info@newbornsepsis.org Advancing Neonatal Care Worldwide

Publications

Peer-reviewed research from NSS-affiliated investigators advancing our understanding of neonatal sepsis.

Showing 150+ publications · Sorted by date (newest first)

Original Research

Multicenter Validation of a Combined Biomarker Panel for Early-Onset Sepsis in Preterm Neonates

Vasquez E, Tanaka Y, Lindgren A, Mitchell S, Sharma R, Ochieng J, et al.

Lancet Child & Adolescent Health, 2026; 10(3): 215-228

Prospective study across 12 NICUs demonstrating 94% sensitivity and 89% specificity for a combination of IL-6, presepsin, and CRP measured at 6 hours of life for early-onset sepsis diagnosis in preterm neonates <32 weeks.

Systematic Review

Antibiotic Duration for Culture-Negative Suspected Neonatal Sepsis: A Systematic Review and Meta-Analysis

Lindgren A, Mitchell S, Chen M, Al-Rashid F, et al.

JAMA Pediatrics, 2025; 179(12): 1098-1112

Meta-analysis of 28 studies (n=12,450) showing no difference in adverse outcomes between 36-48h and 5-7 day empiric antibiotic courses in culture-negative suspected sepsis.

Registry Report

Global Patterns of Neonatal Sepsis Aetiology: First Report from the NSS International Sepsis Registry

Ochieng J, Santos M, Diallo A, Sharma R, Vasquez E, et al.

Pediatric Infectious Disease Journal, 2025; 44(8): 612-625

Analysis of 15,000 neonatal sepsis episodes across 45 centres in 30 countries, revealing marked geographic variation in causative organisms, resistance patterns, and mortality rates.

Guideline

NSS Clinical Practice Guidelines for Neonatal Sepsis: 2025 Update

Newborn Sepsis Society Guideline Committee. Vasquez E, Ochieng J, Lindgren A, et al.

Neonatal Sepsis Journal, 2025; 3(1): 1-68

Comprehensive update of the NSS EOS and LOS guidelines incorporating novel biomarker evidence, revised antimicrobial recommendations, and AI-assisted risk stratification algorithms.

Original Research

Machine Learning for Early Prediction of Neonatal Sepsis: A Multicentre Validation Study

Tanaka Y, Chen M, Mitchell S, Vasquez E, et al.

Nature Medicine, 2025; 31(11): 3245-3256

Development and external validation of a machine learning model using continuous vital sign monitoring data to predict sepsis onset 6-12 hours before clinical deterioration, with AUROC of 0.91.

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