Development and internal validation of a metabolism-related model for predicting 30-day mortality in neonatal sepsis
Abstract Objective Neonatal sepsis, a severe infectious disease associated with high mortality rates, is characterized by metabolic disturbances that play a crucial role in its progression. The aim of this study is to develop a metabolism-related model for assessing 30-day mortality in neonatal seps...
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BMC
2025-01-01
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| Series: | BMC Infectious Diseases |
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| Online Access: | https://doi.org/10.1186/s12879-025-10527-z |
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| author | Xiangwen Tu Junkun Chen Wen Liu |
| author_facet | Xiangwen Tu Junkun Chen Wen Liu |
| author_sort | Xiangwen Tu |
| collection | DOAJ |
| description | Abstract Objective Neonatal sepsis, a severe infectious disease associated with high mortality rates, is characterized by metabolic disturbances that play a crucial role in its progression. The aim of this study is to develop a metabolism-related model for assessing 30-day mortality in neonatal sepsis. Methods The clinical data of neonatal sepsis at Ganzhou Women and Children’s Health Care Hospital from January 2019 to December 2022 were retrospectively analyzed. Neonatal sepsis cases were divided into survival and non-survival groups. Multivariate logistic regression analysis was used to identify the independent risk factors for 30-day mortality. A nomogram model was developed based on these risk factors. Internal validation of the model was performed using 10-fold cross-validation. The predictive performance was evaluated through receiver operating characteristic (ROC) curves and calibration curve analyses. Decision curve analysis (DCA) was conducted to evaluate the clinical applicability of the developed model. Results The study included a total of 156 cases of neonatal sepsis. Multivariate logistic regression analysis revealed that alanine(ALA), citrulline(CIT)), octadecanoylcarnitine(C18) and methionine(MET) were identified as independent risk factors for 30-day mortality of neonatal sepsis. The ROC curve showed an area under the curve of AUC = 0.866 (95% CI 0.796–0.936, P < 0.05). The calibration curve and DCA indicated excellent performance of the model. Conclusion This study establishes a predictive model for neonatal sepsis-associated 30-day mortality, effectively capturing the perturbations in amino acid metabolism and fatty acid oxidation, thereby demonstrating robust predictive capabilities. |
| format | Article |
| id | doaj-art-bfb53210e6d647ceb39dfe13ebc8b904 |
| institution | Kabale University |
| issn | 1471-2334 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Infectious Diseases |
| spelling | doaj-art-bfb53210e6d647ceb39dfe13ebc8b9042025-02-02T12:10:43ZengBMCBMC Infectious Diseases1471-23342025-01-012511910.1186/s12879-025-10527-zDevelopment and internal validation of a metabolism-related model for predicting 30-day mortality in neonatal sepsisXiangwen Tu0Junkun Chen1Wen Liu2Laboratory of Eugenics Genetics, GanZhou Women and Children’s Health Care HospitalLaboratory of Eugenics Genetics, GanZhou Women and Children’s Health Care HospitalNeonatal intensive care Unit, GanZhou Women and Children’s Health Care HospitalAbstract Objective Neonatal sepsis, a severe infectious disease associated with high mortality rates, is characterized by metabolic disturbances that play a crucial role in its progression. The aim of this study is to develop a metabolism-related model for assessing 30-day mortality in neonatal sepsis. Methods The clinical data of neonatal sepsis at Ganzhou Women and Children’s Health Care Hospital from January 2019 to December 2022 were retrospectively analyzed. Neonatal sepsis cases were divided into survival and non-survival groups. Multivariate logistic regression analysis was used to identify the independent risk factors for 30-day mortality. A nomogram model was developed based on these risk factors. Internal validation of the model was performed using 10-fold cross-validation. The predictive performance was evaluated through receiver operating characteristic (ROC) curves and calibration curve analyses. Decision curve analysis (DCA) was conducted to evaluate the clinical applicability of the developed model. Results The study included a total of 156 cases of neonatal sepsis. Multivariate logistic regression analysis revealed that alanine(ALA), citrulline(CIT)), octadecanoylcarnitine(C18) and methionine(MET) were identified as independent risk factors for 30-day mortality of neonatal sepsis. The ROC curve showed an area under the curve of AUC = 0.866 (95% CI 0.796–0.936, P < 0.05). The calibration curve and DCA indicated excellent performance of the model. Conclusion This study establishes a predictive model for neonatal sepsis-associated 30-day mortality, effectively capturing the perturbations in amino acid metabolism and fatty acid oxidation, thereby demonstrating robust predictive capabilities.https://doi.org/10.1186/s12879-025-10527-zAmino acidsAcylcarnitinesMortalityNeonatal sepsisPrognosis |
| spellingShingle | Xiangwen Tu Junkun Chen Wen Liu Development and internal validation of a metabolism-related model for predicting 30-day mortality in neonatal sepsis BMC Infectious Diseases Amino acids Acylcarnitines Mortality Neonatal sepsis Prognosis |
| title | Development and internal validation of a metabolism-related model for predicting 30-day mortality in neonatal sepsis |
| title_full | Development and internal validation of a metabolism-related model for predicting 30-day mortality in neonatal sepsis |
| title_fullStr | Development and internal validation of a metabolism-related model for predicting 30-day mortality in neonatal sepsis |
| title_full_unstemmed | Development and internal validation of a metabolism-related model for predicting 30-day mortality in neonatal sepsis |
| title_short | Development and internal validation of a metabolism-related model for predicting 30-day mortality in neonatal sepsis |
| title_sort | development and internal validation of a metabolism related model for predicting 30 day mortality in neonatal sepsis |
| topic | Amino acids Acylcarnitines Mortality Neonatal sepsis Prognosis |
| url | https://doi.org/10.1186/s12879-025-10527-z |
| work_keys_str_mv | AT xiangwentu developmentandinternalvalidationofametabolismrelatedmodelforpredicting30daymortalityinneonatalsepsis AT junkunchen developmentandinternalvalidationofametabolismrelatedmodelforpredicting30daymortalityinneonatalsepsis AT wenliu developmentandinternalvalidationofametabolismrelatedmodelforpredicting30daymortalityinneonatalsepsis |