Efficacy of the Cardiac Implantable Electronic Device Multisensory Triage-HF Algorithm in Heart Failure Care: A Real-World Clinical Experience.
Ugur AslanSaskia Lambertha Maria Anna BeeresMichelle FeijenGerlinde M MulderJ Wouter JukemaAnastasia D EgorovaPublished in: Sensors (Basel, Switzerland) (2024)
Heart failure (HF) admissions are burdensome, and the mainstay of prevention is the timely detection of impending fluid retention, creating a window for medical treatment intensification. This study evaluated the accuracy and performance of a Triage-HF-guided carepath in real-world ambulatory HF patients in daily clinical practice. In this prospective, observational study, 92 adult HF patients (71 males (78%), with a median age of 69 [IQR 59-75] years) with the Triage-HF algorithm activated in their cardiac implantable electronic devices (CIEDs), were monitored. Following high-risk alerts, an HF nurse contacted patients to identify signs and symptoms of fluid retention. The sensitivity and specificity were 83% and 97%, respectively. The positive predictive value was 89%, and negative predictive value was 94%. The unexplained alert rate was 0.05 alerts/patient year, and the false negative rate was 0.11 alerts/patient year. Ambulatory diuretics were initiated or escalated in 77% of high-risk alert episodes. In 23% (n = 6), admission was ultimately required. The median alert handling time was 2 days. Fifty-eight percent (n = 18) of high-risk alerts were classified as true positives in the first week, followed by 29% in the second-third weeks (n = 9), and 13% (n = 4) in the fourth-sixth weeks. Common sensory triggers included an elevated night ventricular rate (84%), OptiVol (71%), and reduced patient activity (71%). The CIED-based Triage-HF algorithm-driven carepath enables the timely detection of impending fluid retention in a contemporary ambulatory setting, providing an opportunity for clinical action.
Keyphrases
- heart failure
- end stage renal disease
- emergency department
- acute heart failure
- newly diagnosed
- ejection fraction
- chronic kidney disease
- left ventricular
- healthcare
- blood pressure
- primary care
- machine learning
- peritoneal dialysis
- prognostic factors
- clinical practice
- case report
- patient reported outcomes
- atrial fibrillation
- real time pcr
- placebo controlled