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

PLOS Digit Health. 2023 Nov 1;2(11):e0000306. doi: 10.1371/journal.pdig.0000306. eCollection 2023 Nov.

Abstract

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.

Grants and funding

This work was supported by the NYUAD Center for Interacting Urban Networks (CITIES) funded by Tamkeen under the NYUAD Research Institute Award CG001 (to F.E.S, G.O.G, P.W, & V.N.L), and the NYUAD Center for Artificial Intelligence & Robotics (CAIR) funded by Tamkeen under the NYUAD Research Institute Award CG010 (to F.E.S). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.