Association of Gastroesophageal Reflux Disease with Preterm Birth: Machine Learning Analysis

Abstract

Background: This study used machine learning and population data for testing the associations of preterm birth with gastroesophageal reflux disease (GERD) and periodontitis. Methods: Retrospective cohort data came from Korea National Health Insurance Service claims data for all women who aged 25-40 years and gave births for the first time as singleton pregnancy during 2015-2017 (405,586 women). The dependent variable was preterm birth during 2015-2017 and the independent variables were GERD (coded as no vs. yes) for each of the years 2002-2014, periodontitis (coded as no vs. yes) for each of the years 2002-2014, age (year) in 2014, socioeconomic status in 2014 measured by an insurance fee, and region (city) (coded as no vs. yes) in 2014. Random forest variable importance was adopted for finding main predictors of preterm birth and testing its associations with GERD and periodontitis. Results: Based on random forest variable importance, main predictors of preterm birth during 2015-2017 were socioeconomic status in 2014, age in 2014, GERD for the years 2012, 2014, 2010, 2013, 2007 and 2009, region (city) in 2014 and GERD for the year 2006. The importance rankings of periodontitis were relatively low. Conclusion: Preterm birth has a stronger association with GERD than with periodontitis. For the prevention of preterm birth, preventive measures for GERD would be essential together with the improvement of socioeconomic status for pregnant women. Especially, it would be vital to promote active counseling for general GERD symptoms (neglected by pregnant women).