AIMC Topic: Renal Insufficiency, Chronic

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Network-based machine learning reveals cardiometabolic multimorbidity patterns and modifiable lifestyle factors: a community-focused analysis of NHANES 2015-2018.

BMC public health
Cardiometabolic Multimorbidity (CMM) has emerged as one of the primary threats to human health globally due to its high incidence, disability, and mortality rates. Accurate identification of CMM patterns is crucial for CMM classification and health m...

Exploring the association between volatile organic compound exposure and chronic kidney disease: evidence from explainable machine learning methods.

Renal failure
BACKGROUND: Chronic Kidney Disease (CKD) affects approximately 697.5 million people worldwide. Volatile organic compounds (VOCs) are emerging as potential risk factors, but their complex relationships with CKD may be underestimated by traditional lin...

A novel predictive model and therapeutic potential of quercetin derivatives in chronic kidney disease progression.

Biochemical pharmacology
Chronic kidney disease (CKD) remains a pressing global health issue with limited therapeutic options. The loss of nephrons is a crucial pathological change driving CKD progression, influenced by diverse programmed cell death (PCD) pathways, yet the p...

Leveraging pulse wave signal properties for coronary artery calcification screening in CKD patients.

Computers in biology and medicine
BACKGROUND AND AIMS: Chronic kidney disease (CKD) patients are particularly susceptible to coronary atherosclerosis, which can be assessed using computed tomography (CT)-based coronary artery calcium (CAC) score. However, such a costly examination mi...

Reappraising machine learning models for vascular calcification in CKD: methodological concerns and clinical gaps.

International urology and nephrology
Lin et al. (Int Urol Nephrol, 2025) contribute to the literature on abdominal aortic calcification (AAC) in chronic kidney disease (CKD) using interpretable machine learning. However, several limitations hinder its clinical applicability. The cross-s...

Machine learning-based prediction model for cognitive impairment risk in patients with chronic kidney disease.

PloS one
BACKGROUND: The high prevalence of cognitive impairment (CI) in Chronic kidney disease (CKD) patients impacts their quality of life and prognosis, yet risk prediction models for CI in this population remain underexplored.

Prediction of depression risk in middle-aged and elderly Cardiovascular-Kidney-Metabolic syndrome patients by social and environmental determinants of health: an interpretable machine learning approach using longitudinal data from China.

Journal of health, population, and nutrition
BACKGROUND: Cardiovascular-Kidney-Metabolic (CKM) syndrome is a systemic disease characterized by pathophysiological interactions between the cardiovascular system, chronic kidney disease, and metabolic risk factors. In China, the prevalence of CKM i...

Developing a CT radiomics-based model for assessing split renal function using machine learning.

Japanese journal of radiology
PURPOSE: This study aims to investigate whether non-contrast computed tomography radiomics can effectively reflect split renal function and to develop a radiomics model for its assessment.

Development Of the VAMPCT Score for Predicting Mortality in CKD Patients with COVID-19.

International journal of medical sciences
Chronic kidney disease (CKD) patients with coronavirus disease 2019 (COVID-19) are at significant risk of death. However, clinical identification of high-risk individuals remains suboptimal despite the recognition of many pathophysiological and como...

Prediction of acute and chronic kidney diseases during the post-covid-19 pandemic with machine learning models: utilizing national electronic health records in the US.

EBioMedicine
BACKGROUND: COVID-19 has been linked to acute kidney injury (AKI) and chronic kidney disease (CKD), but machine learning (ML) models predicting these risks post-pandemic have been absent. We aimed to use large electronic health records (EHR) and ML a...