Risk prediction of bronchopulmonary dysplasia in preterm infants by the nomogram model

Abstract

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Backgrounds and AimsBronchopulmonary dysplasia (BPD) has serious immediate and long-term sequelae as well as morbidity and mortality. The objective of this study is to develop a predictive model of BPD for premature infants using clinical maternal and neonatal parameters.MethodsThis single-center retrospective study enrolled 237 cases of premature infants with gestational age less than 32 weeks. The research collected demographic, clinical and laboratory parameters. Univariate logistic regression analysis was carried out to screen the potential risk factors of BPD. Multivariate and LASSO logistic regression analysis was performed to further select variables for the establishment of nomogram models. The discrimination of the model was assessed by C-index. The Hosmer-Lemeshow test was used to assess the calibration of the model.ResultsMultivariate analysis identified maternal age, delivery option, neonatal weight and age, invasive ventilation, and hemoglobin as risk predictors. LASSO analysis selected delivery option, neonatal weight and age, invasive ventilation, hemoglobin and albumin as the risk predictors. Both multivariate (AUC = 0.9051; HL P = 0.6920; C-index = 0.910) and LASSO (AUC = 0.8935; HL P = 0.7796; C-index = 0.899) - based nomograms exhibited ideal discrimination and calibration as confirmed by validation dataset.ConclusionsThe probability of BPD in a premature infant could be effectively predicted by the nomogram model based on the clinical maternal and neonatal parameters. However, the model required external validation using larger samples from multiple medical centers.

Keywords

• BPD
• logistic regression analysis
• risk prediction
• preterm infants
• nomogram model