Clinical Evidence

ClinicalEVIDENCE Newsletter

Newsletter #2 – May 2023

ClinicalEVIDENCE newsletter #2 – May 2023

Welcome to the latest edition of ClinicalEVIDENCE, 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.

In this issue, we focus on HeartLogic tool, a multiparametric approach to heart failure diagnosis that is gaining increased attention in today’s world.

We hope you will find this issue informative and engaging, as we strive to deliver the most up-to-date and relevant content to our readers.

HEARTLOGIC PUBLICATIONS: The three HeartLogicᵀᴹ articles

HeartLogic and All-cause Mortality Prediction

More recently, Dr. D’Onofrio et al.¹ has been published  on Heart Rhythm Journal a new analysis, accompanied by a positive editorial article by Dr. Varma², titled:

 “Predicting all-cause mortality by means of a multisensory implantable defibrillator algorithm for heart failure monitoring” 

Predicting mortality in an HF population is challenging as heart failure has multiple etiologies with different risk profiles: numerous clinical variables and investigations are needed in order to obtain prognostic information. The use of multiparametric algorithm could explore a large variety of parameters that are objective measurements of the underlying pathophysiology associated with worsening HF.

Methods and Endpoint:

Heart Failure (HF) patients receiving an ICD or CRT –D device equipped with the HeartLogic algorithm have been enrolled in the Registry and followed up in accordance with the standard practice of the centres. 

This analysis  evaluated whether remotely monitored data from HeartLogic could be used to identify patients at high risk for mortality.

The study endpoint  was death due to any cause.


The HeartLogic was activated in 568 patients and during a median follow-up of 26 months 1200 HeartLogic alerts were recorded in 370 (65%) patients. 

Overall, the time IN the alert state was:

  • 13% of the total observation period in general population
  • 20% of the follow-up period of the 370 patients with alerts. 

During follow-up, 55 patients died (46 in the group with alerts), 33 deaths were from cardiovascular causes.

Rate of death was 0.25 per patient/year with the HeartLogic in the alert state and 0.02 per patient/year OUT of the alert state with an incidence rate ratio of 13.72 (95% CI 7.62–25.60; P < 0.001)

  • Experiencing any alert episode was associated with a substantially increased risk of death [hazard ratio (HR): 2.08, 95% confidence interval (CI): 1.16–3.73, P = 0.039].
  • A time IN alert ≥20% was associated with death (HR: 4.07, 95%CI: 2.19-7.54, p<0.001).

According to the time-dependent Cox Model, the IN-alert state was associated with the death due to any cause, after adjustment for age, ischemic cardiomyopathy, chronic kidney disease, atrial fibrillation on implantation: HR: 9.18, 95% CI:5.27-15.99, p<0.001.  (Fig. 3).

Figure 1 Fig.3: Time-dependent Cox model: association between IN-alert state and death for any cause, after adjusting for clinical variables.


  • This analysis demonstrated the ability of HeartLogic to identify subject at high risk for all-cause death.
  • Among patients who had experienced at least 1 HeartLogic alert, a 2-fold higher risk of death was observed and a 4-fold higher risk among patients who had spent .20% of their follow-up period IN the alert state.
  • The rate of fatal events was substantially higher with the HeartLogic IN the alert state, and the association between the alert state and mortality was demonstrated.

Key take-away:

  • The HeartLogic index state identifies periods of significantly increased risk of all-cause death in HF patients.
  • The occurrence of at least 1 HeartLogic alert and a time-IN alert ≥ 20% were significantly associated with mortality due to any cause.

 “...the ability to identify a patient at risk of impending demise, whether HF-related, is valuable. It provides the ability to convey expectations and outline management plans. This study by D’Onofrio et al is the first to show that this may be possible” (Dr. Varma²).

HeartLogic and Ventricular Therapy

Among heart failure patients, ventricular arrhythmias are very common and represent a major cause of mortality , but their prediction is very challenging with available tools in implantable cardioverter defibrillator.

In the last HeartLogic original article  “Implantable defibrillator-detected heart failure status predicts ventricular tachyarrhythmias” published on Journal of Cardiovascular Electrophysiology, Dr. Compagnucci et al.³, sought to analyze the association between HeartLogic index and the incidence of appropriate ICD therapies.

Methods and Endpoint:

Consecutive HF patients with reduced left ventricular ejection fraction (≤35% at the time of implantation) who had received in accordance with standard indications an ICD or CRT-D device endowed with HeartLogic diagnostic tool and were enrolled in the LATITUDE remote monitoring platform were included in the Registry.

All patients were followed up in accordance with the standard practice of the participating centers. Data on the clinical events occurred were collected in the prospective registry.

The primary endpoint was the first appropriate ICD shock therapy.

The secondary endpoint was the first appropriate ICD therapy (a composite of appropriate anti-tachycardia pacing  - ATP- and shock) for ventricular tachycardia or ventricular fibrillation.


HeartLogic was activated in 568 patients who had received an ICD or CRT-D.

During a media follow-up of 25 months:

  • one or more appropriate ICD shocks were documented in 74 (13%) patients.
  • an appropriate ICD therapy (ATP or shock) for ventricular tachycardia or ventricular  fibrillation  was  delivered  in  122  (21%)  patients.

A HeartLogic index ≥ 16 was more frequently measured during the weeks in which the device delivered appropriate shocks (22%, p = .002) or any appropriate therapies for ventricular tachycardia or ventricular fibrillation (15%, p = .048) than in the remaining weeks (10%). 

Conversely, the rate of both shocks and shocks or ATPs increased with greater weekly  HeartLogic Index values (Figure  4).

Figure 4 Fig.4: Rates of shocks and overall appropriate therapies according to weekly HeartLogic index values

In a time-dependent Cox model, the weekly IN‐alert state was independently associated with appropriate ICD shocks (HR: 2.94, 95% CI:1.73–5.01, p < .001), after correction for age, secondary prevention, and use of CRT. and with appropriate ATP or shocks (HR: 1.72, 95% CI: 1.08–2.73, p = .022).

Figure 5 shows the Kaplan–Meier plot of the time to the first appropriate ICD shock and the time to the first appropriate antitachycardia pacing or shock, in the IN- and OUT-of alert states.

Figure 5
Figure 6

Fig. 5: (A) Kaplan–Meier plot of the time to the first appropriate ICD shock in the IN- and OUT-of-alert states. (B) Kaplan–Meier plot of the time to the first appropriate anti-tachycardia pacing or shock in the IN- and OUT-of-alert states.


  • An association between HeartLogic index values continuously measured by the ICD and the occurrence of appropriate shocks and any appropriate ICD therapies delivered has been observed.
  • In this analysis a three-fold higher risk of appropriate ICD shocks during IN‐alert state weeks,  have been observed. A similar association between IN-alert state and any appropriate ICD therapy was found.

Key take-away:

  • The HeartLogic index and its physiological parameters proved to be sensitive markers of increased risk of ventricular arrhythmias, showing significant changes several weeks before appropriate ventricular device therapy.
  • Patients who experienced worsening HF status measured by HeartLogic index were more likely to receive appropriate ICD therapies (shocks or ATP).
  • Changes in both the HeartLogic index and individual physiological parameters preceded device therapies by approximately one month.

HeartLogic and Apnea Index to predict Atrial Fibrillation Events

Following the first publication in the last year about the association between HeartLogic Index and Atrial Fibrillation events, Dr. Bertini et al.⁴ performed a new analysis about the combination of HeartLogic and Apnea Indexes for the prediction of  Atrial High-Rate Events (AHRE). Indeed, atrial fibrillation (AF) and sleep-disordered breathing (SDB), which include obstructive sleep apnea (SA) and central sleep apnea (CSA), are two highly prevalent clinical conditions especially in heart failure patients.

Combination of an implantable defibrillator multi-sensor heart failure index and an apnea index for the prediction of atrial high-rate events” has been published in January 2023 on Europace Journal  as a clinical research article.

Methods and Endpoint:

Data were prospectively collected from consecutive Heart Failure (HF) patients receiving an ICD or CRT with defibrillator (CRT-D) endowed with the HeartLogic algorithm and the ApneaScan diagnostic feature. Patients were enrolled in the LATITUDE remote monitoring platform and followed up in accordance with the standard practice of the centres.

This analysis explored the association between Respiratory Disturbance Index (RDI), using ApneaScan feature, and the HF status, using HeartLogic Index, and the incidence of AHRE episodes, as surrogate of AF episodes.

The endpoints were: daily AF burden of ≥5minutes, ≥6hours and ≥23hours.


A total of  411 patients were enrolled in this analysis. followed for a median observation period of 26 months (25th–75th percentile: 16–35).

During a median follow-up of 26 months:

  • the time IN-alert HF state was 13% of the total observation period, 
  • the RDI value odes/h (severe SA) during 58% of the observation period

 The AF burdens of:

  • ≥5 minutes/day was documented in 139 (34%) patients
  • ≥6 hours/day was documenwas ≥30 episted in 89 (22%) patients
  • ≥23 hours/day was documented in 68 (17%) patients

The combined distribution of weekly HF index values and RDI values during the follow-up period is reported in Fig. 1.

The red area identifies the combination of HF alert and severe SA conditions and accounts for only 6% of the follow-up period.

Figure 3 Fig. 1: Combined distribution of weekly HeartLogic values and RDI values

The time-dependent Cox model showed that IN-alert HF state was independently associated with AF regardless of the daily burden threshold: hazard ratios from 2.17 for ≥5 min/day to 3.43 for ≥23 h/day (p<0.01), while the occurrence of severe SA was associated only with AF burden ≥5 min/day (hazard ratio 1.55 [95%CI:1.11-2.16], p=0.001). 

Event rates calculated according to the HF alert status (IN vs. OUT) and to the weekly RDI value (≥ vs. <30 episodes/h) and for the combination of HF alert and RDI conditions are reported in Fig. 2: 

  • OUT of HF alert period or low RDI values had low AHRE occurrence rates.
  • IN of HF alert periods, if high RDI values were also recorded (6% of follow-up), the AHRE occurrence was much higher (yellow bars).
Figure 3 Fig. 2: Event rates for HeartLogic alert status and RDI value individually and in combination


  • This analysis demonstrated that the HF status was independently associated with AF regardless of the daily burden, while severe SA was mainly associated with shorter AF episodes. 
  • The coexistence of worsening HF and sleep apnea occurs rarely but is associated with a very high rate of AF occurrence.

Key take-aways:

  • The analysis provided evidence on the use of HeartLogic and ApneaScan ICD indexes for a dynamic AF risk stratification.
  • These two indexes should be considered to integrate clinical assessment of patients for AF stratification, resulting in improved decision-making. 


  1. D'Onofrio A, Vitulano G, Calò et al. Predicting all-cause mortality by means of a multisensor implantable defibrillator algorithm for heart failure monitoring. Heart Rhythm. 2023 Mar 24:S1547-5271(23)00331-4. doi: 10.1016/j.hrthm.2023.03.026
  2. Varma N. Alert Notifications for Impending Patient Demise- Widening the Powers of Automatic Remote Monitoring of Cardiac Implantable Electronic Devices. Heart Rhythm. 2023 Apr 18:S1547-5271(23)02119-7. doi: 10.1016/j.hrthm.2023.04.012 
  3. Compagnucci P, Casella M, Bianchi et al. Implantable defibrillator-detected heart failure status predicts ventricular tachyarrhythmias. J Cardiovasc Electrophysiol. 2023 Mar 30. doi: 10.1111/jce.15898 
  4. Bertini M, Vitali F, D'Onofrio et al. Combination of an implantable defibrillator multi-sensor heart failure index and an apnea index for the prediction of atrial high-rate events. Europace. 2023 Apr 15;25(4):1467-1474. doi: 10.1093/europace/euad052

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