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.