A dynamic risk score to identify increased risk for heart failure decompensation.

Abstract

A method for combining heart failure (HF) diagnostic information in a Bayesian belief network (BBN) framework to improve the ability to identify when patients are at risk for HF hospitalization (HFH) is investigated in this paper. Implantable devices collect HF related diagnostics, such as intrathoracic impedance, atrial fibrillation (AF) burden, ventricular rate during AF, night heart rate, heart rate variability, and patient activity, on a daily basis. Features were extracted that encoded information regarding out of normal range values as well as temporal changes at weekly and monthly time scales. A BBN is used to combine the features to generate a risk score defined as the probability of a HFH given the diagnostic evidence. Patients with a very high risk score at follow-up are 15 times more likely to have a HFH in the next 30 days compared to patients with a low-risk score. The combined score has improved ability to identify patients at risk for HFH compared to the individual diagnostic parameters. A score of this nature allows clinicians to manage patients by exception; a patient with higher risk score needs more attention than a patient with lower risk score.