Urine volatile organic compounds profiling via GC-IMS combined with machine learning: a powerful diagnostic and...

The diagnostic delay associated with standard urine culture necessitates rapid, accurate alternatives for urinary tract infection (UTI) management. Volatile organic compounds (VOCs) emitted by microbes represent a promising source of metabolic biomarkers for infection diagnosis.

To develop and validate a diagnostic model for UTI by integrating urine VOCs profiles obtained via gas chromatography-ion mobility spectrometry (GC-IMS) with clinical features using machine learning.

We conducted a prospective cohort study of 258 adults with suspected UTI. Clean-catch midstream urine samples were collected for clinical urinalysis, culture (reference standard), and GC-IMS-based VOCs analysis. VOCs and clinical data were used to train and test machine learning models (Logistic Regression, Random Forest, Support Vector Machine). Model performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and decision curve analysis.

Among 258 enrolled patients, 152 (58.9%) were culture-positive. We identified 11 differentially expressed VOCs between infected and non-infected groups, with acetic acid, benzaldehyde, and furan being the most significant (Bonferroni-adjusted p < 0.05). A Random Forest model integrating both VOCs and clinical features demonstrated superior performance (AUC of 0.914, with an accuracy of 82.1% (95% CI: 71.8-89.8%), sensitivity of 87.0%, specificity of 75.0%, and an F1-score of 0.851) compared to models using clinical-only (AUC 0.831) or VOC-only (AUC 0.850). Multivariate analysis confirmed acetic acid (OR 3.27) and benzaldehyde (OR 4.95) as strong independent predictors of UTI. Furthermore, VOCs profiles allowed moderate discrimination between Gram-positive and Gram-negative bacterial infections (AUC 0.800) and exhibited pathogen-specific patterns.

The integration of urine VOCs profiles obtained by GC-IMS with routine clinical parameters using machine learning achieves high diagnostic accuracy for UTI and shows potential for rapid pathogen differentiation. This strategy could improve UTI diagnostics, enabling faster, more precise antibiotic therapy.