Purpose: During the first waves of the coronavirus pandemic, evidence on potential effective treatments was urgently needed. Results from observational studies on the effectiveness of... Show morePurpose: During the first waves of the coronavirus pandemic, evidence on potential effective treatments was urgently needed. Results from observational studies on the effectiveness of hydroxychloroquine (HCQ) were conflicting, potentially due to biases. We aimed to assess the quality of observational studies on HCQ and its relation to effect sizes.Methods: PubMed was searched on 15 March 2021 for observational studies on the effectiveness of in-hospital use of HCQ in COVID-19 patients, published between 01/01/2020 and 01/03/2021 on. Study quality was assessed using the ROBINS-I tool. Association between study quality and study characteristics (journal ranking, publication date, and time between submission and publication) and differences between effects sizes found in observational studies compared to those found in RCTs, were assessed using Spearman's correlation.Results: Eighteen of the 33 (55%) included observational studies were scored as critical risk of bias, eleven (33%) as serious risk and only four (12%) as moderate risk of bias. Biases were most often scored as critical in the domains related to selection of participants (n = 13, 39%) and bias due to confounding (n = 8, 24%). There were no significant associations found between the study quality and the characteristics nor between the study quality and the effect estimates.Discussion: Overall, the quality of observational HCQ studies was heterogeneous. Synthesis of evidence of effectiveness of HCQ in COVID-19 should focus on RCTs and carefully consider the added value and quality of observational evidence. Show less
Electronic health record (EHR) data not only offer many exciting research opportunities but also come with their own inherent limitations. Researchers may not always realise the challenges... Show moreElectronic health record (EHR) data not only offer many exciting research opportunities but also come with their own inherent limitations. Researchers may not always realise the challenges associated with the use of EHR data for research, or the fact that using large datasets of 'real-world data' does not necessarily provide valuable real-world evidence. This article discusses some of the main differences between EHR data and data collected primarily for research purposes, and the challenges encountered when using EHR data for research. It also offers suggestions on how to deal with these challenges based on worked-out examples. It therefore serves as a quick guide for researchers interested in either reading or performing EHR-based research. Show less
Background: Knowledge about adverse drug reactions (ADRs) in the population is limited because of underreporting, which hampers surveillance and assessment of drug safety. Therefore, gathering... Show moreBackground: Knowledge about adverse drug reactions (ADRs) in the population is limited because of underreporting, which hampers surveillance and assessment of drug safety. Therefore, gathering accurate information that can be retrieved from clinical notes about the incidence of ADRs is of great relevance. However, manual labeling of these notes is time-consuming, and automatization can improve the use of free-text clinical notes for the identification of ADRs. Furthermore, tools for language processing in languages other than English are not widely available. Objective: The aim of this study is to design and evaluate a method for automatic extraction of medication and Adverse Drug Reaction Identification in Clinical Notes (ADRIN). Methods: Dutch free-text clinical notes (N=277,398) and medication registrations (N=499,435) from the Cardiology Centers of the Netherlands database were used. All clinical notes were used to develop word embedding models. Vector representations of word embedding models and string matching with a medical dictionary (Medical Dictionary for Regulatory Activities [MedDRA]) were used for identification of ADRs and medication in a test set of clinical notes that were manually labeled. Several settings, including search area and punctuation, could be adjusted in the prototype to evaluate the optimal version of the prototype. Results: The ADRIN method was evaluated using a test set of 988 clinical notes written on the stop date of a drug. Multiple versions of the prototype were evaluated for a variety of tasks. Binary classification of ADR presence achieved the highest accuracy of 0.84. Reduced search area and inclusion of punctuation improved performance, whereas incorporation of the MedDRA did not improve the performance of the pipeline. Conclusions: The ADRIN method and prototype are effective in recognizing ADRs in Dutch clinical notes from cardiac diagnostic screening centers. Surprisingly, incorporation of the MedDRA did not result in improved identification on top of word embedding models. The implementation of the ADRIN tool may help increase the identification of ADRs, resulting in better care and saving substantial health care costs. Show less
Introduction Chest pain or discomfort affects 20%-40% of the general population over the course of their life and may be a symptom of myocardial ischaemia. For the diagnosis of obstructive... Show moreIntroduction Chest pain or discomfort affects 20%-40% of the general population over the course of their life and may be a symptom of myocardial ischaemia. For the diagnosis of obstructive macrovascular coronary artery disease (CAD), algorithms have been developed; however, these do not exclude microvascular angina. This may lead to false reassurance of symptomatic patients, mainly women, with functionally significant, yet non-obstructive coronary vascular disease. Therefore, this study aims to estimate the prevalence of both macrovascular and microvascular coronary vascular disease in women and men presenting with chest pain or discomfort, and to subsequently develop a decision-support tool to aid cardiologists in referral to cardiovascular imaging for both macrovascular and microvascular CAD evaluation. Methods and analysis Women and men with chest pain or discomfort, aged 45 years and older, without a history of cardiovascular disease, who are referred to an outpatient cardiology clinic by their general practitioner are eligible for inclusion. Coronary CT angiography is used for anatomical imaging. Additionally, myocardial perfusion imaging by adenosine stress cardiac MRI is performed to detect functionally significant coronary vascular disease. Electronic health record data, collected during regular cardiac work-up, including medical history, cardiovascular risk factors, physical examination, echocardiography, (exercise) ECG and blood samples for standard cardiovascular biomarkers and research purposes, are obtained. Participants will be classified as positive or negative for coronary vascular disease based on all available data by expert panel consensus (a cardiovascular radiologist and two cardiologists). After completion of the clinical study, all collected data will be used to develop a decision support tool using predictive modelling and machine-learning techniques. Ethics and dissemination The study protocol was approved by the Institutional Review Board of the University Medical Center Utrecht. Results will be disseminated through national and international conferences and in peer-reviewed journals in cardiovascular disease. Show less
