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Preterm birth and maternal heart disease: A machine learning analysis using the Korean national health insurance
Jue Seong Lee1, Eun-Saem Choi2, Yujin Hwang2,3
1Department of Pediatric Cardiology, Korea University College of Medicine, Korea University Anam Hospital, Seoul, Korea.
Insights
Maternal heart disease, particularly arrhythmia and ischemic heart disease (IHD), is linked to preterm birth (PTB). Machine learning effectively predicts PTB, highlighting the importance of managing maternal heart conditions during pregnancy.
Area of Science:
- Cardiology
- Obstetrics
- Data Science
Background:
- Maternal heart disease is a suspected risk factor for preterm birth (PTB), but evidence is limited.
- This study addresses the need for validated research on the maternal heart disease-PTB association.
Purpose of the Study:
- To develop a machine learning model for predicting PTB using nationwide population data.
- To investigate the specific associations between various maternal heart diseases and PTB.
Main Methods:
- A retrospective cohort study of 174,926 primiparous women in South Korea (2017).
- Utilized the Korea National Health Insurance claims database.
- Employed random forest variable importance and Shapley additive explanation for analysis of PTB determinants and maternal heart diseases (arrhythmia, IHD, etc.).
Main Results:
- The machine learning model demonstrated high predictive accuracy (AUC 88.53-95.31, accuracy 89.59-95.22).
- Socioeconomic status and maternal age were key PTB predictors.
- Arrhythmia and ischemic heart disease (IHD) showed strong associations with PTB, with atrial fibrillation/flutter being a significant risk factor.
Conclusions:
- Machine learning provides an effective prediction model for PTB.
- Managing maternal heart conditions like arrhythmia and IHD is crucial for reducing PTB rates.
Background:
Maternal heart disease is suspected to affect preterm birth (PTB); however, validated studies on the association between maternal heart disease and PTB are still limited. This study aimed to build a prediction model for PTB using machine learning analysis and nationwide population data, and to investigate the association between various maternal heart diseases and PTB.
Methods:
A population-based, retrospective cohort study was conducted using data obtained from the Korea National Health Insurance claims database, that included 174,926 primiparous women aged 25-40 years who delivered in 2017. The random forest variable importance was used to identify the major determinants of PTB and test its associations with maternal heart diseases, i.e., arrhythmia, ischemic heart disease (IHD), cardiomyopathy, congestive heart failure, and congenital heart disease first diagnosed before or during pregnancy.
Results:
Among the study population, 12,701 women had PTB, and 12,234 women had at least one heart disease. The areas under the receiver-operating-characteristic curves of the random forest with oversampling data were within 88.53 to 95.31. The accuracy range was 89.59 to 95.22. The most critical variables for PTB were socioeconomic status and age. The random forest variable importance indicated the strong associations of PTB with arrhythmia and IHD among the maternal heart diseases. Within the arrhythmia group, atrial fibrillation/flutter was the most significant risk factor for PTB based on the Shapley additive explanation value.
Conclusions:
Careful evaluation and management of maternal heart disease during pregnancy would help reduce PTB. Machine learning is an effective prediction model for PTB and the major predictors of PTB included maternal heart disease such as arrhythmia and IHD.
