Prediction of neonatal sepsis in Hakka population: PCT time interval analysis and nomogram model incorporating perinatal factors and disease characteristics
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 retrospectiv...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | The Journal of Maternal-Fetal & Neonatal Medicine |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/14767058.2025.2532091 |
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| _version_ | 1849318201354092544 |
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| author | Kun Wu Ao Zhenzhen Ping Liu Jia Wang |
| author_facet | Kun Wu Ao Zhenzhen Ping Liu Jia Wang |
| author_sort | Kun Wu |
| collection | DOAJ |
| description | 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 Elevated PCT levels were significantly associated with an increased risk of neonatal sepsis (OR = 13.59, 95% CI: 9.01–20.50, p < 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. |
| format | Article |
| id | doaj-art-cb6f86f7f15c487eb0bafc33301ef12d |
| institution | Kabale University |
| issn | 1476-7058 1476-4954 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | The Journal of Maternal-Fetal & Neonatal Medicine |
| spelling | doaj-art-cb6f86f7f15c487eb0bafc33301ef12d2025-08-20T03:50:58ZengTaylor & Francis GroupThe Journal of Maternal-Fetal & Neonatal Medicine1476-70581476-49542025-12-0138110.1080/14767058.2025.2532091Prediction of neonatal sepsis in Hakka population: PCT time interval analysis and nomogram model incorporating perinatal factors and disease characteristicsKun Wu0Ao Zhenzhen1Ping Liu2Jia Wang3Department of Medical Genetics Laboratory, Heyuan Women and Children’s Hospital, Heyuan City, Guangdong Province, ChinaDepartment of Pediatrics, Heyuan Women and Children’s Hospital, Heyuan City, Guangdong Province, ChinaDepartment of Medical Genetics Laboratory, Heyuan Women and Children’s Hospital, Heyuan City, Guangdong Province, ChinaDepartment of Medical Genetics Laboratory, Heyuan Women and Children’s Hospital, Heyuan City, Guangdong Province, ChinaObjective 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 Elevated PCT levels were significantly associated with an increased risk of neonatal sepsis (OR = 13.59, 95% CI: 9.01–20.50, p < 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.https://www.tandfonline.com/doi/10.1080/14767058.2025.2532091Hakka populationneonatal sepsisProcalcitonin (PCT)nomogram prediction modelmaternal perinatal factors |
| spellingShingle | Kun Wu Ao Zhenzhen Ping Liu Jia Wang Prediction of neonatal sepsis in Hakka population: PCT time interval analysis and nomogram model incorporating perinatal factors and disease characteristics The Journal of Maternal-Fetal & Neonatal Medicine Hakka population neonatal sepsis Procalcitonin (PCT) nomogram prediction model maternal perinatal factors |
| title | Prediction of neonatal sepsis in Hakka population: PCT time interval analysis and nomogram model incorporating perinatal factors and disease characteristics |
| title_full | Prediction of neonatal sepsis in Hakka population: PCT time interval analysis and nomogram model incorporating perinatal factors and disease characteristics |
| title_fullStr | Prediction of neonatal sepsis in Hakka population: PCT time interval analysis and nomogram model incorporating perinatal factors and disease characteristics |
| title_full_unstemmed | Prediction of neonatal sepsis in Hakka population: PCT time interval analysis and nomogram model incorporating perinatal factors and disease characteristics |
| title_short | Prediction of neonatal sepsis in Hakka population: PCT time interval analysis and nomogram model incorporating perinatal factors and disease characteristics |
| title_sort | prediction of neonatal sepsis in hakka population pct time interval analysis and nomogram model incorporating perinatal factors and disease characteristics |
| topic | Hakka population neonatal sepsis Procalcitonin (PCT) nomogram prediction model maternal perinatal factors |
| url | https://www.tandfonline.com/doi/10.1080/14767058.2025.2532091 |
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