Comparison of SOFA Score, SIRS, qSOFA, and qSOFA + L Criteria in the Diagnosis and Prognosis of Sepsis

Abstract

Objective: Sepsis has been defined as a life-threatening organ dysfunction that develops as a result of impaired host response to infection. This study aimed to investigate sequential organ failure assessment (SOFA)score, systemic inflammatory response syndrome (SIRS), quick SOFA (qSOFA), and qSOFA + lactate criteria(qSOFA+L) in the diagnosis and prognosis of sepsis.Materials and Methods: A retrospective study was performed that included all patients diagnosed withsepsis between January 1, 2013 and December 31, 2017 in Izmir Tepecik Training and Research HospitalInfectious Diseases and Clinical Microbiology Clinic.Results: A total of 976 patients diagnosed with sepsis (mean age 72.5±13.7 years, 52.7% women) over fiveyears were included in this study. Of all patients admitted to the emergency department and diagnosed withsepsis, 37.4% (n=365) were hospitalized and 52.3% (n=191) of these patients died. Emergency departmentmortality was 12.5% (n=122). The mortality rate was higher in patients with qSOFA and qSOFA+L criteria≥2 in the emergency department. There was no statistically significant difference in terms of SIRS, qSOFA,or qSOFA+L criteria among patients who died in the hospital. The SOFA score (area under receiver operator characteristic curve, AUC=0.89) was highly discriminative in predicting sepsis. When the SOFA scorewas>11, its sensitivity and negative predictive values were both 100%. The SOFA score (AUC=0.75 and0.72, respectively) was also highly discriminative in predicting emergency and in-hospital mortality. Whenthe SOFA score was>11, the sensitivity and specificity of predicting emergency department mortality were63.5% and 78.8%, respectively. The sensitivity was 65.8% and the specificity was 75.5% when describing inhospital mortality for SOFA scores>9.Conclusion: The SOFA score was highly sensitive and predictive in the diagnosis of sepsis. The SOFA scorehad a high discriminative ability to predict emergency and in-hospital mortality