Heart failure & cardiomyopathies
Prediction of Heart Failure Events Based on Physiologic Sensor Data in HINODE Defibrillator Patients
Authors' conclusion
Results suggest that signs of worsening HF can be detected successfully with remote patient follow-up.
Study context
- Population
- ICD and CRT-D patients with remote monitoring (HINODE study)81 ICD, 63 CRT-D
- Participants
- 144 participants
- Comparison
- HeartLogic alert state vs out of alert state
- Outcome
- Sensitivity of HeartLogic alert for worsening HF events (HFEs)
Methodological detailsretrospective simulation of alerts, clinicians and patients blinded to alerts, independent HFE adjudication committee
- Retrospective simulation of alerts
- Clinicians and patients blinded to alerts
- Independent HFE adjudication committee
Performance
As reported in the abstract
Sensitivity for HFEs at nominal threshold (16)
Heart failure events (HFEs) detected by HeartLogic alert (sensitivity), 95% CI 69.9%-89.1%
80.8%
Further findings
HFE rate in vs out of alert
event rate ratio 8.4 · P < 0.001
False positive alert rate
- FPR, alerts per patient-year
- 1.16 (95% CI 0.98-1.38)
Alert rate
- 311 alerts across 106 patients; alerts per patient-year
- 1.27
Time from first true positive alert to HFE
- Median time, days
- 53
Original publication
Prediction of Heart Failure Events Based on Physiologic Sensor Data in HINODE Defibrillator Patients. ESC Heart Fail. Published 2 July 2024. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →