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INTRODUCTION
Chronic heart failure (CHF) is a very important public health problem and is associated with poor outcomes and high rates of death and hospitalization. Acute and repeat episodes of decompensation lead to the progressive deterioration of cardiac performance and are also associated with a worse outcome and progressive multiorgan failure.1 Acute decompensated heart failure (ADHF) is a common disease observed in clinical practice. Its physiopathology is incompletely understood, and its treatment resources are limited.1 Moreover, ADHF remains the main reason for hospital stay in patients over 65 years old and is associated with high mortality and morbidity and mounting financial cost.2
Brain natriuretic peptides (BNP) are released by the ventricles as a response to increased pressure or fluid overload and have been recommended for CHF treatment.3 Cardiac power is an index of cardiac contractility, and the cardiac power index (CPI) is the product of simultaneously measured mean arterial blood pressure and cardiovascular flow; it decreases as pressure and fluid overload increase and has been demonstrated to be the strongest predictor of outcome in ADHF.4

SonR is a hemodynamic sensor able to detect the acceleration of the endocardial wall of the left ventricle. This sensor is encapsulated at the tip of the atrial pacing lead attached to the Platinium SonR CRTD 1841 cardiac resynchronization therapy defibrillator system (Microport CRM Clamart France). The SonR sensor transforms cardiac vibrations into an electric signal, and SonR signals have shown a high correlation with right and left ventricular dP/dT max. SonR is able to detect changes in myocardial contractility, unlike echocardiography.5
The aim of our study was to establish a correlation between SonR system signals and BNP levels in CHF patients with reduced ejection fraction. Increasing levels of BNP, reflecting elevations in pressure and fluid levels, could be matched with a simultaneous decrease in cardiac contractility properties measured by the SonR signal, allowing the use of SonR values as a guide to CHF treatment.

METHODS
The Added Value Changes in Signal SonR in Patients with left ventricular ejection fraction less or equal than 30% (AVCS-SONR) was a pilot, prospective, multicenter, nonrandomized, observational study.

Inclusion criteria: Patients were enrolled if they had ischemic or non-ischemic cardiomyopathy, a left ventricular ejection fraction (LVEF) less than or equal to 30%, and were candidates for a single or double chamber automatic implantable cardioverter defibrillator (AICD) for primary or secondary sudden cardiac death prevention. Implanted AICD had to be the Platinium SonR CRTD 1841 (Microport CRM Clamart) AICD, which was able to detect, measure and record the SonR signal with the left ventricular port unused since cardiac resynchronization therapy (CRT) was not allowed in the study. They should have had at least one hospital admission due to ADHF in the year prior to the inclusion date.

Exclusion criteria: The exclusion criteria included end-stage renal failure in hemodialysis, planned cardiac transplant or CRT, pregnancy, participation in other investigation protocols, and inability or unwillingness to follow the requirements of the protocol for any reason.
The follow-up period was 1 year after the implant, during which an N-terminal pro-brain natriuretic peptide (NT-proBNP) measurement was performed monthly, and the SonR signal value registered on the date as close as possible to the NT-proBNP extraction was obtained from the AICD system. Hospital admission due to ADHF episodes was recorded.
The primary objective of the study was to evaluate the relationship between the SonR signals recorded by the AICD and the paired NT-proBNP values monthly for 1 year. The secondary objectives were to analyze the ADHF episodes and to establish whether the SonR values and NT-proBNP levels registered 1 month prior to the admission date could have an inverse relationship.

Statistical analysis: Continuous variables are presented as the mean ± SD. Discrete variables are presented as percentages. Associations were considered statistically significant when p < 0.05. To explore the linear relationship between NT- proBNP and the paired measurement of SonR values, Spearman’s rank correlation coefficient was used. Using the same approach, we explored the relationship between the natural logarithm of the NT-proBNP levels and SonR measurements. All analyses were performed using SPSS v. 23 (IBM Corp, Armonk, NY, USA).

Research ethics: The local Ethics Committee approved the study protocol, and all patients provided written informed consent to participate.

RESULTS
Patient characteristics
A total of 27 patients were enrolled in the study by 5 Spanish hospitals from April 2018 to July 2019. The indication for AICD was primary prevention in 92.6% of patients, and mean LVEF was 26.3%. Global baseline characteristics are shown in Table 1.

All 27 patients but one provided valid paired data to the global database. We obtained 203 NT- proBNP values and 212 SonR values (62% and 65%, respectively, of the total number of data expected). The missing data could not be retrieved mostly due to logistical and/or technical problems. In addition, two patients died a short time after the implant due to ACHF (one after 48 days and the other 78 days after), and no data could be retrieved from the first one, and only a few data pairs could be retrieved from the second one. There was no relationship between the cause of death and the previous implant procedure. All NT-proBNP levels obtained were collected with the SonR value obtained at the same time. Therefore, a total of 324 pairs or data points were expected, but only 203 (62%) could be retrieved and analyzed. In the data analysis, an inverse correlation between linear NT-proBNP levels and the SonR signals was obtained, R= -0,309 (p< 0.0001). Additionally, an inverse correlation between logarithmic NT pro- BNP levels and corresponding SonR signals was observed, R= -0,410 (p<0.000001). (Fig 1)

There were only 5 ADHF events in 4 patients, leading to death in two patients. One patient had 2 hospital admissions due to ADHF in a short period of time, and the evolution of the SonR values related to both admission dates is shown in Fig 2.

DISCUSSION
We demonstrated in this study that NT-proBNP levels (linear or logarithmic) were inversely correlated with SonR signal values, both with high statistical significance. Congestion is considered the most important cause of hospitalization in patients with CHF and severe left ventricular dysfunction.2 Increases in intracardiac and pulmonary artery pressures are the cause of this congestion and occur several days or even weeks before signs and symptoms of decompensation occur, suggesting that intensifying the current treatment to reduce these pressures may reduce admission risk.6 In the CHAMPION study, the use of the CardioMEMS device enabled significantly less hospitalization in patients with CHF in New York Heart Association (NYHA) class III than usual care, as pressure information given to physicians in the active arm led to improved CHF management in the short (6 months) 7 and long (31 months) term.8 The use of CardioMEMS requires the invasive implant of an expensive device and the continuous collaboration of the patient, who must be trained before taking the measurements daily, under precise conditions and postures.

Other studies that aimed to provide a sensitive and accurate predictor of impending ADHF were published. The MultiSENSE study used a multiparameter-based algorithm (HeartLogic) to detect imminent decompensation and obtained a 70% sensitivity rate, with a very low positive predictive value (11.3%).9
Recent scientific evidence suggests that BNP-guided treatment is useful to reduce the hospitalization rate as a marker of increasing pressure and fluid overload. Once this marker indicates to the physician that the patient’s condition is worsening, even when the symptoms of decompensation are not present, treatment may be adjusted, preventing hospital admission. In the STARS-BNP study, a BNP- based treatment strategy was able to reduce the risk of hospitalization-induced death and stay.10, and in the PROTECT study 11, the NT-proBNP- guided treatment strategy was superior to the standard of care, reducing the event rate, improving quality of life and demonstrating favorable effects on cardiac remodeling. Another study demonstrated an all-cause mortality reduction using BNP-guided treatment in patients aged younger than 75 years.12

Although some conflicting results were published in other studies,13 one systematic meta-analysis demonstrated a reduction in CHF hospitalization but not in total mortality,14 and another meta-analysis, which included the negative results of the GUIDE-IT study,13 found BNP-guided therapy beneficial in reducing CHF admissions and all-cause mortality.15 Based on this information, some guidelines regarding the clinical use of BNP consider that the combination of symptoms, weight gain and BNP concentration would be the best strategy to diagnose the early rise of pressures and fluid overload 16 or even suggest that NT-proBNP levels should be measured regularly in patients with CHF with reduced LVEF to decrease hospitalizations and potentially reduce mortality, mainly in individuals younger than 75 years old.3 This BNP-based treatment strategy requires continuous and regular BNP measurements and may involve disregarding BNP elevations not coincident with the schedule of the measurements, making it difficult to observe the decompensation warning in a timely manner, which is needed to adjust treatment and avoid hospitalization when pressures and fluid overload start to increase and symptoms are not present yet. While it is known that NT-proBNP levels are not linear in cases of ADHF 16 and instead show rapid changes as decompensation progresses or improves, we assessed the relationship between logarithmic NT- proBNP levels and SonR values, with even better results than the evaluation of linear NT-proBNP levels.

The SonR system is a hemodynamic sensor embedded in the tip of the right atrial lead attached to the Platinium SonR CRTD 1841 AICD. This sensor is a detector of left ventricle endocardial wall acceleration, and its signal has been demonstrated to closely correlate with left ventricular dP/dT max, which is currently considered the gold standard for the assessment of myocardial contractility.17 This system was able to show a close correlation with echocardiographic shortening fraction and noninvasive blood pressure and dP/dT max monitoring in patients with severely impaired LV ejection fraction who underwent an isometric stress test. The hemodynamic and echocardiographic parameters correlated very closely with the SonR signal both during rest and postexercise periods.5 Moreover, a clinical benefit in terms of NYHA class was demonstrated in the pilot CLEAR study,18 in which the system was used to automatically adjust the AV and VV delays in cardiac resynchronization pacemaker patients who were compared with a control group with usual adjustments. The SonR system was also used in the long-term follow-up RESPOND-CRT trial 19 with a similar design, in which a significant reduction in HF hospitalization was demonstrated in the SonR group.

In our study, we found an inverse and highly significant correlation between the SonR system signal, which can be used to assess the patient’s myocardial performance, and the NT-proBNP levels measured monthly, which can be used to detect an early decompensation in the CHF patient. As there are evident logistic problems in continuously measuring NT-proBNP levels, it seems logical to identify another correlated measure that can be used to detect early HF decompensation and to allow the physician to intensively treat the patient before the situation worsens, leading to hospital admission. In patients implanted with an AICD featuring the SonR system, these measurements might be used as a surrogate for NT-proBNP measurements, providing a daily assessment of the status of myocardial contractility, which may play an important role in the management of these patients.

LIMITATIONS
This was a pilot study with a small number of patients. There was a significant inverse correlation between SonR signal values and NT-proBNP levels, showing that SonR signals might be considered a surrogate of NT-proBNP levels in CHF patient management, but this should be assessed in a future study with the appropriate design.

CONCLUSIONS
The SonR system values showed an inverse and statistically significant relationship with NT-proBNP levels during a 1-year period. This finding confirms our primary hypothesis that SonR values might be of value in CHF patient management.

Conflicts of Interest Statement: JJGG and JFCC have received fees for lectures and advice from St Jude Medical, Medtronic and Boston Scientific. IFL have received fees for lectures and advice from Abbot and Biotronik. JGF has received fees for lectures and advice from Biotronik, Medtronic and Boston Scientific

Funding Statement: The present study has no funding.

Acknowledgments: We would like to thank Arrhytnet Company for supporting this project with enthusiasm.

REFERENCES
1.    Njoroge JN, Teerlink JR. Pathophysiology and therapeutic approaches to acute decompensated heart failure. Circ Res. 2021;128:1468-1486. DOI: 10.1161/CIRCRESAHA.121.318186

2.    Tinoco Mesquita E, Lagoeiro Jorge AJ, Morais Rabelo L, Vale Souza C Jr. Understanding hospitalization in patients with heart failure. Int J Cardiovasc Sci. 2017;30:81-90. DOI: 10.5935/2359-4802.20160060

3.    Ezekowitz JA, O Meara E, McDonald MA, et al. 2017 Comprehensive Update of the Canadian Cardiovascular Society guidelines for the management of meart mailure. Can J Cardiol. 2017;33:1342-1433.    DOI: 10.1016/j.cjca.2017.08.022

4.    Cotter G, Moshkovitz Y, Kaluskia E, et al. The role of cardiac power and systemic vascular resistance in the pathophysiology and diagnosis of patients with acute congestive heart failure. Eur J Heart Fail. 2003;5:443-451. DOI: 10.1016/s1388-9842(03)00100-4

5.    Sacchi S, Paoletti Perini A, Attana P, et al. Assessment of myocardial montractility by SonR sensor in patients undergoing cardiac resynchronization therapy. PACE 2016;39:268- 274. DOI: 10.1111/pace.12795

6.    Zile MR, Bennett TD, Sutton MSJ, et al. Transition from chronic compensated to acute decompensated heart failure pathophysiological insights obtained from continuous monitoring of intracardiac pressures. Circulation. 2008;118:1433-1441. DOI: 10.1161/CIRCULATIONAHA.108.783910

7.    Abraham WT, Adamson PB, Bourge RC, et al. Wireless pulmonary artery haemodynamic monitoring in chronic heart failure: a randomised controlled trial. Lancet. 2011; 377: 658-66. DOI: 10.1016/S0140-6736(11)60101-3

8.    Abraham WT, Stevenson LW, Bourge RC, Lindenfeld JA, Bauman JG, Adamson PB. Sustained efficacy of pulmonary artery pressure to guide adjustment of chronic heart failure therapy: complete follow-up results from the CHAMPION randomised trial. Lancet. 2016;387:453-61. DOI: 10.1016/S0140-6736(15)00723-0

9.    Boehmer JP, Hariharan R, Devecchi FG, et al. Multisensor algorithm predicts heart failure events in patients with implanted devices. Results from the MultiSENSE study. J Am Coll Cardiol HF. 2017;5:216-25. DOI: 10.1016/j.jchf.2016.12.011

10.    Jourdain P, Jondeau G, Funck F, et al. Plasma brain    natriuretic    peptide-guided    therapy    to improve outcome in heart failure. The STARS-BNP Multicenter Study. J Am Coll Cardiol. 2007;49:1733-1739.
DOI: 10.1016/j.jacc.2006.10.081

11.    Januzzi JL, Rehman SU, Mohammed AA, et al. Use of amino-terminal pro–B-type natriuretic peptide to guide outpatient therapy of patients with chronic left ventricular systolic dysfunction. J Am Coll Cardiol. 2011;58:1881-1889.
DOI: 10.1016/j.jacc.2011.03.072

12.    Troughton RW, Frampton CM, Brunner-La Rocca HP, et al. Effect of B-type natriuretic peptide- guided treatment of chronic heart failure on total mortality and hospitalization: an individual patient metaanalysis. Eur Heart J. 2014;35:1559-1567. DOI: 10.1093/eurheartj/ehu090

13.    Felker GM, Anstrom KJ, Adams KF, et al. Effect of natriuretic peptide-guided therapy on hospitalization or cardiovascular mortality in high- risk patients with heart failure and reduced ejection fraction. A randomized clinical trial. JAMA. 2017;318:713-720.
DOI: 10.1001/jama.2017.10565

14.    Pufulete P, Maishman R, Dabner L, et al. B-type natriuretic peptide-guided therapy for heart failure (HF): a systematic review and meta-analysis of individual participant data (IPD) and aggregate data. Syst Rev. 2018;7:112. DOI: 10.1186/s13643-018-0776-8

15.    McLellan J, Bankhead CR, Oke JL, Hobbs FDR, Taylor CJ, Perera R. Natriuretic peptide-guided treatment for heart failure: a systematic review and meta-analysis. BMJ Evid Based Med. 2020;25:33- 37. DOI: 10.1136/bmjebm-2019-111208

16.    Mueller C, McDonald K, de Boer RA, et al. Heart Failure Association of the European Society of Cardiology practical guidance on the use of natriuretic peptide concentrations. Eur J Heart Fail. 2019;21:715-731. DOI: 10.1002/ejhf.1494

17.    Sacchi S, Contardi D, Pieragnoli P, Ricciardi G, Giomi A, Padeletti L. Hemodynamic Sensor in Cardiac Implantable Electric Devices: The cendocardial acceleration technology. J Healthc Eng. 2013;4:453-464.cDOI: 10.1260/2040-2295.4.4.453

18.    Ritter P, Delnoy PP, Padeletti L, et al. A randomized pilot study of optimization of cardiac resynchronization therapy in sinus rhythm patients using a peak endocardial acceleration sensor vs. standard methods. Europace. 2012;14:1324- 1333. DOI: 10.1093/europace/eus059

19.    Brugada J, Delnoy PP, Brachmann J, et al. Contractility sensor-guided optimization of cardiac resynchronization therapy: results from the RESPOND-CRT trial. Eur Heart J. 2017;38:730- 738. DOI: 10.1093/eurheartj/ehw526