A multi-sensor algorithm may predict heart failure (HF) events in patients with implanted devices, according to the results of the MultiSENSE Study presented at AHA 2016.

John P. Boehmer, MD, FACC, et al., sought to develop and validate an algorithm combining information from a diverse set of implanted device-based sensors that can effectively detect worsening HF. Overall, 900 patients had sensor data collection enabled and followed for up to a year. The primary endpoint of unexplained alert rate per patient year was evaluated using the 320 patient years of follow-up data and 50 composite index scores in the Test Set group.

Results show that the composite index and alert effectively detected 70 percent of worsening HF episodes with a low rate of unexplained detections of less than 1.5 per patient year, demonstrating superior performance to other published implantable device features. Performance of the alert was prospectively validated using an independent test data set. The median advance warning time prior to HF events was 34 days, providing clinicians with time for corrective treatment.

The authors conclude that "the multi-sensor algorithm provides a timely alert predicting impending HF decompensation."

"The next step would be a study proving that the Detection program can lead to changes in therapy that improve patient outcomes," commented Kim A. Eagle, MD, MACC, editor-in-chief of ACC.org.

Keywords: AHA16, American Heart Association, AHA Annual Scientific Sessions, Algorithms, Heart Failure, Monitoring, Physiologic

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