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Meta-Analysis
. 2025 Jul 8;12(2):e003474.
doi: 10.1136/openhrt-2025-003474.

Evaluating an alert-based multiparametric algorithm for predicting heart failure hospitalisations in patients with implantable cardioverter-defibrillators: a meta-cohort study

Alan Bulava  1 , João De Sousa  2 , Laurence Guédon-Moreau  3 , Morio Shoda  4 , Tobias Timmel  5 , Sally Thompson Hilpert  5 , Antonio D'Onofrio  6
Affiliations
Meta-Analysis

Evaluating an alert-based multiparametric algorithm for predicting heart failure hospitalisations in patients with implantable cardioverter-defibrillators: a meta-cohort study

Alan Bulava et al. Open Heart. .

Abstract

Background: The alert-based HeartInsight algorithm predicts risk of worsening heart failure hospitalisations (WHFHs) by evaluating temporal trends of seven physiologic parameters obtained through automatic daily remote monitoring of implantable cardioverter-defibrillators. The aim of the present study was to evaluate the predictive performance of HeartInsight in a larger and more heterogeneous meta-cohort of patients, incorporating newer device generations and including patients managed with the most recent guideline-directed medical therapy (GDMT).

Methods: The HeartInsight algorithm was retrospectively applied to data from four clinical trials in which WHFH events were adjudicated by independent external boards and remote monitoring was activated to provide relevant parameter trends. The analysis comprised 1352 patients with New York Heart Association (NYHA) class II/III, and no long-standing atrial fibrillation.

Results: During a median follow-up of 599 days, 110 patients (median age 68 years (IQR, 61-75), 75.7% male) had a total of 165 WHFHs. The estimated sensitivity of WHFH prediction, as determined by generalised estimating equations, was 51.5% (95% CI 43.0% to 59.9%). The false alert rate was 0.85 per patient-year, the median alerting time was 34 days (IQR, 16-78) and the specificity was 81.4% (95% CI 80.4 to 82.4%). The results were verified in the multivariable analysis with two adjusting covariates (newer/older device generation and quadruple/other GDMT) and in the univariable analysis of prespecified patient subgroups according to NYHA class, aetiology and sex, showing no significant differences.

Conclusions: Study results underscore the robustness of the predictive algorithm in a heterogeneous and contemporarily managed heart failure population.

Keywords: Defibrillators, Implantable; HEART FAILURE; Telemedicine.

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Conflict of interest statement

Competing interests: AB received consultant fees and/or grant support from Abbott, Biotronik, and Boston Scientific. JDS received grant support and/or consultant fees from Microport and Medtronic outside the submitted work. MS received grant support from Abbott, Biotronik, Boston Scientific, and Medtronic. LG-M received fees for lectures and/or consulting from Novartis, Microport, Boston Scientific, and Medtronic outside the submitted work. Lille University Hospital Center received grants from BMS, Abbott, Biotronik, Boston Scientific, Microport and Medtronic outside the submitted work. TT and STH are employees of Biotronik SE & Co. KG, Berlin, Germany.

Figures

Figure 1
Figure 1. HeartInsight sensitivity, specificity and alerting time across patient subgroups from table 3, using a default nominal alert threshold of 45. For each subgroup comparison, the level of significance is indicated. d, days; n.s., not significant; NYHA, New York Heart Association class.
See this image and copyright information in PMC

References

    1. Samsky MD, Ambrosy AP, Youngson E, et al. Trends in Readmissions and Length of Stay for Patients Hospitalized With Heart Failure in Canada and the United States. JAMA Cardiol. 2019;4:444–53. doi: 10.1001/jamacardio.2019.0766. - DOI - PMC - PubMed
    1. Givertz MM, Yang M, Hess GP, et al. Resource utilization and costs among patients with heart failure with reduced ejection fraction following a worsening heart failure event. ESC Heart Fail. 2021;8:1915–23. doi: 10.1002/ehf2.13155. - DOI - PMC - PubMed
    1. Ziaeian B, Fonarow GC. Epidemiology and aetiology of heart failure. Nat Rev Cardiol. 2016;13:368–78. doi: 10.1038/nrcardio.2016.25. - DOI - PMC - PubMed
    1. D’Onofrio A, Solimene F, Calò L, et al. Combining home monitoring temporal trends from implanted defibrillators and baseline patient risk profile to predict heart failure hospitalizations: results from the SELENE HF study. EP Europace. 2022;24:234–44. doi: 10.1093/europace/euab170. - DOI - PMC - PubMed
    1. Levy WC, Mozaffarian D, Linker DT, et al. The Seattle Heart Failure Model. Circulation. 2006;113:1424–33. doi: 10.1161/CIRCULATIONAHA.105.584102. - DOI - PubMed

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