Predicting Duration of CKD Progression in Patients with Hypertension and Diabetes

Khannara, Warangkana; Iam-On, Natthakan; Boongoen, Tossapon

doi:10.1007/978-3-319-27000-5_11

Cited by 4 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Patients with chronic nephritis and normal renal function may present with dyslipidemia, such as nephrotic syndrome, due to the disease itself. Additionally, even patients with renal insufficiency and few urinary proteins also suffer from lipoprotein metabolism disorders, dyslipidemia, and atherosclerosis due to weakening renal function [34,35]. Uncontrolled hyperglycemia and hyperlipidemia increase the risk of cardiovascular disease and accelerate the progression of CKD to advanced stages, regardless of whether CKD is caused by diabetes or hyperlipidemia [36].…”

Section: Discussionmentioning

confidence: 99%

Establishment and Evaluation of Artificial Intelligence-Based Prediction Models for Chronic Kidney Disease under the Background of Big Data

Yan

Lü

et al. 2022

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

Objective. To establish a prediction model for the risk evaluation of chronic kidney disease (CKD) to guide the management and prevention of CKD. Methods. A total of 1263 patients with CKD and 1948 patients without CKD admitted to the Tongde Hospital of the Zhejiang Province from January 1, 2008, to December 31, 2018, were retrospectively analyzed. Spearman’s correlation was used to analyze the relationship between CKD and laboratory parameters. XGBoost, random forest, Naive Bayes, support vector machine, and multivariate logistic regression algorithms were employed to establish prediction models for the risk evaluation of CKD. The accuracy, precision, recall, F1 score, and area under the receiver operating curve (AUC) of each model were compared. The new bidirectional encoder representations from transformers with light gradient boosting machine (MD-BERT-LGBM) model was used to process the unstructured data and transform it into researchable unstructured vectors, and the AUC was compared before and after processing. Results. Differences in laboratory parameters between CKD and non-CKD patients were observed. The neutrophil ratio and white blood cell count were significantly associated with the occurrence of CKD. The XGBoost model demonstrated the best prediction effect (accuracy = 0.9088, precision = 0.9175, recall = 0.8244, F1 score = 0.8868, AUC = 0.8244), followed by the random forest model (accuracy = 0.9020, precision = 0.9318, recall = 0.7905, F1 score = 0.581, AUC = 0.9519). Comparatively, the predictions of the Naive Bayes and support vector machine models were inferior to those of the logistic regression model. The AUC of all models was improved to some extent after processing using the new MD-BERT-LGBM model. Conclusion. The new MD-BERT-LGBM model with the inclusion of unstructured data has contributed to the higher accuracy, sensitivity, and specificity of the prediction models. Clinical features such as age, gender, urinary white blood cells, urinary red blood cells, thrombin time, serum creatinine, and total cholesterol were associated with CKD incidence.

show abstract

Section: Discussionmentioning

confidence: 99%

Establishment and Evaluation of Artificial Intelligence-Based Prediction Models for Chronic Kidney Disease under the Background of Big Data

Yan

Lü

et al. 2022

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

show abstract

“…In addition to albumin, serum creatinine and EGFR, we also identified TG and LDL as prediction factors in our models. It was also previously reported that a distinct panel of lipid-related features may improve the prediction of CKD progression beyond EGFR and proteinuria [47].…”

Section: Discussionmentioning

confidence: 99%

Comparison and development of machine learning tools in the prediction of chronic kidney disease progression

et al. 2019

View full text Add to dashboard Cite

Background Urinary protein quantification is critical for assessing the severity of chronic kidney disease (CKD). However, the current procedure for determining the severity of CKD is completed through evaluating 24-h urinary protein, which is inconvenient during follow-up. Objective To quickly predict the severity of CKD using more easily available demographic and blood biochemical features during follow-up, we developed and compared several predictive models using statistical, machine learning and neural network approaches. Methods The clinical and blood biochemical results from 551 patients with proteinuria were collected. Thirteen blood-derived tests and 5 demographic features were used as non-urinary clinical variables to predict the 24-h urinary protein outcome response. Nine predictive models were established and compared, including logistic regression, Elastic Net, lasso regression, ridge regression, support vector machine, random forest, XGBoost, neural network and k-nearest neighbor. The AU-ROC, sensitivity (recall), specificity, accuracy, log-loss and precision of each of the models were evaluated. The effect sizes of each variable were analysed and ranked. Results The linear models including Elastic Net, lasso regression, ridge regression and logistic regression showed the highest overall predictive power, with an average AUC and a precision above 0.87 and 0.8, respectively. Logistic regression ranked first, reaching an AUC of 0.873, with a sensitivity and specificity of 0.83 and 0.82, respectively. The model with the highest sensitivity was Elastic Net (0.85), while XGBoost showed the highest specificity (0.83). In the effect size analyses, we identified that ALB, Scr, TG, LDL and EGFR had important impacts on the predictability of the models, while other predictors such as CRP, HDL and SNA were less important. Conclusions Blood-derived tests could be applied as non-urinary predictors during outpatient follow-up. Features in routine blood tests, including ALB, Scr, TG, LDL and EGFR levels, showed predictive ability for CKD severity. The developed online tool can facilitate the prediction of proteinuria progress during follow-up in clinical practice. Electronic supplementary material The online version of this article (10.1186/s12967-019-1860-0) contains supplementary material, which is available to authorized users.

show abstract

Prediction of Adult Chronic Kidney Disease with Class-Imbalanced Datasets

Zhu-Cuiling

Yuan-Jiucun

Yang-Chengwei

2021

2021 IEEE International Conference on Big Data (Big Data)

View full text Add to dashboard Cite

Predicting Duration of CKD Progression in Patients with Hypertension and Diabetes

Cited by 4 publications

References 16 publications

Establishment and Evaluation of Artificial Intelligence-Based Prediction Models for Chronic Kidney Disease under the Background of Big Data

Establishment and Evaluation of Artificial Intelligence-Based Prediction Models for Chronic Kidney Disease under the Background of Big Data

Comparison and development of machine learning tools in the prediction of chronic kidney disease progression

Prediction of Adult Chronic Kidney Disease with Class-Imbalanced Datasets

Contact Info

Product

Resources

About