Key result
An automatic lead dysfunction identification algorithm demonstrated 97.1% sensitivity and 85.7% positive predictive value for detecting ICD lead dysfunctions.
Why the study?
Does an automatic lead dysfunction identification algorithm accurately detect lead dysfunctions in ICD patients?
Population
1,756 ICD patients enrolled in a 13-center long-term lead study and 35 patients who presented with…
Comparison
Automatic lead dysfunction identification… vs Stored ICD diagnostics and clinical manifestation.
Design
Cohort
Follow-up
average 18.3 patient-months
Authors
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May facilitate preemptive ICD lead intervention; hypothesis-generating and requires prospective validation before practice change.
Observational (n=1,756)
Yes
Does an automatic lead dysfunction identification algorithm accurately detect lead dysfunctions in ICD patients?
An automatic algorithm utilizing ICD memory diagnostics and intracardiac EGMs can identify lead dysfunctions with high sensitivity and positive predictive value prior to clinical manifestation.
Gunderson et al. (2005) conducted an observational in ICD lead dysfunction (n=1,756). Automatic lead dysfunction identification algorithm vs. Clinical confirmation via stored ICD diagnostics was evaluated on Sensitivity and positive predictive value (PPV) of the algorithm. An automatic lead dysfunction identification algorithm demonstrated 97.1% sensitivity and 85.7% positive predictive value for detecting ICD lead dysfunctions.
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