ABSTRACT

Background: Sepsis remains a critical healthcare challenge worldwide, demanding prompt identification and treatment to improve patient outcomes. Given the absence of a definitive gold-standard diagnostic test, adjunct diagnostic tools are imperative to aid in early sepsis detection and guide effective treatment strategies. This study introduces a novel 3-step model to identify and classify sepsis, integrating current knowledge and clinical guidelines to enhance diagnostic precision.

Methods: This longitudinal study was conducted at a tertiary care teaching hospital in northern India. Adult patients admitted with suspected sepsis underwent screening using predefined criteria. The 3-step model consisted of assessing dysregulated host response using a National Early Warning Score-2 (NEWS-2) score of ≥6 (step 1); evaluating risk factors for infection (step 2); and assessing infection presence through clinical, supportive, or confirmatory evidence (step 3). Based on this Model, patients were categorized into asepsis, possible sepsis, probable sepsis, or confirmed sepsis at various intervals during hospitalization.

Results: A total of 230 patients were included. Initial categorization on Day 1 showed 13.0% in asepsis, 35.2% in possible sepsis, 51.3% in probable sepsis, and 0.4% in confirmed sepsis. By Day 7, shifts were observed with 49.7% in asepsis, 9.5% in possible sepsis, 25.4% in probable sepsis, and 15.4% in confirmed sepsis. At discharge or death by day 28, categories were 60.4% asepsis, 5.2% possible sepsis, 21.7% probable sepsis, and 12.6% confirmed sepsis. Transitions between categories were noted throughout hospitalization, demonstrating the dynamic nature of sepsis progression and response to treatment.

Conclusion: The 3-step Model effectively stratifies sepsis status over hospitalization, facilitating early identification and classification of septic patients. This approach holds promise for enhancing diagnostic accuracy, guiding clinical decision-making, and optimizing antimicrobial stewardship practices. Further validation across diverse patient cohorts and healthcare settings must confirm its utility and generalizability.