Heart Failure / HeartLogic™ Heart Failure Diagnostic / Clinical Data / Real-World Evidence

HeartLogic Performance in Clinical Practice

HeartLogic reduces hospitalisations for decompensated heart failure and significantly reduces the costs per patient for the healthcare system, as described in the Heggermont et al publication.1

image
 HeartLogic™ Heart Failure Diagnostic provided consistent heart failure (HF) detection performance with low unexplained alert rates in real-world analyses of nearly 500 patients across four studies.
 
  MultiSENSE2
(Validation Data Set)
Capucci
et al.3
(ESC HF 2019)
Santini, Heggermont
et al.4
(Clin Card 2020)
RE-HEART
Phase I5
(ESC HF 2020)
RE-HEART
Phase II6
(ESC HF 2020)
PATIENTS 400 58 104 104 215
FOLLOW-UP
(MEDIAN)
12 months 5 months 13 months 8 months 5 months
HF HOSPITALISATION RATE
(PER PATIENT-YEAR)
0.20 0.21 0.15 N/A N/A
SENSITIVITY 70% 100%* 69%* N/A N/A
UNEXPLAINED ALERT RATE
(PER PATIENT-YEAR)
1.47 0.41 0.37 0.25 0.13
TIME IN HEARTLOGIC ALERT (%) 17% 12% 15% N/A N/A
 

 

Retrospective Analysis: Capucci, et al.

 

Preliminary experience with the multisensor HeartLogic algorithm for heart failure monitoring: a retrospective case series report

In a real-world scenario, HeartLogic alerts preceded heart failure symptoms by a median of 12 days and HF events by 38 days, which demonstrates the opportunity to identify patients earlier in the time course of HF decompensation.3

Pre-Symptomatic Insights7

  Early Warning Time
(Days)
Maximum HeartLogic Index
HF HOSPITALISATIONS 38 [15-61] 40 [28-40]
HF VISITS 12 [1-19] 24 [20-30]

Sensors with Worsening on the Day of the Alert Threshold Crossing3

S3 Heart Sounds S3/S1 Heart Sounds Night Heart Rate Thoracic Impedance Respiration Rate Rapid Shallow Breathing Index
84% 88% 60% 44% 36% 36%
 

 

Prospective Analysis: Santini, et al.

 
Prospective evaluation of the multisensor HeartLogic algorithm for heart failure monitoring

Primary Objectives

  • Evaluate the clinic workflow of HeartLogic alert management.
  • Compare an alert-based, follow-up strategy with scheduled monthly remote transmissions.
  • Evaluate the association between HeartLogic alert state and signs and symptoms of worsening heart failure.

Conclusions

  • This is the first publication to prospectively evaluate the HeartLogic algorithm in clinical practice utilizing a standardised remote-monitoring protocol.
  • This analysis demonstrated HeartLogic alerts to be frequently clinically meaningful and associated with impending HF events.
  • The analysis confirmed that the volume of alert-driven, remote follow-ups is low (0.93 per patient-year) when compared with a monthly remote follow-up protocol (10.3 per patient-year) and demonstrates that an alert-based management strategy may be more efficient than a scheduled monthly remote follow-up strategy.
  • The probability of detecting common signs and symptoms of HF at regular remote or in-office assessment is extremely low when the patient is out of HeartLogic alert state.

Evaluating the Clinic Workflow of HeartLogic Alert Management

Of the 60 clinically meaningful alerts during the study period, 80% provided new information to clinicians, which highlights HeartLogic’s ability to proactively identify patients at risk.4

Workflow graphic showing the number of clinically meaningful alerts, actionable alerts and alerts that provided new information.
Physician Perspectives
Clinical and economic impact of HeartLogic™ in device patients with HF
Episode 1 featuring Dr. Heggermont on “What can we learn from an economic perspective on HeartLogic?”


Episode 2 featuring Dr. Petkar on “Streamlining your clinic using HeartLogic”


Episode 3 featuring Dr. Garcia “How to optimize heart failure patient management using HeartLogic?”


Listen on Apple Podcast Listen on Google Podcast Listen on Spotify Podcast

These educational podcasts were produced in cooperation with Dr. Heggermont, Dr. Petkar, Dr. Garcia and Prof. Gardner. Results from case studies are not predictive of results in other cases. Results in other cases may vary.

 
doctor

“We learned that HeartLogic is very powerful in stratifying patient with a very low risk. When we have a patient in an out-of-alert condition, we can also give the attention to other patients.”

Luca Santini, MD

 

 

RE-HEART Registry Phase I

 

Preliminary Results of the Spanish Multicentric HeartLogic (RE-HEART) Registry: A Blinded Analysis

Phase 1 of the RE-HEART Registry was a retrospective, blinded analysis designed to analyze the association between clinical events and HeartLogic alerts in patients from 12 Spanish centers.

Maximum HeartLogic Alert Index5

The RE-HEART Registry analysis found a longer duration of alert and higher maximum HeartLogic index for patients with heart failure hospitalisations compared to those with minor events.

 

 

RE-HEART Registry Phase II

 

Preliminary Results of the Spanish Multicentric HeartLogic (RE-HEART) Registry: Adoption of an Alert-Based Heart Failure Management Approach

Phase II of the RE-HEART Registry showed a diverse array of interventions were taken for a diverse patient population. HeartLogic contributing sensors at time of alert were primarily driven by heart sounds.

Actions Triggered by Alerts6

Bar chart showing the actions triggered by HeartLogic alerts, including medication changes, hospitalization and more.

HeartLogic Contributing Sensors at Time of Alert6

Chart showing the contributing HeartLogic sensors at time of alert, including heart sounds, impedance & respiratory/heart rates.
 

 
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