Objective: To investigate the effects of aspirin on the distribution of birthweight and its impact on the rates of large-for-gestational age (LGA) neonates. Design: Secondary analysis of the Combined Multimarker Screening and Randomized Patient Treatment with Aspirin for Evidence-based Preeclampsia Prevention (ASPRE) trial. Setting: Thirteen hospitals in England, Spain, Belgium, Greece, Italy, and Israel. Population: Participants of the ASPRE trial at increased risk of preterm pre-eclampsia (PE) who had a live birth. Methods: We compared the birthweight distributions and the rates of LGA neonates between the trial groups. Analyses were stratified according to the presence of pre-existing diabetes mellitus and the development of pre-eclampsia, and logistic regression was used to investigate independent predictors of LGA neonates. Main Outcome Measures: Birthweight distribution and rate of LGA neonates. Results: Among 1,571 singleton, live neonates (777 from the aspirin group and 794 from the placebo group), aspirin was associated with a shift in birthweight from below 2,500 to between 2,500 and 4,000 grams, and birthweight percentile from below the 25 th to between the 25 th and 75 th percentiles, with no significant increase in LGA neonates (5.5% vs. 6.2%, p=0.667). Logistic regression demonstrated a significant interaction between treatment and pre-existing diabetes (p-value 0.034), and a positive association between maternal weight and LGA neonates (adjusted odds ratio 1.040, 95% confidence interval 1.030 – 1.051, p<0.001). Conclusions: Aspirin use is associated with increased birthweight without increasing the rate of LGA neonates. Among women with pre-existing diabetes, however, aspirin may lead to a higher rate of LGA neonates.

Dear Sir We congratulate Dr Guy and colleagues on their paper1which demonstrates that implementation of combined screening using the FMF algorithm2 is feasible in practice and is better than the existing NICE guidelines in prevention of preeclampsia, especially preterm preeclampsia with delivery before 34 weeks. We hope that this will lead to wider application of combined screening for prediction and prevention of preeclampsia.The authors acknowledge that treatment with aspirin will have led to underestimation of screening performance. We would like to highlight this and emphasise the importance of accounting for the effect of aspirin when assessing predictive performance. To make the point, consider the most extreme case with 100% compliance with a treatment that prevents 100% of cases. In the screen positive group, all cases would be prevented by the treatment and classified as false positives. Adopting the same analysis presented in this paper would result in a detection rate and positive predictive value of zero regardless of performance without treatment.In the data presented in this study, for the FMF algorithm with 99% compliance to aspirin at a dose of 150 mg / day and assuming 62% reduction in risk,3 99%×62% = 61.4% of cases of preterm preeclampsia would be prevented and classed as false positives. The remaining 100-61.4% = 38.6% would be classed as true positives so the 15 cases of preterm preeclampsia which led to the detection rate of 15/27 = 55.6% represent just 38.6% of the cases of preterm preeclampsia detected. An estimate of the number detected, including those prevented by aspirin is, 15/0.386 = 39. The estimated number of cases in total is therefore 39 + 12 = 51, obtained by adding the false negatives 27-15 = 12 to the estimated true positives. This gives a detection rate of 39/51 = 76% compared to the figure of 55.6% given in Table 2. Applying similar calculations to the positive predictive value (i.e. proportion of women in the screen positive group who would, without aspirin, have developed preterm preeclampsia) of 9.8%. This should be compared with the 3.8% presented in the paper. Applying the same arithmetic to the NICE group gives a detection rate of 41.6% and a positive predictive value of 2.4%. These are much closer to the figures in Table 2 of the paper because of the relatively low compliance in the NICE group. Other measures of screening performance presented on this paper including the likelihood ratios, negative predictive value the receiver operating characteristic (ROC) curve analysis are also affected by this problem.The arithmetic presented above is intended for illustration; for the SPREE study4 we applied Markov chain monte carlo (MCMC) methods for inferences about screening performance. These or similar methods should be applied in future studies of screening performance.Dave Wright,1 Kypros Nicolaides2Institute of Health Research, University of Exeter, Exeter, UKHarris Birthright Research Centre for Fetal Medicine, King’s College Hospital, London, UK.