Establishment of a predictive nomogram model for predicting the death of very preterm infants during hospitalization.
10.7499/j.issn.1008-8830.2202027
- Author:
Zhen-Zhen JUE
1
;
Juan SONG
1
;
Zhu-Ye ZHOU
1
;
Wen-Dong LI
1
;
Yu-Yang YUE
1
;
Fa-Lin XU
1
Author Information
1. Department of Neonatology, Third Affiliated Hospital of Zhengzhou University/Henan Provincial Key Laboratory of Pediatric Brain Injury/Henan Provincial Clinical Research Center of Pediatric Diseases/Advanced Medical Research Center of Zhengzhou University, Zhengzhou 450052, China.
- Publication Type:Randomized Controlled Trial
- Keywords:
Death;
Nomogram;
Predictive model;
Risk factor;
Very preterm infant
- MeSH:
Cesarean Section;
Female;
Fetal Growth Retardation;
Hospitalization;
Humans;
Infant;
Infant, Newborn;
Infant, Premature;
Infant, Premature, Diseases;
Nomograms;
Pregnancy;
Retrospective Studies
- From:
Chinese Journal of Contemporary Pediatrics
2022;24(6):654-661
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES:To establish a nomogram model for predicting the risk of death of very preterm infants during hospitalization.
METHODS:A retrospective analysis was performed on the medical data of 1 714 very preterm infants who were admitted to the Department of Neonatology, the Third Affiliated Hospital of Zhengzhou University, from January 2015 to December 2019. These infants were randomly divided into a training cohort (1 179 infants) and a validation cohort (535 infants) at a ratio of 7∶3. The logistic regression analysis was used to screen out independent predictive factors and establish a nomogram model, and the feasibility of the nomogram model was assessed by the validation set. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to assess the discriminatory ability, accuracy, and clinical applicability of the model.
RESULTS:Among the 1 714 very preterm infants, 260 died and 1 454 survived during hospitalization. By the multivariate logistic regression analysis of the training set, 8 variables including gestational age <28 weeks, birth weight <1 000 g, severe asphyxia, severe intraventricular hemorrhage (IVH), grade III-IV respiratory distress syndrome (RDS), and sepsis, cesarean section, and use of prenatal glucocorticoids were selected and a nomogram model for predicting the risk of death during hospitalization was established. In the training cohort, the nomogram model had an AUC of 0.790 (95%CI: 0.751-0.828) in predicting the death of very preterm infants during hospitalization, while in the validation cohort, it had an AUC of 0.808 (95%CI: 0.754-0.861). The Hosmer-Lemeshow goodness-of-fit test showed a good fit (P>0.05). DCA results showed a high net benefit of clinical intervention in very preterm infants when the threshold probability was 10%-60% for the training cohort and 10%-70% for the validation cohort.
CONCLUSIONS:A nomogram model for predicting the risk of death during hospitalization has been established and validated in very preterm infants, which can help clinicians predict the probability of death during hospitalization in these infants.
