OBJECTIVES: This study sought to use implantable cardioverter-defibrillator (ICD) diagnostics to discriminate ICD lead fractures from normally functioning leads with high impedance and from connection problems between the lead and header. BACKGROUND: ICD diagnostics facilitate identification of fractures, but there are no accepted criteria for discriminating fractures from other causes of high impedance and/or nonphysiological "noise" oversensing. METHODS: We analyzed a development set of 91 leads to construct a stepwise algorithm based on ICD diagnostics. It included 40 fractures, 30 connection problems, and 21 functioning leads that triggered high-impedance alerts. Then we applied this algorithm to an independent test set of 100 leads: 70 fractures and 30 intact leads with connection problems that were misdiagnosed clinically as fractures. In the algorithm, either extremely high maximum impedance or noise oversensing with a normal impedance trend indicated a fracture. A short interval from surgery to impedance rise or prolonged stable impedance after an abrupt rise indicated a connection problem. A gradual impedance increase or stable, high impedance indicated a functioning lead. RESULTS: In the test set, the algorithm correctly classified 100% of fractures (95% confidence interval [CI]: 95% to 100%) and 87% of connection problems that were misdiagnosed as fractures (95% CI: 70% to 95%). CONCLUSIONS: An algorithm using only ICD diagnostics identifies leads with oversensing or high impedance as fractures or connection problems with a high degree of accuracy.
OBJECTIVES: This study sought to use implantable cardioverter-defibrillator (ICD) diagnostics to discriminate ICD lead fractures from normally functioning leads with high impedance and from connection problems between the lead and header. BACKGROUND: ICD diagnostics facilitate identification of fractures, but there are no accepted criteria for discriminating fractures from other causes of high impedance and/or nonphysiological "noise" oversensing. METHODS: We analyzed a development set of 91 leads to construct a stepwise algorithm based on ICD diagnostics. It included 40 fractures, 30 connection problems, and 21 functioning leads that triggered high-impedance alerts. Then we applied this algorithm to an independent test set of 100 leads: 70 fractures and 30 intact leads with connection problems that were misdiagnosed clinically as fractures. In the algorithm, either extremely high maximum impedance or noise oversensing with a normal impedance trend indicated a fracture. A short interval from surgery to impedance rise or prolonged stable impedance after an abrupt rise indicated a connection problem. A gradual impedance increase or stable, high impedance indicated a functioning lead. RESULTS: In the test set, the algorithm correctly classified 100% of fractures (95% confidence interval [CI]: 95% to 100%) and 87% of connection problems that were misdiagnosed as fractures (95% CI: 70% to 95%). CONCLUSIONS: An algorithm using only ICD diagnostics identifies leads with oversensing or high impedance as fractures or connection problems with a high degree of accuracy.
Authors: Bruce L Wilkoff; Laurent Fauchier; Martin K Stiles; Carlos A Morillo; Sana M Al-Khatib; Jesœs Almendral; Luis Aguinaga; Ronald D Berger; Alejandro Cuesta; James P Daubert; Sergio Dubner; Kenneth A Ellenbogen; N A Mark Estes; Guilherme Fenelon; Fermin C Garcia; Maurizio Gasparini; David E Haines; Jeff S Healey; Jodie L Hurtwitz; Roberto Keegan; Christof Kolb; Karl-Heinz Kuck; Germanas Marinskis; Martino Martinelli; Mark McGuire; Luis G Molina; Ken Okumura; Alessandro Proclemer; Andrea M Russo; Jagmeet P Singh; Charles D Swerdlow; Wee Siong Teo; William Uribe; Sami Viskin; Chun-Chieh Wang; Shu Zhang Journal: J Arrhythm Date: 2016-02-01