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- Title
Can the application of machine learning to electronic health records guide antibiotic prescribing decisions for suspected urinary tract infection in the Emergency Department?
- Authors
Rockenschaub, Patrick; Gill, Martin J.; McNulty, Dave; Carroll, Orlagh; Freemantle, Nick; Shallcross, Laura
- Abstract
Urinary tract infections (UTIs) are a major cause of emergency hospital admissions, but it remains challenging to diagnose them reliably. Application of machine learning (ML) to routine patient data could support clinical decision-making. We developed a ML model predicting bacteriuria in the ED and evaluated its performance in key patient groups to determine scope for its future use to improve UTI diagnosis and thus guide antibiotic prescribing decisions in clinical practice. We used retrospective electronic health records from a large UK hospital (2011–2019). Non-pregnant adults who attended the ED and had a urine sample cultured were eligible for inclusion. The primary outcome was predominant bacterial growth ≥104 cfu/mL in urine. Predictors included demography, medical history, ED diagnoses, blood tests, and urine flow cytometry. Linear and tree-based models were trained via repeated cross-validation, re-calibrated, and validated on data from 2018/19. Changes in performance were investigated by age, sex, ethnicity, and suspected ED diagnosis, and compared to clinical judgement. Among 12,680 included samples, 4,677 (36.9%) showed bacterial growth. Relying primarily on flow cytometry parameters, our best model achieved an area under the ROC curve (AUC) of 0.813 (95% CI 0.792–0.834) in the test data, and achieved both higher sensitivity and specificity compared to proxies of clinician's judgement. Performance remained stable for white and non-white patients but was lower during a period of laboratory procedure change in 2015, in patients ≥65 years (AUC 0.783, 95% CI 0.752–0.815), and in men (AUC 0.758, 95% CI 0.717–0.798). Performance was also slightly reduced in patients with recorded suspicion of UTI (AUC 0.797, 95% CI 0.765–0.828). Our results suggest scope for use of ML to inform antibiotic prescribing decisions by improving diagnosis of suspected UTI in the ED, but performance varied with patient characteristics. Clinical utility of predictive models for UTI is therefore likely to differ for important patient subgroups including women <65 years, women ≥65 years, and men. Tailored models and decision thresholds may be required that account for differences in achievable performance, background incidence, and risks of infectious complications in these groups. Author summary: Urinary tract infections (UTIs) often lead to emergency hospital visits, but they can be difficult to diagnose. As a result, antibiotics are often prescribed inappropriately. We created a machine learning model to help doctors better diagnose UTIs and prescribe antibiotics only when needed. We used health records from a large UK hospital and considered factors such as patient age, sex, ethnicity, urinary symptoms, laboratory tests, and medical history. Our model was good at predicting UTIs and performed better than doctors' guesses in many cases. It worked well for both white patients and ethnic minorities, but there were some differences in how well it did for older people, men, and patients who already had a suspected UTI. In summary, our study suggests that machine learning can help improve UTI diagnosis and antibiotic prescribing decisions in the emergency department. However, we might need to customize the model for different patient groups, as its performance varied based on patient characteristics.
- Subjects
URINARY tract infection diagnosis; ANTIBIOTICS; HOSPITAL emergency services; CONFIDENCE intervals; URINARY tract infections; MACHINE learning; RETROSPECTIVE studies; TREATMENT effectiveness; DRUGS; DESCRIPTIVE statistics; RESEARCH funding; ELECTRONIC health records; DECISION making in clinical medicine; PREDICTION models; DATA analysis software
- Publication
PLoS Digital Health, 2023, Vol 1, Issue 6, p1
- ISSN
2767-3170
- Publication type
Article
- DOI
10.1371/journal.pdig.0000261