Abstract
BACKGROUND: Clinical decision-making in urinary tract infections depends heavily on accurately distinguishing between pathogenic and non-pathogenic organisms. The interpretation of urine culture results is influenced by proper sample collection, the patient's clinical context, and organism-specific characteristics. However, there is currently no definitive method to determine whether a urinary isolate is truly pathogenic. This distinction is critical, as treatment decisions hinge on it. This pioneering study systematically applies a stepwise model to differentiate pathogenic from non-pathogenic urinary isolates-an approach not previously described. AIM: To determine whether a urinary isolate is pathogenic (commensal, colonizer, or direct pathogen) or non-pathogenic (commensal, colonizer, or contaminant) using a structured, stepwise approach. METHODS: This prospective, longitudinal, exploratory study was conducted over 24 months, starting in January 2022, at All India Institute of Medical Sciences Rishikesh, following approval from the Institutional Ethics Committee. A stepwise model developed by the investigators was applied to assess the nature of the isolates. Data recorded using REDCap, and analysis was performed using SPSS Version 25. RESULTS: A total of 275 consecutive patients aged over 18 years with positive urine cultures-initially treated with antibiotics based on microbiological and clinical assessment-were included. The stepwise model classified 90.54% of cases as pathogenic (commensals: 61.81%, colonizers: 14.18%, and direct pathogens: 14.54%) and 9.45% as non-pathogenic. The model showed that there could be a significant reduction in average hospital stay by over 13 days, along with saving approximately Rs. 981 per patient in antibiotic costs in non-pathogenic cohort. CONCLUSION: This novel model identified that approximately one in ten urinary isolates, initially considered pathogenic and treated with antibiotics, were in fact non-pathogenic. The model is safe, feasible, and potentially valuable in resource-limited settings, warranting broader validation and implementation.