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CorVue algorithm efficacy to predict heart failure in real life: Unnecessary and potentially misleading information?

Julia Anna Palfy1, Juan Benezet-Mazuecos2, Juan Martinez Milla1

  • 1General Cardiology, Cardiology Department of Hospital, Universitario Fundación Jiménez Diaz-Quironsalud, Madrid, Spain.

Pacing and Clinical Electrophysiology : PACE
|June 2, 2018
PubMed

Insights

The CorVue™ algorithm demonstrated low sensitivity in predicting heart failure hospitalizations in real-world patients. Routine activation may provide misleading data, impacting patient care.

Area of Science:

  • Cardiology
  • Biomedical Engineering

Background:

  • Heart failure (HF) hospitalizations significantly impact patient quality of life and incur substantial healthcare costs.
  • Intrathoracic impedance (ITI) monitoring via cardiac devices is explored for predicting HF hospitalizations.
  • The long-term efficacy of the CorVue™ algorithm for HF prediction in a real-world setting remains under-evaluated compared to other algorithms.

Purpose of the Study:

  • To evaluate the long-term efficacy of the CorVue™ algorithm in predicting heart failure (HF) hospitalizations in a real-world cohort.
  • To assess the sensitivity, specificity, and predictive values of CorVue™ in detecting HF events.

Main Methods:

  • The CorVue™ algorithm was activated in implantable cardioverter defibrillator (ICD)/cardiac resynchronization therapy defibrillator (CRT-D) patients to record intrathoracic impedance (ITI) measures.
  • Clinical events (HF requiring treatment/hospitalization) and CorVue™ data were collected quarterly.
  • Appropriate CorVue™ detection was defined as an event occurring within 4 weeks prior to a clinical HF event.

Main Results:

  • Fifty-three patients (26 ICD, 27 CRT-D) were followed for a mean of 17 months.
  • CorVue™ generated 105 ITI drop alarms in 32 patients (60%), with only 6 (true positives) correctly predicting hospitalization.
  • The algorithm had a sensitivity of 24%, specificity of 70%, positive predictive value of 6%, and negative predictive value of 93%, with 76% of clinical HF episodes undetected.

Conclusions:

  • The CorVue™ algorithm exhibits low sensitivity for predicting heart failure events.
  • Routine activation of CorVue™ may generate misleading information for clinicians.
  • Further research is needed to improve the accuracy of impedance-based HF prediction algorithms.
Abstract

Keywords:
CorVueTMheart failureintrathoracic impedance

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