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.

BIOTRONIK SE and Co KG Berlin Germany

Cardiology Unidade Local de Saúde de Santa Maria EPE Lisboa Portugal

Ceske Budejovice Hospital University of South Bohemia in Ceske Budejovice Ceske Budejovice Czech Republic

CHU Lille Lille University Hospital Center Lille France

Department of Cardiology Tokyo Women's Medical University Tokyo Japan

Unità Operativa di Elettrofisiologia Studio e Terapia delle Aritmie Monaldi Hospital Naples Italy

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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. PubMed DOI PMC

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. PubMed DOI PMC

Ziaeian B, Fonarow GC. Epidemiology and aetiology of heart failure. Nat Rev Cardiol. 2016;13:368–78. doi: 10.1038/nrcardio.2016.25. PubMed DOI PMC

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. PubMed DOI PMC

Levy WC, Mozaffarian D, Linker DT, et al. The Seattle Heart Failure Model. Circulation. 2006;113:1424–33. doi: 10.1161/CIRCULATIONAHA.105.584102. PubMed DOI

Hindricks G, Theuns DA, Bar-Lev D, et al. Ability to remotely monitor atrial high-rate episodes using a single-chamber implantable cardioverter-defibrillator with a floating atrial sensing dipole. Europace. 2023;25:euad061. doi: 10.1093/europace/euad061. PubMed DOI PMC

Botto GL, Sinagra G, Bulava A, et al. Predicting worsening heart failure hospitalizations in patients with implantable cardioverter defibrillators: is it all about alerts? A pooled analysis of nine trials. Europace. 2024;26:euae032. doi: 10.1093/europace/euae032. PubMed DOI PMC

McDonagh TA, Metra M, Adamo M, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. 2021;42:3599–726. doi: 10.1093/eurheartj/ehab368. PubMed DOI

Lindberg F, Lund LH, Benson L, et al. Trajectories in New York Heart Association functional class in heart failure across the ejection fraction spectrum: data from the Swedish Heart Failure Registry. Eur J Heart Fail. 2022;24:2093–104. doi: 10.1002/ejhf.2644. PubMed DOI PMC

Zeitler EP, Austin AM, Leggett CG, et al. Complications and Mortality Following CRT-D Versus ICD Implants in Older Medicare Beneficiaries With Heart Failure. JACC: Heart Failure . 2022;10:147–57. doi: 10.1016/j.jchf.2021.10.012. PubMed DOI

Martens P, Nijst P, Verbrugge FH, et al. Profound differences in prognostic impact of left ventricular reverse remodeling after cardiac resynchronization therapy relate to heart failure etiology. Heart Rhythm. 2018;15:130–6. doi: 10.1016/j.hrthm.2017.08.021. PubMed DOI

Leyva F, Qiu T, Zegard A, et al. Sex-Specific Differences in Survival and Heart Failure Hospitalization After Cardiac Resynchronization Therapy With or Without Defibrillation. J Am Heart Assoc. 2019;8:e013485. doi: 10.1161/JAHA.119.013485. PubMed DOI PMC

Schmitt J, Wenzel B, Brüsehaber B, et al. Impact of lockdown during COVID‐19 pandemic on physical activity and arrhythmia burden in heart failure patients. Pacing Clinical Electrophis . 2022;45:471–80. doi: 10.1111/pace.14443. PubMed DOI

Klein C, Kouakam C, Lazarus A, et al. Comprehensive vs. standard remote monitoring of cardiac resynchronization devices in heart failure patients: results of the ECOST-CRT study. Europace. 2024;26:3599. doi: 10.1093/europace/euae233. PubMed DOI PMC

Hindricks G, Taborsky M, Glikson M, et al. Implant-based multiparameter telemonitoring of patients with heart failure (IN-TIME): a randomised controlled trial. The Lancet. 2014;384:583–90. doi: 10.1016/S0140-6736(14)61176-4. PubMed DOI

Varma N, Love CJ, Michalski J, et al. Alert-Based ICD Follow-Up. JACC: Clinical Electrophysiology. 2021;7:976–87. doi: 10.1016/j.jacep.2021.01.008. PubMed DOI

Al-Zaiti SS, Alghwiri AA, Hu X, et al. A clinician’s guide to understanding and critically appraising machine learning studies: a checklist for Ruling Out Bias Using Standard Tools in Machine Learning (ROBUST-ML) Eur Heart J Digit Health . 2022;3:125–40. doi: 10.1093/ehjdh/ztac016. PubMed DOI PMC

Averbuch T, Sullivan K, Sauer A, et al. Applications of artificial intelligence and machine learning in heart failure. Eur Heart J Digit Health . 2022;3:311–22. doi: 10.1093/ehjdh/ztac025. PubMed DOI PMC

Boehmer JP, Hariharan R, Devecchi FG, et al. A Multisensor Algorithm Predicts Heart Failure Events in Patients With Implanted Devices. JACC: Heart Failure . 2017;5:216–25. doi: 10.1016/j.jchf.2016.12.011. PubMed DOI

White-Williams C, Rossi LP, Bittner VA, et al. Addressing Social Determinants of Health in the Care of Patients With Heart Failure: A Scientific Statement From the American Heart Association. Circulation. 2020;141:e841–63. doi: 10.1161/CIR.0000000000000767. PubMed DOI

Metra M, Tomasoni D, Adamo M, et al. Worsening of chronic heart failure: definition, epidemiology, management and prevention. A clinical consensus statement by the Heart Failure Association of the European Society of Cardiology. European J of Heart Fail. 2023;25:776–91. doi: 10.1002/ejhf.2874. PubMed DOI

Malik A, Gill GS, Lodhi FK, et al. Prior Heart Failure Hospitalization and Outcomes in Patients with Heart Failure with Preserved and Reduced Ejection Fraction. Am J Med. 2020;133:84–94. doi: 10.1016/j.amjmed.2019.06.040. PubMed DOI PMC

Butler J, Yang M, Manzi MA, et al. Clinical Course of Patients With Worsening Heart Failure With Reduced Ejection Fraction. J Am Coll Cardiol. 2019;73:935–44. doi: 10.1016/j.jacc.2018.11.049. PubMed DOI

Zanotto G, Capucci A. HeartInsight: from SELENE HF to implementation in clinical practice. Eur Heart J Suppl. 2023;25:C337–43. doi: 10.1093/eurheartjsupp/suad030. PubMed DOI PMC