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Pseudo-pacemaker syndrome (PPMS) is a rare complication of first-degree atrio-ventricular (AV) block in which a very prolonged PR interval causes AV dyssynchrony and subsequent symptoms of hemodynamic instability in the absence of an implanted pacemaker. The aim of this manuscript was to describe a unique case of PPMS and to provide a comprehensive review of the topic to help clinicians in the diagnosis and management of this condition. Through systematic research on PubMed, Google Scholar, EBSCO, and Ovid MEDLINE and using the search strings "pseudo-pacemaker syndrome" and "symptomatic first-degree AV block," we identified 14 articles accounting for 17 cases of PPMS, including our case report. The most common age group for PPMS was middle-aged and young adults, with an average age of 47 years. Palpitations were the most common presenting symptom and four main etiologies of PPMS were identified, as follows: (1) Idiopathic PPMS with evidence of impaired conduction over the AV node (20% of cases), (2) PPMS associated with reversable inflammatory causes (13%) or (3) associated with iatrogenic surgical or interventional procedures leading to the permanent damage of the normal AV conduction system (20%), and, finally, (4) PPM related to dual AV nodal physiology (DAVNP) as a primary finding (27%) or occurring after fast or slow pathway ablation for treatment of AV nodal re-entrant tachycardia (AVNRT) (20%). Treatment should be patient-tailored and based on the specific etiology once identified. However, the treatment of PPMS due to DAVNP without AVNRT presentation is yet to be clarified., (© 2023 Wiley Periodicals LLC.)

Application of artificial intelligence techniques in medicine has rapidly expanded in recent years. Two algorithms for identification of cardiac implantable electronic devices using chest radiography were recently developed: The PacemakerID algorithm, available as a mobile phone application (PIDa) and a web platform (PIDw) and The Pacemaker Identification with Neural Networks (PPMnn), available via web platform. In this study, we assessed the relative accuracy of these algorithms. The machine learning algorithms (PIDa, PIDw, PPMnn) were used to predict device manufacturer using chest X-rays for patients with implanted devices. Each prediction was considered correct if predicted certainty was >75%. For comparative purposes, accuracy of each prediction was compared to the result using the CARDIA-X algorithm. 500 X-rays were included from a convenience sample. Raw accuracy was PIDa 89%, PIDw 73%, PPMnn 71% and CARDIA-X 85%. In conclusion, machine learning algorithms for identification of cardiac devices are accurate at determining device manufacturer, have capacity for improved accuracy with additional training sets and can utilize simple user interfaces. These algorithms have clinical utility in limiting potential infectious exposures and facilitate rapid identification of devices as needed for device reprogramming., (Copyright © 2020 Elsevier Inc. All rights reserved.)