especially LDL-3 and LDL-4 levels, were significantly positively correlated with AIS. Furthermore, there is a significant positive correlation between small dense LDL (sd-LDL, that is LDL-3 to 7) levels and serum lipids including total cholesterol (TC), Low density lipoprotein cholesterol (LDL-C), and Triglyceride (TG). Increased LDL-3 and LDL-4 as well as decreased LDL-1 and LDL-2 were correlated to the occurrence of AIS, even in the people with normal LDL-C levels. A new prediction model including 12 variables can accurately predict the AIS risk in Chinese patients (AUC = 0.82 ± 0.04). Conclusions Levels of LDL subclasses should be considered in addition to serum LDL-C in assessment and management of AIS. A new prediction model based on clinical variables including LDL subtractions can help clinicians identify high of AIS, even in the people with norm.

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Estimation of The LDL Subclasses in Ischemic Stroke as A Risk Factor in A Chinese Population

10.21203/rs.3.rs-65582/v1 ◽

2020 ◽

Author(s):

Ruisheng Duan ◽

Wenjun Xue ◽

Kunpeng Wang ◽

Nan Yin ◽

Hongyu Hao ◽

...

Keyword(s):

Ischemic Stroke ◽

High Risk ◽

Prediction Model ◽

Serum Lipids ◽

Pearson Correlation ◽

Polyacrylamide Gel Electrophoresis ◽

Fasting Blood Glucose ◽

Chinese Patients ◽

Model Based ◽

The People

Abstract PURPOSE: To clarify the role of LDL subclasses in the occurrence of acute ischemic stroke (AIS) and develop a risk prediction model based on these characteristics to identify high-risk people.METHODS: Five hundred and sixty-six patients with AIS and 197 non-AIS controls were included in this study. Serum lipids and other baseline characteristics including fasting blood glucose (GLU), serum creatinine (Scr), and blood pressure were investigated in relation to occurrence of AIS. The LDL subfractions were classified and measured with the Lipoprint System by a polyacrylamide gel electrophoresis technique.RESULTS: Levels of LDL-3, LDL-4 and LDL-5 subclasses were significantly higher in the AIS group compared to the non-AIS group and lower level of LDL-1 was prevalent in the AIS patients. Consistently, Pearson correlation analysis demonstrated that sd‐LDL levels, especially LDL-3 and LDL-4 levels, were significantly positively correlated with AIS. Furthermore, there is a significant positive correlation between sd-LDL levels and serum lipids including TC, LDL‐C, and TG. Increased LDL-3 and LDL-4 as well as decreased LDL-1 and LDL-2 were correlated to the occurrence of AIS, even in the people with normal LDL-C levels. A new prediction model including 12 variables can accurately predict the AIS risk in Chinese patients (AUC=0.82±0.04).CONCLUSIONS: Levels of LDL subclasses should be considered in addition to serum LDL-C in assessment and management of AIS. A new prediction model based on clinical variables including LDL subtractions can help clinicians identify high‐risk patients for better prevention.

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Estimation of the LDL Subclasses in Ischemic Stroke as a Risk Factor in a Chinese population

10.21203/rs.3.rs-65582/v3 ◽

2020 ◽

Author(s):

Ruisheng Duan ◽

Wenjun Xue ◽

Kunpeng Wang ◽

Nan Yin ◽

Hongyu Hao ◽

...

Keyword(s):

Ischemic Stroke ◽

Prediction Model ◽

Serum Lipids ◽

Polyacrylamide Gel Electrophoresis ◽

Fasting Blood Glucose ◽

Density Lipoprotein ◽

Chinese Patients ◽

Low Density ◽

Model Based ◽

The People

Abstract Background: Acute ischemic stroke (AIS) is one of the leading causes of mortality and long-term disability worldwide. Our study aims to clarify the role of low-density lipoproteins (LDL) subclasses in the occurrence of AIS and develop a risk prediction model based on these characteristics to identify high-risk people.Methods: Five hundred and sixty-six patients with AIS and 197 non-AIS controls were included in this study. Serum lipids and other baseline characteristics including fasting blood glucose (GLU), serum creatinine (Scr), and blood pressure were investigated in relation to occurrence of AIS. The LDL subfractions were classified and measured with the Lipoprint System by a polyacrylamide gel electrophoresis technique.Results: Levels of LDL-3, LDL-4 and LDL-5 subclasses were significantly higher in the AIS group compared to the non-AIS group and lower level of LDL-1 was prevalent in the AIS patients. Consistently, Spearman correlation coefficient demonstrated that sd‐demonevels, especially LDL-3 and LDL-4 levels, were significantly positively correlated with AIS. Furthermore, there is a significant positive correlation between small dense LDL (sd-LDL, that is LDL-3 to 7) levels and serum lipids including total cholesterol (TC), Low density lipoprotein cholesterol (LDL‐C), and Triglyceride (TG). Increased LDL-3 and LDL-4 as well as decreased LDL-1 and LDL-2 were correlated to the occurrence of AIS, even in the people with normal LDL-C levels. A new prediction model including 12 variables can accurately predict the AIS risk in Chinese patients (AUC=0.82±0.04).Conclusions: Levels of LDL subclasses should be considered in addition to serum LDL-C in assessment and management of AIS. A new prediction model based on clinical variables including LDL subtractions can help clinicians identify high of AIS, even in the people with norm.

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Fasting Blood Glucose Change Prediction Model Based on Medical Examination Data and Data Mining Techniques

2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity) ◽

10.1109/smartcity.2015.157 ◽

2015 ◽

Cited By ~ 3

Author(s):

Wenxiang Xao ◽

Fengjing Shao ◽

Jun Ji ◽

Rencheng Sun ◽

Chunxiao Xing

Keyword(s):

Data Mining ◽

Blood Glucose ◽

Prediction Model ◽

Fasting Blood Glucose ◽

Medical Examination ◽

Data Mining Techniques ◽

Model Based ◽

Change Prediction

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Development and Validation of a LASSO Prediction Model for Better Identification of Ischemic Stroke: A Case-Control Study in China

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2021.630437 ◽

2021 ◽

Vol 13 ◽

Author(s):

Zirui Meng ◽

Minjin Wang ◽

Shuo Guo ◽

Yanbing Zhou ◽

Mingxue Zheng ◽

...

Keyword(s):

Ischemic Stroke ◽

Prediction Model ◽

Area Under The Curve ◽

Smartphone Application ◽

Good Prognosis ◽

Demographic Variables ◽

Derivation Cohort ◽

Model Based ◽

Selection Operator ◽

Time Critical

BackgroundTimely diagnosis of ischemic stroke (IS) in the acute phase is extremely vital to achieve proper treatment and good prognosis. In this study, we developed a novel prediction model based on the easily obtained information at initial inspection to assist in the early identification of IS.MethodsA total of 627 patients with IS and other intracranial hemorrhagic diseases from March 2017 to June 2018 were retrospectively enrolled in the derivation cohort. Based on their demographic information and initial laboratory examination results, the prediction model was constructed. The least absolute shrinkage and selection operator algorithm was used to select the important variables to form a laboratory panel. Combined with the demographic variables, multivariate logistic regression was performed for modeling, and the model was encapsulated within a visual and operable smartphone application. The performance of the model was evaluated on an independent validation cohort, formed by 304 prospectively enrolled patients from June 2018 to May 2019, by means of the area under the curve (AUC) and calibration.ResultsThe prediction model showed good discrimination (AUC = 0.916, cut-off = 0.577), calibration, and clinical availability. The performance was reconfirmed in the more complex emergency department. It was encapsulated as the Stroke Diagnosis Aid app for smartphones. The user can obtain the identification result by entering the values of the variables in the graphical user interface of the application.ConclusionThe prediction model based on laboratory and demographic variables could serve as a favorable supplementary tool to facilitate complex, time-critical acute stroke identification.

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N-Terminal Pro-brain Natriuretic Peptide Is Associated With Hemorrhagic Transformation and Poor Outcomes in Patients With Stroke Treated With Intravenous Thrombolysis

Frontiers in Molecular Neuroscience ◽

10.3389/fnmol.2021.758915 ◽

2021 ◽

Vol 14 ◽

Author(s):

Ke-Jia Zhang ◽

Hang Jin ◽

Rui Xu ◽

Peng Zhang ◽

Zhen-Ni Guo ◽

...

Keyword(s):

Ischemic Stroke ◽

Prediction Model ◽

Brain Natriuretic Peptide ◽

Acute Ischemic Stroke ◽

Natriuretic Peptide ◽

Functional Outcomes ◽

Intravenous Thrombolysis ◽

Hemorrhagic Transformation ◽

Model Based ◽

Individualized Prediction

Background: N-terminal pro-brain natriuretic peptide (NT-proBNP) levels are a promising biomarker for predicting stroke outcomes; however, their prognostic validity is not well-understood in patients who have undergone intravenous thrombolysis. This study was designed to evaluate the prognostic value of NT-proBNP levels in patients with acute ischemic stroke treated with intravenous thrombolysis.Methods: Patients with ischemic stroke who underwent intravenous thrombolysis between April 2015 and December 2020 were analyzed. Demographic information, information related to intravenous thrombolysis, medical history, and laboratory test results were collected. Outcomes, such as hemorrhagic transformation, early neurologic deterioration, poor 3-month functional outcomes, and 3-month mortality were recorded. Correlations between NT-proBNP levels and the above outcomes were analyzed, an individualized prediction model based on NT-proBNP levels for functional outcomes was developed, and a nomogram was drafted.Results: A total of 404 patients were included in the study. Elevated NT-proBNP levels were independently associated with hemorrhagic transformation, poor 3-month functional outcomes, and 3-month mortality, while early neurological deterioration was not. An association between NT-proBNP levels and hemorrhagic transformation was noted. An individualized prediction model for poor functional outcomes was established, which was composed of ln(NT-proBNP), National Institutes of Health Stroke Scale (NIHSS), and baseline glucose, with good discrimination [area under the curve (AUC) 0.764] and calibration (P > 0.05).Conclusion: To the best of our knowledge, this is the first report on the association between NT-proBNP levels and hemorrhagic transformation in patients who have undergone intravenous thrombolysis. The 3-month functional outcomes and mortality were found to be associated with NT-proBNP levels. An individualized prediction model based on NT-proBNP levels to predict the 3-month functional outcomes was established. Our results suggest that NT-proBNP levels could be used as a prognostic biomarker in patients with acute ischemic stroke treated with intravenous thrombolysis.

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Power Generation Prediction Model Based on Improved PSO-BPNN

Journal of Physics Conference Series ◽

10.1088/1742-6596/2083/3/032060 ◽

2021 ◽

Vol 2083 (3) ◽

pp. 032060

Author(s):

Yan Yu

Keyword(s):

Prediction Model ◽

Electricity Generation ◽

Back Propagation ◽

Electric Power Industry ◽

Back Propagation Neural Network ◽

Constructive Theory ◽

Swarm Optimization ◽

The Past ◽

Model Based ◽

The People

Abstract Electricity generation greatly impacts economic development, and electricity is indispensable for production, transportation, and living. Therefore, forecasting electricity generation accurately is of great research significance for the development of the country and the livelihood of the people. Because of the nonlinear relationship between electricity generation and the influencing factors, this paper, supported by the above data in China over the past 20 years, describes a prediction model based on Improved Particle Swarm Optimization (PSO) -- Back Propagation Neural Network (BPNN) to optimize the algorithm about forecasting electricity generation. The experimental results have shown that the accuracy and stability of the prediction model were constructed in this paper, which was improved by about 2%-6% compared with the traditional ones. In addition, the application of this model could provide a constructive theory for some relevant works in the electric-power industry.

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