Artificial intelligence in cardiology: a machine learning model for supraventricular tachycardia discrimination Articles uri icon

authors

  • DE LA LINDE VALDES, ANGEL
  • RIOS MUÑOZ, GONZALO RICARDO
  • López-Dóriga Costales, Juan
  • CARTA BERGAZ, ALEJANDRO
  • GONZALEZ-TORRECILLA, ESTEBAN
  • ATIENZA FERNÁNDEZ, FELIPE
  • ARENAL MAIZ, ANGEL
  • CALVO CUERVO, DAVID
  • GOMEZ SANCHEZ, ROBERTO
  • BERMEJO THOMAS, JAVIER
  • AVILA ALONSO, PABLO

publication date

  • October 2024

start page

  • ehae666.34

issue

  • 1

volume

  • 45

International Standard Serial Number (ISSN)

  • 0195-668X

Electronic International Standard Serial Number (EISSN)

  • 1522-9645

abstract

  • Background: Distinguishing atrioventricular nodal reentrant tachycardia (AVNRT) from orthodromic atrioventricular reentrant tachycardia (AVRT) presents a diagnostic challenge, with clinical and electrocardiogram (ECG) features playing a crucial role in differential diagnosis.
    Purpose: The aim of this study was to devise and validate a predictive algorithm that utilises basic clinical and ECG parameters to accurately classify AVNRT versus AVRT. Furthermore, this algorithm was integrated into a user-friendly, web-based tool to support clinical
    decision-making.
    Methods: We conducted a prospective analysis of 704 patients (median age: 53.6 years; 59.7% female) with electrophysiologically confirmed AVNRT (n=535) or AVRT (n=169). A comprehensive evaluation was performed using a structured questionnaire tailored to arrhythmia-specific symptoms. The study utilised advanced machine learning techniques for classification, employing feature selection methods to enhance predictive accuracy. The analysis included 19 demographic, clinical, and ECG variables, adopting an 80-20% split for training and internal validation. The algorithm's performance was evaluated against a model comprising all variables (Full Model), expert ECG interpretations (ExpertECG), objective ECG criteria (ECG model), and a baseline dummy classifier, with efficacy assessed by the Area Under the Receiver Operating Characteristic curve (AUC) and the Area Under the Precision-Recall Curve (AUPRC).
    Results: The optimised model, utilising the XGBoost algorithm with six critical variables (initial age at first episode, biological sex, presence of neck palpitations, pseudoR wave in lead V1/pseudoS wave in inferior leads, visibility of retrograde P-waves, and tachycardia cycle length), exhibited superior performance, achieving an AUC of 0.916 and an AUPRC of 0.823. This model significantly outperformed other comparative models in terms of predictive accuracy (Figure 1). Implementation of the model into a web-based interface was accomplished using Shiny, enhancing its accessibility for clinical application (Figure 2).
    Conclusions: This study illustrates the efficacy of employing machine learning algorithms to utilise bedside clinical and ECG variables for accurately differentiating between AVNRT and AVRT. The creation of a web-based calculator marks a significant advancement, empowering clinicians, especially non-specialists, with a potent tool for providing personalised diagnostic insights. Future research should focus on external validation to ascertain the model's generalisability and its applicability in broader clinical settings.

subjects

  • Biology and Biomedicine