Abstract
Background
Pre-eclampsia affects around 2–3% of all pregnancies, and is associated with potential serious complications for the woman and the baby. Once diagnosed, progression of the syndrome can be unpredictable, and decisions around timing of delivery need to take into account evolving maternal complications and perinatal morbidity. Novel prognostic models and blood biomarkers for determination of need for delivery in pregnancies with pre-eclampsia are now emerging.
Objective
The objective of the study was to establish a prognostic model to inform optimal timing of delivery in women with late preterm pre-eclampsia (34+ 0 to 36+ 6 weeks’ gestation), comparing novel candidate biomarkers (e.g. placental growth factor) with clinical and routinely collected blood/urinary parameters [incorporated into the PREP-S (Prediction models for Risk of Early-onset Pre-eclampsia – Survival) model] to determine clinically indicated need for delivery for pre-eclampsia (or related complications) within 7 days of assessment.
Methods
Prospective recruitment of women in whom blood samples for placental growth factor and soluble fms-like tyrosine kinase-1 testing was obtained, alongside clinical data, for use within the PREP-S model. Candidate variables were compared using standard methods (sensitivity, specificity, receiver operator curve areas). Estimated probability of early delivery from PREP-S was compared with actual event rates by calibration.
Setting
The PEACOCK (Prognostic indicators of severe disEAse in women with late preterm pre-eClampsia tO guide deCision maKing on timing of delivery) study was a prospective cohort study, nested within the PHOENIX (Pre-eclampsia in HOspital: Early iNductIon or eXpectant management) trial.
Participants
Women between 34+ 0 and 36+ 6 weeks’ gestation, with a diagnosis of pre-eclampsia, in whom a plasma (ethylenediaminetetraacetic acid) blood sample for placental growth factor testing was obtained, alongside clinical data for the assessment of variables in a prognostic model.
Main outcome measures
Clinically indicated need for delivery for pre-eclampsia within 7 days of assessment. Statistical analysis: both PREP-S and placental growth factor were assessed and compared using standard methods (sensitivity and specificity for placental growth factor thresholds of 100 pg/ml and < 12 pg/ml, and receiver operating characteristic areas for continuous measurements). The estimated probability of early delivery from PREP-S was compared with actual event rates for women with similar probabilities by calibration. Calibration using logistic regression was also used.
Results
Between 27 April 2016 and 24 December 2018, 501 women were recruited to the study. Although placental growth factor testing had high sensitivity (97.9%) for delivery within 7 days, the negative predictive value was only 71.4% and the specificity was low (8.4%). The area under the curve for the clinical prediction model (PREP-S) and placental growth factor in this cohort in determining need for delivery within 7 days was 0.64 (standard error 0.03) and 0.60 (standard error 0.03), respectively, and 0.65 (standard error 0.03) in combination.
Limitations
A high proportion of women in this cohort already had low placental growth factor concentrations at the time of confirmed diagnosis, which reduced the ability of the biomarker to further predict adverse outcomes.
Conclusions
In this group of women with late preterm pre-eclampsia, placental growth factor measurement is not likely to add to the current clinical assessment to help plan care for late preterm pre-eclampsia regarding timing of delivery. Existing models developed in women with early-onset pre-eclampsia to predict complications cannot be used to predict clinically indicated need for delivery in women with late preterm pre-eclampsia.
Future work
Further statistical modelling and subgroup analysis is being considered to assess if improved model performance in the whole cohort or a subgroup can be achieved. Addition of other biomarkers to the model may also be of use and will be explored.
Pre-eclampsia affects around 2–3% of all pregnancies, and is associated with potential serious complications for the woman and the baby. Once diagnosed, progression of the syndrome can be unpredictable, and decisions around timing of delivery need to take into account evolving maternal complications and perinatal morbidity. Novel prognostic models and blood biomarkers for determination of need for delivery in pregnancies with pre-eclampsia are now emerging.
Objective
The objective of the study was to establish a prognostic model to inform optimal timing of delivery in women with late preterm pre-eclampsia (34+ 0 to 36+ 6 weeks’ gestation), comparing novel candidate biomarkers (e.g. placental growth factor) with clinical and routinely collected blood/urinary parameters [incorporated into the PREP-S (Prediction models for Risk of Early-onset Pre-eclampsia – Survival) model] to determine clinically indicated need for delivery for pre-eclampsia (or related complications) within 7 days of assessment.
Methods
Prospective recruitment of women in whom blood samples for placental growth factor and soluble fms-like tyrosine kinase-1 testing was obtained, alongside clinical data, for use within the PREP-S model. Candidate variables were compared using standard methods (sensitivity, specificity, receiver operator curve areas). Estimated probability of early delivery from PREP-S was compared with actual event rates by calibration.
Setting
The PEACOCK (Prognostic indicators of severe disEAse in women with late preterm pre-eClampsia tO guide deCision maKing on timing of delivery) study was a prospective cohort study, nested within the PHOENIX (Pre-eclampsia in HOspital: Early iNductIon or eXpectant management) trial.
Participants
Women between 34+ 0 and 36+ 6 weeks’ gestation, with a diagnosis of pre-eclampsia, in whom a plasma (ethylenediaminetetraacetic acid) blood sample for placental growth factor testing was obtained, alongside clinical data for the assessment of variables in a prognostic model.
Main outcome measures
Clinically indicated need for delivery for pre-eclampsia within 7 days of assessment. Statistical analysis: both PREP-S and placental growth factor were assessed and compared using standard methods (sensitivity and specificity for placental growth factor thresholds of 100 pg/ml and < 12 pg/ml, and receiver operating characteristic areas for continuous measurements). The estimated probability of early delivery from PREP-S was compared with actual event rates for women with similar probabilities by calibration. Calibration using logistic regression was also used.
Results
Between 27 April 2016 and 24 December 2018, 501 women were recruited to the study. Although placental growth factor testing had high sensitivity (97.9%) for delivery within 7 days, the negative predictive value was only 71.4% and the specificity was low (8.4%). The area under the curve for the clinical prediction model (PREP-S) and placental growth factor in this cohort in determining need for delivery within 7 days was 0.64 (standard error 0.03) and 0.60 (standard error 0.03), respectively, and 0.65 (standard error 0.03) in combination.
Limitations
A high proportion of women in this cohort already had low placental growth factor concentrations at the time of confirmed diagnosis, which reduced the ability of the biomarker to further predict adverse outcomes.
Conclusions
In this group of women with late preterm pre-eclampsia, placental growth factor measurement is not likely to add to the current clinical assessment to help plan care for late preterm pre-eclampsia regarding timing of delivery. Existing models developed in women with early-onset pre-eclampsia to predict complications cannot be used to predict clinically indicated need for delivery in women with late preterm pre-eclampsia.
Future work
Further statistical modelling and subgroup analysis is being considered to assess if improved model performance in the whole cohort or a subgroup can be achieved. Addition of other biomarkers to the model may also be of use and will be explored.
| Original language | English |
|---|---|
| Journal | Health Technology Assessment |
| DOIs | |
| Publication status | Published - 1 May 2021 |
