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A Novel Heart Failure Diagnostic Risk Score Using a Minimally Invasive Subcutaneous Insertable Cardiac Monitor.
JACC. Heart Failure 2023 October 27
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 ± SD 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).
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 ± SD 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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