Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization

Abstract

Background: Implantable cardiac sensors have shown promise in reducing rehospitalization for heart failure (HF), but the efficacy of noninvasive approaches has not been determined. The objective of this study was to determine the accuracy of noninvasive remote monitoring in predicting HF rehospitalization. Methods: The LINK-HF study (Multisensor Non-invasive Remote Monitoring for Prediction of Heart Failure Exacerbation) examined the performance of a personalized analytical platform using continuous data streams to predict rehospitalization after HF admission. Study subjects were monitored for up to 3 months using a disposable multisensor patch placed on the chest that recorded physiological data. Data were uploaded continuously via smartphone to a cloud analytics platform. Machine learning was used to design a prognostic algorithm to detect HF exacerbation. Clinical events were formally adjudicated. Results: One hundred subjects aged 68.4±10.2 years (98% male) were enrolled. After discharge, the analytical platform derived a personalized baseline model of expected physiological values. Differences between baseline model estimated vital signs and actual monitored values were used to trigger a clinical alert. There were 35 unplanned nontrauma hospitalization events, including 24 worsening HF events. The platform was able to detect precursors of hospitalization for HF exacerbation with 76% to 88% sensitivity and 85% specificity. Median time between initial alert and readmission was 6.5 (4.2–13.7) days. Conclusions: Multivariate physiological telemetry from a wearable sensor can provide accurate early detection of impending rehospitalization with a predictive accuracy comparable to implanted devices. The clinical efficacy and generalizability of this low-cost noninvasive approach to rehospitalization mitigation should be further tested. Registration: URL: https://www.clinicaltrials.gov . Unique Identifier: NCT03037710.

The Role of Wearables in Heart Failure

“…To overcome the limitations of single channel monitoring, multiparameter monitoring wearable devices are being investigated. The LINK-HF study used a multiparameter patch sensor with ECG monitoring, intrathoracic impedance detection, accelerometry and temperature sensor on 100 patients with heart failure [ 51 ••]. A machine learning algorithm was then used on the data to create a personalised baseline for each individual, and then, a further predictive algorithm for decompensation was derived which demonstrated a sensitivity of 76% and specificity of 85% for a decompensation alert 10 days before the event.…”

Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance

“…In the heart failure care field, wearable sensors coupled with ML analytics can be potentially used to improve clinical outcomes and reduce hospitalizations [ 36 – 38 ]. Heart failure is a chronic disease with acute exacerbations that reports high rates of hospitalization and mortality year after year (one-year hospital readmission rate of more than 50%, and one-year mortality rates of 30%) [ 39 ] and involves a worldwide expenditure of around $31 billion [ 40 ] yearly.…”

“…Due to the high cost of hospitalizations (the average length is 5–10 days) [ 41 ] and the high rates of morbidity and mortality (especially between the elderly population), the potential of IoT-based devices stands out. In the LINK-HF study [ 36 ], it was demonstrated that machine learning models that use data from VitalPatch®, a wearable sensor, can more accurately forecast heart failure exacerbation than invasive devices. The sensor layer used in the mentioned study was made up of a multisensory patch placed on the chest that recorded physiological data.…”

eHealth – Smart Devices revolutionieren die Kardiologie

“…Es gelang sogar, das Risiko für eine erst Jahre später entstandene Herzinsuffizienz bei Patienten zu ermitteln, die zum Zeitpunkt der EKG-Aufzeichnung noch eine normale Pumpfunktion aufwiesen [3]. Mithilfe von Informationen aus einem Brustpatch, der neben EKG auch Temperatur, Hautimpedanz und Bewegung aufzeichnet, konnte in einer Studie an Herzinsuffizienzpatienten eine kardiale Dekompensation mit einem zeitlichen Vorlauf von knapp 7 Tagen vorhersagt werden [24]. Solch ein präziser Blick in die Zukunft erlaubt eine rechtzeitige medizinische Intervention, um zukünftige kardiale Dekompensation in diesem Patientenkollektiv zu verhindern.…”