Research Article
Published: 15 May, 2023 | Volume 8 - Issue 2 | Pages: 042-050
Background: Since 2019, remote patient monitoring (RPM) for patients with chronic heart failure (CHF) has been supported by the European Society of Cardiology. However, real-world data on the use of such solutions has been limited and not primarily based on patient-reported outcomes. The aim of this study was to describe the Satelia® Cardio solution in France within the French ETAPES funding program and assess the security and performance of its clinical algorithm.
Methods: A retrospective observational study was conducted on CHF patients monitored by RPM through Satelia® Cardio. From September 1, 2018, to June 30, 2020, patients were included if they had completed over six months of follow-up. The risk of a possible CHF decompensation was categorized by the system in three levels: green, orange and red. The algorithm security and performance were assessed through the negative predictive value (NPV) of the prediction of hospitalization of a patient within seven days.
Results: In total, 331 patients were included in this study with 36,682 patient self-administered questionnaires answered. Patients were mostly males (70.4%) and had a mean age of 68.1 years. The mean left ventricular ejection fraction (LVEF) was 35.4% (± 12.3) and 73.3% of patients had a LVEF ≤ 40%. The questionnaire response rate was 90.9%. A green status was generated for 95.3% of answers. There were 4.5% (n = 1,499) orange alerts and 0.2% (n = 74) red alerts. Overall, 92.1% of patients had at least one CHF related hospitalization and 31.7% (n = 105) of these cases were non-scheduled. The NPV at seven days was 99.43%.
Conclusion: Satelia® Cardio is a feasible, relevant and reliable solution to safely monitor the cohorts of patients with CHF, reassuring cardiologists about patient stability.
Read Full Article HTML DOI: 10.29328/journal.jccm.1001152 Cite this Article Read Full Article PDF
Chronic heart failure; Telemedicine; Telehealth; Remote patient monitoring; Real-world evidence; Clinical algorithm
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