Aims Arrhythmogenic right ventricular cardiomyopathy (ARVC) is diagnosed by a complex set of clinical tests as per 2010 Task Force Criteria (TFC). Avoiding misdiagnosis is crucial to prevent sudden cardiac death as well as unnecessary implantable cardioverter-defibrillator implantations. This study aims to validate the overall performance of the TFC in a real-world cohort of patients referred for ARVC evaluation. Methods and results We included patients consecutively referred to our centres for ARVC evaluation. Patients were diagnosed by consensus of three independent clinical experts. Using this as a reference standard, diagnostic performance was measured for each individual criterion as well as the overall TFC classification. Of 407 evaluated patients (age 38 ± 17 years, 51% male), the expert panel diagnosed 66 (16%) with ARVC. The clinically observed TFC was false negative in 7/66 (11%) patients and false positive in 10/69 (14%) patients. Idiopathic outflow tract ventricular tachycardia was the most common alternative diagnosis. While the TFC performed well overall (sensitivity and specificity 92%), signal-averaged electrocardiogram (SAECG, P = 0.43), and several family history criteria (P ≥ 0.17) failed to discriminate. Eliminating these criteria reduced false positives without increasing false negatives (net reclassification improvement 4.3%, P = 0.019). Furthermore, all ARVC patients met at least one electrocardiogram (ECG) or arrhythmia criterion (sensitivity 100%). Conclusion The TFC perform well but are complex and can lead to misdiagnosis. Simplification by eliminating SAECG and several family history criteria improves diagnostic accuracy. Arrhythmogenic right ventricular cardiomyopathy can be ruled out using ECG and arrhythmia criteria alone, hence these tests may serve as a first-line screening strategy among at-risk individuals.

The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated. However, the clinical performance and applicability of created algorithms are yet unknown. In this narrative review, opportunities and threats of AI in the field of electrophysiology are described, mainly focusing on ECGs. Current opportunities are discussed with their potential clinical benefits as well as the challenges. Challenges in data acquisition, model performance, (external) validity, clinical implementation, algorithm interpretation as well as the ethical aspects of AI research are discussed. This article aims to guide clinicians in the evaluation of new AI applications for electrophysiology before their clinical implementation.

Fragmented QRS complexes (fQRS) are common in patients with arrhythmogenic cardiomyopathy (ACM). A new method of fQRS quantification may aid early disease detection in pathogenic variant carriers and assessment of prognosis in patients with early stage ACM. Patients with definite ACM (n = 221, 66%), carriers of a pathogenic ACM-associated variant without a definite ACM diagnosis (n = 57, 17%) and control subjects (n = 58, 17%) were included. Quantitative fQRS (Q-fQRS) was defined as the total amount of deflections in the QRS complex in all 12 electrocardiography (ECG) leads. Q-fQRS was scored by a single observer and reproducibility was determined by three independent observers. Q-fQRS count was feasible with acceptable intra- and inter-observer agreement. Q-fQRS count is significantly higher in patients with definite ACM (54 ± 15) and pathogenic variant carriers (55 ± 10) compared to controls (35 ± 5) (p < 0.001). In patients with ACM, Q-fQRS was not associated with sustained ventricular arrhythmia (p = 0.701) at baseline or during follow-up (p = 0.335). Both definite ACM patients and pathogenic variant carriers not fulfilling ACM diagnosis have a higher Q-fQRS than controls. This may indicate that increased Q-fQRS is an early sign of disease penetrance. In concealed and early stages of ACM the role of Q-fQRS for risk stratification is limited.