Early detection of fluid retention with a multisensory algorithm in chronic heart failure patients

Abstract

Abstract Funding Acknowledgements Type of funding sources: Public hospital(s). Main funding source(s): Leiden University Medical Center Background Hospitalizations for decompensated heart failure are a marker for poor prognosis and pose a burden on patients and resources. The mainstay in preventing these hospitalizations is early detection of fluid retention and timely pharmacological intervention. The multisensory cardiac implantable electronic device (CIED) based HeartLogic™ algorithm can alert in case of upcoming congestion. The cumulative HeartLogic™ index is based on the following sensors: heart sounds, thoracic impedance, respiratory rate, night heart rate and patient activity levels. The current analysis investigates the performance of the HeartLogicTM algorithm in a real-world ambulant heart failure population. Methods All consecutive heart failure patients with a CIED and an activated HeartLogic™ algorithm were included for analysis. Patients were followed from 01-01-2018 until 01-09-2022 according to the heart failure care path (figure 1). HeartLogic™ automatically generated an alert if the index surpassed the preset threshold of 16. An alert was either true positive (≥2 signs/symptoms of fluid retention on top of the alert) or false positive (≤1 signs/symptoms). Without an alert a patient was true negative (≤1 signs/symptoms) or false negative (≥2 signs/symptoms). A logistic regression model with linear mixed models was used. Furthermore, patients with ≥2 true positive alerts and ≤1 false positive alerts per year were compared to patients without alerts to identify characteristics of patients who benefit most from the HeartLogic™ algorithm supported management. Results Data of 138 patients were included, median age was 69 [60 – 77], 78% was male and 50% had an ischemic etiology of heart failure. Majority of the patients had a CRT-D (n=90, 65%) and the remaining 48 patients had an ICD (35%). Total follow-up entailed of 297 patient years, median follow-up was 26 months [14 – 36]. During follow-up, 231 alerts were observed. After exclusion of 14 alerts (incomplete clinical information), 217 alerts were available for analysis. Majority of these alerts were true positive for fluid retention(n=161, 74%). Of interest, 21 of these alerts (13%) were not primarily heart failure related, but prompted clinical action (e.g. pneumonia or anemia). The remaining 59 (26%) alerts were deemed false positive. The sensitivity to detect impending fluid retention was 86%, the specificity 88%. The positive predictive value was 73% and the negative predictive value was 94%. Patients with HeartLogicTM alerts had a significantly higher baseline NT-Pro BNP, when compared to patients without alerts, p<0.05 (Figure 2). No differential response was observed based on age, gender or BMI. Conclusions In a real world heart failure population the HeartLogic™ algorithm supported care path adequately detects impending fluid retention. Patients who benefited most had higher levels of NT-Pro BNP at baseline.