Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits

Urine culture is often considered the gold standard for detecting the presence of bacteria in the urine. Since culture is expensive and often requires 24-48 hours, clinicians often rely on urine dipstick test, which is considerably cheaper than culture and provides instant results. Despite its ease of use, urine dipstick test may lack sensitivity and specificity. In this paper, we use a real-world dataset consisting of 17,572 outpatient encounters who underwent urine cultures, collected between 2015 and 2021 at a large multi-specialty hospital in Abu Dhabi, United Arab Emirates. We develop and evaluate a simple parsimonious prediction model for positive urine cultures based on a minimal input set of ten features selected from the patient’s presenting vital signs, history, and dipstick results. In a test set of 5,339 encounters, the parsimonious model achieves an area under the receiver operating characteristic curve (AUROC) of 0.828 (95% CI: 0.810-0.844) for predicting a bacterial count ≥ 105 CFU/ml, outperforming a model that uses dipstick features only that achieves an AUROC of 0.786 (95% CI: 0.769-0.806). Our proposed model can be easily deployed at point-of-care, highlighting its value in improving the efficiency of clinical workflows, especially in low-resource settings.Author summary: Urine culture tests are often ordered to help early detection of bacteria in the urine in various clinical settings. Notwithstanding their importance in clinical decision-making, urine culture tests add cost and burden on medical staff as they require a long waiting time. In this work, we propose a low-cost machine learning model to provide real-time predictions of urine culture results at point-of-care. The proposed approach is based on a simple model that requires a minimal feature set, making it easy to implement in real-clinical settings. By developing and validating the model on real-world outpatient data from Abu Dhabi, we found that our model outperformed the clinical baselines. Our findings underscore the potential of machine learning models in optimizing clinical workflow efficiency by providing timely predictions.