Diagnostic Accuracy of Presepsin for Early Diagnosis of Sepsis in Critically Ill Obstetrics Patients
Year: 2025
Type: article
Study design: Observational Study
Abstract: BACKGROUND: Sepsis is a significant maternal health issue; presepsin (sCD14-ST) has emerged as a novel biomarker for diagnosis of sepsis. AIM: The aim of this study was to evaluate the diagnostic accuracy of presepsin for prediction of sepsis in critically ill obstetric patients. METHODS: A prospective observational study was conducted in the Obstetrics Intensive Care Unit (ICU), over 18-month duration, involving 156 critically ill obstetric patients. Biomarkers including presepsin, C-reactive protein (CRP), and total leukocyte count (TLC) were measured on day 1 and day 3 of ICU admission. Presepsin was measured in serum sample by enzyme-linked immunosorbent assay (ELISA) technique using ELISA kit. RESULTS: 44.9% critically ill obstetrics patients developed sepsis in ICU. The most common comorbidity was acute kidney injury (AKI; 20.0%), and AKI was 28.5% in septic patients. Maternal death occurred in 17.9%, with a significantly higher mortality among those who had sepsis (32.9%) compared to those without sepsis (5.8%). Presepsin levels significantly increased from day 1 value of 260 pg/mL to day 3 value of 350 pg/ml, after the development of sepsis ( P < 0.001). Those who did not develop sepsis (controls) levels were decreased. Receiver operating characteristic analysis showed presepsin had good diagnostic accuracy for prediction of sepsis presepsin at a cutoff value of ≥245 pg/ml had a sensitivity of 84.6% and specificity of 80.0%, at the area under the ROC curve (AUC) = 0.892, with confidence interval of 95% (AUC day 3 > day 1). Presepsin outperformed CRP and TLC in early sepsis detection. CONCLUSION: Presepsin is a reliable early marker for sepsis in critically ill obstetric patients, with better diagnostic accuracy than CRP and TLC. Its serial monitoring may offer valuable prognostic insight.
Source: Annals of African Medicine
Institution: King George's Medical University
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4416738562