Key result
ICM-based risk score predicts worsening HF events with ~68% sensitivity.
Why the study?
The authors tested whether physiological data from sensors in a minimally invasive subcutaneous insertable cardiac monitor could create a dynamic risk score to predict worsening heart failure events across the ejection fraction spectrum.
Does a multiparameter heart failure risk score derived from an insertable cardiac monitor predict worsening heart failure events in patients with NYHA class II/III heart failure?
Observational (n=136)
Double-blind
Does a multiparameter heart failure risk score derived from an insertable cardiac monitor predict worsening heart failure events in patients with NYHA class II/III heart failure?
A novel multiparameter heart failure risk score derived from a subcutaneous insertable cardiac monitor can predict worsening heart failure events with moderate sensitivity and provide early warning.
ICM-based HF risk score may enable early detection in NYHA II/III patients; hypothesis-generating and requires prospective outcome trials before adoption.
BACKGROUND: The authors tested the hypothesis that physiological information from sensors within a minimally invasive, subcutaneous, insertable cardiac monitor (ICM) could be used to develop an ambulatory heart failure risk score (HFRS) to accurately identify heart failure (HF) patients, across the ejection fraction spectrum, at high risk of an impending worsening heart failure event (HFE). OBJECTIVES: The purpose of this study was to examine performance of ICM-based, multiparameter, dynamic HFRS to predict HFEs in patients with NYHA functional class II/III HF. METHODS: In 2 observational cohorts, HF patients were implanted with an ICM; subcutaneous impedance, respiratory rate, heart rate and variability, atrial fibrillation burden, ventricular rate during atrial fibrillation, and activity duration were combined into an HFRS to identify the probability of HFE within 30 days. Patients and providers were blinded to the data. HFRS sensitivity and unexplained detection rate were defined in 2 independent patient population data sets. HFEs were defined as hospitalization, observation unit, or emergency department visit with a primary diagnosis of HF, and intravenous diuretic treatment. RESULTS: First data set (development): 42 patients had 19 HFE; second data set (validation): 94 patients had 19 HFE (mean age 66 ± 11 years, 63% men, 50% with LVEF ≥40%, 80% NYHA functional class III). Using a high-risk threshold = 7.5%, development and validation data sets: sensitivity was 73.7% and 68.4%; unexplained detection rate of 1.4 and 1.5 per patient-year; median 47 and 64 days early warning before HFE. CONCLUSIONS: ICM-HFRS provides a multiparameter, integrated diagnostic method with the ability to identify when HF patients are at increased risk of heart failure events. (Reveal LINQ Evaluation of Fluid [REEF]; NCT02275923, Reveal LINQ Heart Failure [LINQ HF]; NCT02758301, Algorithm Using LINQ Sensors for Evaluation and Treatment of Heart Failure [ALLEVIATE-HF]; NCT04452149).
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Zile et al. (2023) conducted an observational in Heart failure (NYHA functional class II/III) (n=136). Insertable cardiac monitor-based heart failure risk score (HFRS) was evaluated on Sensitivity and unexplained detection rate for predicting worsening heart failure events. An insertable cardiac monitor-based heart failure risk score predicted worsening heart failure events with 68.4% sensitivity and 1.5 unexplained detections per patient-year in the validation cohort.
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