A multiparametric ICD algorithm for heart failure risk stratification and management: an analysis in clinical practice

Funding Acknowledgements

Type of funding sources: None.

Introduction

The HeartLogic algorithm combines multiple implantable cardioverter defibrillator (ICD) sensors to identify patients at risk of heart failure (HF) events.

Purpose

We sought to evaluate the risk stratification ability of this algorithm in clinical practice. We also analyzed the alert management strategies adopted in the study group and their association with the occurrence of HF events.

Methods

The HeartLogic feature was activated in 366 ICD and cardiac resynchronization therapy ICD patients at 22 centers. The HeartLogic algorithm automatically calculates a daily HF index and identifies periods IN or OUT of an alert state on the basis of a configurable threshold (in this analysis set to 16).

Results

The HeartLogic index crossed the threshold value 273 times (0.76 alerts/patient-year) in 150 patients over a median follow-up of 11 months [25-75 percentile: 6-16]. Overall, the time IN the alert state was 11% of the total observation period. Patients experienced 36 HF hospitalizations and 8 patients died of HF (rate: 0.12 events/patient-year) during the observation period. Thirty-five events were associated with the IN alert state (0.92 events/patient-year versus 0.03 events/patient-year in the OUT of alert state). The hazard ratio in the IN/OUT of alert state comparison was (HR: 24.53, 95% CI: 8.55-70.38, p < 0.001), after adjustment for baseline clinical confounders. Alerts followed by clinical actions were associated with a lower rate of HF events (HR: 0.37, 95% CI: 0.14-0.99, p = 0.047). No differences in event rates were observed between in-office and remote alert management. By contrast, verification of HF symptoms during post-alert examination was associated with a higher risk of HF events (HR: 5.23, 95% CI: 1.98-13.83, p < 0.001).

Conclusions

This multiparametric ICD algorithm identifies patients during periods of significantly increased risk of HF events. The rate of HF events seemed lower when clinical actions were undertaken in response to alerts. Extra in-office visits did not seem to be required in order to effectively manage HeartLogic alerts, while post-alert verification of symptoms seemed useful in order to better stratify patients at risk of HF events.

855Performance of a multisensor icd algorithm in heart failure patient management

Funding Acknowledgements

No funding

Background

The HeartLogic index combines data from multiple implantable cardioverter-defibrillator (ICD)-based sensors and has proved to be a sensitive and timely predictor of impending heart failure (HF) decompensation.

Purpose

To describe a multicenter experience of remote HF management of patients who received a HeartLogic-enabled ICD or cardiac resynchronization therapy ICD (CRT-D).

Methods

The HeartLogic feature was activated in 104 patients (76 male, 71 ± 10 years, left ventricular ejection fraction 29 ± 7%). In accordance with a standardized follow-up protocol, remote data reviews and patient phone contacts were performed monthly and at the time of HeartLogic alerts (when the index crossed the nominal threshold value of 16), to assess the patient decompensation status. In-office visits were performed every 6 months or when deemed necessary.

Results

During a median follow-up of 13[11-18] months, 100 HeartLogic alerts were reported (0.82 alerts/pt-year) in 53 patients. 60 HeartLogic alerts were judged clinically meaningful (i.e. associated with worsening of HF or resulted in active clinical actions). Specifically, multiple associated conditions were reported: 45 (75%) symptoms or signs of clinical deterioration of HF, 13 (22%) discontinuations or reductions of prescribed HF therapy, 11 (18%) declines in CRT percentage (with or without new onset atrial fibrillation), 8 (13%) recurrences of previous HF events. For 48 out of 60 alerts the clinician was not previously aware of the condition. Of these, 43 alerts triggered multiple clinical actions. Alert-triggered actions were: 30 (70%) diuretic dosage increases, 15 (35%) other drug adjustments, 6 (14%) HF hospitalizations, 3 (7%) device reprogramming/revisions, 1 (2%) cardioversion, 1 (2%) patient education on therapy adherence. Out of 40 non-clinically meaningful alerts (0.33 alerts/pt-year), 8 (20%) were associated with non-HF therapy changes or interventions, 3 (8%) with pulmonary events, 29 (72%) remained unexplained. The overall number of HF hospitalizations was 16 (rate 0.13 hospitalizations/pt-year). Five HF hospitalizations were not preceded by HeartLogic alert (0.04 hospitalizations/pt-year).

Conclusions

The HeartLogic index provided clinically meaningful information and allowed to remotely identify relevant HF related clinical conditions, with a low rate of unexplained detections and undetected HF events. In this experience, remote monitoring using HeartLogic alerts allowed to drive HF care and take effective clinical actions.

Trauma care in Italy: evidence of in-hospital preventable deaths

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