Algorithm predicts heart failure hospitalisations


Biotronik has announced the results from a new study published in EP Europace this week confirming that heart failure (HF) decompensation can be predicted early when monitored using an algorithm that combines existing remote monitoring trends and baseline risk stratification.

The multicentre SELENE HF study concluded that the algorithm predicted two thirds of first, post-implant HF hospitalisations with a median alerting time of 42 days, with a false positive rate of only 0.7 alerts per patient per year. An alert based on this algorithm could allow clinicians time to initiate preventive measures that may improve patient outcomes and reduce hospitalisations.

Patients with acute HF continue to suffer from poor prognosis and high rehospitalisation rates. This results in poor patient outcomes as well as a significant burden on healthcare systems. Early  prevention of decompensation may increase therapy options for patients and decrease rehospitalisation rates.

“A heart failure alert would benefit both patients and clinicians. Detecting worsening heart failure early and proactively stratifying patients at risk may help improve quality of care and avoid rehospitalisations. This also may alleviate overloaded clinics and help efficiently allocate resources,” said Antonio D’Onofrio, principal investigator of the SELENE HF study and head of Electrophysiology and Cardiac Pacing Unit, Monaldi Hospital, Naples, Italy.

The algorithm automatically analyses relevant patient parameters and remote monitoring trends. The resulting predictions may free up clinicians’ time and, importantly, get them crucial information about their patients’ conditions faster.

“Selene HF builds on the results and experience from the IN-TIME study and underscores Biotronik’s commitment to improving scientific knowledge and driving innovation in the field of heart failure,” said Klaus Contzen, vice-president clinical affairs, CRM, BIOTRONIK. “It has the potential to assist the development of an easy-to-use solution that can identify patients at higher risk of worsening heart failure hospitalisations so clinicians can proactively intervene.”



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