ABSTRACT
BACKGROUND: Atrial fibrillation (AF) may occur asymptomatically and can be diagnosed only with electrocardiography (ECG) while the arrhythmia is present. OBJECTIVES: The aim of this study was to independently validate the approach of using artificial intelligence (AI) to identify underlying paroxysmal AF from a 12-lead ECG in sinus rhythm (SR). METHODS: An AI algorithm was trained to identify patients with underlying paroxysmal AF, using electrocardiographic data from all in- and outpatients from a single center with at least 1 ECG in SR. For patients without AF, all ECGs in SR were included. For patients with AF, all ECGs in SR starting 31 days before the first AF event were included. The patients were randomly allocated to training, internal validation, and testing datasets in a 7:1:2 ratio. In a secondary analysis, the AF prevalence of the testing group was modified. Additionally, the performance of the algorithm was validated at an external hospital. RESULTS: The dataset consisted of 494,042 ECGs in SR from 142,310 patients. Testing the model on the first ECG of each patient (AF prevalence 9.0%) resulted in accuracy of 78.1% (95% CI: 77.6%-78.5%), area under the receiver-operating characteristic curve of 0.87 (95% CI: 0.86-0.87), and area under the precision recall curve (AUPRC) of 0.48 (95% CI: 0.46-0.50). In a low-risk group (AF prevalence 3%), the AUPRC decreased to 0.21 (95% CI: 0.18-0.24). In a high-risk group (AF prevalence 30%), the AUPRC increased to 0.76 (95% CI: 0.75-0.78). This performance was robust when validated in an external hospital. CONCLUSIONS: The approach of using an AI-enabled electrocardiographic algorithm for the identification of patients with underlying paroxysmal AF from ECGs in SR was independently validated.
Subject(s)
Atrial Fibrillation , Humans , Atrial Fibrillation/diagnosis , Artificial Intelligence , Electrocardiography/methods , Algorithms , ROC CurveSubject(s)
Antibodies, Viral , COVID-19 Vaccines/immunology , COVID-19/immunology , COVID-19/prevention & control , SARS-CoV-2/immunology , 2019-nCoV Vaccine mRNA-1273 , Adult , Antibody Formation/immunology , BNT162 Vaccine , Female , Health Personnel , Humans , Male , Prospective Studies , VaccinationABSTRACT
The LISA application, developed by the University Hospitals Leuven, permits referring physicians to consult the electronic medical records of their patients over the internet in a highly secure way. We decided to completely change the way we secured the application, discard the existing web application and build a completely new application, based on the in-house developed hospital information system, used in the University Hospitals Leuven. The result is a fat Java client, running on a Windows Terminal Server, secured by a commercial SSL-VPN solution.
