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.
Humans ; Singapore/epidemiology* ; Retrospective Studies ; Female ; Risk Factors ; Premature Birth/ethnology* ; Pregnancy ; Adult ; Infant, Newborn ; Asian People/statistics & numerical data* ; Gestational Age ; Body Mass Index ; Maternal Age ; Logistic Models ; Ethnicity

INTRODUCTION: An antenatal scoring system for vaginal birth after caesarean section (VBAC) categorises patients into a low or high probability of successful vaginal delivery. It enables counselling and preparation before labour starts. The current study aims to evaluate the role of Grobman nomogram and the Kalok scoring system in predicting VBAC success in Singapore. METHODS: This is a retrospective study on patients of gestational age 37 weeks 0 day to 41 weeks 0 day who underwent a trial of labour after 1 caesarean section between September 2016 and September 2017 was conducted. Two scoring systems were used to predict VBAC success, a nomogram by Grobman et al. in 2007 and an additive model by Kalok et al. in 2017. RESULTS: A total of 190 patients underwent a trial of labour after caesarean section, of which 103 (54.2%) were successful. The Kalok scoring system (area under curve [AUC] 0.740) was a better predictive model than Grobman nomogram (AUC 0.664). Patient's age (odds ratio [OR] 0.915, 95% CI [confidence interval] 0.844-0.992), body mass index at booking (OR 0.902, 95% CI 0.845-0.962), and history of successful VBAC (OR 4.755, 95% CI 1.248-18.120) were important factors in predicting VBAC. CONCLUSION Neither scoring system was perfect in predicting VBAC among local women. Further customisation of the scoring system to replace ethnicity with the 4 races of Singapore can be made to improve its sensitivity. The factors identified in this study serve as a foundation for developing a population-specific antenatal scoring system for Singapore women who wish to have a trial of VBAC.
Area Under Curve ; Cesarean Section ; Female ; Humans ; Infant ; Pregnancy ; Retrospective Studies ; Trial of Labor ; Vaginal Birth after Cesarean