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

Abstract

AimTo provide a risk prediction for bronchopulmonary dysplasia (BPD) in premature infants under the new diagnostic criteria and establish a prediction model.MethodsIn 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.ResultA 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.ConclusionThis study made a preliminary exploration of the risk model for early prediction of BPD and indicated good discrimination and calibration in premature infants.