Development of risk prediction models to predict urine culture growth for adults with suspected urinary tract infection in the emergency department: protocol for an electronic health record study from a single UK university hospital

Diagnostic and Prognostic Research

Orlagh Carroll¹  Martin J. Gill²  David McNulty³  Nick Freemantle⁴  Laura Shallcross⁵  Patrick Rockenschaub⁵ 

[1] Department of Medical Statistics, London School of Hygiene and Tropical Medicine, Keppel Street, WC1E 7HT, London, UK;Department of Microbiology, Queen Elizabeth Hospital Birmingham, University Hospitals Birmingham NHS Foundation Trust, Mindelsohn Way, B15 2TH, Birmingham, UK;Health Informatics, University Hospitals Birmingham NHS Foundation Trust, 11-13 Frederick Road, Edgbaston, B15 1JD, Birmingham, UK;Institute of Clinical Trials and Methodology, University College London, 90 High Holborn, WC1V 6LJ, London, UK;Institute of Health Informatics, University College London, 222 Euston Road, NW1 2DA, London, UK;

关键词: Protocol;    Diagnosis;    Urinary tract infection;    Prediction models;    Hospital;   

DOI  :  10.1186/s41512-020-00083-2

来源: Springer

【 摘 要 】

BackgroundUrinary tract infection (UTI) is a leading cause of hospital admissions and is diagnosed based on urinary symptoms and microbiological cultures. Due to lags in the availability of culture results of up to 72 h, and the limitations of routine diagnostics, many patients with suspected UTI are started on antibiotic treatment unnecessarily. Predictive models based on routinely collected clinical information may help clinicians to rule out a diagnosis of bacterial UTI in low-risk patients shortly after hospital admission, providing additional evidence to guide antibiotic treatment decisions.MethodsUsing electronic hospital records from Queen Elizabeth Hospital Birmingham (QEHB) collected between 2011 and 2017, we aim to develop a series of models that estimate the probability of bacterial UTI at presentation in the emergency department (ED) among individuals with suspected UTI syndromes. Predictions will be made during ED attendance and at different time points after hospital admission to assess whether predictive performance may be improved over time as more information becomes available about patient status. All models will be externally validated for expected future performance using QEHB data from 2018/2019.DiscussionRisk prediction models using electronic health records offer a new approach to improve antibiotic prescribing decisions, integrating clinical and demographic data with test results to stratify patients according to their probability of bacterial infection. Used in conjunction with expert opinion, they may help clinicians to identify patients that benefit the most from early antibiotic cessation.

【 授权许可】

CC BY