Data from implantable cardiac devices can help clinicians predict when a patient may be hospitalized for heart failure (HF), according to new research published in EP Europace. The key to those accurate predictions, researchers explained, was an advanced AI model that offers real-time evaluations of certain patient parameters and ongoing monitoring trends.

The Selene HF trial included more than 900 patients with a median age of 69 years old and a median ejection fraction of 30%. Eighty-one percent of participants were men. All patients had either an implantable cardioverter-defibrillator (ICD) or cardiac resynchronization therapy defibrillator (CRT-D) manufactured by Biotronik. Each participant was enrolled from one of 34 sites in Italy and Spain.

The research team’s algorithm focused on seven key factors: diurnal and nocturnal heart rates, ventricular extrasystoles, atrial tachyarrhythmia burden, heart rate variability, physical activity and thoracic impedance. It was designed to alert clinicians when they needed to step in and provide care, potentially avoiding any further issues.

“A HF alert would benefit both patients and clinicians,” lead author Antonio D’Onofrio, MD, a cardiologist at Monaldi Hospital in Italy, said in a statement. “Detecting worsening HF early and proactively stratifying patients at risk may help improve quality of care and avoid re-hospitalizations. This also may alleviate overloaded clinics and help efficiently allocate resources.”