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Is There a Logic in Using Device-Based Algorithms to Prevent Heart Failure Decompensations?

2021; Lippincott Williams & Wilkins; Volume: 14; Issue: 10 Linguagem: Inglês

10.1161/circheartfailure.121.008770

1941-3297

Liviu Klein,

Heart Failure Treatment and Management

HomeCirculation: Heart FailureVol. 14, No. 10Is There a Logic in Using Device-Based Algorithms to Prevent Heart Failure Decompensations? Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBIs There a Logic in Using Device-Based Algorithms to Prevent Heart Failure Decompensations? Liviu Klein, MD, MS Liviu KleinLiviu Klein Correspondence to: Liviu Klein, MD, MS, Division of Cardiology, University of California San Francisco, 505 Parnassus Ave, M1178, San Francisco, CA 94143. Email E-mail Address: [email protected] https://orcid.org/0000-0002-0547-1480 Division of Cardiology, University of California San Francisco. Originally published12 Oct 2021https://doi.org/10.1161/CIRCHEARTFAILURE.121.008770Circulation: Heart Failure. 2021;14:e008770This article is a commentary on the followingMultiparametric Implantable Cardioverter-Defibrillator Algorithm for Heart Failure Risk Stratification and Management: An Analysis in Clinical PracticeOther version(s) of this articleYou are viewing the most recent version of this article. Previous versions: October 12, 2021: Ahead of Print Action is the foundational key to all success.—Pablo PicassoSee Article by Calò et alOne of the greatest challenges in the management of patients with heart failure is forecasting and preventing development of acute worsening. Data from recent large, randomized studies suggest that worsening heart failure events, irrespective of the place of treatment (eg, intravenous diuretics in the office, Emergency Department, or hospital setting), are associated with a 4-fold to 6-fold increase in cardiovascular mortality.1,2 Decades of research using implantable hemodynamic monitors has led to improved understanding of early development of hemodynamic (subclinical) congestion and the gradual transition to a decompensated (clinically manifest) state, thus identifying time periods at risk with opportunities for intervention. Timely intervention has the potential to reduce not only heart failure hospitalizations but also cardiovascular mortality.3In the last 2 decades, implantable and wearable technologies have been developed to capture physiological parameters associated with hemodynamic congestion (Figure). Various heart failure sensors have been added to cardiovascular implantable electronic devices used for sudden cardiac death prevention or biventricular pacing in patients with heart failure and reduced ejection fraction. Heart failure management assisted by sensors has not been shown to improve hospitalization rates compared with usual care.4–7 The explanation for such failures is not completely clear, but a number of possibilities exist. It may be that multiparametric rather than single-feature designs optimally predict heart failure decompensations,4,5 it may be the lack of standardized criteria to define subclinical worsening or an absence of effective approaches to treat such decompensations.6,7 It may be a combination of all of these—device diagnostics are complex to study and impact on patient clinical status difficult to prove.Download figureDownload PowerPointFigure. Assessment of hemodynamic (subclinical) versus clinical congestion in heart failure. Adapted from Adamson12 with permission. Copyright © 2009, Springer Nature.In this context, the current study by Calò et al8 in this issue of Circulation: Heart Failure provides new, practical evidence that proactive management of subclinical alerts derived from a more sophisticated cardiovascular implantable electronic devices algorithm is safe and may lead to a reduction in episodes of decompensation. The investigators enrolled 366 stable outpatients with heart failure and reduced ejection fraction (60% with New York Heart Association functional class I/II symptoms) from 22 clinical practices across Italy, all implanted with cardiovascular implantable electronic devices that had the HeartLogic algorithm enabled. The HeartLogic algorithm incorporates sensor data from first and third heart sounds, thoracic impedance, respiration rate, relative tidal volume, heart rate, and patient activity, assigning point values to abnormal readings. The HeartLogic index value is updated daily, and an alert is issued when the index value crosses an alert threshold. Based on prior studies validated against clinical events and to minimize the false positive rate, the nominal alert threshold has been set at 16 for abnormal threshold crossing (alert) and of 6 for recovery of the signal to a baseline state.9 Based on these nominal values, patients can spend time in alert, from the time the HeartLogic index has crossed 16 until it returns back to 6, or out of alerts when the HeartLogic has not crossed 16. Individual practices downloaded remote transmissions routinely and initiated in-depth review if the HeartLogic index threshold was crossed. The management of an alert state was left to the investigators' discretion, although general guidance was provided. Over a median follow-up of almost a year, 40% of patients experienced at least 1 alert (0.76 alerts per patient-year [PPY]). During the follow-up period, the clinical event rate (heart failure hospitalizations or mortality) was 0.12 PPY, consistent with the relatively low-risk population enrolled (for comparison, the event rate in the PARADIGM HF trial [Prospective Comparison of ARNI with ACEI to Determine Impact on Global Mortality and Morbidity in Heart Failure] was 0.096 PPY).2 Importantly, most events occurred while patients were in an alert state; in fact, the event rate was 0.92 PPY for in alert versus 0.03 PPY for out of alert state. The in-alert event rate is remarkably similar to what was seen in the contemporary CardioMEMS Post-Approval study that enrolled New York Heart Association functional class III patients,10 suggesting that hemodynamic congestion is the great equalizer across the symptomatic spectrum of heart failure.A few other observations are noteworthy from the study by Calò et al: (1) the average time from triggering an alert (HeartLogic threshold crossing) to a clinical event was 29 days, which is comparable to the 34 days seen in the MultiSENSE study (Multisensor Chronic Evaluation in Ambulatory Heart Failure Patients)9 that led to development of the HeartLogic algorithm, providing real-world validation; (2) if untreated, the median time in alert was 42 days, and patients spent ≈11% of the time in an alert state, suggesting that untreated hemodynamic congestion can linger for a long time; (3) <40% of alerts were associated with heart failure symptoms, suggesting once again that waiting for symptoms to initiate treatment may be less successful in preventing clinical events. Indeed, the current study shows that if action was taken in response to an alert, there was a 66% reduction in subsequent clinical events, compared with alerts not acted upon.8 Overall, 43% of alerts triggered an action, the majority being diuretic increases (66%) or adjustments of guideline-directed medical therapy (34%); only a minority of alerts were addressed by patient education without pharmacological intervention. The specific interventions in the study by Calò et al8 explain the difference in outcomes compared with prior studies, where most actions taken involved patient education, or advice to contact the physician office, and where pharmacological interventions were initiated in <20% of alerts.6,7 In addition, although participants in the current study had regular remote downloads of cardiovascular implantable electronic device data, attention was given only to those who had a threshold crossing (alert). This management by exception approach is key to a lean remote management strategy and avoids data overload and overburdening clinician practices that was seen in prior studies.5,6 In the current study, most alerts were managed remotely, with only 10% of all alerts resulting in unscheduled office visits.8 This has important implications in a post–COVID-19 world that has seen dramatic changes in heart failure care delivery.11The current study is limited by its observational, nonrandomized nature. Whether a structured approach to HeartLogic Index alert management will be associated with improved outcomes is being studied in the ongoing prospective, randomized controlled MANAGE-HF trial (Multiple Cardiac Sensors for the Management of Heart Failure; https://www.clinicaltrials.gov; unique identifier: NCT03237858).Strong evidence now exists that hemodynamic congestion precedes clinical congestion and heart failure events by several weeks; we have data that such clinical events not only result in unscheduled office visits or hospitalizations but may contribute to increased cardiovascular mortality; we have a large arsenal of diagnostic devices that accurately detect hemodynamic congestion (Figure). Just as millions of diabetic patients are treated based on home glucose measurements without waiting for development of diabetic ketoacidosis, we as cardiologists must reject the watchful waiting mentality (ie, development of symptoms) and act now to treat heart failure patients at the initiation of congestion and stave off decompensations. As the great master of Cubism once said, "Action is the foundational key to all success."; it is time for us to practice such action.Disclosures Dr Klein has received modest compensation for participating in the Steering Committee of the MANAGE-HF trial (Multiple Cardiac Sensors for the Management of Heart Failure).FootnotesFor Disclosures, see page 1082.The opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.Correspondence to: Liviu Klein, MD, MS, Division of Cardiology, University of California San Francisco, 505 Parnassus Ave, M1178, San Francisco, CA 94143. Email liviu.[email protected]eduReferences1. Okumura N, Jhund PS, Gong J, Lefkowitz MP, Rizkala AR, Rouleau JL, Shi VC, Swedberg K, Zile MR, Solomon SD, et al.; PARADIGM-HF Investigators and Committees*. Importance of clinical worsening of heart failure treated in the outpatient setting: evidence from the prospective comparison of ARNI with ACEI to determine impact on global mortality and morbidity in heart failure trial (PARADIGM-HF).Circulation. 2016; 133:2254–2262. doi: 10.1161/CIRCULATIONAHA.115.020729LinkGoogle Scholar2. Vaduganathan M, Cunningham JW, Claggett BL, Causland FM, Barkoudah E, Finn P, Zannad F, Pfeffer MA, Rizkala AR, Sabarwal S, et al.. Worsening heart failure episodes outside a hospital setting in heart failure with preserved ejection fraction: the PARAGON-HF trial.JACC Heart Fail. 2021; 9:374–382. doi: 10.1016/j.jchf.2021.01.014CrossrefMedlineGoogle Scholar3. Klein L. Treating hemodynamic congestion is the key to prevent heart failure hospitalizations.JACC Heart Fail. 2016; 4:345–347. doi: 10.1016/j.jchf.2016.03.004CrossrefMedlineGoogle Scholar4. van Veldhuisen DJ, Braunschweig F, Conraads V, Ford I, Cowie MR, Jondeau G, Kautzner J, Aguilera RM, Lunati M, Yu CM, et al.; DOT-HF Investigators. Intrathoracic impedance monitoring, audible patient alerts, and outcome in patients with heart failure.Circulation. 2011; 124:1719–1726. doi: 10.1161/CIRCULATIONAHA.111.043042LinkGoogle Scholar5. Böhm M, Drexler H, Oswald H, Rybak K, Bosch R, Butter C, Klein G, Gerritse B, Monteiro J, Israel C, et al.; OptiLink HF Study Investigators. Fluid status telemedicine alerts for heart failure: a randomized controlled trial.Eur Heart J. 2016; 37:3154–3163. doi: 10.1093/eurheartj/ehw099CrossrefMedlineGoogle Scholar6. Morgan JM, Kitt S, Gill J, McComb JM, Ng GA, Raftery J, Roderick P, Seed A, Williams SG, Witte KK, et al.. Remote management of heart failure using implantable electronic devices.Eur Heart J. 2017; 38:2352–2360. doi: 10.1093/eurheartj/ehx227CrossrefMedlineGoogle Scholar7. Boriani G, Da Costa A, Quesada A, Ricci RP, Favale S, Boscolo G, Clementy N, Amori V, Mangoni di S Stefano L, Burri H; MORE-CARE Study Investigators. Effects of remote monitoring on clinical outcomes and use of healthcare resources in heart failure patients with biventricular defibrillators: results of the MORE-CARE multicentre randomized controlled trial.Eur J Heart Fail. 2017; 19:416–425. doi: 10.1002/ejhf.626CrossrefMedlineGoogle Scholar8. Calò L, Bianchi V, Ferraioli D, Santini L, Dello Russo A, Carriere C, Santobuono VE, Andreoli C, La Grecca C, Arena G, et al.. Multiparametric implantable cardioverter-defibrillator algorithm for heart failure risk stratification and management: an analysis in clinical practice.Circ Heart Fail. 2021; 14:e008134. doi: 10.1161/CIRCHEARTFAILURE.120.008134LinkGoogle Scholar9. Boehmer JP, Hariharan R, Devecchi FG, Smith AL, Molon G, Capucci A, An Q, Averina V, Stolen CM, Thakur PH, et al.. A multisensor algorithm predicts heart failure events in patients with implanted devices: results from the MultiSENSE Study.JACC Heart Fail. 2017; 5:216–225. doi: 10.1016/j.jchf.2016.12.011CrossrefMedlineGoogle Scholar10. Shavelle DM, Desai AS, Abraham WT, Bourge RC, Raval N, Rathman LD, Heywood JT, Jermyn RA, Pelzel J, Jonsson OT, et al.; CardioMEMS Post-Approval Study Investigators. Lower rates of heart failure and all-cause hospitalizations during pulmonary artery pressure-guided therapy for ambulatory heart failure: one-year outcomes from the CardioMEMS Post-Approval Study.Circ Heart Fail. 2020; 13:e006863. doi: 10.1161/CIRCHEARTFAILURE.119.006863LinkGoogle Scholar11. Sayer G, Horn EM, Farr MA, Axsom K, Kleet A, Gjerde C, Latif F, Sobol I, Kelley N, Lancet E, et al.. Transition of a large tertiary heart failure program in response to the COVID-19 pandemic: changes that will endure.Circ Heart Fail. 2020; 13:e007516. doi: 10.1161/CIRCHEARTFAILURE.120.007516LinkGoogle Scholar12. Adamson PB. Pathophysiology of the transition from chronic compensated and acute decompensated heart failure: new insights from continuous monitoring devices.Curr Heart Fail Rep. 2009; 6:287–292. doi: 10.1007/s11897-009-0039-zCrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsRelated articlesMultiparametric Implantable Cardioverter-Defibrillator Algorithm for Heart Failure Risk Stratification and Management: An Analysis in Clinical PracticeLeonardo Calò, et al. Circulation: Heart Failure. 2021;14 October 2021Vol 14, Issue 10 Advertisement Article InformationMetrics © 2021 American Heart Association, Inc.https://doi.org/10.1161/CIRCHEARTFAILURE.121.008770PMID: 34634918 Originally publishedOctober 12, 2021 KeywordsEditorialshemodynamicslogicalgorithmsheart failurePDF download Advertisement SubjectsCardiomyopathyCatheter Ablation and Implantable Cardioverter-DefibrillatorHeart Failure