Etiologies and predictors for cardiac implantable electronic device implantation in young patients hospitalized for complete atrioventricular block

Abstract

Abstract Introduction Information regarding the etiologies of complete atrioventricular block (CAVB) in young patients is scarce. We aimed to investigate the potential causes for non-iatrogenic CAVB in young patients, as well as to identify possible predictors for need of implantable cardioverter-defibrillator (ICD) or permanent pacemaker (PPM) implantation in young patients presenting with CAVB. Methods Using the National Inpatient Sample (NIS) database, we identified patients aged 18–60 hospitalized with CAVB in the US between 2015 (last quarter)-2019. Patients who had concurrent cardiac surgery or electrophysiological procedures were excluded. Baseline demographics, clinical characteristics, potential etiologies for CAVB as well as outcomes including the need for temporary cardiac pacing (TCP), ICD and PPM implantation were analyzed. Multivariable logistic regression models were used to identify predictors of ICD or PPM implantation in patients with unknown etiology. Results and discussion An estimated total of 56,385 patients aged 18–60 with CAVB and no concurrent surgical or EP interventions were identified. The mean (±SD) age was 49 (±10) years and 59% were males. Approximately 55% of patients had no identified cause for CAVB (Table 1). While 16% of patients received TCP, 32% and 6% of patients were eventually implanted with a PPM and ICD, respectively (Figure 1), In patients with CAVB of unknown etiology, advanced age and need for TCP emerged in multivariable analyses as independent predictors of the need for both PPM and ICD implantation. Female gender was identified as a predictor for PPM implantation but was associated with a lower chance of ICD implantation. Conclusion The majority of non-iatrogenic young CAVB patients had no identified etiology in a nationwide database and less than 40% of the patients received an implantable cardiac device. Better risk stratification and diagnostic algorithms are needed for this population of patients. Funding Acknowledgement Type of funding sources: None.