INTRODUCTION
Stillbirth is a devastating event and yet it harbours an up to 22-fold recurrence risk in future pregnancies.1, 2 With the aim to reduce the annual stillbirth rate worldwide, the ability to predict the likelihood of such event by accurate risk stratification may prompt both parents and clinicians to embark upon a suitable and targeted antenatal surveillance program.
Prediction models for stillbirths are most commonly defined as “models, scores or clinical decision tools” which aid in estimating the risk of stillbirth in a pregnant woman based upon certain variables.3 In a recent review, the most commonly used variables in prediction models for stillbirths have been identified to be maternal age, body mass index (BMI) and maternal diabetes, yet strongest evidence of association with stillbirth was for nulliparity, pre-existing hypertension and maternal obesity.4 As about 11.2 to 64.9% of stillbirths in high income countries are due to placental dysfunction,5 a triad of the latter factors is most likely contributing to such. Whilst the pathomechanisms of the individual risk denominators might work differently on the axis leading to fetal death, the synthesis of these variables into a prediction model is helpful for early recognition and intervention to prevent adverse perinatal outcome. To date, 69 prediction models for stillbirths have been described in literature.3
By this study we aim to apply the demographic model of the Fetal Medicine Foundation (FMF) Stillbirth Risk Calculator6 based upon maternal characteristics (weight, ethnicity and smoking), medical history [diabetes, chronic hypertension, systemic lupus erythematosus (SLE) and anti-phospholipid syndrome (APS)] and obstetric history (parity, stillbirth and/or preeclampsia in previous pregnancies)7 in our single-centre cohort of intrauterine fetal deaths (IUFD) and matched live births as an independent dataset for external validation of this prediction tool.