Predictors for adverse events during cardiac lead extraction - Experience from a large single centre

Background: As the use of cardiac implantable electronic devices (CIED) has increased in recent years, the need for transvenous lead extraction (TLE) has also steadily increased. However, the TLE procedure could lead to serious complications and even death. Clinical decision-making tools are necessary for predicting these adverse events, but the appropriate tools have not yet been developed. Objective: To explore the possible predictors and develop a clinical model to predict TLE related adverse events. Methods: All the patients who were admitted to our cardiac center for TLE from January 2014 to January 2021 were included in this study. The patient information, device baseline characteristics, procedure-related information, complications and outcomes were recorded. Independent predictors of TLE related adverse events were identified by univariate, LASSO and multivariate analysis. A nomogram for predicting these adverse events was developed based on these independent predictors. Calibration and decision curve analysis were conducted to evaluate the nomogram. Results: One thousand and one hundred patients were included in this study, 778 (70.7%) were male and the median age was 68 years old. A total of 2,208 leads were extracted and 2.01 +/- 0.74 leads were extracted per procedure. Fifty-five patients (5%) developed adverse events including minor complications (2.4%), major complications (2.3%) and death (0.27%). Seven independent predictors for TLE related adverse events were identified and selected to establish the nomogram including BMI, female gender, hypoalbuminemia, number of extracted leads>3, longest dwell time of the extracted leads and manual traction. The area under the receiver operating characteristic (ROC) curve (AUC) for the prediction model was 0.774. Calibration curve and decision curve analysis showed that the nomogram had good prediction performance. Conclusion: TLE related adverse events are some of the key issues that concern clinicians. We have identified seven independent factors and established a predictive model that may help clinicians identify at-risk patients and create better plans for lead extraction.