Prediction for late-onset sepsis in preterm infants based on data from East China
AimTo construct a prediction model based on the data of premature infants and to apply the data in our study as external validation to the prediction model proposed by Yuejun Huang et al. to evaluate the predictive ability of both models.MethodsIn total, 397 premature infants were randomly divided i...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Pediatrics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2022.924014/full |
| _version_ | 1797995042718089216 |
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| author | Xianghua Shuai Xiaoxia Li Yiling Wu |
| author_facet | Xianghua Shuai Xiaoxia Li Yiling Wu |
| author_sort | Xianghua Shuai |
| collection | DOAJ |
| description | AimTo construct a prediction model based on the data of premature infants and to apply the data in our study as external validation to the prediction model proposed by Yuejun Huang et al. to evaluate the predictive ability of both models.MethodsIn total, 397 premature infants were randomly divided into the training set (n = 278) and the testing set (n = 119). Univariate and multivariate logistic analyses were applied to identify potential predictors, and the prediction model was constructed based on the predictors. The area under the curve (AUC) value, the receiver operator characteristic (ROC) curves, and the calibration curves were used to evaluate the predictive performances of prediction models. The data in our study were used in the prediction model proposed by Yuejun Huang et al. as external validation.ResultsIn the current study, endotracheal intubation [odds ratio (OR) = 10.553, 95% confidence interval (CI): 4.959–22.458], mechanical ventilation (OR = 10.243, 95% CI: 4.811–21.806), asphyxia (OR = 2.614, 95% CI: 1.536–4.447), and antibiotics use (OR = 3.362, 95% CI: 1.454–7.775) were risk factors for late-onset sepsis in preterm infants. The higher birth weight of infants (OR = 0.312, 95% CI: 0.165–0.588) and gestational age were protective factors for late-onset sepsis in preterm infants. The training set was applied for the construction of the models, and the testing set was used to test the diagnostic efficiency of the model. The AUC values of the prediction model were 0.760 in the training set and 0.796 in the testing set.ConclusionThe prediction model showed a good predictive ability for late-onset sepsis in preterm infants. |
| first_indexed | 2024-04-11T09:55:16Z |
| format | Article |
| id | doaj.art-9e6870402d6444b5a27dec1750e28085 |
| institution | Directory Open Access Journal |
| issn | 2296-2360 |
| language | English |
| last_indexed | 2024-04-11T09:55:16Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj.art-9e6870402d6444b5a27dec1750e280852022-12-22T04:30:37ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602022-09-011010.3389/fped.2022.924014924014Prediction for late-onset sepsis in preterm infants based on data from East ChinaXianghua ShuaiXiaoxia LiYiling WuAimTo construct a prediction model based on the data of premature infants and to apply the data in our study as external validation to the prediction model proposed by Yuejun Huang et al. to evaluate the predictive ability of both models.MethodsIn total, 397 premature infants were randomly divided into the training set (n = 278) and the testing set (n = 119). Univariate and multivariate logistic analyses were applied to identify potential predictors, and the prediction model was constructed based on the predictors. The area under the curve (AUC) value, the receiver operator characteristic (ROC) curves, and the calibration curves were used to evaluate the predictive performances of prediction models. The data in our study were used in the prediction model proposed by Yuejun Huang et al. as external validation.ResultsIn the current study, endotracheal intubation [odds ratio (OR) = 10.553, 95% confidence interval (CI): 4.959–22.458], mechanical ventilation (OR = 10.243, 95% CI: 4.811–21.806), asphyxia (OR = 2.614, 95% CI: 1.536–4.447), and antibiotics use (OR = 3.362, 95% CI: 1.454–7.775) were risk factors for late-onset sepsis in preterm infants. The higher birth weight of infants (OR = 0.312, 95% CI: 0.165–0.588) and gestational age were protective factors for late-onset sepsis in preterm infants. The training set was applied for the construction of the models, and the testing set was used to test the diagnostic efficiency of the model. The AUC values of the prediction model were 0.760 in the training set and 0.796 in the testing set.ConclusionThe prediction model showed a good predictive ability for late-onset sepsis in preterm infants.https://www.frontiersin.org/articles/10.3389/fped.2022.924014/fullsepsislate-onsetpredictionpreterm infantsEast China |
| spellingShingle | Xianghua Shuai Xiaoxia Li Yiling Wu Prediction for late-onset sepsis in preterm infants based on data from East China Frontiers in Pediatrics sepsis late-onset prediction preterm infants East China |
| title | Prediction for late-onset sepsis in preterm infants based on data from East China |
| title_full | Prediction for late-onset sepsis in preterm infants based on data from East China |
| title_fullStr | Prediction for late-onset sepsis in preterm infants based on data from East China |
| title_full_unstemmed | Prediction for late-onset sepsis in preterm infants based on data from East China |
| title_short | Prediction for late-onset sepsis in preterm infants based on data from East China |
| title_sort | prediction for late onset sepsis in preterm infants based on data from east china |
| topic | sepsis late-onset prediction preterm infants East China |
| url | https://www.frontiersin.org/articles/10.3389/fped.2022.924014/full |
| work_keys_str_mv | AT xianghuashuai predictionforlateonsetsepsisinpreterminfantsbasedondatafromeastchina AT xiaoxiali predictionforlateonsetsepsisinpreterminfantsbasedondatafromeastchina AT yilingwu predictionforlateonsetsepsisinpreterminfantsbasedondatafromeastchina |