Bronchopulmonary Dysplasia Predicted by Developing a Machine Learning Model of Genetic and Clinical Information
Dan Dai1, Huiyao Chen2, Xinran Dong2
1Division of Pulmonary Medicine, Children's Hospital of Fudan University, Shanghai, China.
Insights
Genetic factors improve bronchopulmonary dysplasia (BPD) risk prediction in premature infants. Combining genetic risk gene sets (RGS) with clinical factors offers accurate BPD risk stratification.
Area of Science:
- Neonatal Medicine
- Genetics
- Computational Biology
Background:
- Early and accurate risk evaluation for bronchopulmonary dysplasia (BPD) in premature infants is crucial for implementing preventive strategies.
- Current BPD risk prediction models often lack genetic factors, leading to complexity or poor discrimination.
- This study investigates the role of genetic factors in early and accurate BPD risk prediction.
Purpose of the Study:
- To identify genetic factors contributing to BPD risk.
- To develop and validate predictive models for BPD and severe BPD (sBPD) risk by integrating clinical and genetic data.
- To assess the performance of these models in stratifying BPD risk.
Main Methods:
- Exome sequencing in 245 premature infants (131 with BPD, 114 controls).
- Gene burden testing to identify risk genes and define BPD-RGS and sBPD-RGS.
- Development and evaluation of predictive models using clinical and genetic features, assessed by AUROC.
Main Results:
- Thirty genes identified for BPD-RGS and 21 for sBPD-RGS.
- A predictive model combining BPD-RGS and clinical factors showed significantly improved BPD risk discrimination (AUROC 0.915 vs. 0.814).
- Similarly, the sBPD predictive model integrating genetic and clinical factors demonstrated enhanced performance (AUROC 0.907 vs. 0.826).
Conclusions:
- Genetic information plays a role in BPD susceptibility.
- The developed predictive model, incorporating BPD-RGS and clinical factors, accurately stratifies BPD risk in premature infants.
- This approach enhances early and precise BPD risk assessment for improved clinical management.
Background:
An early and accurate evaluation of the risk of bronchopulmonary dysplasia (BPD) in premature infants is pivotal in implementing preventive strategies. The risk prediction models nowadays for BPD risk that included only clinical factors but without genetic factors are either too complex without practicability or provide poor-to-moderate discrimination. We aim to identify the role of genetic factors in BPD risk prediction early and accurately.
Methods:
Exome sequencing was performed in a cohort of 245 premature infants (gestational age <32 weeks), with 131 BPD infants and 114 infants without BPD as controls. A gene burden test was performed to find risk genes with loss-of-function mutations or missense mutations over-represented in BPD and severe BPD (sBPD) patients, with risk gene sets (RGS) defined as BPD-RGS and sBPD-RGS, respectively. We then developed two predictive models for the risk of BPD and sBPD by integrating patient clinical and genetic features. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUROC).
Results:
Thirty and 21 genes were included in BPD-RGS and sBPD-RGS, respectively. The predictive model for BPD, which combined the BPD-RGS and basic clinical risk factors, showed better discrimination than the model that was only based on basic clinical features (AUROC, 0.915 vs. AUROC, 0.814, P = 0.013, respectively) in the independent testing dataset. The same was observed in the predictive model for sBPD (AUROC, 0.907 vs. AUROC, 0.826; P = 0.016).
Conclusion:
This study suggests that genetic information contributes to susceptibility to BPD. The predictive model in this study, which combined BPD-RGS with basic clinical risk factors, can thus accurately stratify BPD risk in premature infants.
