Interpretable machine learning model for comparing and validating three diagnostic criteria for bronchopulmonary dysplasia in predicting value of respiratory prognosis of preterm infants: a retrospective cohort study

可解释的机器学习模型用于比较和验证三种支气管肺发育不良诊断标准在预测早产儿呼吸预后中的价值:一项回顾性队列研究

阅读:1

Abstract

BACKGROUND: Comparison and validation of the predictive value of three diagnostic criteria for bronchopulmonary dysplasia for respiratory prognosis of preterm infants with gestational age <32 weeks. METHODS: This retrospective cohort study was conducted to collect clinical data of 397 preterm infants. On the basis of the follow-up results, the enrolled population was divided into a respiratory adverse outcome group and a normal outcome group. The 2001 NICHD, the 2018 NICHD, and the 2019 NRN criteria were used to diagnose and grade BPD in preterm infants. The dataset was randomly divided, with 70% used for model training and 30% used for model validation. The extreme gradient boosting machine learning algorithm was used for model training. Furthermore, the SHapley additive exPlanation analysis method was utilized to visually interpret the results of the machine learning model. RESULTS: A total of 397 preterm infants were included. In the training set, prediction models based on the 2001 NICHD, 2018 NICHD, and 2019 NRN criteria achieved AUC values of 0.747, 0.804, and 0.789, with corresponding accuracies of 0.740, 0.765, and 0.765. In the test set, the respective AUC values were 0.694, 0.747, and 0.752, and accuracies were 0.750, 0.800, and 0.750. Based on the DeLong's method, comparisons of ROC curves between the training and test sets revealed that both the 2018 NICHD and 2019 NRN criteria demonstrated significantly higher AUC than the 2001 NICHD criteria (training set: Z = -3.514, -2.110, both P < 0.05; test set: Z = -2.137, -2.199, both P < 0.05). However, there was no statistically significant difference in the AUC between the 2018 NICHD and 2019 NRN criteria for either the training set (Z = 0.863, P = 0.388) or the test set (Z = -0.176, P = 0.861). The SHAP revealing that the two most important features affecting the respiratory prognosis of preterm infants were the severity of BPD and early invasive ventilation. CONCLUSIONS: Both the 2018 NICHD and 2019 NRN criteria for BPD show better and similar predictive values for respiratory adverse outcomes in preterm infants, and both are superior to the 2001 NICHD criteria. The top two factors affecting the respiratory prognosis of preterm infants are the severity of BPD and early invasive mechanical ventilation.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。