Time course of oversensing and impedance changes in developing implantable cardioverter-defibrillator lead fracture.

Background: Pace-sense conductors comprise a pacing coil to the tip electrode and cable to the ring-electrode. Implantable cardioverter-defibrillator (ICD) lead-monitoring diagnostics include pacing impedance (direct current resistance [DCR]) and measures of oversensing. How they change as fractures progress is unknown.

Objectives: To characterize the relationship between oversensing, impedance, and structural changes in ICD leads developing pace-sense conductor fractures.

Methods: We performed bending tests on 39 leads connected to ICD generators in an electrolyte bath with simulated electrograms. DCR was recorded every 3 minutes; electrograms were telemetered continuously. Twenty-two leads were tested to develop partial or complete fracture criteria confirmed by imaging, using DCR or DCR variability measured by standard deviation (σ). Results are reported for 17 other test leads.

Results: Initial oversensing occurred with partial pacing coil fracture vs complete ring cable fracture and correlated with bending-induced DCR peaks. These peaks were too small to be detected by clinical impedance measurements and were characterized by small increases in σ (≥0.5 Ω). Impedance threshold alerts occurred at complete pacing coil fracture but only later for ring cable fractures. The oversensing alert triggered before device-detected ventricular fibrillation more frequently than impedance alerts (94% vs 17%; = .00002).

Conclusions: In conductor fracture, early oversensing corresponds to partial pacing coil fracture or complete ring cable fracture and correlates with transient bending-induced impedance increases, which are detected by impedance variability but too small to trigger clinical impedance alerts. This explains why clinical oversensing alerts provide more warning for device-detected ventricular fibrillation than impedance alerts and suggests how to improve impedance diagnostics based on short-term variability.