Risk Score to Identify Increased Risk for Heart Failure Prediction

Risk Score to Identify Increased Risk for Heart Failure Prediction

A method for combining heart failure (HF) diagnostic information in a Bayesian belief network (BBN) framework to im- prove the ability to identify when patients are at risk for HF hospi- talization (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 al- lows clinicians to manage patients by exception; a patient with
higher risk score needs more attention than a patient with lower risk score. Risk Score to Identify Increased Risk for Heart Failure Prediction

HEART failure (HF) is the most common cardiovascular disease that causes significant economic burden, mor- bidity, and mortality. In the U.S., more than 5.7 million have HF . The primary cause of a significant proportion of hospitalizations is HF, with close to one million discharges for HF in the U.S. in 2007 [1]. The primary cause of HF hospitalization (HFH) is volume overload in which patients retain excess amount of fluid. The primary HF management strategy is to control excess fluid volume using diuretic therapy [2]. Further, ACE-Inhibitors, which control blood pressure, and β-blockers,
which control heart rate, are known to reduce mortality in HF patients .
Implantable medical devices, such as pacemakers, im- plantable cardioverter defibrillator, cardiac resynchronization therapy defibrillator (CRT-D) and implantable loop recorders, provide daily measurements of several diagnostic parame- ters for possible evaluation of HF status in patients. Wire-less transmission capabilities in these devices allow for auto- matic remote monitoring of the diagnostic parameters over the

Implanted medical devices monitor several clinical diagnostic parameters that may include IMP, AF burden, ventricular rate during atrial fibrillation (VRAF), ACT, NHR, and HRV. These parameters are monitored continuously and the device stores sample data points for each parameter daily. IMP is measured across the thorax between an electrode inside the heart and the device [3]. IMP is a surrogate measure for increasing fluid in the thorax, with an increase in fluid volume leading to a reduction in IMP. HRV is measured as the standard deviation of 5 min median of atrial (PP) intervals during a 24 h period. The HRV is a long-term measure of sympathetic tone changes, with reducing HRV implying increases in sympathetic tone. HRVis not measured during AF. NHR is measured as the average
heart rate between midnight and 4 A. M. and is a measure for resting heart rate. ACT is measured as the number of minutes in a 24 h period the patient is active. A patient is considered active over a minute if the number of deflections of an accelerometer in the device exceeds a threshold.

Conclusion:
In conclusion, machine learning holds great potential for predicting heart failure and improving patient outcomes. By leveraging large datasets and powerful algorithms, healthcare professionals can identify high-risk patients and intervene early, potentially saving lives. However, the successful implementation of machine learning in healthcare requires collaboration between data scientists, healthcare professionals, and policymakers to address challenges and ensure ethical and responsible use of this technology. With continued advancements in machine learning and healthcare, we can expect further improvements in heart failure prediction and overall patient care.