Existing Data Sources in Clinical Epidemiology: Database of Community Acquired Infections Requiring Hospital Referral in Eastern Denmark (DCAIED) 2018-2021.
Jon Gitz HollerJens-Ulrik Stæhr JensenFrederik Neess EngsigMorten Heiberg BestleBirgitte LindegaardJens Henning RasmussenHenning BundgaardFinn Erland NielsenKasper Karmark IversenJesper Juul LarsenBarbara Juliane HolzknechtJonas Bredtoft BoelPradeesh SivapalanTheis Skovsgaard ItenovPublished in: Clinical epidemiology (2023)
Infectious diseases are major health care challenges globally and a prevalent cause of admission to emergency departments. Epidemiologic characteristics and outcomes based on population level data are limited. The Database of Community Acquired Infections in Eastern Denmark (DCAIED) 2018-2021 was established with the aim to explore and estimate the population characteristics, and outcomes of patients suffering from community acquired infections at the emergency departments in the Capital Region and the Zealand Region of Denmark using data from electronic medical records. Adult patients (≥18 years) presenting to the emergency department with suspected or confirmed infection are included in the cohort. Presence of sepsis and organ failure are assessed using modified criteria from the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). During the inclusion period from January 2018 to January 2022, 2,241,652 adult emergency department visits have been registered. Of these, 451,825 were unique encounters of which 60,316 fulfilled criteria of suspected infection and 28,472 fulfilled sepsis criteria and 8,027 were defined as septic shock. The database covers the entire Capital and Zealand Region of Denmark with an uptake area of 2.6 million inhabitants and includes demographic, laboratory and outcome indicators, with complete follow-up. The database is well-suited for epidemiological research for future national and international collaborations.
Keyphrases
- septic shock
- emergency department
- adverse drug
- healthcare
- electronic health record
- infectious diseases
- mental health
- end stage renal disease
- big data
- pulmonary embolism
- chronic kidney disease
- newly diagnosed
- south africa
- ejection fraction
- primary care
- prognostic factors
- peritoneal dialysis
- type diabetes
- intensive care unit
- case report
- acute kidney injury
- artificial intelligence
- adipose tissue
- drug induced
- deep learning
- patient reported