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Objectives We incorporated the Korean Electronic Data Interchange (EDI) vocabulary into Observational Medical Outcomes Partnership (OMOP) vocabulary using a semi-automated process. The goal of this study was to improve the Korean EDI as a standard medical ontology in Korea. Methods We incorporated the EDI vocabulary into OMOP vocabulary through four main steps. First, we improved the current classification of EDI domains and separated medical services into procedures and measurements. Second, each EDI concept was assigned a unique identifier and validity dates. Third, we built a vertical hierarchy between EDI concepts, fully describing child concepts through relationships and attributes and linking them to parent terms. Finally, we added an English definition for each EDI concept. We translated the Korean definitions of EDI concepts using Google.Cloud.Translation.V3, using a client library and manual translation. We evaluated the EDI using 11 auditing criteria for controlled vocabularies. Results We incorporated 313,431 concepts from the EDI to the OMOP Standardized Vocabularies. For 10 of the 11 auditing criteria, EDI showed a better quality index within the OMOP vocabulary than in the original EDI vocabulary. Conclusions The incorporation of the EDI vocabulary into the OMOP Standardized Vocabularies allows better standardization to facilitate network research. Our research provides a promising model for mapping Korean medical information into a global standard terminology system, although a comprehensive mapping of official vocabulary remains to be done in the future.

Comorbid conditions appear to be common among individuals hospitalised with coronavirus disease 2019 (COVID-19) but estimates of prevalence vary and little is known about the prior medication use of patients. Here, we describe the characteristics of adults hospitalised with COVID-19 and compare them with influenza patients. We include 34,128 (US: 8362, South Korea: 7341, Spain: 18,425) COVID-19 patients, summarising between 4811 and 11,643 unique aggregate characteristics. COVID-19 patients have been majority male in the US and Spain, but predominantly female in South Korea. Age profiles vary across data sources. Compared to 84,585 individuals hospitalised with influenza in 2014-19, COVID-19 patients have more typically been male, younger, and with fewer comorbidities and lower medication use. While protecting groups vulnerable to influenza is likely a useful starting point in the response to COVID-19, strategies will likely need to be broadened to reflect the particular characteristics of individuals being hospitalised with COVID-19., Detailed knowledge of the characteristics of COVID-19 patients helps with public health planning. Here, the authors use routinely-collected data from seven databases in three countries to describe the characteristics of >30,000 patients admitted with COVID-19 and compare them with those admitted for influenza in previous years.

Importance COVID-19 is causing high mortality worldwide. Developing models to quantify the risk of poor outcomes in infected patients could help develop strategies to shield the most vulnerable during de-confinement. Objective To develop and externally validate COVID-19 Estimated Risk (COVER) scores that quantify a patient9s risk of hospital admission (COVER-H), requiring intensive services (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis. Design Multinational, distributed network cohorts. Setting We analyzed a federated network of electronic medical records and administrative claims data from 13 data sources and 6 countries, mapped to a common data model. Participants Model development used a patient population consisting of >2 million patients with a general practice (GP), emergency room (ER), or outpatient (OP) visit with diagnosed influenza or flu-like symptoms any time prior to 2020. The model was validated on patients with a GP, ER, or OP visit in 2020 with a confirmed or suspected COVID-19 diagnosis across four databases from South Korea, Spain and the United States. Outcomes Age, sex, historical conditions, and drug use prior to index date were considered as candidate predictors. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. Results Overall, 43,061 COVID-19 patients were included for model validation, after initial model development and validation using 6,869,127 patients with influenza or flu-like symptoms. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, and kidney disease) which combined with age and sex could discriminate which patients would experience any of our three outcomes. The models achieved high performance in influenza. When transported to COVID-19 cohorts, the AUC ranges were, COVER-H: 0.73-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.82-0.90. Calibration was overall acceptable, with overestimated risk in the most elderly and highest risk strata. Conclusions and relevance A 9-predictor model performs well for COVID-19 patients for predicting hospitalization, intensive services and death. The models could aid in providing reassurance for low risk patients and shield high risk patients from COVID-19 during de-confinement to reduce the virus9 impact on morbidity and mortality.

IMPORTANCE: Current guidelines recommend ticagrelor as the preferred P2Y12 platelet inhibitor for patients with acute coronary syndrome (ACS), primarily based on a single large randomized clinical trial. The benefits and risks associated with ticagrelor vs clopidogrel in routine practice merits attention. OBJECTIVE: To determine the association of ticagrelor vs clopidogrel with ischemic and hemorrhagic events in patients undergoing percutaneous coronary intervention (PCI) for ACS in clinical practice. DESIGN, SETTING, AND PARTICIPANTS: A retrospective cohort study of patients with ACS who underwent PCI and received ticagrelor or clopidogrel was conducted using 2 United States electronic health record–based databases and 1 nationwide South Korean database from November 2011 to March 2019. Patients were matched using a large-scale propensity score algorithm, and the date of final follow-up was March 2019. EXPOSURES: Ticagrelor vs clopidogrel. MAIN OUTCOMES AND MEASURES: The primary end point was net adverse clinical events (NACE) at 12 months, composed of ischemic events (recurrent myocardial infarction, revascularization, or ischemic stroke) and hemorrhagic events (hemorrhagic stroke or gastrointestinal bleeding). Secondary outcomes included NACE or mortality, all-cause mortality, ischemic events, hemorrhagic events, individual components of the primary outcome, and dyspnea at 12 months. The database-level hazard ratios (HRs) were pooled to calculate summary HRs by random-effects meta-analysis. RESULTS: After propensity score matching among 31 290 propensity-matched pairs (median age group, 60-64 years; 29.3% women), 95.5% of patients took aspirin together with ticagrelor or clopidogrel. The 1-year risk of NACE was not significantly different between ticagrelor and clopidogrel (15.1% [3484/23 116 person-years] vs 14.6% [3290/22 587 person-years]; summary HR, 1.05 [95% CI, 1.00-1.10]; P = .06). There was also no significant difference in the risk of all-cause mortality (2.0% for ticagrelor vs 2.1% for clopidogrel; summary HR, 0.97 [95% CI, 0.81-1.16]; P = .74) or ischemic events (13.5% for ticagrelor vs 13.4% for clopidogrel; summary HR, 1.03 [95% CI, 0.98-1.08]; P = .32). The risks of hemorrhagic events (2.1% for ticagrelor vs 1.6% for clopidogrel; summary HR, 1.35 [95% CI, 1.13-1.61]; P = .001) and dyspnea (27.3% for ticagrelor vs 22.6% for clopidogrel; summary HR, 1.21 [95% CI, 1.17-1.26]; P