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  • Title: Diagnostic power and healthcare resource consumption of a dedicated workflow algorithm designed to manage thoracic impedance alerts in heart failure patients by remote monitoring.
    Author: Ricci RP, Morichelli L, Porfili A, Quarta L, Sassi A.
    Journal: J Cardiovasc Med (Hagerstown); 2018 Mar; 19(3):105-112. PubMed ID: 29283915.
    Abstract:
    PURPOSE: Modern cardiac implantable devices provide diagnostic information on several physiological variables which are associated with worsening heart failure, creating an opportunity for early intervention to prevent heart failure symptoms and hospitalizations. We evaluated diagnostic accuracy and workload of a remote monitoring (RM) workflow algorithm which leverages intrathoracic impedance and other device diagnostics. METHODS: In our RM workflow a team of expert nurses was responsible for continuity of care, direct relationship with patients and implementation of a specific protocol to evaluate RM alerts and to limit unnecessary resource consumption. Each patient was univocally assigned to a reference nurse. End points were diagnostic accuracy, healthcare utilization, defined as any hospital admission, and actionability of alerts, defined as medication change or other clinical action. RESULTS: One-hundred twenty-six consecutive patients with implantable cardioverter defibrillator/cardiac resynchronization therapy defibrillator were followed for a median time of 23 months. Out of 2176 remote transmissions, 893 (41%) in 111 patients (88.1%) showed clinically relevant events triggered by 574 alerts [2.2 (95% confidence interval = 2.0-2.4) per patient per year]. Among 309 alerts with intrathoracic impedance crossing, heart failure deterioration was confirmed in 116 (37.5%). Clinical actions followed 76/116 (65.5%) true heart failure alerts and 17/193 (8.8%) false-positive alerts (P < 0.001). In particular, drug therapy change followed 72/116 (62.1%) true heart failure alerts and 15/193 (7.8%) false-positive alerts (P < 0.001). Healthcare utilization occurred in 65.5% true heart failure alerts and in 24.9% false-positive alerts (P < 0.001). CONCLUSION: A dedicated workflow algorithm results in more focused clinical surveillance leading to prompt detection and treatment of acute heart failure events without wasting healthcare resource.
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