Stepwise explorative model to determine pathogenicity of cultured blood isolates

Blood culture remains the gold standard for diagnosing bloodstream infections (BSIs); however, distinguishing true pathogens from contaminants remains a critical challenge. Misclassification can lead to inappropriate antimicrobial use, prolonged hospitalization, and increased healthcare burden. This study proposes a structured, stepwise clinical-pathological model to differentiate pathogenic from non-pathogenic organisms in blood cultures. In this prospective study at a tertiary hospital in northern India, 205 blood culture-positive adults were evaluated between August and October 2024. Organisms were classified as pathogenic or non-pathogenic using microbiological data and clinical indicators, including SOFA score, time to positivity, and site concordance, through a seven-step algorithm with 28-day outcome follow-up. Among 1600 blood culture samples received, 205 isolates were positive for an organism, 160 (78.0%) were identified as pathogenic and 45 (22.0%) as non-pathogenic. The most common pathogens were Klebsiella pneumoniae (20.0%), Acinetobacter baumannii (9.3%), and Pseudomonas aeruginosa (6.3%), while non-pathogens were mainly coagulase-negative staphylococci (CONS, 18.5%) and Stenotrophomonas maltophilia (8.3%). Mean Time to Positivity (TTP) was significantly shorter in pathogens (16.3 {+/-} 8.0 hours) compared to non-pathogens (21.5 {+/-} 10.1 hours; p < 0.001). Discordance was observed in 7 cases (3.4%) where clinicians labelled isolates as non-pathogens but microbiologists disagreed, and in 26 cases (12.7%) with the opposite interpretation. Overall agreement was 65.4%, with a Cohens kappa of 0.25, indicating fair inter-rater reliability. This stepwise clinical-microbiological model offers an effective framework for distinguishing pathogens from non-pathogens in BSIs. Incorporating SOFA score, TTP, and culture concordance enhances diagnostic stewardship, informs antimicrobial decisions, and supports prognostication, especially in resource-limited and high-burden healthcare settings.