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Review
. 2021 Feb 15;21(4):1361.
doi: 10.3390/s21041361.

Early Detection of Fluid Retention in Patients with Advanced Heart Failure: A Review of a Novel Multisensory Algorithm, HeartLogicTM

Michelle Feijen  1 , Anastasia D Egorova  1 , Saskia L M A Beeres  1 , Roderick W Treskes  1
Affiliations
Review

Early Detection of Fluid Retention in Patients with Advanced Heart Failure: A Review of a Novel Multisensory Algorithm, HeartLogicTM

Michelle Feijen et al. Sensors (Basel). .

Abstract

Heart failure (HF) hospitalisations due to decompensation are associated with shorter life expectancy and lower quality of life. These hospitalisations pose a significant burden on the patients, doctors and healthcare resources. Early detection of an upcoming episode of decompensation may facilitate timely optimisation of the ambulatory medical treatment and thereby prevent heart-failure-related hospitalisations. The HeartLogicTM algorithm combines data from five sensors of cardiac implantable electronic devices into a cumulative index value. It has been developed for early detection of fluid retention in heart failure patients. This review aims to provide an overview of the current literature and experience with the HeartLogicTM algorithm, illustrate how the index can be implemented in daily clinical practice and discuss ongoing studies and potential future developments of interest.

Keywords: HeartLogicTM; admissions; cardiac implantable electronic devices; fluid retention; heart failure; multi sensor remote monitoring.

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Conflict of interest statement

A.D.E. is a local sub-investigator for the PREEMPT-HF study. R.W.T. received a speaker’s honorarium from Boston Scientific in the past 5 years. The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The sensors incorporated in the HeartLogicTM index. S1, 1st heart sound; S3, 3rd heart sound; LV, left ventricle.
Figure 2
Figure 2
An example of a HeartLogicTM report. The report states the cumulative HeartLogicTM index (top left corner) and provides additional information on the data collected by the individual sensors (contributing trends and trend graph). S1, 1st heart sound; S3, 3rd heart sound.
Figure 3
Figure 3
Overview of the literature search strategy and selection of relevant studies.
Figure 4
Figure 4
Schematic illustration of how the HeartLogicTM index can be implemented in a clinical care path. HF, heart failure; HL, HeartLogicTM.
Figure 5
Figure 5
The course of HeartLogicTM index of patient described in Case 1. A (3 January): Inclusion. HeartLogicTM index 8, eGFR (estimated glomerular filtration rate) 44 mL/min/1.73 m2, NT pro-BNP (N-terminal B-type natriuretic peptide) 6474 ng/L. B (5 February): Phone contact. HeartLogicTM index 17. C (15 February): Phone contact. HeartLogicTM index 25. D (20 February): Out-patient visit. HeartLogicTM index 22, eGFR 33 mL/min/1.73 m2, NT pro-BNP 9650 ng/L. E (2 April): out-patient visit. HeartLogicTM index 0, eGFR 42 mL/min/1.73 m2, NT pro-BNP 4173 ng/L.
Figure 6
Figure 6
The course of HeartLogicTM index of patient described in Case 2. Ambulance (19 April): Presentation with a ventricular tachycardia. A (29 May): Phone contact. HeartLogicTM index exceeded the threshold of 16. B (12 June): Phone contact. HeartLogicTM index 0.
Figure 7
Figure 7
The course of HeartLogic index of patient described in Case 3. A (17 July): Phone contact. HeartLogicTM index raises to 38. B (14 August): Phone contact. HeartLogicTM index 50, NT-pro BNP (N-terminal B-type natriuretic peptide) 22.919 ng/L. C (26 October): Phone contact. Gradual decline of HeartLogicTM until 0.
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