Diagnostic accuracy of presepsin in infants younger than 3 months with fever without a source: preliminary data
Niccolò Parri
7. Department of Emergency Medicine and Trauma Center, Meyer University Children's Hospital, Florence, Italy
ESPID Education. Pierantoni L. May 8, 2019; 263076 Disclosure(s): Nothing to disclose
Dr. Luca Pierantoni
Dr. Luca Pierantoni
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P-SEP could be a promising biomarker for invasive or severe bacterial infection in children
Infants with fever without source (FWS), have an increased risk of severe (SBI) and invasive (IBI) bacterial infection. Differentiate between infants with IBI, SBI or a viral infection remains a challenge. Procalcitonin (PCT) and C-reactive protein (CRP) were proven accurate biomarkers for bacterial infections. The sCD14-subtypes (Presepsin, P-SEP) seems to be a promising biomarker for sepsis in neonates and adults.
Objective of the study was to determine the accuracy of P-SEP in identifying IBI and SBI in infants younger than 3 months with FWS.
Materials and methods: multi-center, prospective, clinical trial of infants younger than 3 months with FWS presenting to 6 pediatric emergency departments. P-SEP, CRP and PCT levels were measured, urinary dipstick and a blood culture were performed.
Results: Of the 284 enrolled patients, 16 had IBI and 60 SBI. P-SEP achieved the best accuracy for IBI at the cutoff of 449 pg/mL (sensitivity 81.2%, specificity 76.1%). The ROC curve of P-SEP values had the area under the curve (AUC) of 0.85 (95% CI 0.70–0.90), compared an AUC of 0.82 for PCT (95% CI 0.69–0.95). At the same cutoff PSEP showed inadequate accuracy for SBI (sensitivity 36.7%, specificity 75.4%).
Conclusions: P-SEP is a promising new biomarker. P-SEP accuracy for IBI was comparable to PCT, and due to its NPV seems to be the best marker for ruling out the presence of IBI. However, P-SEP is probably not enough accurate to be use as a stand-alone marker to rule in an IBI.
Future researches should demonstrate if P-SEP could be used in combination with other biomarkers or clinical findings in clinical prediction models to increase the accuracy to discriminate high risk patients.
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