Developing a logistic regression model to predict spontaneous preterm birth from maternal socio-demographic and obstetric history at initial pregnancy registration

Background

Current predictive machine learning techniques for spontaneous preterm birth heavily rely on a history of previous preterm birth and/or costly techniques such as fetal fibronectin and ultrasound measurement of cervical length to the disadvantage of those considered at low risk and/or those who have no access to more expensive screening tools.

Aims and objectives We aimed to develop a predictive model for spontaneous preterm delivery < 37 weeks using socio-demographic and clinical data readily available at booking -an approach which could be suitable for all women regardless of their previous obstetric history.

Methods

We developed a logistic regression model using seven feature variables derived from maternal socio-demographic and obstetric history from a preterm birth (n = 917) and a matched full-term (n = 100) cohort in 2018 and 2020 at a tertiary obstetric unit in the UK. A three-fold cross-validation technique was applied with subsets for data training and testing in Python® (version 3.8) using the most predictive factors. The model performance was then compared to the previously published predictive algorithms.

Results

The retrospective model showed good predictive accuracy with an AUC of 0.76 (95% CI: 0.71–0.83) for spontaneous preterm birth, with a sensitivity and specificity of 0.71 (95% CI: 0.66–0.76) and 0.78 (95% CI: 0.63–0.88) respectively based on seven variables: maternal age, BMI, ethnicity, smoking, gestational type, substance misuse and parity/obstetric history.

Conclusion

Pending further validation, our observations suggest that key maternal demographic features, incorporated into a traditional mathematical model, have promising predictive utility for spontaneous preterm birth in pregnant women in our region without the need for cervical length and/or fetal fibronectin.