Automatic Identification of Clinical Lead Dysfunctions

Abstract

Implantable cardioverter defibrillators (ICD) lead dysfunctions can cause inappropriate shocks. Current ICDs store lead diagnostics and detected episodes. This stored information with intracardiac electrograms (EGM) and sensed RR interval patterns may characterize the ICD lead performance. The aim of this analysis was to determine the sensitivity and positive predictive value (PPV) of an automatic lead dysfunction identification algorithm. This algorithm uses RR and EGM data to distinguish noncardiac oversensing (OS), for example, due to conductor fracture, and cardiac OS, for example, T-wave OS, from detected episodes. The algorithm also uses lead diagnostics: sensing integrity counter trends (e.g., RR intervals <140 ms), nonsustained tachyarrhythmias episodes with a mean RR <200 ms and impedance trends to identify lead fractures. The PPV was determined using the stored memory from 1,756 ICD patients enrolled in a 13-center long-term lead study with an average follow-up of 18.3 patient-months. Sensitivity was determined in 35 patients who presented with OS or lead fracture-related adverse events confirmed by stored ICD diagnostics. The algorithm sensitivity was 97.1% (34/35). There were 43 additional patients identified by the algorithm without an adverse event. Stored ICD diagnostics confirmed lead dysfunctions in 32 of 43 patients corresponding with an 85.7% PPV (66/77). ICD memory diagnostics and episodes with intracardiac EGM may be used to identify ICD lead dysfunctions with high sensitivity and PPV. This algorithm may be implemented in postprocessing ICD environments (e.g., remote server, programmer) to rapidly identify lead dysfunction prior its clinical manifestation.

Failure of impedance monitoring to prevent adverse clinical events caused by fracture of a recalled high-voltage implantable cardioverter-defibrillator lead

“…All inappropriately shocked patients whose sensing function could be evaluated had evidence of oversensing based on sensing integrity counter data. This observation suggests that P/S conductor impedance changes may occur too late to be useful for preventing inappropriate shocks, whereas an alert system based on a sensing algorithm (such as described by Gunderson et al 11,12 ) may be more effective. Such algorithms must be able to differentiate lead noise caused by fracture or insulation defects from physiological signals like T-waves.…”

Implementation of Lead Safety Recommendations

“…However, these impedance measurements alone seemed to be insufficient for inappropriate therapy prevention 13,14 . Accordingly, the research focus shifted to the combined use of impedance measurements with quantitative measures of oversensing resulting in a better and earlier lead failure detection 15,16 . Swerdlow and co‐workers developed an uploadable algorithm based on a nonphysiologically short R–R interval detection, the sensing integrity counter, and the nonsustained tachycardia log 17 .…”

Extraction of Transvenous Pacing and ICD Leads

“…Many ICD leads suffer from conductor fractures and insulation failures. To prevent current shunting from the newer shock coil into the older coil, and to prevent noise from the metal components of the leads touching each other, removal of inactive ICD coils is frequently performed 39 40 .…”