Predictive modeling of bronchopulmonary dysplasia in premature infants: the impact of new diagnostic standards.

Abstract

Aim: To provide a risk prediction for bronchopulmonary dysplasia (BPD) in premature infants under the new diagnostic criteria and establish a prediction model.

Methods: In this study, we retrospectively collected case data on preterm infants admitted to the NICU from August 2015 to August 2018. A lasso analysis was performed to identify the risk factors associated with the development of BPD. A nomogram predictive model was constructed in accordance with the new diagnostic criteria for BPD.

Result: A total of 276 preterm infants were included in the study.The incidence of BPD under the 2018 diagnostic criteria was 11.2%. Mortality was significantly higher in the BPD group than the non-BPD group under the 2018 diagnostic criteria (P < 0.05). Fourteen possible variables were selected by the Lasso method, with a penalty coefficient λ=0.0154. The factors that eventually entered the logistic regression model included birth weight [BW, OR = 0.9945, 95% CI: 0.9904-0.9979], resuscitation way (OR = 4.8249, 95% CI: 1.3990-19.4752), intrauterine distress (OR = 8.0586, 95% CI: 1.7810-39.5696), score for SNAPPE-II (OR = 1.0880, 95% CI: 1.0210-1.1639), hematocrit (OR = 1.1554, 95% CI: 1.0469-1.2751) and apnea (OR = 7.6916, 95% CI: 1.4180-52.1236). The C-index after adjusting for fitting deviation was 0.894.

Conclusion: This study made a preliminary exploration of the risk model for early prediction of BPD and indicated good discrimination and calibration in premature infants.