Predictive modeling of bronchopulmonary dysplasia in premature infants: the impact of new diagnostic standards - Tang, Lijun; Wu, Weibin; Huang, Weimin; Bi, Guangliang (Frontiers in Pediatrics, 2024)
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 =<sans-serif> 0</sans-serif>.9945, 95% CI: 0.9904–0.9979], resuscitation way (OR =<sans-serif> 4</sans-serif>.8249, 95% CI: 1.3990–19.4752), intrauterine distress (OR =<sans-serif> 8</sans-serif>.0586, 95% CI: 1.7810–39.5696), score for SNAPPE-II (OR =<sans-serif> 1</sans-serif>.0880, 95% CI: 1.0210–1.1639), hematocrit (OR =<sans-serif> 1</sans-serif>.1554, 95% CI: 1.0469–1.2751) and apnea (OR =<sans-serif> 7</sans-serif>.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.