A structured stepwise model to distinguish pathogens from non-pathogens in blood culture isolates: A prospective clinical-microbiological study.

BACKGROUND

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-microbiological model to differentiate pathogenic from non-pathogenic organisms in blood cultures.

METHODS

The study aimed to determine proportions of pathogens and non-pathogens in automated blood culture isolates. In this prospective cohort study at a tertiary hospital in northern India, blood culture-positive adults were evaluated in 2024. Organisms were classified as pathogenic or non-pathogenic using the SOFA score, time to positivity (TTP), and site concordance, through a seven-step algorithm, with a 28-day follow-up.

RESULTS

Among 1600 blood culture samples received, 205 isolates were positive for an organism, 160 (78%) were identified as pathogenic, and 45 (22%) as non-pathogenic. The most common pathogens were Klebsiella (20.0%), Acinetobacter (9.3%), and Pseudomonas (6.3%), while non-pathogens were mainly coagulase-negative staphylococci (CONS, 18.5%) and Stenotrophomonas (8.3%). Mean TTP was significantly shorter in pathogens (16.3 ± 8.0 h; bacterial pathogens with 15.01 h, while fungal pathogens with 26.33 h) compared to non-pathogens (21.5 ± 10.1 h; p < 0.001). Discordance was observed in 7 cases (3.4%) where clinicians labeled isolates as non-pathogens but microbiologists disagreed, and in 26 cases (12.7%) with the opposite interpretation. Overall agreement was 65.4%, with a Cohen's kappa of 0.28.

CONCLUSION

This 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 healthcare settings.