A prediction model of pulmonary hypertension in preterm infants with bronchopulmonary dysplasia

• Published in: Frontiers in pediatrics 2022-07, Vol.10, p.925312-925312
• Main Authors: Wang, Chenhong, Ma, Xiaolu, Xu, Yanping, Chen, Zheng, Shi, Liping, Du, Lizhong
• Format: Article
• Language: English
• Subjects: bronchopulmonary dysplasia; nomogram; Pediatrics; prediction model; preterm infant; pulmonary hypertension
• ISSN: 2296-2360; 2296-2360
• DOI: 10.3389/fped.2022.925312

ObjectivePulmonary hypertension (PH) is a severe cardiovascular complication of bronchopulmonary dysplasia (BPD) that contributes to the high mortality rates for preterm infants. The objective of this study is to establish a prediction model of BPD-associated PH (BPD-PH) by integrating multiple predictive factors for infants with BPD. MethodA retrospective investigation of the perinatal clinical records and data of echocardiography in all the preterm infants with BPD was performed from January 2012 to December 2019. A prediction model of BPD-PH was established based on the univariate and multivariate logistic regression analysis of the clinical data and evaluated by using the area under the receiver operating characteristic (ROC) curve (AUC), combined with the Hosmer-Lemeshow (HL) test. Internal validation was performed with bootstrap resampling. ResultA total of 268 infants with BPD were divided into the BPD-PH group and the no-PH group. Multivariate logistic regression analysis showed that the independent predictive factors of BPD-PH were moderate to severe BPD, small for gestational age, duration of hemodynamically significant patent ductus arteriosus ≥ 28 days, and early PH. A prediction model was established based on the β coefficients of the four predictors. The area under the ROC curve of the prediction model was 0.930. The Hosmer-Lemeshow test (p = 0.976) and the calibration curve showed good calibration. ConclusionThe prediction model based on the four risk factors predicts the development of BPD-PH with high sensitivity and specificity and might help clinicians to make individualized interventions to minimize the disease risk.