Machine learning in diagnostic stewardship: A technical approach in improving diagnostic accuracy, optimizing antibiotic use in false positive CSF cultures.

Diagnostic stewardship emphasises ordering the right tests, at the right time for the patient and also promotes the judicious use of rapid and accurate molecular diagnostic tools to enable the initiation of proper antibiotic therapy, while avoiding excessive use of broad-spectrum antibiotics hence antimicrobial stewardship. Proper interpretation of the test is crucial to avoid over-diagnosis and excessive healthcare costs. This study aimed to access the CSF diagnostic accuracy by evaluating contamination rate through machine learning models, including ROC analysis and principal component analysis (PCA). Multivariate correlation analysis showed glucose was negatively correlated with lactate and protein. Receiver operating characteristic curve analysis demonstrated high model performance, with Area Under the Curve values of 0.956 for pathogen detection, 0.971 for no-growth, and 0.955 for contamination. The principal component analysis revealed a variance of around 61% for the data set and identified unique pattern for few organisms, potentially supporting early infection detection. By improving the identification of true infections in CSF samples, this approach contributes in reducing false positives and enhancing diagnostic stewardship.