Objectives: Conditions that have similar initial presentations as sepsis may make early recognition of sepsis in an emergency room (ER) difficult. We investigated whether selected physiologic and metabolic parameters can be reliably used in the emergency department to differentiate sepsis from other disease states that mimic it, such as dehydration and stroke. Methods: Loess regression on retrospective follow-up chart data of patients with sepsis-like symptoms (N 5 664) aged 18ţ in a large ER in Hungary was used to visualize/identify cutoff points for sepsis risk. A multivariate logistic regression model based on standard triage data was constructed with its corresponding receiver operating characteristic (ROC) curve and compared with another model constructed based on current sepsis guidelines. Results: Age, bicarbonate, HR, lactate, pH, and body temperature had U, V, W, or reverse U-shaped associations with identifiable inflexion points, but the cutoff values we identified were slightly different from guideline cutoff values. In contrast to the guidelines, no inflexion points could be observed for the association of sepsis with SBP, DPB, MAP, and RR and therefore were treated as continuous variables. Compared to the guidelines-based model, the triage data-driven final model contained additional variables (age, pH, bicarbonate) and did not include lactate. The data-driven model identified about 85% of sepsis cases correctly, while the guidelines-based model identified only about 70% of sepsis cases correctly. Conclusion: Our findings contribute to the growing body of evidence for the necessity of finding improved tools to identify sepsis at early time points, such as in the ER.  Kulcsszavak: sepsis, SIRS, SOFA, clinical patient data, guidelines