Clinical Utility of HeartLogic, a Multiparametric Telemonitoring System, in Heart Failure

López‐Azor, Juan Carlos; Torre, Noelia de la; García‐Cosio, María Dolores; Pérez, Pedro Caravaca; Munera, Catalina; Clement, Irene Marco; León, Rocío Cózar; Álvarez‐García, Jesús; Pachón, Marta; Arribas, Fernando; Salguero‐Bodes, Rafael; Delgado-Jiménez, Juan; Bagudá, Javier de Juan

doi:10.15420/cfr.2021.35

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…Their HeartLogic™ multisensor index and alert algorithm provides a sensitive and timely predictor of impending ADHF decompensation. Details of their signal evaluation can be found in [96,97]. Both implant-based prediction technologies have been comprehensively analyzed and evaluated in [76].…”

Section: Prediction Models Using Implantsmentioning

confidence: 99%

Clinical Predictive Modeling of Heart Failure: Domain Description, Models’ Characteristics and Literature Review

Odrobina

2024

Diagnostics

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This study attempts to identify and briefly describe the current directions in applied and theoretical clinical prediction research. Context-rich chronic heart failure syndrome (CHFS) telemedicine provides the medical foundation for this effort. In the chronic stage of heart failure, there are sudden exacerbations of syndromes with subsequent hospitalizations, which are called acute decompensation of heart failure (ADHF). These decompensations are the subject of diagnostic and prognostic predictions. The primary purpose of ADHF predictions is to clarify the current and future health status of patients and subsequently optimize therapeutic responses. We proposed a simplified discrete-state disease model as an attempt at a typical summarization of a medical subject before starting predictive modeling. The study tries also to structure the essential common characteristics of quantitative models in order to understand the issue in an application context. The last part provides an overview of prediction works in the field of CHFS. These three parts provide the reader with a comprehensive view of quantitative clinical predictive modeling in heart failure telemedicine with an emphasis on several key general aspects. The target community is medical researchers seeking to align their clinical studies with prognostic or diagnostic predictive modeling, as well as other predictive researchers. The study was written by a non-medical expert.

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Section: Prediction Models Using Implantsmentioning

confidence: 99%

Clinical Predictive Modeling of Heart Failure: Domain Description, Models’ Characteristics and Literature Review

Odrobina

2024

Diagnostics

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“…A percutaneous approach for the treatment of HF secondary to mitral or tricuspid regurgitation is increasingly performed with the advent of transcatheter edge-to-edge leaflet repair, improving prognosis in selected patients [ 10 , 11 ], and some patients with advanced HF benefit from left ventricular assist devices [ 12 ]. In thismodern era, devices can also be useful tools in the follow-up of patients, for example, to identify those with impending HF decompensation, in order to avoidhospitalizations [ 13 , 14 ].…”

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confidence: 99%

Personalized Therapy and Clinical Outcome for Heart Failure

Aceña

Bagudá

Rincón

2022

JCM

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Prediction of heart failure events based on physiologic sensor data in HINODE defibrillator patients

Nishii,

Sakata,

Murohara

et al. 2024

ESC Heart Failure

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AimsHospitalizations are common in patients with heart failure and are associated with high mortality, readmission and economic burden. Detecting early signs of worsening heart failure may enable earlier intervention and reduce hospitalizations. The HeartLogic algorithm is designed to predict worsening heart failure using diagnostic data from multiple device sensors. The main objective of this analysis was to evaluate the sensitivity of the HeartLogic alert calculation in predicting worsening heart failure events (HFEs). We also evaluated the false positive alert rate (FPR) and compared the incidence of HFEs occurring in a HeartLogic alert state to those occurring out of an alert state.MethodsThe HINODE study enrolled 144 patients (81 ICD and 63 CRT‐D) with device sensor data transmitted via a remote monitoring system. HeartLogic alerts were then retrospectively simulated using relevant sensor data. Clinicians and patients were blinded to calculated alerts. Reported adverse events with HF symptoms were adjudicated and classified by an independent HFE committee. Sensitivity was defined as the ratio of the number of detected usable HFEs (true positives) to the total number of usable HFEs. A false positive alert was defined as an alert with no usable HFE between the alert onset date and the alert recovery date plus 30 days. The patient follow‐up period was categorized as in alert state or out of alert state. The event rate ratio was the HFE rate calculated in alert to out of alert.ResultsThe patient cohort was 79% male and had an average age of 68 ± 12 years. This analysis yielded 244 years of follow‐up data with 73 HFEs from 37 patients. A total of 311 HeartLogic alerts at the nominal threshold (16) occurred across 106 patients providing an alert rate of 1.27 alerts per patient‐year. The HFE rate was 8.4 times greater while in alert compared with out of alert (1.09 vs. 0.13 events per patient‐year; P < 0.001). At the nominal alert threshold, 80.8% of HFEs were detected by a HeartLogic alert [95% confidence interval (CI): 69.9%–89.1%]. The median time from first true positive alert to an adjudicated clinical HFE was 53 days. The FPR was 1.16 (95% CI: 0.98–1.38) alerts per patient‐year.ConclusionsResults suggest that signs of worsening HF can be detected successfully with remote patient follow‐up. The use of HeartLogic may predict periods of increased risk for HF or clinically significant events, allowing for early intervention and reduction of hospitalization in a vulnerable patient population.

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Clinical Utility of HeartLogic, a Multiparametric Telemonitoring System, in Heart Failure

Cited by 5 publications

References 35 publications

Clinical Predictive Modeling of Heart Failure: Domain Description, Models’ Characteristics and Literature Review

Clinical Predictive Modeling of Heart Failure: Domain Description, Models’ Characteristics and Literature Review

Personalized Therapy and Clinical Outcome for Heart Failure

Prediction of heart failure events based on physiologic sensor data in HINODE defibrillator patients

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