Preterm birth trends and risk factors in a multi-ethnic Asian population: A retrospective study from 2017 to 2023, can we screen and predict this? - Rachel Phoy Cheng Chun; Hiu Gwan Chan; Gilbert Yong San Lim; Devendra Kanagalingam; Pamela Partana; Kok Hian Tan; Tiong Ghee Teoh; Ilka Tan (Annals, Academy of Medicine, Singapore)
Introduction: Preterm birth (PTB) remains a leading cause of perinatal morbidity and mortality worldwide. Understanding Singapore’s PTB trends and associated risk factors can inform effective strategies for screening and intervention. This study analyses PTB trends in Singapore from 2017 to 2023, identifies risk factors in this multi-ethnic population and evaluates a predictive model for PTB. Method: A retrospective analysis of all PTBs between 22+0 and 36+6 weeks of gestation, from 1 January 2017 to 31 December 2023, was performed by extracting maternal and neonatal data from electronic medical records. These PTBs were taken from the registry of births for Singapore and SingHealth cluster data. Cochran-Armitage trend test and multinomial logistic regression were used. An extreme gradient boosting (XGBoost) model was developed to test and predict the risk of PTB. Results: The PTB rate in Singapore did not show a significant change. However, there was modest downward trend in the SingHealth population from 11.3% to 10.2%, mainly in late spontaneous PTBs (sPTBs). sPTBs accounted for ~60% of PTBs. Risk factors for very/extreme sPTB included Chinese ethnicity, age ≥35 years, body mass index (BMI) ≥23 kg/m², being unmarried, primiparity, twin pregnancy and maternal blood group AB. The XGBoost model achieved an area under the receiver operating characteristic curve of 0.75, indicating moderate ability to predict PTB. Conclusion: The overall PTB rate in Singapore has not improved. This study underscores the importance of local factors, particularly advanced maternal age, BMI, primiparity, unmarried, Chinese ethnicity and maternal blood group AB influencing PTB risk. Artificial intelligence methods show promise in improving PTB risk stratification, ultimately supporting personalised care and intervention.