Stepwise model to differentiate pathogenic from non-pathogenic organisms in lower-respiratory isolates: Effectiveness and feasibility prospective cohort study

BACKGROUND: Lower respiratory tract infections remain a major cause of morbidity and mortality among hospitalized patients. However, isolating organisms from respiratory samples often leads to diagnostic uncertainty due to the coexistence of colonizers, commensals, and contaminants. To address this challenge, this study employed a structured, stepwise exploratory model to differentiate true pathogens from non-pathogens in aerobic respiratory cultures and multiplex polymerase chain reaction (Biofire FilmArray) results. AIM: non-pathogen in lower respiratory tract isolates. METHODS: This prospective, longitudinal time-bound study was conducted over three months (August 2024 to October 2024) at a tertiary care center in Northern India. Adult patients (≥ 18 years) with positive lower respiratory tract samples were enrolled. Each isolate was independently classified by the treating clinician, microbiologist, and study investigator using a six-step clinical-microbiological algorithm that incorporated clinical signs, Sequential Organ Failure Assessment score trends, alternative infection sources, host factors, and outcome data. The final classification was determined by the investigator. Outcomes, including treatment response and mortality at 28 days, were compared across pathogen and non-pathogen groups. RESULTS: Of the 145 included patients, 131 (90.3%) were classified as pathogens and 14 (9.7%) as non-pathogens. Cohen's Kappa between investigator and microbiologist classifications was 0.28, indicating fair agreement. Among pathogen cases, 68 (51.9%) responded to treatment. In contrast, 12 of 14 non-pathogen cases (85.7%) were not treated, with favorable outcomes in most, and only one unrelated death (7.1%). CONCLUSION: The structured clinico-microbiological model strongly correlates with treatment outcomes, making it useful for differentiating infection from colonization. Crucially, microbiological detection alone doesn't determine pathogenicity. Integrating clinical, laboratory, and outcome data is essential for rational antibiotic use and effective antimicrobial stewardship.