Risk prediction models to guide antibiotic prescribing: a study on adult patients with uncomplicated upper respiratory tract infections in an emergency department

• Main Authors: Joshua Guoxian Wong, Aung-Hein Aung, Weixiang Lian, David Chien Lye, Chee-Kheong Ooi, Angela Chow
• Format: Article
• Language: English
• Published: BMC 2020-11-01
• Series: Antimicrobial Resistance and Infection Control
• Subjects: Adult; ED; URTI; Antibiotic prescribing; Machine learning; Prediction model
• Online Access: http://link.springer.com/article/10.1186/s13756-020-00825-3

Abstract Background Appropriate antibiotic prescribing is key to combating antimicrobial resistance. Upper respiratory tract infections (URTIs) are common reasons for emergency department (ED) visits and antibiotic use. Differentiating between bacterial and viral infections is not straightforward. We aim to provide an evidence-based clinical decision support tool for antibiotic prescribing using prediction models developed from local data. Methods Seven hundred-fifteen patients with uncomplicated URTI were recruited and analysed from Singapore’s busiest ED, Tan Tock Seng Hospital, from June 2016 to November 2018. Confirmatory tests were performed using the multiplex polymerase chain reaction (PCR) test for respiratory viruses and point-of-care test for C-reactive protein. Demographic, clinical and laboratory data were extracted from the hospital electronic medical records. Seventy percent of the data was used for training and the remaining 30% was used for validation. Decision trees, LASSO and logistic regression models were built to predict when antibiotics were not needed. Results The median age of the cohort was 36 years old, with 61.2% being male. Temperature and pulse rate were significant factors in all 3 models. The area under the receiver operating curve (AUC) on the validation set for the models were similar. (LASSO: 0.70 [95% CI: 0.62–0.77], logistic regression: 0.72 [95% CI: 0.65–0.79], decision tree: 0.67 [95% CI: 0.59–0.74]). Combining the results from all models, 58.3% of study participants would not need antibiotics. Conclusion The models can be easily deployed as a decision support tool to guide antibiotic prescribing in busy EDs.