Automated urinalysis for predicting urine culture positivity and presumptive Gram classification: a retrospective study at a cancer hospital
- 1Department of Clinical Laboratory Medicine, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
Abstract
Introduction: Automated urinalysis may help prioritize urine cultures, but whether multivariable models improve on bacterial count alone remains uncertain. We assessed their comparative performance and evaluated presumptive Gram classification in a cancer-hospital cohort.
Methods: We retrospectively analyzed 621 urine specimens from 477 patients with paired urinalysis and clinically requested cultures at a cancer hospital (June 2023–June 2024). Urinalysis measurements were obtained using the Sysmex UF-4000 and UC-3500. Culture outcomes were determined from final laboratory reports; 136 specimens classified as likely contaminated were excluded from the primary analysis. LASSO, random forest, and gradient boosting were compared with bacterial count alone using repeated, nested cross-validation grouped by patient. An exploratory temporal evaluation included later specimens from patients absent from the training cohort.
Results: The primary analysis included 485 specimens, of which 236 were culture-positive. Mean pooled cross-validated area under the receiver operating characteristic curve (AUC) was 0.892 for bacterial count. Corresponding AUCs were 0.895 for LASSO, 0.897 for random forest, and 0.893 for gradient boosting. Patient-cluster 95% confidence intervals for AUC differences versus bacterial count included zero. In temporal evaluation involving 114 specimens from 101 patients, AUCs were 0.927 (95% CI 0.877–0.965) for bacterial count and 0.919 (95% CI 0.860–0.963) for LASSO. Among 211 culture-positive specimens yielding a single bacterial organism, 153 (72.5%) had definite Gram classifications. Accuracy was 94.1% in this subset, compared with 85.6% for an always-Gram-negative prediction. Correct definite classifications were obtained for 68.2% of all 211 specimens.
Discussion: Bacterial count alone provided good discrimination of urine culture positivity in this cancer-hospital cohort, with no demonstrated improvement from multivariable models. Presumptive Gram classification added microbiological information for specimens with definite flags. These findings support prospective evaluation of bacterial count for urine culture triage, with Gram classification as an adjunct.
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BibTeX
@article{cai2026automated,
title = {{Automated urinalysis for predicting urine culture positivity and presumptive Gram classification: a retrospective study at a cancer hospital}},
author = {Xinjian Cai and Xijun Liu and Qiuxia Lu and Yiteng Lin},
journal = {Frontiers in Cellular and Infection Microbiology},
year = {2026},
month = oct,
doi = {10.3389/fcimb.2026.1935652},
url = {https://doi.org/10.3389/fcimb.2026.1935652},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Automated urinalysis for predicting urine culture positivity and presumptive Gram classification: a retrospective study at a cancer hospital
AU - Xinjian Cai
AU - Xijun Liu
AU - Qiuxia Lu
AU - Yiteng Lin
PY - 2026
DA - 2026/10/12
JO - Frontiers in Cellular and Infection Microbiology
DO - 10.3389/fcimb.2026.1935652
UR - https://doi.org/10.3389/fcimb.2026.1935652
AB - Introduction: Automated urinalysis may help prioritize urine cultures, but whether multivariable models improve on bacterial count alone remains uncertain. We assessed their comparative performance and evaluated presumptive Gram classification in a cancer-hospital cohort. Methods: We retrospectively analyzed 621 urine specimens from 477 patients with paired urinalysis and clinically requested cultures at a cancer hospital (June 2023–June 2024). Urinalysis measurements were obtained using the Sysmex UF-4000 and UC-3500. Culture outcomes were determined from final laboratory reports; 136 specimens classified as likely contaminated were excluded from the primary analysis. LASSO, random forest, and gradient boosting were compared with bacterial count alone using repeated, nested cross-validation grouped by patient. An exploratory temporal evaluation included later specimens from patients absent from the training cohort. Results: The primary analysis included 485 specimens, of which 236 were culture-positive. Mean pooled cross-validated area under the receiver operating characteristic curve (AUC) was 0.892 for bacterial count. Corresponding AUCs were 0.895 for LASSO, 0.897 for random forest, and 0.893 for gradient boosting. Patient-cluster 95% confidence intervals for AUC differences versus bacterial count included zero. In temporal evaluation involving 114 specimens from 101 patients, AUCs were 0.927 (95% CI 0.877–0.965) for bacterial count and 0.919 (95% CI 0.860–0.963) for LASSO. Among 211 culture-positive specimens yielding a single bacterial organism, 153 (72.5%) had definite Gram classifications. Accuracy was 94.1% in this subset, compared with 85.6% for an always-Gram-negative prediction. Correct definite classifications were obtained for 68.2% of all 211 specimens. Discussion: Bacterial count alone provided good discrimination of urine culture positivity in this cancer-hospital cohort, with no demonstrated improvement from multivariable models. Presumptive Gram classification added microbiological information for specimens with definite flags. These findings support prospective evaluation of bacterial count for urine culture triage, with Gram classification as an adjunct.
SN - 2235-2988
ER -