ArticleJournal of personalized medicine2021
Predictive Model for High Coronary Artery Calcium Score in Young Patients with Non-Dialysis Chronic Kidney Disease.
Article in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review.BMJ digital health & AI · 2026Article
- Integrating predictive modeling and causal inference for advancing medical science.Childhood kidney diseases · 2024Review
- Development and Validation of Machine Learning-Based Models for Prediction of Intensive Care Unit Admission and In-Hospital Mortality in Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Disease.Chronic obstructive pulmonary diseases (Miami, Fla.) · 2024Article
- Coronary artery calcification score as the determinant of coronary artery disease in chronic kidney disease patients: A preliminary study.ARYA atherosclerosis · 2024Article
- A Hybrid Risk Factor Evaluation Scheme for Metabolic Syndrome and Stage 3 Chronic Kidney Disease Based on Multiple Machine Learning Techniques.Healthcare (Basel, Switzerland) · 2022Article
- Explainable Machine Learning-Based Risk Prediction Model for In-Hospital Mortality after Continuous Renal Replacement Therapy Initiation.Diagnostics (Basel, Switzerland) · 2022Article
- Explainable Machine Learning Model for Predicting First-Time Acute Exacerbation in Patients with Chronic Obstructive Pulmonary Disease.Journal of personalized medicine · 2022Article
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
Abstract
Cardiovascular disease is a major complication of chronic kidney disease. The coronary artery calcium (CAC) score is a surrogate marker for the risk of coronary artery disease. The purpose of this study is to predict outcomes for non-dialysis chronic kidney disease patients under the age of 60 with high CAC scores using machine learning techniques. We developed the predictive models with a chronic kidney disease representative cohort, the Korean Cohort Study for Outcomes in Patients with Chronic Kidney Disease (KNOW-CKD). We divided the cohort into a training dataset (70%) and a validation dataset (30%). The test dataset incorporated an external dataset of patients that were not included in the KNOW-CKD cohort. Support vector machine, random forest, XGboost, logistic regression, and multi-perceptron neural network models were used in the predictive models. We evaluated the model's performance using the area under the receiver operating characteristic (AUROC) curve. Shapley additive explanation values were applied to select the important features. The random forest model showed the best predictive performance (AUROC 0.87) and there was a statistically significant difference between the traditional logistic regression model and the test dataset. This study will help identify patients at high risk of cardiovascular complications in young chronic kidney disease and establish individualized treatment strategies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.