Clinical Evidence

ClinicalEVIDENCE Newsletter

Newsletter #4 – September 2023

ClinicalEVIDENCE Newsletter 2023 #4 – September 2023

Welcome to the ClinicalEVIDENCE newsletter, your go to source for the latest clinical data on heart diseases, diagnostics, and monitoring.

Our aim is to provide you with cutting-edge information that will keep you up-to-date on the most recent advancements in the field of heart failure management.

to have the opportunity to share the key messages of the latest HeartLogicᵀᴹ and Remote Monitoring publications.

We will also provide you with the highlights of the abstracts presented at the most important European Congress of Cardiology held in Amsterdam last August.

HeartLogicᵀᴹ and Remote Monitoring publications & abstracts from ESC Congress 

Remote Multiparametric Monitoring Publication

The multiparametric evaluation is consolidating as a standard in the clinical practice for the heart failure (HF) patient monitoring. Many papers have been published about the reliability of this approach to reduce the HF worsening events.

Prof. Boriani et al. published on European Journal of Internal Medicine an interesting review about recent data on multiparametric monitoring of HF patients through cardiac implantable electronic devices (CIEDs).

“Remote multiparametric monitoring and management of heart failure patients through cardiac implantable electronic devices”

Authors explored a large amount of published data related to the multiparametric management of HF patients through CIEDs and provided interesting suggestions on timing and management of alerts to prevent HF events.

HF monitoring with CIEDs

A very exhaustive overview of HF diagnostics and multiparametric algorithms implemented on CIEDs and available on the market was reported.

Table 1: Device diagnostics and algorithms for management of patients with heart failure.

The Figure 1 shows the underlying pathophysiological changes occurring in the 30 days preceding an HF event that can be detected by multisensory HF monitoring through CIEDs.

Figure2 Figure 1

As reported in Table 1, HeartLogic is the only HF monitoring system able to monitor heart sounds by mean of an accelerometer located in the CIED.

Clinical significance of CIED alerts and subsequent clinical actions in heart failure patients

The potential clinical value of alerts from a CIED triggered by changes in parameters predictive of HF decompensation depends on several factors, including the accuracy of the alert in the monitored population, the time between the alert and patient contact, patient-related factors like adherence to advice, symptom recognition, and physician decisions.

An analysis of the MANAGE-HF study¹ showed that the response to HeartLogic alerts varied across sites involved in the study.

  • Diuretic adjustment was the main response to alerts, leading to shorter alert durations.
  • Guideline-directed medical treatment, such as ACE inhibitors/ARBs/ARNIs, was not consistently increased.

Authors strongly suggested to consider disease-modifying agents like ARNIs, ACE inhibitors/ARBs, and SGLT2 inhibitors in response to alerts without overt congestion, rather than increase in diuretics. Indeed a close follow-up and an intensive treatment of rapid up-titration of medical therapy have been demonstrated to reduce the HF symptoms, HF readmission and all-cause death.

Data from the MultiSENSE study² indicated that the time between an alert and an HF event should be used to provide a targeted decisions to prevent the event. 

Flowchart for decision making in CIED patients under remote monitoring

The challenge for physicians is to take the appropriate decision at the right time in response to the HF alerts.

Patient data and alerts gathered through CIEDs are remotely transmitted to a platform that can be accessed by the remote monitoring team. Authors suggested that after accurate data revision and interpretation, the physician should contact the patient and perform a remote evaluation.

Figure2 figure 2

In the Figure 2 authors proposed a list of parameters to evaluate at the patient contact after an HF alert.

Multidisciplinary team management

An effective remote monitoring of CIEDs in clinical practice needs specific organization models and collaboration among different healthcare professionals in the multidisciplinary care of HF patients in order to decrease mortality and hospitalizations and to improve the quality of life.

A range of healthcare professionals, including cardiologists, HF specialists, electrophysiologists, allied professionals (such as HF/electrophysiology nurses, cardiology technicians, etc.), and potentially general practitioners, should be involved in the remote management of HF patients.

A multidisciplinary approach to patient management should adhere to local regulations and available resources.

Authors proposed that once an actionable alert is identified by physician, specialized nurse or cardiology technician, it depends on the center organization, an electrophysiologist or cardiologist should be informed.

Figure3 figure 3

Figure 3 presents a proposed flowchart outlining potential scenarios and steps for informed decision-making in HF patients with CIEDs. This approach aims to address both device-related and patient-related issues, in order to decrease hospitalizations and improve outcomes.

The collaboration with general practitioner might play a crucial role, particularly for the initial in-person patient evaluation after a persistent alert (>2 weeks) and possibly for prescribing laboratory tests, investigations (like echocardiograms or thoracic echography), or changes in medication.

Future steps

The unique randomized controlled trial³ available showed that at 1 year patients in the telemonitoring arm had less odds of worsening of a composite of all-cause death, overnight hospital admission for HF, change in NYHA class, and change in patient global self-assessment (odds ratio 0.63, 95% CI 0.43–0.90).

On the other hand, some studies showed remote monitoring had neutral effects on hard outcomes as compared to usual care.
Even if sensitivity of multiparametric CIED alerts in predicting HF events is high (specificity is still suboptimal), current HF guidelines do not grant a high level of recommendation for remote monitoring of HF patients (Class IIb, level of evidence B).

The cost-effectiveness of remote monitoring of CIED in HF patients is still unclear.
For these reasons, there is need to carry out studies evaluating the effectiveness of physician’s decision making on the basis of remote monitoring alerts, the implementation and organization of remote monitoring  service and the cost-effectiveness analysis.

Key Messages

  • In the absence of clinical signs of congestion, decision-making in reaction to an [multiparametric] alert […] should strongly consider the possibility of escalating disease-modifying agents, such as ARNI, ACEi/ARB, and sodium-glucose cotransporter-2 (SGLT2) inhibitors, rather than increase in diuretics.”
  • “The time between an alert and the occurrence of an HF event, frequently leading to HF hospitalization, is a time that should be characterized by a decision making targeted to avoid the event.”
  • “It will be of great importance to assess in the next future if up-titration of optimized medical therapy, found effective in patients with overt, clinically manifest HF, will be beneficial even when HF is in a pre-clinical stage, as it may happen when an alert is derived from multiparametric HF monitoring in the absence of overt HF.”

HeartLogic Review Publication

As known the early detection of HF worsening may allow outpatient treatment before an overt decompensation event and reduce HF hospitalizations and related costs. The HeartLogic is proven to be effective in predicting the risk of incipient HF decompensation.
In this context Dr. Mariani et al. published on European Heart Journal Supplements a review mainly focused on the economic effects and the organizational revolution associated with the use of HeartLogic index:

“HeartLogic™: real-world data—efficiency, resource consumption, and workflow optimization”

In order to optimize HF management and reduce HF hospitalizations, several telemonitoring (TM) strategies and remote monitoring of CIEDs devices have been implemented in the last decade, with a drastic adoption acceleration during the COVID-19 pandemics to follow-up patients maintaining social distancing imposed by the authorities. The implementation of multiparametric algorithm on CIEDs, as HeartLogic, to detect patients at higher risk of HF decompensation increased the importance of CIEDs remote monitoring in clinical practice.

The HeartLogic Index details

Authors reported an exhaustive description of HeartLogic pathophysiological rationale and functioning. For each sensors included in the HeartLogic index (first heart sound (S1), third heart sound (S3), S3/S1 ratio, thoracic impedance, respiratory rate and tidal volume, night heart rate, and patient activity level) the authors explained the relation with HF pathophysiology and many details about the calibration and functioning.

The results of MultiSENSE and sub-analysis have been report highlighting the high sensitivity and specificity and the prognostic value of the index that was independent from the value of NT-proBNP.

The HeartLogic Index: real-world data and ongoing studies

This section reports an excursus of the main real-data published in the last years. All these studies confirmed the results of the MultiSENSE study, as reported in the Table 1.

  • An alert-based remote monitoring was found as effective as in-office management in reducing HF event probability (HR 0.99, 95% CI 0.37–2.68, P-value 0.993), underscoring the possibility of managing HF patients without increasing the clinic workload in terms of in-person visits and of scheduled remote transmissions review.⁴
  • The in-alert state was associated with a 24-fold increased risk of HF events as compared with an out-of-alert state in a multi-variate model adjusted for potential confounders as AF or chronic kidney disease (HR 24.53, 95% CI 8.55–70.38, P-value<0.001).⁴
  • Patients who reported symptoms at the time of HeartLogic™ threshold crossing had a five-fold increased risk of HF events as compared with patients not reporting symptoms (HR 5.23, 95% CI 1.98–13.83, P-value<0.001).⁴
  • The total number of HF hospitalizations significantly declined from the pre- activation period to the post-activation period (P-value 0.003), and also the number of  patients hospitalized for HF decompensation in the pre-activation period was significantly higher than patients hospitalized for HF in the post-activation period (P-value 0.005).⁵

The HeartLogic™ index: economic impact, resource consumption, and workflow optimization

Despite the use of remote monitoring is recommended by the latest international guidelines, it is still underused in clinical practice due to the lack of reimbursement and the need of changes in hospital organization.

Some studies (TARIFF⁶ and MORE-CARE⁷) demonstrated a significant reduction in the mean cost, mainly driven by the reduction of composite endpoint of healthcare resource utilization for HF patients followed by remote follow-up as compare with patients followed by in-person visits alone.

The analysis of Treskes et al⁵ of the resource utilization demonstrated a substantial decrease in overall healthcare costs with the activation of the HeartLogic™ algorithm in clinical practice. A significant reductions in average total costs, average hospitalization costs, daily hospital costs, and medical imaging expenses per patient was observed. 

The organizational model for remote monitoring endorsed by current guidelines, the ‘primary nursing model’ is based on the dynamic interaction between a nurse/technician and a physician. (Figure 1)

The nursing model for remote monitoring was evaluated in the HomeGuide Registry⁸ confirming that remote monitoring was highly effective in detecting and managing clinical events in CIED patients with low resources consumption.

Figure2 Figure 1: Workflow organization for remote management and follow-up of heart failure patients using the HeartLogic™ algorithm.

The crucial aspect of heart failure patients managements is the multi-disciplinary team: well-trained nurses working closely with heart failure specialists allow proactive response to alerts, preventing heart failure events. The HeartLogic™ daily check, is well aligned with this structure identifying high-risk patients and facilitationg the allocation of resources.

Key Messages

  • The HeartLogic algorithm has shown good performance even in daily clinical practice.
  • The HeartLogic seems a useful tool in identifying patients at increased risk of worsening HF, allowing time-effective actions to avoid HF event and hospitalization.
  • The alert-based remote system seems associated with reduced health economic costs without increasing workload because it allows to redistribute resources from low-risk patients to high- risk patients. 

News from ESC Congress

The ESC Congress, one of the most important European congress of cardiology, took place in Amsterdam from 25 to 28 August.

The congress covered topics across the whole of cardiovascular medicine; on the one hand presenting and discussing the latest scientific findings and on the other, providing in-depth clinical teaching and practice-changing education to 30.000 attendees.

Specifically 3 abstracts were presented about HeartLogic: 1 oral presentation and 2 moderated posters.

News from ESC Congress #1 – Oral presentation

Prof. Boriani (Modena - Italy) held a great presentation titled:

“Performance of a multisensor implantable defibrillator algorithm for HF monitoring in the presence of AF”

This analysis evaluated and compared the performance of the HeartLogic algorithm during sinus rhythm and during long-lasting atrial arrhythmias episodes.


  • Rate of HeartLogic alerts.
  • Rate of HF hospitalizations.
  • Incidence rate ratio (IRR) of HF hospitalizations between IN alert and OUT of alert periods.


568 patients were enrolled in 26 Italian centers with a median follow-up of 26 months.

Matched groups were identified:

  • AHRE group: 53 patients who experienced an AHRE burden ≥20 hours/day in addition to an AHRE burden <1 hour/day for ≥30 consecutive days.
  • Non-AHRE group: 106 patients with an AHRE burden <1 hour/day.
  • Patients received more alerts during long-lasting periods of AHRE.
Figure5 Figure 1
  • The ability of the algorithm to identify increased risk of HF events was confirmed during AHRE, despite a lower IN/OUT-of-alert IRR in comparison with non-AHRE periods and non-AHRE patients.
Figure5 Figure 2

Key Messages

  • In the specific setting of atrial arrhythmias, the IN- / OUT-of-alert state was able to identify periods when patients were at increased risk of HF hospitalizations.
  • The risk stratification ability of the algorithm seemed to persist during AHRE periods, but the incidence rate ratio was higher during periods with minimal/no AHRE and in non-AHRE patients.

News from ESC Congress #2 – Poster session

“A multisensory algorithm for detection of upcoming congestion in chronic heart failure patients”

has been presented by Dr. Feijen (Leiden University Medical Center -- Netherlands (The) ) as Moderated Poster.

The aim of this analysis is to evaluate the performance of the HeartLogic algorithm in a real-world heart failure population.

An alert was considered true positive (≥2 signs/symptoms of congestion were observed at the time of alert) or be false positive (≤1 signs/symptoms). Without an alert a patient was either true negative (≤1 signs/symptoms) or false negative (≥2 signs/symptoms).

139 patients were included in the analysis and during a median follow-up period of 2.4 years 274 usable HeartLogic alerts were observed. The performance of the HeartLogic algorithm  in detecting the upcoming congestion was:

  • positive predictive value = 72%
  • negative predictive value = 98%
  • sensitivity = 86%
  • specificity = 88%
Figure2 Figure 1: Comparison between patients with > 65% of true positive alerts and patients without alerts.

Figure 1 shows that patients with more than 65% of true positive HeartLogic™ alerts had a significantly lower left ventricular ejection fraction (Panel a), higher degree of mitral regurgitation (Panel b) and higher levels of NT-proBNP (Panel c), when compared to patients without alerts.

Key messages

  • HeartLogic algorithm adequately detects impending fluid retention in a real-world heart failure population.
  • Patients who benefited most of HeartLogic algorithm had a lower left ventricular ejection fraction, more severe mitral regurgitation and higher NT-proBNP levels at baseline.

Dott. D’Onofrio (Naples – Italy) presented the Moderated Poster:

“Prediction of all-cause mortality using a multisensor implantable defibrillator algorithm for HF monitoring”

This abstract has been already presented in the ClinicalEVIDENCE Newsletter #2_2023 as full-length paper published on Heart Rhythm Journal.

Figure8 Figure 1: Kaplan–Meier analysis of time to death due to any cause. Patients are stratified according to the occurrence of at least one HeartLogic alert (Panel a) and a time IN alert ≥20% (Panel b).

Key Messages

  • The occurrence of at least one HeartLogic alert and a time IN alert ≥ 20% were significantly associated with mortality due to any cause.
  • The rate of fatal events is substantially higher with the HeartLogic™ IN the alert state.


  1. Hernandez AF, Albert NM, Allen LA, et al. Multiple cardiac sensors for management of heart failure (manage-hf) - phase i evaluation of the integration and safety of the heartlogic multisensor algorithm in patients with heart failure. J Card Fail 2022;28(8):1245–54.
  2. Boehmer JP, Hariharan R, Devecchi FG, et al. A Multisensor algorithm predicts heart failure events in patients with implanted devices: results from the multiSENSE study. JACC Heart Fail 2017;5(3):216–25.
  3. Hindricks G, Taborsky M, Glikson M, et al. Implant-based multiparameter telemonitoring of patients with heart failure (IN-TIME): a randomised controlled trial. Lancet 2014;384(9943):583–90.
  4. Calò L, Bianchi V, Ferraioli D, Santini L, Dello Russo A, Carriere C 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
  5. Treskes RW, Beles M, Caputo ML, Cordon A, Biundo E, Maes E, et al. Clinical and economic impact of HeartLogic™ compared with standard care in heart failure patients. ESC Heart Fail 2021;8:1541–1551.
  6. Ricci RP, Vicentini A, D’Onofrio A, Sagone A, Rodaris G, Padeletti L et al. Economic analysis of remote monitoring of cardiac implantable electronic devices: results of the Health Economics Evaluation Registry for Remote Follow-up (TARIFF) study. Heart Rhythm 2017;14:50–57.
  7. Boriani G, Da Costa A, Quesada A, Ricci RP, Favale S, Boscolo G et al. 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.
  8. Ricci RP, Morichelli L, D’Onofrio A, Calò L, Vaccari D, Zanotto G et al. Effectiveness of remote monitoring of DIED in detection and treatment of clinical and device-related cardiovascular events in daily practice: the HomeGuide Registry. Europace 2013;15:970–977.

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