Atrial fibrillation (AF)--an irregular, often rapid heart rate--is common in patients with chronic kidney disease (CKD) and is associated with poor kidney and cardiovascular outcomes.
For the results, researchers at the University of Washington in the US conducted a study to see if a new prediction model could be used to identify patients with chronic kidney disease at the highest risk of experiencing atrial fibrillation.
The team compared a previously published atrial fibrillation prediction model with a model developed using machine learning (a type of artificial intelligence) based on clinical variables and cardiac markers.
In an analysis of information on 2,766 participants in the Chronic Renal Insufficiency Cohort (CRIC), the model based on machine learning was superior to the previously published model for predicting atrial fibrillation.
"The application of such a model could be used to identify patients with chronic kidney disease who may benefit from enhanced cardiovascular care and also to identify a selection of patients for clinical trials of AF therapies," said study lead author Leila Zelnick from the University of Washington.
The findings come from a study is scheduled to be presented online during ASN Kidney Week 2020 Reimagined from October 19-October 25.