Latest Study Shows New Algorithm Can Predict Heart Failure Hospitalizations
SELENE HF adds to existing evidence that HF decompensation starts several weeks before hospitalization
A study published in EUROPACE this week confirms 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 multi-center SELENE HF study concluded that the algorithm predicted two-thirds of first, post-implant HF hospitalizations with a median alerting time of 42 days, with a false positive rate of only 0.7 alerts per patient, per year1. An alert based on this algorithm could allow clinicians time to initiate preventive measures that may improve patient outcomes and reduce hospitalizations.
Patients with acute HF continue to suffer from poor prognosis and high re-hospitalization rates. This results in poor patient outcomes as well as a significant burden on healthcare systems. Nearly 1 in 4 HF patients are readmitted within 30 days of discharge and approximately half are readmitted within 6 months3. Early prevention of decompensation may increase therapy options for patients and decrease re-hospitalization 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 re-hospitalizations. This also may alleviate overloaded clinics and help efficiently allocate resources,” said Dr. 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 analyzes 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 Dr. 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 hospitalizations so clinicians can proactively intervene”.
Key points from the study:
- A HF predictor which combines seven remote monitoring temporal trends and baseline risk stratification was developed and validated.
- The study enrolled 918 patients across multiple centers with a median follow-up of 22.5 months.
- Two thirds of first post-implant HF hospitalizations were predicted early with a median prediction time of 42 days.
- There were only 0.7 false alerts per patient, per year.
- D’Onofrio A, Solimene F, Calò L, Calvi V, Viscusi M, Melissano D, et al. Combining home monitoring temporal trends from implanted defibrillators and baseline patient risk profile to predict heart failure hospitalisations: results from the SELENE HF study. Europace 2021; in print. DOI: 10.1093/europace/euab170.
- Zile MR, Bennett TD, St John Sutton M, Cho YK, Adamson PB, Aaron MF, et al. Transition from chronic compensated to acute decompensated heart failure: pathophysiological insights obtained from continuous monitoring of intracardiac pressures. Circulation 2008;118:1433–41.
Khan MS, Sreenivasan J, Lateef N, Abougergi MS, Greene SJ, Ahmad T, et al. Trends in 30- and 90-day readmission rates for heart failure. Circ Heart Fail. 2021;14(4):e008335.
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