Heart failure & cardiomyopathies
Machine learning–powered, device-embedded heart sound measurement can optimize atrioventricular (AV) delay in patients with cardiac resynchronization therapy (CRT)
Authors' conclusion
Heart sound sensors embedded in a CRT device, powered by a ML algorithm, provide a reliable assessment of optimal AV delays and absolute LVPmax and LVdP/dtmax.
Study context
- Population
- Patients undergoing CRT device implantation with a pacing protocol at different AV delays∼30,000 heartbeats
- Comparison
- Machine learning (ML)-powered device-embedded heart sound estimation vs invasive LV pressure measurement for AV delay optimization
- Outcome
- Estimated vs measured optimal AV delay (difference)
Performance
As reported in the abstract
Difference
−5.6 ± 17.1 ms
LVPmax; not significantly different
Difference
+5.1 ± 6.7 ms
LVdP/dtmax; not significantly different
Further findings
Most prominent features for AV delay optimization
- S1 amplitude, S2 amplitude and S1 integral (S1 energy for LVdP/dtmax)
Parts of this Brief are withheld: the automatic checks could not confirm them against the abstract.
Original publication
Machine learning–powered, device-embedded heart sound measurement can optimize AV delay in patients with CRT. Heart Rhythm. Published 27 May 2023. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →