Biomedical subjects

Chad A Bounds

Publications and source records attributed to Chad A Bounds.

2 recordsLinked to original sources

PubMed2005

Automatic identification of clinical lead dysfunctions.

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.

Algorithms↗

PubMed2004

An algorithm to predict implantable cardioverter-defibrillator lead failure.

OBJECTIVES: The goal of this analysis was to test an algorithm that identifies implantable cardioverter-defibrillator (ICD) lead problems before clinical failure and/or inappropriate therapy. BACKGROUND: The ICD lead failures typically present as inappropriate shock therapy. Identifying lead failures before their clinical presentation may prevent patient discomfort, improve device longevity, and avoid device-induced proarrhythmia. METHODS: We tested an algorithm that uses two measures of oversensing and one measure of abnormal impedance to detect a lead failure. The oversensing measures consisted of a counter for RR intervals <140 ms and nonsustained ventricular tachycardia episodes with mean RR interval <200 ms. The impedance measure tracked lead impedances every day and each week. Abnormal impedance was defined as a decrease in impedances or an outlier value compared with baseline. Lead failures were identified when both oversensing measures were met or abnormal impedance and one oversensing measure occurred. The stored data from 696 patients with an ICD were analyzed to determine the sensitivity and specificity of the algorithm to detect lead failures. RESULTS: Twenty-nine patients demonstrated clinical lead failures with an average of 6 +/- 9 inappropriate shocks per patient. The two oversensing measures used in the algorithm predicted 72% (21 of 29) of the lead failures. Fulfilling at least two of the three impedance and oversensing measures, the sensitivity of our algorithm was 83% (24 of 29) with a 100% (667 of 667) specificity. CONCLUSION: Oversensing combined with abnormal impedance trends may be used to identify ICD lead failures with high sensitivity and very high specificity.

Algorithms↗