Diagnostic accuracy of presepsin in infants younger than 3 months with fever without a source: preliminary data
Author(s):
Niccolò Parri
Affiliations:
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
Background:
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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