Principal findings of this study
The SPREE study has demonstrated that risk assessment for pre-eclampsia as currently recommended by NICE guidelines8 identifies approximately 30% of women who would develop pre-eclampsia and about 40% of those that will develop severe pre-eclampsia leading to preterm birth, at a SPR of 10%.
Compliance with the NICE recommendation that women at high-risk for pre-eclampsia should be treated with aspirin from the first trimester to the end of pregnancy was only 23%. Such low compliance may, at least in part, be attributed to the generally held belief, based on the results of a meta-analysis in 2007,6 that aspirin reduces the risk of pre-eclampsia by only about 10%.
The performance of screening by competing risks model, which combines maternal factors with biomarkers,17,33 was superior to that of risk assessment by NICE guidelines. At the same SPR as for the NICE method, the DR for all pre-eclampsia in screening by Mat-CHs, MAP and serum PAPP-A was 42.5% and the DR for preterm pre-eclampsia by a combination of Mat-CHs, MAP, UTA-PI and PLGF was 82.4%, which is significantly higher than that of the NICE guidelines.
Strengths and limitations of this study
The strengths of the study include prospective examination of a large number of pregnant women in several maternity units covering a wide spectrum of demographic and racial characteristics. The results are therefore likely to be generalisable across the UK. More than 90% of patients attending for routine care agreed to participate in the study. Measurement of all biomarkers was recorded in all cases and complete follow-up was obtained from > 98% of participants. Consistency in data collection was maintained throughout the study period by ensuring adequate training for all investigators based on standardised protocols, regular UCL-CCTU monitoring, and external validation and quality assurance of biomarker measurements.
A potential limitation of the study is lack of formal health economic assessment concerning the implementation of combined screening for pre-eclampsia. Such assessment was beyond the scope of this study, but it is currently being carried out.
Comparison with results of previous studies
The performance of screening for preterm pre-eclampsia by the competing risks model, utilising Mat-CHs, MAP, UTA-PI and PLGF, observed in this study is comparable to that reported in several previous studies of singleton pregnancies at 11–13 weeks’ gestation.17,19,39,40 The algorithm was originally developed from a study19 of 58,884 pregnancies. The DR of preterm pre-eclampsia was 77% at a FPR of 10%.19 Subsequently, we used data from prospective screening in 35,948 pregnancies to update the original algorithm. The DR of preterm pre-eclampsia was 75% at a FPR of 10%.17 The diagnostic accuracy of this algorithm was examined in a prospective multicentre study39 of 8775 pregnancies. The DR of preterm pre-eclampsia was 75% at a FPR of 10%.39 In the screened population in the ASPRE trial,40 involving 25,797 pregnancies from 13 maternity hospitals in six countries, the DR of preterm pre-eclampsia after adjustment for the effect of aspirin was 77% at a FPR of 9.2%.40 None of these studies found evidence that PAPP-A improved screening achieved by MAP, UTA-PI and PLGF.
Other first-trimester combined prediction models have been developed in different populations. Specifically, two Spanish cohort studies27,29 have developed models with the use of Mat-CHs, UTA-PI, MAP and biochemical markers that have demonstrated similar predictive performance in comparison with the Bayes’ theorem-based model. In contrast, three combined prediction algorithms, established from cohort studies in the USA, demonstrate lower predictive performance than the Bayes’ theorem-based model.25,28,41
A recent systematic review has compared the performance of simple risk models (i.e. Mat-CHs only) with that of specialised models that include specialised tests (e.g. measurements of MAP, UTA-PI and/or biochemical markers) for the prediction of pre-eclampsia.42 Seventy models from 29 studies were identified (17 models to predict pre-eclampsia of any gestation, 31 models to predict early-onset pre-eclampsia and 22 models to predict late-onset pre-eclampsia). Among them, 22 were simple risk models, whereas 48 were classified as specialised models. Comparing simple and specialised models, the latter performed better than the simple models in predicting both early- and late-onset pre-eclampsia.42 The specialised models have been shown to increase the DR of pre-eclampsia by 18% (95% CI 0% to 56%) at a fixed FPR of 5% or 10%.42 Such results further confirm that our approach to screening using a combination of various tests rather than a single test is better for the prediction of pre-eclampsia.
Implications on clinical practice
Recent evidence suggests that first-trimester risk assessment should focus on prediction of pre-eclampsia leading to preterm birth primarily with the aim of preventing preterm birth through treatment with aspirin from 11–13 weeks’ gestation. Aspirin is considerably more effective than previously thought in reducing the risk of preterm pre-eclampsia, provided the daily dose of the drug is ≥ 100 mg and the gestational age at onset of therapy is < 16 weeks.1 In the ASPRE trial,7,43 use of aspirin (150 mg/day) starting from 11–14 weeks’ gestation reduced the risk of preterm pre-eclampsia by 62% and a secondary analysis of the trial reported that the reduction was even greater (75%) if the compliance was ≥ 90%. Against this background, there are ongoing debates about prediction and prevention of preterm pre-eclampsia centred on two questions: (1) whether or not aspirin should be recommended for all women or to a subpopulation of those women predicted to be at increased risk of developing pre-eclampsia and (2) if a strategy of prediction and prevention is to be used, what method should be used for prediction.
The arguments in favour of recommending aspirin to all women are that it avoids the need for prediction and the whole population benefits from the prophylactic treatment with aspirin. Arguments against this are that (1) compliance is likely to be worse when aspirin is applied to the whole population than when recommended to a subpopulation selected, and counselled, based on risk and (2) there is a need to balance the benefit from aspirin in prevention of preterm pre-eclampsia with potential harm from aspirin due to haemorrhagic and other adverse effects. Assuming that the entire population took aspirin, an incidence of 0.8% and a relative reduction in risk of preterm pre-eclampsia of 60%, 208 women would be exposed to aspirin treatment for every case of preterm pre-eclampsia prevented. Using risk stratification with Mat-CHs, MAP, UTA-PI and PLGF with the same SPR as NICE, 16 women would need to be exposed to aspirin to prevent one case compared with 30 women using the NICE guidelines.
Regarding the method of prediction, the debate centres around screening performance, costs and practical issues of implementation.
The main focus of this report has been on the DR achieved by using the competing risk model compared with that of the NICE method. For the same SPR as NICE, the DR for preterm pre-eclampsia achieved by combining Mat-CHs with MAP, UTA-PI and PLGF is 79.6% (95% CI 72.7% to 86.5%) compared with 44.1% (95% CI 35.7% to 52.6%) when using the NICE method. Using these estimates and with an incidence of preterm pre-eclampsia of 0.8% the positive predictive values are 1 in 16 compared with 1 in 29 for the competing risk model and NICE method, respectively. Among women who screen negative, the proportions with preterm pre-eclampsia (i.e. 1 – negative predictive value) are 1 in 550 compared with 1 in 200 for the competing risk model and NICE method, respectively.
The main argument against the use of risk algorithms, such as the competing risk model, is that they are too complex to use in practice. Simple methods, such as the NICE criteria or cut-off points applied to biomarker measurements or their ratios, should be preferred because they are easy to implement in practice. In fact, the essential features of our approach of using Bayes’ theorem to update likelihoods from biomarker MoM values to update a prior based on maternal factors have been used for many decades in screening for aneuploidies. These algorithms have been built into commercial software used extensively in practice. The commercial software suppliers have implemented the competing risk algorithm for pre-eclampsia screening into their software systems.
Regarding the approaches based on application of cut-off points to individual markers or ratios of different markers, the following points need to be considered. First, they do not provide individualised risks for decision-making. Second, their performance is inferior to approaches based on probability theory to make optimal use of the available information. Last, because biomarkers are affected by covariates such as ethnicity, they are likely to be inequitable in the way they perform across different groups within the population.
In the clinical implementation of the first-trimester combined test for preterm pre-eclampsia, recording Mat-CHs and medical history, measurement of blood pressure and hospital attendance at 11–13 weeks’ gestation for an ultrasound scan are an integral part of routine antenatal care. Measurement of UTA-PI can be carried out by the same sonographers and ultrasound machines used for the routine scan at 11–13 weeks’ gestation; however, the sonographers will require training to carry out this test and the measurement would add 2–3 minutes to the current 20–30 minutes used for the scan. Serum PLGF can be measured in the same blood sample and by the same automated platforms that are currently used for measurement of serum PAPP-A as part of routine clinical practice in screening for fetal trisomies in all maternity hospitals in England; however, there is an additional cost for the reagents. Extensive research has established reference ranges for MAP, PLGF and UTA-PI, has described the Mat-CHs that affect the measurements and has developed the infrastructure for auditing of results.44–46
One decade ago, effective first-trimester screening for fetal trisomies was implemented in all maternity hospitals in the UK within a few months of the appropriate decision being taken by the UK National Screening Committee and NICE.47 The same infrastructure can now be used to expand the aims of first-trimester screening to include identification of women at high risk of developing preterm pre-eclampsia and substantially reducing such risk through the prophylactic use of the appropriate dose of aspirin.48
Conclusion
The SPREE study has demonstrated that the performance of first trimester screening for pre-eclampsia by a combination of maternal factors and biomarkers is superior to that achieved by the risk assessment method recommended by the current NICE guidelines.
