Surveillance and Alert-based Multiparameter Monitoring to Reduce Worsening Heart Failure Events: Results From SCALE-HF 1

Abstract

Background

There is a need for better noninvasive remote monitoring solutions that prevent hospitalizations through the early prediction and management of heart failure (HF). SurveillanCe and Alert-Based Multiparameter Monitoring to ReducE Worsening Heart Failure Events (SCALE-HF 1) evaluated the performance of a novel congestion index that alerts to fluid accumulation preceding HF events.

Methods and Results

SCALE-HF 1 was a multicenter, prospective, observational study investigating HF event prediction using data from the cardiac scale. Participants with HF took measurements at home by standing barefoot on the scale for approximately 20 seconds each day. The congestion index was applied retrospectively, and an alert was generated when the index exceeded a fixed threshold established in prior studies. HF events were defined as unplanned administration of IV diuretics or admissions with a primary diagnosis of HF. Sensitivity was defined as the ratio of correctly identified HF events to the total number of HF events. We enrolled 329 participants (mean age 64 ± 14 years; 43% women; 32% Black; 56% with reduced ejection fraction) across 8 sites with 238 participant-years of follow-up and 69 usable HF events. The congestion index predicted 48 of the 69 HF events (70%) at 2.58 alerts per participant-year. In contrast, the standard weight rule (weight gain of >3 lb in 1 day or >5 lb in 7 days) predicted only 24 of the 69 HF events (35%) at 4.18 alerts per participant-year. The congestion index alerts had a significantly higher sensitivity (P < .01) at a lower alert rate than the standard weight rule.

Conclusions

The congestion index alerts demonstrated sensitive prediction of HF events at a low alert rate, significantly exceeding the performance of weight-based monitoring.

ClinicalTrials.gov Identifier

NCT04882449