Biomarkers of neonatal sepsis in children with multiple organ dysfunction syndrome: review
- Authors: Golomidov A.V.1, Grigoriev E.V.2, Mozes V.G.3
-
Affiliations:
- S.V. Belyaev Kuzbass Regional Clinical Hospital
- Research Institute for Complex Issues of Cardiovascular Diseases
- Kemerovo State University
- Issue: Vol 26, No 2 (2026)
- Pages: 257-270
- Section: Reviews
- Submitted: 26.01.2026
- Accepted: 13.06.2026
- Published: 30.06.2026
- URL: https://rps-journal.ru/jour/article/view/1993
- DOI: https://doi.org/10.17816/psaic1993
- EDN: https://elibrary.ru/JFFTWC
- ID: 1993
Cite item
Abstract
Neonatal sepsis is a life-threatening condition with a high mortality rate, despite significant advances in perinatology and neonatology. The clinical signs of sepsis in newborns are nonspecific and can mimic a wide range of non-infectious pathologies, making early diagnosis challenging. The aim of this review was to analyze current data on laboratory markers of neonatal sepsis. A search was conducted in the PubMed database using the keywords neonatal sepsis, biomarker, prognosis. The search yielded 66 publications, an additional 9 publications were retrieved from the eLibrary database using the keywords биомаркеры (biomarkers), сепсис (sepsis), прогноз (prognosis). The search period was 5 years (2020–2025), in accordance with PRISMA 2020 guidelines. A review of the current literature confirms that no single laboratory marker, whether classical (C-reactive protein, procalcitonin, interleukin-6) or novel (presepsin, CD64, serum amyloid A, sTREM-1), is ideal due to limited specificity, the influence of non-infectious factors, and variability with gestational age. The most promising approach to diagnosing neonatal sepsis is a combined approach that includes simultaneous measurement of several biomarkers alongside clinical data. Artificial intelligence and machine learning technologies, which can analyze multidimensional data, predict sepsis at a preclinical stage, and create personalized diagnostic algorithms, show particular promise. Further research should therefore focus on developing integrated diagnostic systems to improve the accuracy, speed, and efficacy of sepsis detection in newborns.
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About the authors
Alexandr V. Golomidov
S.V. Belyaev Kuzbass Regional Clinical Hospital
Author for correspondence.
Email: golomidov.oritn@yandex.ru
ORCID iD: 0000-0001-7522-9094
SPIN-code: 4406-2065
MD, Cand. Sci. (Medicine)
Russian Federation, KemerovoEvgeny V. Grigoriev
Research Institute for Complex Issues of Cardiovascular Diseases
Email: grigorievev@hotmail.com
ORCID iD: 0000-0001-8370-3083
SPIN-code: 2316-2287
MD, Dr. Sci. (Medicine), Professor, Corresponding Member of the Russian Academy of Sciences
Russian Federation, KemerovoVadim G. Mozes
Kemerovo State University
Email: vadimmoses@mail.ru
ORCID iD: 0000-0002-3269-9018
SPIN-code: 5854-6890
MD, Dr. Sci. (Medicine), Professor
Russian Federation, KemerovoReferences
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