RESULTS
Of the 309 samples from 253 pregnancies with an sFlt-1/PlGF ratio of 38
or below, none delivered with early-onset pre-eclampsia in the
subsequent week and none of the pregnancies developed early-onset
pre-eclampsia. Conversely, 42.3% (52 of 123) of pregnancies with an
sFlt-1/PlGF ratio above 38 delivered with early-onset pre-eclampsia.
However, only 22.1% (47 of 213 samples from 123 pregnancies) were
diagnosed with early-onset pre-eclampsia leading to delivery within one
week. Note that five pregnancies that delivered with early-onset
pre-eclampsia did not have any blood test during the last week of
pregnancy. Table S1 (supporting information) shows the epidemiological
and clinical characteristics of the population and samples,
respectively, by study group. NT-proBNP did not vary significantly
during the considered gestational weeks in pregnancies that did not
develop pre-eclampsia (Pearson correlation= -0.092 p= 0.149) (see Figure
S2). In contrast, sFlt-1, PlGF and sFlt-1/PlGF ratio changed with
gestational age. Equations describing sFlt-1, PlGF and sFlt-1/PlGF ratio
medians per gestational week are described in supporting information
(see Table S2).
As the assessed endpoint is a subrogated marker of severity, we compared
this endpoint with the definition of severe pre-eclampsia in our sample
(13). We found that 97.9% (46/47) of pregnancies with early-onset
pre-eclampsia leading to delivery within one week were cases of severe
pre-eclampsia. However, the assessed endpoint was observed in only
48.9% (46/94) cases of severe pre-eclampsia. Therefore, we conclude
that the assessment of early-onset pre-eclampsia leading to delivery
within one week is a more restrictive criterion of severe pre-eclampsia
than the ACOG definition of severe pre-eclampsia.
When assessing individual marker prediction performances, we observed a
significantly lower estimate of the AUC of the model based on
gestational age and PlGF MoM than the AUC obtained with the model that
includes gestational age and sFlt-1 MoM (p< 0.001) (Figure 2).
Model development
We developed two types of linear mixed model. One type including the raw
marker values and another considering the gestational age-corrected
markers. Marker values were logarithmized to overcome the skewness in
the data.
Addition of NT-proBNP to sFlt-1/PlGF ratio
We compared the prediction ability of the raw value marker models from
the respective ROC curves (Figure 3, left panel). The estimate of the
AUC of the model that includes gestational age, sFlt-1/PlGF ratio and
NT-proBNP was significantly greater (DeLong test, p= 0.013) than the
estimate of the AUC of the model without NT-proBNP.
Use of sFlt-1/PlGF ratio
PlGF MoM was excluded from the MoM transformed marker model during its
construction due to low prediction ability. We did not consider
including sFlt-1/PlGF ratio MoM in the models as the inclusion of PlGF
MoM was non-informative.
The estimate of the AUC of the model that combines gestational age,
sFlt-1 MoM and NT-proBNP was significantly greater (p= 0.031) than the
estimate of the AUC of the model without NT-proBNP (Figure 3, right
panel). Therefore, the selected model for the prognostic prediction tool
included gestational age, sFlt-1 MoM and NT-proBNP. Description and
predictions of the selected model for each possible value are summarized
in Table S3 and Figure S1 (supporting information), respectively.
There were no significant differences between the AUC of the raw value
marker model that combines gestational age, sFlt-1/PlGF ratio and
NT-proBNP and the gestational age-adjusted model that includes
gestational age and sFlt-1 MoM and NT-proBNP (p= 0.648).
Subgroup analysis
Prediction ability of the model that combines gestational age and sFlt-1
MoM and NT-proBNP did not differ from the model without NT-proBNP in
pregnancies with intrauterine growth restriction (p= 0.200) or chronic
hypertension (p= 0.361).
Model performance
The area under the AUC for early-onset pre-eclampsia diagnosis leading
to delivery within one week was 0.882 (95% CI 0.822-0.934) for the
model that combines gestational age, sFlt-1 MoM and NT-proBNP and 0.826
(95% CI 0.752-0.892) for the model that combines sFlt-1/PlGF ratio raw
values and gestational age model (P = 0.044).
At a 5% false positive rate cut-off level the model that combines
gestational age, sFlt-1 MoM and NT-proBNP reached a detection rate of
59.6%, which was significantly greater (p= 0.001) than the model
without NT-proBNP (31.9%). At this cut-off level, the model with
NT-proBNP resulted positive in 16.9% of the sample and the likelihood
ratio of a positive test was 12.4 (95% CI: 6.0-25.3). In other words,
the odds of a developing the event is increased twelvefold when the
prognostic prediction tool result is positive.
At the sFlt-1/PlGF ratio 655 cut-off the detection ratio was 31.9%
(19.1-47.1) with false positive rate of 4.2% (1.7-8.5), predicting
early-onset pre-eclampsia diagnosis leading to delivery within one week.
With the same false positive rate, the detection rate with the
prognostic prediction tool was 53.2% (38.1-67.9) (P=0.03).
Globally, if we compare the application of the criteria based on the
sFlt-1/PlGF ratio cut-off value of 38 with the application of the
prognostic assessment tool (to pregnancies with an sFlt-1/PlGF ratio
above 38), the latter reduced the false positive rate from 34.9% to
1.7% increasing positive predictive value from 22.1% to 77.8%, at the
expense of including 33.9% inconclusive valid results (Table 1).