Sequential Application of Time-Stratified Demographic, Vital, Clinical–Laboratory, and Microbiology Variables for Accurate and Rapid Identification of Sepsis

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Background: Accurate early identification of sepsis remains a major clinical challenge due to its heterogeneous presentation and overlap of clinical signs with the non-infectious systemic inflammatory response syndrome (SIRS). Timely differentiation is crucial for improving patient outcomes, meeting sepsis bundle requirements and reducing inappropriate antimicrobial use. We hypothesized that clinical–laboratory data available within the first three hours of patient presentation could be used to identify patients with sepsis at a clinically useful level of diagnostic accuracy, in lieu of traditional microbiology results which would not become available until at least 12–24 h. Data from two independent studies were used to quantify the diagnostic value of demographic, vital, clinical–laboratory, and microbiological data available at three time points for distinguishing retrospectively diagnosed critically ill patients with either sepsis or non-infectious SIRS. A particular focus of this work was an assessment of the utility of SeptiCyte RAPID (Immunexpress Inc., Seattle, WA, USA) as an aid to sepsis diagnosis, producing actionable data within one hour. Methods: Data from two independent study cohorts were analyzed. The “510(k) cohort” consisted of 419 adult patients in intensive care (ICU) (MARS, VENUS, and NEPTUNE studies). The “Andalusian cohort” consisted of 353 ICU patients from the PANGEA study. Logistic regression models, selected by a greedy search algorithm and validated by repeated cross-validation, were used to determine the contributions of different variables to diagnostic accuracy. Diagnostic performance was quantified by the area under the receiver operating characteristic curve (AUC). Results: For the 510(k) cohort, a baseline AUC of 0.69–0.73 was observed using five to seven vital and demographic variables assessed immediately upon presentation (time T1). The addition of clinical–laboratory variables, in particular SeptiCyte RAPID, within one to three hours post-presentation (time T2) increased the AUC to 0.85–0.86. Finally, the addition of microbiological data 12–24 h post-presentation (time T3) further improved the AUC to 0.90–0.91. Similar results were obtained for the Andalusian cohort. AUC values at the three time points were as follows: At time T1, AUC = 0.67 based solely on vital signs and demographics; at time T2, AUC = 0.87 based on vitals + demographics + SeptiCyte RAPID ± other clinical–laboratory data; at time T3, AUC = 0.93 based on vitals + demographics + SeptiCyte RAPID ± other clinical–laboratory data + microbiology results. For both cohorts, the most significant variables included temperature, mean arterial pressure, respiratory rate, suspected infection site, SeptiCyte RAPID, procalcitonin, confirmed bacterial infection and positive blood culture confirmation. In summary, the AUC for diagnosing sepsis rose progressively from T1 (510(k) 0.69–0.73; Andalusian 0.67) to T2 (510(k) 0.85–0.86; Andalusian 0.87) with the addition of SeptiCyte RAPID to T3 (510(k) 0.90–0.91; Andalusian 0.93) as more clinical information became available over time. Conclusions: The accuracy of identification of sepsis increases markedly as demographics and vital signs are supplemented with clinical–laboratory information, and ultimately with microbiological culture results. The AUC improves in the shortest time within the first three hours when laboratory data, and particularly SeptiCyte RAPID results, become available. Integrating rapid host response testing with SeptiCyte RAPID into time-based diagnostic frameworks may enhance early sepsis recognition, improve antimicrobial stewardship, and support guideline-driven clinical decisions.