The inclusion of a Holter Reading software in the clinical practice of cardiology shows a multi-level high positive impact in healthcare: a real-world implementation study in three Spanish hospitals.

Abstract

Aims: Holter monitoring is a high prevalent technique to detect various heart pathologies. Its use has progressively increased over time with the consequent expenditure of time to interpret its results. We aim to evaluate the validity of the Cardiologs software as well as the clinical utility and potential benefits derived from the inclusion of an artificial intelligence (AI)-based software in the clinical routine of the cardiology service.

Methods and results: Concordance analyses were performed to determine the degree of correlation between the results reported by the Cardiologs software and cardiologists regarding a list of variables for 498 Holter records included in the study. Sensitivity, specificity, positive and negative prediction values, positive and negative likelihood ratios, and odds ratio were calculated. The preliminary analysis reported good correlation between the reported observations by the cardiologists involved in this study (Kappa = 0.67; P < 0001). Furthermore, an excellent concordance was found between software and cardiologists in the detection of atrial fibrillation, ventricular extrasystoles and sinus pauses of >3 s, moderate for supraventricular extrasystoles (Kappa > 0.80 in all cases), but weak or poor correlations in the rest of the variables studied. The global correlation was moderate (Kappa = 0.43; P < 0.001). Notably, the software showed sensitivity of 99.4%, negative predictive value of 99.5%, and negative likelihood ratio of 0.010, highlighting its clinical usefulness in correctly identify normal tests.

Conclusion: The inclusion of an AI-based software for reading Holter tests may have great impact in distinguishing normal Holter tests, leading to time savings and improved clinical efficiency.