Vishal S Mehta, YingLiang Ma, Nadeev Wijesuriya, Felicity DeVere, Sandra Howell, Mark K Elliott, Nilanka N Mannkakara, Tatiana Hamakarim, Tom Wong, Hugh O'Brien, Steven Niederer, Reza Razavi, Christopher A Rinaldi
BACKGROUND: Machine learning (ML) models have been proposed to predict risk related to transvenous lead extraction (TLE). OBJECTIVE: We tested if integrating imaging data into an existing ML model increases its ability to predict major adverse events (MAE: procedure-related major complications and procedure-related deaths) and lengthy procedures (≥100 minutes). METHODS: We hypothesised certain features: i) lead angulation ii) coil percentage inside the superior vena cava (SVC), and iii) number of overlapping leads in the SVC, detected from a pre-TLE plain anterior-posterior (AP) chest x-ray (CXR) would improve prediction of MAE and long procedure times...
February 12, 2024: Heart Rhythm: the Official Journal of the Heart Rhythm Society