Key result
HeartLogic index runs ~2-fold higher during clinically stable periods in patients with HF events.
Why the study?
The study aimed to characterize implantable device-based sensor values in patients with heart failure during clinically stable periods compared with the time course of acute decompensation and recovery from HF events.
Does continuous monitoring with device-based physiologic sensors detect progressive worsening leading up to HF events and differentiate risk during clinically stable periods in ambulatory patients with heart failure?
Observational (n=900)
Single-blind
Yes
Does continuous monitoring with device-based physiologic sensors detect progressive worsening leading up to HF events and differentiate risk during clinically stable periods in ambulatory patients with heart failure?
Multiparameter device-based sensors can identify ambulatory heart failure patients at higher risk of decompensation even during clinically stable periods and track physiological worsening prior to events.
Answers are grounded in this paper and cite their sources.
May support sensor-guided risk stratification in stable HF; hypothesis-generating for prospective outcome trials.
AIMS: This study aims to characterize the range of implantable device-based sensor values including heart sounds, markers of ventilation, thoracic impedance, activity, and heart rate for patients with heart failure (HF) when patients were deemed to be in clinically stable periods against the time course of acute decompensation and recovery from HF events. METHODS AND RESULTS: The MultiSENSE trial followed 900 patients implanted with a COGNIS CRT-D for up to 1 year. Chronic, ambulatory diagnostic sensor data were collected and evaluated during clinically stable periods (CSP: unchanged NYHA classification, no adverse events, and weight change ≤2.27 kg), and in the timeframe leading up to and following HF events (HF admissions or unscheduled visits with intravenous HF treatment). Physiologic sensor data from 1667 CSPs occurring in 676 patients were compared with those data leading up to and following 192 HF events in 106 patients. Overall, the mean age was 66.6 years, and the population were predominantly male (73%). Patients were primarily in NYHA II (67%), with a mean LVEF of 29.6% and median NT-proBNP of 754.5 pg/mL. Sensor values during CSP were poorer in patients who had HF events during the study period than those without HF events, including first heart sound (S1: 2.18 ± 0.84 mG vs. 2.62 ± 0.95 mG, P = 0.002), third heart sound (S3: 1.13 ± 0.36 mG vs. 0.91 ± 0.30 mG, P < 0.001), thoracic impedance (45.66 ± 8.78 Ohm vs. 50.33 ± 8.43 Ohm, P < 0.001), respiratory rate (19.09 ± 3.10 br/min vs. 17.66 ± 2.39 br/min, P = 0.002), night time heart rate (73.39 ± 8.36 b.p.m. vs. 69.56 ± 8.09 b.p.m., P = 0.001), patient activity (1.69 ± 1.84 h vs. 2.56 ± 2.20 h, P = 0.006), and HeartLogic index (11.07 ± 12.14 vs. 5.31 ± 5.13, P = 0.001). Sensor parameters measured worsening status leading up to HF events with recovery of values following treatment. CONCLUSIONS: Device-based physiologic sensors not only revealed progressive worsening leading up to HF events but also differentiated patients at increased risk of HF events when presumed to be clinically stable.
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Gardner et al. (2021) conducted an observational in Heart failure (n=900). Future heart failure events vs. No heart failure events (clinically stable) was evaluated on HeartLogic index during clinically stable periods (p=0.001). Device-based physiologic sensors, including the HeartLogic index, differentiated patients at increased risk of heart failure events even during presumed clinically stable periods.
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