scholarly journals Using Machine Learning to Evaluate the Role of Microinflammation in Cardiovascular Events in Patients With Chronic Kidney Disease

2022 ◽  
Vol 12 ◽  
Author(s):  
Xiao Qi Liu ◽  
Ting Ting Jiang ◽  
Meng Ying Wang ◽  
Wen Tao Liu ◽  
Yang Huang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Density Lipoprotein ◽  
Major Complication ◽  
Machine Learning Algorithms ◽  
Lipid Lowering ◽  
Lipid Metabolism Disorder ◽  
Increased Risk ◽  
Hs Crp

BackgroundLipid metabolism disorder, as one major complication in patients with chronic kidney disease (CKD), is tied to an increased risk for cardiovascular disease (CVD). Traditional lipid-lowering statins have been found to have limited benefit for the final CVD outcome of CKD patients. Therefore, the purpose of this study was to investigate the effect of microinflammation on CVD in statin-treated CKD patients.MethodsWe retrospectively analysed statin-treated CKD patients from January 2013 to September 2020. Machine learning algorithms were employed to develop models of low-density lipoprotein (LDL) levels and CVD indices. A fivefold cross-validation method was employed against the problem of overfitting. The accuracy and area under the receiver operating characteristic (ROC) curve (AUC) were acquired for evaluation. The Gini impurity index of the predictors for the random forest (RF) model was ranked to perform an analysis of importance.ResultsThe RF algorithm performed best for both the LDL and CVD models, with accuracies of 82.27% and 74.15%, respectively, and is therefore the most suitable method for clinical data processing. The Gini impurity ranking of the LDL model revealed that hypersensitive C-reactive protein (hs-CRP) was highly relevant, whereas statin use and sex had the least important effects on the outcomes of both the LDL and CVD models. hs-CRP was the strongest predictor of CVD events.ConclusionMicroinflammation is closely associated with potential CVD events in CKD patients, suggesting that therapeutic strategies against microinflammation should be implemented to prevent CVD events in CKD patients treated by statin.

Stroke in chronic renal failure

2008 ◽  
Vol 149 (15) ◽  
pp. 691-696
Author(s):  
Dániel Bereczki
Keyword(s):  
Risk Factors ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Renal Disease ◽  
Chronic Renal Disease ◽  
Kidney Diseases ◽  
Cerebrovascular Diseases ◽  
Lipid Lowering ◽  
Increased Risk ◽  
Intracerebral Hemorrhages

Chronic kidney diseases and cardiovascular diseases have several common risk factors like hypertension and diabetes. In chronic renal disease stroke risk is several times higher than in the average population. The combination of classical risk factors and those characteristic of chronic kidney disease might explain this increased risk. Among acute cerebrovascular diseases intracerebral hemorrhages are more frequent than in those with normal kidney function. The outcome of stroke is worse in chronic kidney disease. The treatment of stroke (thrombolysis, antiplatelet and anticoagulant treatment, statins, etc.) is an area of clinical research in this patient group. There are no reliable data on the application of thrombolysis in acute stroke in patients with chronic renal disease. Aspirin might be administered. Carefulness, individual considerations and lower doses might be appropriate when using other treatments. The condition of the kidney as well as other associated diseases should be considered during administration of antihypertensive and lipid lowering medications.

scholarly journals Chronic Kidney Disease Prediction using Machine Learning Models

2019 ◽  
Vol 9 (1) ◽  
pp. 6364-6367
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Kidney Failure ◽  
Bone Diseases ◽  
Machine Learning Algorithms ◽  
Knowledge Generation ◽  
Good Prediction ◽  
Worst Case ◽  
Wide Range

The field of biosciences have advanced to a larger extent and have generated large amounts of information from Electronic Health Records. This have given rise to the acute need of knowledge generation from this enormous amount of data. Data mining methods and machine learning play a major role in this aspect of biosciences. Chronic Kidney Disease(CKD) is a condition in which the kidneys are damaged and cannot filter blood as they always do. A family history of kidney diseases or failure, high blood pressure, type 2 diabetes may lead to CKD. This is a lasting damage to the kidney and chances of getting worser by time is high. The very common complications that results due to a kidney failure are heart diseases, anemia, bone diseases, high potasium and calcium. The worst case situation leads to complete kidney failure and necessitates kidney transplant to live. An early detection of CKD can improve the quality of life to a greater extent. This calls for good prediction algorithm to predict CKD at an earlier stage . Literature shows a wide range of machine learning algorithms employed for the prediction of CKD. This paper uses data preprocessing,data transformation and various classifiers to predict CKD and also proposes best Prediction framework for CKD. The results of the framework show promising results of better prediction at an early stage of CKD

Abstract 234: Changes in Plasma Nitration of High-Density and Low-Density Lipoproteins in Patients With Chronic Kidney Disease Receiving Kidney Transplants

2014 ◽  
Vol 34 (suppl_1) ◽  
Author(s):  
Ahmed Bakillah ◽  
Fasika Tedla ◽  
Isabelle Ayoub ◽  
Devon John ◽  
Allen Norin ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Transplantation ◽  
Kidney Disease ◽  
Density Lipoprotein ◽  
High Density ◽  
Lipid Lowering ◽  
Elevated Concentration ◽  
Low Density ◽  
Post Transplant ◽  
Transplant Patients

Background: Functional abnormalities of high-density lipoprotein (HDL) and elevated concentration of low-density lipoprotein (LDL) could contribute to cardiovascular disease (CVD) in chronic kidney disease (CKD) patients. Both qualitative and quantitative changes in HDL have been described in patients with CKD. Specifically, HDL abundance is reduced and HDL acquires a pro-inflammatory properties. In this study, we hypothesized that a functioning kidney transplant reduces serum nitrated apoA-I concentration. Methods: Concentrations of nitrated apoA-I, nitrated apoB, total apoA-I and total apoB were measured using indirect sandwich ELISA on sera collected from each transplant subject pre-transplant and at 1, 3, and 12 months post-transplant. Patients were excluded if they had a history of diabetes, prednisone dose > 15 mg/day, nephrotic range proteinuria, serum creatinine (Cr) > 1.5mg/dL or active inflammatory disease, or were treated with lipid-lowering medication or HIV protease inhibitors. Paired values of percent nitrated Apo A-I or nitrated apoB before and after kidney transplantation were compared using Wilcoxon signed rank sum test. Results: We screened 75 transplant patients, and 14 were found to meet the selection criteria. Amongst these patients, twelve and eight patients had Cr < 1.5 mg/dL at 3 and 12 months post-transplant, respectively. There was a significant reduction in % nitrated apoA-I at 12 months post-transplant compared to pre-transplant values in patients with Cr<1.5 mg/dL (p=0.04) but neither at 3 months post-transplant nor in patients with Cr >1.5. Reduction of nitrated apoA-I was associated with slight increase in HDL levels 12 months post-transplantation. In contrast to apoA-I, there was no significant change in % nitrated apoB at 3 months and 12 months post-transplant. No significant corelation was observed between nitrated lipoproteins and CRP levels. Conclusion: Patients with well functioning grafts had significant reduction in percent nitrated apoA-I without any effect on apoB nitration 12 months after kidney transplantation. Further studies are needed in a large cohort to determine if nitrated apoA-I can be used as a valuable marker for cardiovascular risk stratification in CKD.

Application of Machine Learning in Chronic Kidney Disease Risk Prediction Using Electronic Health Records (EHR)

2021 ◽  
pp. 213-233
Author(s):  
Laxmi Kumari Pathak ◽  
Pooja Jha
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Electronic Health Records ◽  
Bone Diseases ◽  
Machine Learning Algorithms ◽  
Health Records ◽  
Chronic Kidney Disease Risk ◽  
Electronic Health ◽  
Heart Disorders

Chronic kidney disease (CKD) is a disorder in which the kidneys are weakened and become unable to filter blood. It lowers the human ability to remain healthy. The field of biosciences has progressed and produced vast volumes of knowledge from electronic health records. Heart disorders, anemia, bone diseases, elevated potassium, and calcium are the very prevalent complications that arise from kidney failure. Early identification of CKD can improve the quality of life greatly. To achieve this, various machine learning techniques have been introduced so far that use the data in electronic health record (EHR) to predict CKD. This chapter studies various machine learning algorithms like support vector machine, random forest, probabilistic neural network, Apriori, ZeroR, OneR, naive Bayes, J48, IBk (k-nearest neighbor), ensemble method, etc. and compares their accuracy. The study aims in finding the best-suited technique from different methods of machine learning for the early detection of CKD by which medical professionals can interpret model predictions easily.

scholarly journals Chronic Kidney Disease Prediction based on Blood Potassium Levels using Machine Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 4194-4199
Keyword(s):  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Medical Records ◽  
Research Study ◽  
Machine Learning Algorithms ◽  
Disease Prediction ◽  
Blood Potassium ◽  
Gain Access

Machine learning is an artificial intelligence(AI) technology that provides the systems with the knowledge and capability to learn and evolve automatically from specifically programmed experiences. This focuses on designing computer programs that are able to gain access and use information on their own. Kidney damage or decreased activity for more than three months is known as chronic kidney disease.This illness occurs when the kidneys can no longer expel extra water or waste from human blood. The goal of this research study is to prepare a predictive modeling for chronic kidney disease data to analyze the different open source python module and output the results predicted by machine learning algorithms and determine the accuracy by comparing different algorithms such as KNN and Logistic Regression which are primarily used for classification of data. This algorithm makes predictions on a dataset collected from the patient's medical records. It gives us the clarity that if someone has chronic kidney disease or not primarily based on a person's blood potassium levels present in their body.

The Heat Shock Protein Profile in Children with Chronic Kidney Disease

2010 ◽  
Vol 30 (2) ◽  
pp. 227-232 ◽  
Author(s):  
Kinga Musial ◽  
Krystyna Szprynger ◽  
Maria Szczepańska ◽  
Danuta Zwolińska
Keyword(s):  
Chronic Kidney Disease ◽  
Peritoneal Dialysis ◽  
Heat Shock ◽  
Kidney Disease ◽  
Density Lipoprotein ◽  
Lipoprotein Cholesterol ◽  
Control Group ◽  
Hsp 70 ◽  
Markers Of Inflammation ◽  
Hs Crp

ObjectivesChronic kidney disease (CKD) due to inflammation, lipid disorders, and endothelial dysfunction predisposes to accelerated atherosclerosis. Elevated levels of heat shock proteins (HSPs) and antibodies against them have been described in adults with atherosclerotic lesions and cardiovascular events. However, there are no investigations of these variables in children with CKD treated conservatively or on peritoneal dialysis. Therefore, we decided to evaluate the profile of HSPs and their potential role as markers of atherosclerosis in these groups of patients.MethodsThe study group consisted of 37 children with CKD treated conservatively and 19 children and young adults on automated peritoneal dialysis (APD). The control group comprised 15 age-matched subjects with normal kidney function. HSP-60, HSP-70, HSP-90alpha, anti-HSP-60, anti-HSP-70, sE-selectin, and interleukin (IL)-4 serum concentrations were assessed by ELISA; high-sensitivity C-reactive protein (hs-CRP) serum levels were assessed by nephelometry. Serum lipid profiles (total cholesterol, high density lipoprotein cholesterol, low density lipoprotein cholesterol, triglycerides) were also estimated.ResultsHSP-90α x anti-HSP-60, and sE-selectin concentrations in the CKD and APD patients were higher than in the controls and were lower in the predialysis subjects than in the children on dialysis. Median values of anti-HSP-70 were higher in the CKD patients than in the control group. Levels of IL-4 were increased in all patients versus controls. Median values of HSP-60 were decreased in the CKD and APD children versus controls. HSP-70 and hs-CRP concentrations were comparable in all groups.ConclusionsThe altered HSP and anti-HSP concentrations may imply that the response to stress conditions in the course of CKD is disturbed in children; APD does not aggravate that dysfunction in a significant way. Relationships between HSPs, lipid profile, and markers of inflammation suggest a possible role of the selected HSPs as markers of atherosclerosis in children with CKD.

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