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February 9, 2025
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Machine learning models effectively predict CKD progression using large clinical datasets, focusing on kidney function, complications, and etiologies.
Age, sex, and serum albumin are significant variables in predicting CKD progression, with diabetes and hypertension as leading causes.
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In health care, artificial intelligence (AI)—in particular, machine learning models—have demonstrated promise in the optimization of clinical decision-making and diagnosis. Chronic kidney disease (CKD) may serve as an ideal disease state to use machine learning models, primarily because of the large amounts of routinely collected clinical and biochemical data. Authors of a review published in Nephrology aimed to identify the most important variables used in machine learning models to predict the progression of CKD to kidney failure.Image credit: everythingpossible | stock.adobe.comThe study authors searched for studies on the Ovid Medline and EMBASE databases in August 2023. Studies related to CKD, machine learning, and end-stage renal disease were included. Additionally, studies were included in the review if they had the following characteristics: Adults aged 18 years and older The population has CKD at baseline, defined as at least 3 months of kidney damage; estimated glomerular filtration rate (eGFR) 60 mL/min/1.73 m 2 or less; or signs of kidney damage such as proteinuria, hematuria, blood, or imaging abnormalities Machine learning is the focus of the predictive model The study measures kidney failure as the outcome (defined by eGFR, either ≤ 15 or ≤ 10 mL/min/1.73 m 2 , commencement of kidney replacement therapy, dialysis, or transplant) The study describes variables incorporated into the model The authors included a total of 16 articles, which had 297,185 patients with CKD, with study sample sizes ranging from 436 to 184,292 patients (average: 18,574 patients). Additionally, the most common demographics were age and sex, which were included in 16 and 15 studies, respectively. As for comorbidities, vascular disease—including cardiovascular disease, peripheral vascular disease, coronary artery disease, and cerebrovascular disease—occurred in 7 studies, smoking status in 5, and CKD etiology were considered in 4 studies. Half of the studies (n = 8) recorded clinical variables such as blood pressure and body […]
Machine Learning Shows Promise Predicting Progression of CKD to Kidney Failure
