A Prediction Model of Extubation Failure Risk in Preterm Infants
Objectives: This study aimed to identify variables and develop a prediction model that could estimate extubation failure (EF) in preterm infants.Study Design: We enrolled 128 neonates as a training cohort and 58 neonates as a validation cohort. They were born between 2015 and 2020, had a gestational...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-09-01
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| Series: | Frontiers in Pediatrics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2021.693320/full |
| _version_ | 1831589475804774400 |
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| author | Zimei Cheng Ziwei Dong Qian Zhao Jingling Zhang Su Han Jingxian Gong Yang Wang |
| author_facet | Zimei Cheng Ziwei Dong Qian Zhao Jingling Zhang Su Han Jingxian Gong Yang Wang |
| author_sort | Zimei Cheng |
| collection | DOAJ |
| description | Objectives: This study aimed to identify variables and develop a prediction model that could estimate extubation failure (EF) in preterm infants.Study Design: We enrolled 128 neonates as a training cohort and 58 neonates as a validation cohort. They were born between 2015 and 2020, had a gestational age between 250/7 and 296/7 weeks, and had been treated with mechanical ventilation through endotracheal intubation (MVEI) because of acute respiratory distress syndrome. In the training cohort, we performed univariate logistic regression analysis along with stepwise discriminant analysis to identify EF predictors. A monogram based on five predictors was built. The concordance index and calibration plot were used to assess the efficiency of the nomogram in the training and validation cohorts.Results: The results of this study identified a 5-min Apgar score, early-onset sepsis, hemoglobin before extubation, pH before extubation, and caffeine administration as independent risk factors that could be combined for accurate prediction of EF. The EF nomogram was created using these five predictors. The area under the receiver operator characteristic curve was 0.824 (95% confidence interval 0.748–0.900). The concordance index in the training and validation cohorts was 0.824 and 0.797, respectively. The calibration plots showed high coherence between the predicted probability of EF and actual observation.Conclusions: This EF nomogram was a useful model for the precise prediction of EF risk in preterm infants who were between 250/7 and 296/7 weeks' gestational age and treated with MVEI because of acute respiratory distress syndrome. |
| first_indexed | 2024-12-18T00:45:14Z |
| format | Article |
| id | doaj.art-3ae5323324224a288152ba2b1c0eb3e3 |
| institution | Directory Open Access Journal |
| issn | 2296-2360 |
| language | English |
| last_indexed | 2024-12-18T00:45:14Z |
| publishDate | 2021-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj.art-3ae5323324224a288152ba2b1c0eb3e32022-12-21T21:26:47ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602021-09-01910.3389/fped.2021.693320693320A Prediction Model of Extubation Failure Risk in Preterm InfantsZimei ChengZiwei DongQian ZhaoJingling ZhangSu HanJingxian GongYang WangObjectives: This study aimed to identify variables and develop a prediction model that could estimate extubation failure (EF) in preterm infants.Study Design: We enrolled 128 neonates as a training cohort and 58 neonates as a validation cohort. They were born between 2015 and 2020, had a gestational age between 250/7 and 296/7 weeks, and had been treated with mechanical ventilation through endotracheal intubation (MVEI) because of acute respiratory distress syndrome. In the training cohort, we performed univariate logistic regression analysis along with stepwise discriminant analysis to identify EF predictors. A monogram based on five predictors was built. The concordance index and calibration plot were used to assess the efficiency of the nomogram in the training and validation cohorts.Results: The results of this study identified a 5-min Apgar score, early-onset sepsis, hemoglobin before extubation, pH before extubation, and caffeine administration as independent risk factors that could be combined for accurate prediction of EF. The EF nomogram was created using these five predictors. The area under the receiver operator characteristic curve was 0.824 (95% confidence interval 0.748–0.900). The concordance index in the training and validation cohorts was 0.824 and 0.797, respectively. The calibration plots showed high coherence between the predicted probability of EF and actual observation.Conclusions: This EF nomogram was a useful model for the precise prediction of EF risk in preterm infants who were between 250/7 and 296/7 weeks' gestational age and treated with MVEI because of acute respiratory distress syndrome.https://www.frontiersin.org/articles/10.3389/fped.2021.693320/fullpreterm infantextubationmechanical ventilationearly-onset sepsishemoglobin |
| spellingShingle | Zimei Cheng Ziwei Dong Qian Zhao Jingling Zhang Su Han Jingxian Gong Yang Wang A Prediction Model of Extubation Failure Risk in Preterm Infants Frontiers in Pediatrics preterm infant extubation mechanical ventilation early-onset sepsis hemoglobin |
| title | A Prediction Model of Extubation Failure Risk in Preterm Infants |
| title_full | A Prediction Model of Extubation Failure Risk in Preterm Infants |
| title_fullStr | A Prediction Model of Extubation Failure Risk in Preterm Infants |
| title_full_unstemmed | A Prediction Model of Extubation Failure Risk in Preterm Infants |
| title_short | A Prediction Model of Extubation Failure Risk in Preterm Infants |
| title_sort | prediction model of extubation failure risk in preterm infants |
| topic | preterm infant extubation mechanical ventilation early-onset sepsis hemoglobin |
| url | https://www.frontiersin.org/articles/10.3389/fped.2021.693320/full |
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