Temporal Changes in Inflammatory Markers and Laboratory Parameters in Relation to Risk of Neonatal Sepsis
4 affiliations
- 1Department of Medical Laboratory Diagnostics, Faculty of Pharmacy, Wroclaw Medical University, 211 Borowska Street, 50-556 Wroclaw, Poland
- 2Department of Econometrics and Informatics, Faculty of Economics and Finance, Wroclaw University of Economics, Nowowiejska Street, 58-500 Jelenia Góra, Poland
- 3Department of Neonatology and Neonatal Intensive Care, Institute of Mother and Child, Kasprzaka 17a, 01-211 Warsaw, Poland
- 4Department of Epidemiology and Biostatistics, Institute of Mother and Child, Kasprzaka 17A, 01-211 Warsaw, Poland
Abstract
Background/Objective: Early diagnosis of neonatal sepsis remains a challenge due to the nonspecific nature of clinical symptoms and the delayed dynamics of conventional inflammatory markers. The traditional diagnostic approach is based on static cut-off points, which often do not consider the nonlinear nature of the inflammatory response. The aim of this study was to identify dynamic variables associated with sepsis progression, constituting the foundation for future artificial intelligence (AI) models. Methods: A retrospective analysis was performed using anonymised clinical and laboratory data from a cohort of newborns (n = 122) with suspected sepsis, along with maternal clinical data. Using feature engineering, descriptive statistics parameters were generated for selected parameters, including rates of change in the 24 h and 48 h time windows. This was combined with the verification of clinical and perinatal parameters that affect the assessment of clinical status and diagnosis of sepsis. The correlation analysis was performed using Spearman’s rank correlation coefficient (p < 0.05). Results: Dynamic parameters showed significant associations with sepsis progression than static measurements. The highest correlation was recorded for the rate of CRP growth (CRP_abs_change_rate_max, r = 0.613, p < 0.001), compared with the maximum values (CRP_value_max, r = 0.591, p < 0.001). High correlations were also observed for leukocyte parameters, especially neutrophils (NEUT_%_pct_change_rate_max, r = 0.547, p < 0.001) and basophils (BASO_%_value_max, r = 0.606, p < 0.001), with the strongest correlations observed within the first 24 h (r > 0.70). Conclusions: Neonatal sepsis progression may be more comprehensively characterised by temporal changes in laboratory parameters, not by their absolute values. Rates of change in inflammatory markers may provide clinically relevant complementary information on the course of the disease, reflecting the complex and nonlinear nature of the inflammatory response. The results indicate that the analysis of the trajectory of changes in parameters provides a promising basis for the future development and validation of predictive models based on artificial intelligence for earlier and more accurate diagnosis of neonatal sepsis.
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BibTeX
@article{tadel2026temporal,
title = {{Temporal Changes in Inflammatory Markers and Laboratory Parameters in Relation to Risk of Neonatal Sepsis}},
author = {Karolina Tądel and Andrzej Dudek and Agnieszka Drozdowska-Szymczak and Paweł Krajewski and Magdalena Rutkowska and Iwona Bil-Lula},
journal = {Journal of clinical medicine},
year = {2026},
month = oct,
doi = {10.3390/jcm15197625},
url = {https://europepmc.org/article/PMC/PMC13666265},
note = {CC BY},
}
RIS
TY - JOUR
TI - Temporal Changes in Inflammatory Markers and Laboratory Parameters in Relation to Risk of Neonatal Sepsis
AU - Karolina Tądel
AU - Andrzej Dudek
AU - Agnieszka Drozdowska-Szymczak
AU - Paweł Krajewski
AU - Magdalena Rutkowska
AU - Iwona Bil-Lula
PY - 2026
DA - 2026/10/01
JO - Journal of clinical medicine
DO - 10.3390/jcm15197625
UR - https://europepmc.org/article/PMC/PMC13666265
AB - Background/Objective: Early diagnosis of neonatal sepsis remains a challenge due to the nonspecific nature of clinical symptoms and the delayed dynamics of conventional inflammatory markers. The traditional diagnostic approach is based on static cut-off points, which often do not consider the nonlinear nature of the inflammatory response. The aim of this study was to identify dynamic variables associated with sepsis progression, constituting the foundation for future artificial intelligence (AI) models. Methods: A retrospective analysis was performed using anonymised clinical and laboratory data from a cohort of newborns (n = 122) with suspected sepsis, along with maternal clinical data. Using feature engineering, descriptive statistics parameters were generated for selected parameters, including rates of change in the 24 h and 48 h time windows. This was combined with the verification of clinical and perinatal parameters that affect the assessment of clinical status and diagnosis of sepsis. The correlation analysis was performed using Spearman’s rank correlation coefficient (p < 0.05). Results: Dynamic parameters showed significant associations with sepsis progression than static measurements. The highest correlation was recorded for the rate of CRP growth (CRP_abs_change_rate_max, r = 0.613, p < 0.001), compared with the maximum values (CRP_value_max, r = 0.591, p < 0.001). High correlations were also observed for leukocyte parameters, especially neutrophils (NEUT_%_pct_change_rate_max, r = 0.547, p < 0.001) and basophils (BASO_%_value_max, r = 0.606, p < 0.001), with the strongest correlations observed within the first 24 h (r > 0.70). Conclusions: Neonatal sepsis progression may be more comprehensively characterised by temporal changes in laboratory parameters, not by their absolute values. Rates of change in inflammatory markers may provide clinically relevant complementary information on the course of the disease, reflecting the complex and nonlinear nature of the inflammatory response. The results indicate that the analysis of the trajectory of changes in parameters provides a promising basis for the future development and validation of predictive models based on artificial intelligence for earlier and more accurate diagnosis of neonatal sepsis.
SN - 2077-0383
ER -