Prediction of neonatal sepsis in Hakka population: PCT time interval analysis and nomogram model incorporating perinatal factors and disease characteristics
Year: 2025
Type: article
Study design: Observational Study
Abstract: OBJECTIVE: This study aimed to develop a predictive model for neonatal sepsis by analyzing procalcitonin (PCT) levels measured across different age-specific time intervals in the Hakka population, while integrating maternal perinatal factors and neonatal disease characteristics. METHODS: A retrospective analysis was conducted on 13,884 neonates, encompassing both sepsis and non-sepsis cases, to systematically evaluate the predictive capability of PCT across different time intervals, focusing on the first week of life and beyond (up to [7, 8) days). Logistic regression analysis, incorporating categorized PCT levels, maternal perinatal factors, and neonatal disease characteristics, was utilized to develop a nomogram model for predicting sepsis. RESULTS: < 0.001) during the 2-3 day period. During this period, the area under the curve (AUC) for PCT was 0.853 (95% CI: 0.840-0.866), indicating strong predictive performance. The developed nomogram model achieved an AUC of 0.82 and an accuracy of 0.88 in the training set, effectively distinguishing between high- and low-risk sepsis cases. CONCLUSION: This study confirms the critical role of PCT in the early diagnosis of neonatal sepsis and proposes a time-sensitive predictive model specifically tailored to the Hakka population, offering clinicians a personalized risk assessment tool. Nonetheless, further external validation is required to fully establish the clinical applicability of this model.
Authors: Kun Wu, Ao Zhenzhen, Ping Liu, Jia Wang
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
Award: G8155601945
OpenAlex ID: W4412487805