| Abstrakt: |
Introduction: The diagnosis of sepsis is based on expert consensus and does not yet have a "gold standard." With the aim of improving and standardizing diagnostic methods, there have already been three major consensuses on the subject. However, there are still few studies in middle-income countries comparing the methods. This study describes the characteristics of patients diagnosed with sepsis and evaluates and compares the performance of Sepsis-1, 2, and 3 criteria in predicting 28 days, and in-hospital mortality. Patients and Methods: A retrospective observational cohort study was conducted in the intensive care unit of a tertiary hospital. All admissions between January 1, 2018, and December 31, 2019, were reviewed. Patients diagnosed with sepsis were included. Results: During the study period, 653 patients diagnosed with sepsis (by any of the studied criteria) were included in the study. The 28 days mortality rate was 45.8%, and the in-hospital mortality rate was 59.7%. We observed that 72.1% of patients met the minimum criteria for diagnosing sepsis according to the three protocols, and this group also had the highest mortality rate. Age and comorbidities such as cancer and liver cirrhosis were significantly associated with in-hospital mortality. The most common microorganisms were Escherichia coli, Klebsiella spp., and Staphylococcus spp. Conclusions: The study found that most patients met the diagnostic criteria for sepsis using the three methods. Sepsis-2 showed greater sensitivity to predict mortality, while Sequential Organ Failure Assessment showed low accuracy, but was the only significant one. Furthermore, quick Sequential Organ Failure Assessment (qSOFA) had the highest specificity for mortality. Overall, these findings suggest that, although all three methods contribute to the diagnosis and prognosis of sepsis, Sepsis-2 is particularly sensitive in predicting mortality. Sepsis-3 shows some accuracy but requires improvement, and qSOFA exhibits the highest specificity. More research is needed to improve predictive capabilities and patient outcomes. [ABSTRACT FROM AUTHOR] |
