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Abstract Introduction The novel Confirm Rx™ implantable cardiac monitor (ICM) with SharpSense™ technology incorporates a new P‐wave discriminator designed to improve AF detection. This study aimed to evaluate the diagnostic performance of the Confirm Rx™ ICM in detecting AF episodes of varying durations. Methods We conducted a multicenter retrospective analysis of consecutive patients implanted with a Confirm Rx™ ICM (v1.2) across nine UK hospitals, all with documented AF lasting at least 6 min. Electrocardiograms (ECGs) were manually adjudicated by cardiologists. To account for intra‐ and inter‐reviewer variability, a random sample of 10% of ECGs underwent additional review. Disagreements were resolved by a third reviewer. Diagnostic performance was determined by calculating the gross and patient‐averaged positive predictive value (PPV) for AF episodes of different duration. The source of false positive (FP) detection was also categorized. Results Overall, 16,230 individual ECGs from 232 patients were included. The median AF episode duration was 14 min. R‐wave amplitude remained stable during follow‐up (0.52 ± 0.27 mV [initial] vs. 0.54 ± 0.29 mV [end of follow‐up], p = .10). The gross and patient‐averaged PPV were 75.0% and 67.0%, respectively. Diagnostic performance (gross) increased with progressively longer AF episodes: 88.0% for ≥1 h, 97.3% for 6 h, and 100% for 24 h. The main source of FP during tachycardia was T‐wave oversensing (54.2%), while in non‐tachycardic episodes it was predominantly ectopy (71.2%). The AF burden precision was excellent (93.3%). Conclusion The Confirm Rx™ ICM diagnostic performance was modest for all AF episodes (75%), with accuracy increasing for longer AF episodes.

Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary arteries, affecting the blood supply to the heart, one of the leading causes of death around the world. X-ray coronary angiography is the most common procedure for diagnosing coronary artery disease, which uses contrast material and x-rays to observe vascular lesions. With this type of procedure, blood flow in coronary arteries is viewed in real-time, making it possible to detect stenoses precisely and control percutaneous coronary interventions and stent insertions. Angiograms of coronary arteries are used to plan the necessary revascularisation procedures based on the calculation of occlusions and the affected segments. However, their interpretation in cardiac catheterisation laboratories presently relies on sequentially evaluating multiple 2D image projections, which limits measuring lesion severity, identifying the true shape of vessels, and analysing quantitative data. In silico modelling, which involves computational simulations of patient-specific data, can revolutionise interventional cardiology by providing valuable insights and optimising treatment methods. This paper explores the challenges and future directions associated with applying patient-specific in silico models in catheterisation laboratories. We discuss the implications of the lack of patient-specific in silico models and how their absence hinders the ability to accurately predict and assess the behaviour of individual patients during interventional procedures. Then, we introduce the different components of a typical patient-specific in silico model and explore the potential future directions to bridge this gap and promote the development and utilisation of patient-specific in silico models in the catheterisation laboratories.