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Atrial fibrillation (AF) is the leading cause of cardioembolic stroke and is associated with increased stroke severity and fatality. Early identification of AF is essential for adequate secondary prevention but remains challenging due to its often asymptomatic or paroxysmal occurrence. Artificial intelligence (AI) offers new possibilities by integrating biomarkers, clinical phenotypes, established risk factors and imaging features to define a personalised ‘digital twin’ model. The TAILOR study aims to (1) examine prospective detection of AF using monitoring devices, (2) investigate novel prognostic MRI markers in patients with an AF-related stroke (AFRS) and (3) validate AI-based models for outcome prediction in AFRS.
This prospective multicentre observational cohort study includes patients aged 40 years and above, with neuroimaging-confirmed diagnosis of ischaemic stroke, recruited from two sites: Hospital del Mar Barcelona (Spain) and Radboud University Medical Centre (The Netherlands). For the first sub-study (n=300), patients will undergo clinical assessment at baseline, 3 months and 12 months, and patch-based or Holter cardiac monitoring. The second sub-study (n=200) involves repeated brain MRI and cognitive examination after AFRS. Finally, AI-driven ‘digital twin’ models developed on retrospective TARGET datasets will be prospectively evaluated in TAILOR using temporal and centre-stratified analyses for advanced predictive tools for AF and AFRS outcomes.
The TAILOR study was approved by local ethics boards in Barcelona (CPMP/ICH/135/95) and Medical Research Ethics Committee Oost-Nederland (NL86346.091.24). Patients will be included after providing informed consent. Study results will be presented in peer-reviewed journals and at global conferences.
Due to the growing use of high-dimensional data and methodological advances in medical research, reproducibility of research is increasingly dependent on the availability of reproducible code. However, code is rarely made available and too often only partly reproducible. Here, we aim to provide practical and easily implementable recommendations for medical researchers to improve the reproducibility of their code. We reviewed current coding practices in the population-based Rotterdam Study cohort. Based on this review, we formulated the following five recommendations to improve the reproducibility of code used in data analysis: (1) make reproducibility a priority and allocate time and resources; (2) implement systematic code review by peers, as it further strengthens reproducibility. We provide a code review checklist, which serves as a practical tool to facilitate structured code review; (3) write comprehensible code that is well-structured; (4) report decisions transparently, for instance by providing the annotated workflow code for data cleaning, formatting and sample selection; and (5) focus on accessibility of code and data and share both, when possible, via an open repository to foster accessibility. Ideally, this repository should be managed by the institution and should be accessible to everyone. Based on these five recommendations, medical researchers can take actionable steps to improve the reproducibility of their research. Importantly, these recommendations are thought to provide a practical starting point for enhancing reproducibility rather than mandatory guidelines.
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