Publications by authors named "Jan A Kors"

Background: Electronic health records (EHRs) consist of both structured data (eg, diagnostic codes) and unstructured data (eg, clinical notes). It is commonly believed that unstructured clinical narratives provide more comprehensive information. However, this assumption lacks large-scale validation and direct validation methods.

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Background: While medication errors (MEs) have been studied in the European Medicines Agency's EudraVigilance, extensive characterisation and signal detection based on sexes and age groups have not been attempted.

Objectives: The aim of this study was to characterise all ME-related individual case safety reports in EudraVigilance and explore notable signals of disproportionate reporting (SDRs) among sexes and age groups for the 30 most frequently reported drugs.

Methods: Individual case safety reports were used from EudraVigilance reported between 2002 and 2021.

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Limited population-based data on the gender differences and association between arteriosclerotic calcification at different sites and atrial fibrillation (AF) exist. We aimed to investigate the (gender-specific) associations between arteriosclerotic calcification at different sites with the risk of AF in the general population. Arteriosclerotic calcification was quantified using computed tomography examinations between 2003 and 2006 in 2,259 participants free of AF from the population-based Rotterdam Study.

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Background: Identifying bicuspid aortic valve (BAV) patients at risk for cardiac events remains challenging and the role of the electrocardiogram (ECG) has not yet been described. Therefore, this study aims to describe ECG parameters in BAV patients, and investigate their prognostic value.

Methods: In this single-center prospective study patients with BAV without a prior aortic valve replacement (AVR) were included.

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Article Synopsis
  • Researchers aimed to create and validate new models to predict the risk of dementia over the next five years, focusing on ease of implementation and lower complexity.
  • They used logistic regression models across five observational databases, employing regularization methods like L1 and Broken Adaptive Ridge (BAR) to improve model performance with different sets of predictors, including age, sex, and disease-related factors.
  • The study found that BAR was more effective for variable selection compared to L1 and that adding relevant predictors improved model accuracy, although results varied between German and US data, with the BAR model on the clinically relevant predictor set performing best overall.
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Objective: To explore the feasibility of validating Dutch concept extraction tools using annotated corpora translated from English, focusing on preserving annotations during translation and addressing the scarcity of non-English annotated clinical corpora.

Materials And Methods: Three annotated corpora were standardized and translated from English to Dutch using 2 machine translation services, Google Translate and OpenAI GPT-4, with annotations preserved through a proposed method of embedding annotations in the text before translation. The performance of 2 concept extraction tools, MedSpaCy and MedCAT, was assessed across the corpora in both Dutch and English.

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Background: Heart rate variability (HRV), an important marker of autonomic nervous system activity, is usually determined from electrocardiogram (ECG) recordings corrected for extrasystoles and artifacts. Especially in large population-based studies, computer-based algorithms are used to determine RR intervals. The Modular ECG Analysis System MEANS is a widely used tool, especially in large studies.

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Aims: Common genetic variations in the nitric oxide synthase-1 adaptor protein (NOS1AP) gene are associated with QT-interval prolongation. In a previous study, we observed an association between the rs10494366 variant of this gene and an increased QT-interval shortening in digoxin users. As QT-interval shortening is a risk factor for sudden cardiac death (SCD), in this study, we investigated whether the association between digoxin use and risk of SCD differs in participants with different NOS1AP rs10494366 genotypes.

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Objective: Observational studies using electronic health record (EHR) databases often face challenges due to unspecific clinical codes that can obscure detailed medical information, hindering precise data analysis. In this study, we aimed to assess the feasibility of refining these unspecific condition codes into more specific codes in a Dutch general practitioner (GP) EHR database by leveraging the available clinical free text.

Methods: We utilized three approaches for text classification-search queries, semi-supervised learning, and supervised learning-to improve the specificity of ten unspecific International Classification of Primary Care (ICPC-1) codes.

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Background: There is a lack of knowledge on how patients with asthma or chronic obstructive pulmonary disease (COPD) are globally treated in the real world, especially with regard to the initial pharmacological treatment of newly diagnosed patients and the different treatment trajectories. This knowledge is important to monitor and improve clinical practice.

Methods: This retrospective cohort study aims to characterise treatments using data from four claims (drug dispensing) and four electronic health record (EHR; drug prescriptions) databases across six countries and three continents, encompassing 1.

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Article Synopsis
  • Medication errors (MEs) pose significant health risks and financial strains in healthcare, making their characterization essential for developing prevention strategies.* -
  • A study examined ME reports in the FDA's Adverse Event Reporting System (FAERS) from 2004 to 2020, identifying 488,470 reports, predominantly from consumers, with a notable majority linked to females.* -
  • The research found that about one-third of reports involved serious health outcomes; the most common error was incorrect dosing, and adalimumab was frequently associated with MEs, indicating areas for future analysis and prevention efforts.*
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Monoclonal antibodies (mAbs) targeting immunoglobulin E (IgE) [omalizumab], type 2 (T2) cytokine interleukin (IL) 5 [mepolizumab, reslizumab], IL-4 Receptor (R) α [dupilumab], and IL-5R [benralizumab]), improve quality of life in patients with T2-driven inflammatory diseases. However, there is a concern for an increased risk of helminth infections. The aim was to explore safety signals of parasitic infections for omalizumab, mepolizumab, reslizumab, dupilumab, and benralizumab.

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Background: Higher resting heart rate has been described as a risk factor for adverse outcome in healthy individuals and cardiovascular patients. The aim of this study was to evaluate resting heart rate as risk factor in adult congenital heart disease (ACHD).

Methods: In this prospective observational cohort study, patients with moderate or complex ACHD were included at routine outpatient visit.

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Objective: This work aims to explore the value of Dutch unstructured data, in combination with structured data, for the development of prognostic prediction models in a general practitioner (GP) setting.

Materials And Methods: We trained and validated prediction models for 4 common clinical prediction problems using various sparse text representations, common prediction algorithms, and observational GP electronic health record (EHR) data. We trained and validated 84 models internally and externally on data from different EHR systems.

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Aims: We aimed to assess the (shape of the) association and sex differences in the link between electrocardiographic parameters and new-onset atrial fibrillation (AF).

Methods And Results: A total of 12 212 participants free of AF at baseline from the population-based Rotterdam Study were included. Up to five repeated measurements of electrocardiographic parameters including PR, QRS, QT, QT corrected for heart rate (QTc), JT, RR interval, and heart rate were assessed at baseline and follow-up examinations.

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We investigated a stacking ensemble method that combines multiple base learners within a database. The results on external validation across four large databases suggest a stacking ensemble could improve model transportability.

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Feature importance is often used to explain clinical prediction models. In this work, we examine three challenges using experiments with electronic health record data: computational feasibility, choosing between methods, and interpretation of the resulting explanation. This work aims to create awareness of the disagreement between feature importance methods and underscores the need for guidance to practitioners how to deal with these discrepancies.

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Background: Neuropsychiatric symptoms (NPS) are prevalent in the early clinical stages of Alzheimer's disease (AD) according to proxy-based instruments. Little is known about which NPS clinicians report and whether their judgment aligns with proxy-based instruments. We used natural language processing (NLP) to classify NPS in electronic health records (EHRs) to estimate the reporting of NPS in symptomatic AD at the memory clinic according to clinicians.

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Context: Hyperglycemia and autonomic dysfunction are bidirectionally related.

Objective: We investigated the association of longitudinal evolution of heart rate variability (HRV) with incident type 2 diabetes (T2D) among the general population.

Methods: We included 7630 participants (mean age 63.

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Article Synopsis
  • * Findings highlight important pathways connected to heart development, muscle contraction, and overall heart health related to these angles.
  • * The research also shows genetic links between QRS-T angles and various heart conditions like atrial fibrillation and blockages, suggesting potential areas for future research and risk assessments in cardiovascular health.
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Fabry disease (FD) is an X-linked, lysosomal storage disorder leading to severe cardiomyopathy in a significant proportion of patients. To identify ECG markers that reflect early cardiac involvement and disease progression, we conducted a long term retrospective study in a large cohort of FD patients. A total of 1995 ECGs from 133 patients with classical FD (64% females, 80% treated with enzyme replacement therapy), spanning 20 years of follow-up, were compared to ECGs from 3893 apparently healthy individuals.

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Background: Many dementia prediction models have been developed, but only few have been externally validated, which hinders clinical uptake and may pose a risk if models are applied to actual patients regardless. Externally validating an existing prediction model is a difficult task, where we mostly rely on the completeness of model reporting in a published article. In this study, we aim to externally validate existing dementia prediction models.

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Background: Vaccine information in European electronic health record (EHR) databases is represented using various clinical and database-specific coding systems and drug vocabularies. The lack of harmonization constitutes a challenge in reusing EHR data in collaborative benefit-risk studies about vaccines.

Methods: We designed an ontology of the properties that are commonly used in vaccine descriptions, called Ontology of Vaccine Descriptions (VaccO), with a dictionary for the analysis of multilingual vaccine descriptions.

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Background And Objectives: There is an increasing interest to use real-world data to illustrate how patients with specific medical conditions are treated in real life. Insight in the current treatment practices helps to improve and tailor patient care, but is often held back by a lack of data interoperability and a high-level of required resources. We aimed to provide an easy tool that overcomes these barriers to support the standardized development and analysis of treatment patterns for a wide variety of medical conditions.

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