Strengths and limitations
The CPMs in this study relied on information that is routinely known at the time of the first antenatal visit, using variables that were temporally remote from when most maternal morbid events arise – largely around the time of birth.9 Moreover, our source population comprised all pregnancies from gestational week 20. However, our datasets had few routinely collected clinical measures, such as blood pressure and haemoglobin or glucose concentrations, or first-trimester screening biomarkers. In the prediction of preterm preeclampsia, for example, a model that contained a combination of clinical and paraclinical variables (including placental biomarkers) performed better than with either set of variables in isolation.32 BMI was incomplete in our dataset, as is common in most administrative data sources. However, the proportion in any given BMI category and those with missing values was not appreciably different among women with and without the outcome. Thus, while the contribution of BMI to the outcome may thus not have been well represented in our models, this unlikely changed the overall model performance.
Prediction models are often used to estimate an individual’s absolute risk of a serious adverse event that might be mitigated with the use of a particular therapy, while avoiding subjecting individuals at low predicted risk to potential harmful effects of such therapy.33 In obstetrics, serious adverse events are rare, with limited options for targeted prevention. We acknowledge, therefore, the limitations afforded by the C-statistic to discriminate between individuals with and without a rare adverse event, in which a high false positive rate might be justified.34 The LRs add clinical meaning to the CPM and serve as a foundation for what might be considered reasonable predictability of rare but catastrophic obstetric events.