Objective: This study aimed to validate trial patient eligibility screening and baseline data collection using text-mining in electronic healthcare records (EHRs), comparing the results to those of... Show moreObjective: This study aimed to validate trial patient eligibility screening and baseline data collection using text-mining in electronic healthcare records (EHRs), comparing the results to those of an international trial.Study Design and Setting: In three medical centers with different EHR vendors, EHR-based text-mining was used to automatically screen patients for trial eligibility and extract baseline data on nineteen characteristics. First, the yield of screening with automated EHR text-mining search was compared with manual screening by research personnel. Second, the accuracy of extracted baseline data by EHR text mining was compared to manual data entry by research personnel.Results: Of the 92,466 patients visiting the out-patient cardiology departments, 568 (0.6%) were enrolled in the trial during its recruitment period using manual screening methods. Automated EHR data screening of all patients showed that the number of patients needed to screen could be reduced by 73,863 (79.9%). The remaining 18,603 (20.1%) contained 458 of the actual participants (82.4% of participants). In trial participants, automated EHR text-mining missed a median of 2.8% (Interquartile range [IQR] across all variables 0.4-8.5%) of all data points compared to manually collected data. The overall accuracy of automatically extracted data was 88.0% (IQR 84.7-92.8%).Conclusion: Automatically extracting data from EHRs using text-mining can be used to identify trial participants and to collect baseline information. (C) 2020 The Authors. Published by Elsevier Inc. Show less
Linschoten, M.; Peters, S.; Smeden, M. van; Jewbali, L.S.; Schaap, J.; Siebelink, H.M.; ... ; CAPACITY-COVID Collaborative Conso 2020
Aims:To determine the frequency and pattern of cardiac complications in patients hospitalised with coronavirus disease (COVID-19).Methods and results:CAPACITY-COVID is an international patient... Show moreAims:To determine the frequency and pattern of cardiac complications in patients hospitalised with coronavirus disease (COVID-19).Methods and results:CAPACITY-COVID is an international patient registry established to determine the role of cardiovascular disease in the COVID-19 pandemic. In this registry, data generated during routine clinical practice are collected in a standardised manner for patients with a (highly suspected) severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection requiring hospitalisation. For the current analysis, consecutive patients with laboratory confirmed COVID-19 registered between 28 March and 3 July 2020 were included. Patients were followed for the occurrence of cardiac complications and pulmonary embolism from admission to discharge. In total, 3011 patients were included, of which 1890 (62.8%) were men. The median age was 67 years (interquartile range 56-76); 937 (31.0%) patients had a history of cardiac disease, with pre-existent coronary artery disease being most common (n=463, 15.4%). During hospitalisation, 595 (19.8%) patients died, including 16 patients (2.7%) with cardiac causes. Cardiac complications were diagnosed in 349 (11.6%) patients, with atrial fibrillation (n=142, 4.7%) being most common. The incidence of other cardiac complications was 1.8% for heart failure (n=55), 0.5% for acute coronary syndrome (n=15), 0.5% for ventricular arrhythmia (n=14), 0.1% for bacterial endocarditis (n=4) and myocarditis (n=3), respectively, and 0.03% for pericarditis (n=1). Pulmonary embolism was diagnosed in 198 (6.6%) patients.Conclusion:This large study among 3011 hospitalised patients with COVID-19 shows that the incidence of cardiac complications during hospital admission is low, despite a frequent history of cardiovascular disease. Long-term cardiac outcomes and the role of pre-existing cardiovascular disease in COVID-19 outcome warrants further investigation. Show less
Siemelink, M.A.; Laan, S.W. van der; Haitjema, S.; Koeverden, I.D. van; Schaap, J.; Wesseling, M.; ... ; Pasterkamp, G. 2018
