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A new model that uses machine learning, a type of artificial intelligence, can help predict which patients with kidney disease are at particularly high risk of developing heartbeat irregularities. The results come from a study presented online during ASN Kidney Week 2020 Reimagined October 19-25.

Atrial fibrillation (AF) – an irregular, often rapid heart rate – is common in people with chronic kidney disease (CKD) and has been linked to poor kidney and cardiovascular outcomes. The researchers conducted a study to see if a new predictive model could be used to identify patients with CRF at the highest risk for AF. The team compared a previously published AF predictive 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 kidney failure cohort (CRIC), the machine learning-based model was superior to the previously published model for predicting AF.

“Applying such a model could be used to identify patients with CRF who could benefit from improved cardiovascular care and also to identify patient selection for clinical trials with AF therapies,” said lead author Leila Zelnick, Ph.D. (University of Washington, in Seattle)

The model predicts acute kidney injury that will require dialysis in patients with COVID-19

More information:
Study: “Predicting Atrial Fibrillation Using Clinical and Cardiac Biomarker Data: The CRIC Study”

Provided by the American Society of Nephrology

Quote: New model predicts which patients with kidney disease may experience heartbeat irregularities (2020, October 24). Retrieved October 25, 2020 from

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