Prospective evaluation of screening performance of first-trimester prediction models for preterm preeclampsia in an Asian population.

BACKGROUND: The administration of aspirin <16 weeks gestation to women who are at high risk for preeclampsia has been shown to reduce the rate of preterm preeclampsia by 65%. The traditional approach to identify such women who are at risk is based on risk factors from maternal characteristics, obstetrics, and medical history as recommended by the American College of Obstetricians and Gynecologists and the National Institute for Health and Care Excellence. An alternative approach to screening for preeclampsia has been developed by the Fetal Medicine Foundation. This approach allows the estimation of patient-specific risks of preeclampsia that requires delivery before a specified gestational age with the use of Bayes theorem-based model. OBJECTIVE: The purpose of this study was to examine the diagnostic accuracy of the Fetal Medicine Foundation Bayes theorem-based model, the American College of Obstetricians and Gynecologists, and the National Institute for Health and Care Excellence recommendations for the prediction of preterm preeclampsia at 11-13+6 weeks gestation in a large Asian population STUDY DESIGN: This was a prospective, nonintervention, multicenter study in 10,935 singleton pregnancies at 11-13+6 weeks gestation in 11 recruiting centers across 7 regions in Asia between December 2016 and June 2018. Maternal characteristics and medical, obstetric, and drug history were recorded. Mean arterial pressure and uterine artery pulsatility indices were measured according to standardized protocols. Maternal serum placental growth factor concentrations were measured by automated analyzers. The measured values of mean arterial pressure, uterine artery pulsatility index, and placental growth factor were converted into multiples of the median. The Fetal Medicine Foundation Bayes theorem-based model was used for the calculation of patient-specific risk of preeclampsia at <37 weeks gestation (preterm preeclampsia) and at any gestation (all preeclampsia) in each participant. The performance of screening for preterm preeclampsia and all preeclampsia by a combination of maternal factors, mean arterial pressure, uterine artery pulsatility index, and placental growth factor (triple test) was evaluated with the adjustment of aspirin use. We examined the predictive performance of the model by the use of receiver operating characteristic curve and calibration by measurements of calibration slope and calibration in the large. The detection rate of screening by the Fetal Medicine Foundation Bayes theorem-based model was compared with the model that was derived from the application of American College of Obstetricians and Gynecologists and National Institute for Health and Care Excellence recommendations. RESULTS: There were 224 women (2.05%) who experienced preeclampsia, which included 73 cases (0.67%) of preterm preeclampsia. In pregnancies with preterm preeclampsia, the mean multiples of the median values of mean arterial pressure and uterine artery pulsatility index were significantly higher (mean arterial pressure, 1.099 vs 1.008 [P<.001]; uterine artery pulsatility index, 1.188 vs 1.063[P=.006]), and the mean placental growth factor multiples of the median was significantly lower (0.760 vs 1.100 [P<.001]) than in women without preeclampsia. The Fetal Medicine Foundation triple test achieved detection rates of 48.2%, 64.0%, 71.8%, and 75.8% at 5%, 10%, 15%, and 20% fixed false-positive rates, respectively, for the prediction of preterm preeclampsia. These were comparable with those of previously published data from the Fetal Medicine Foundation study. Screening that used the American College of Obstetricians and Gynecologists recommendations achieved detection rate of 54.6% at 20.4% false-positive rate. The detection rate with the use of National Institute for Health and Care Excellence guideline was 26.3% at 5.5% false-positive rate. CONCLUSION: Based on a large number of women, this study has demonstrated that the Fetal Medicine Foundation Bayes theorem-based model is effective in the prediction of preterm preeclampsia in an Asian population and that this method of screening is superior to the approach recommended by American College of Obstetricians and Gynecologists and the National Institute for Health and Care Excellence. We have also shown that the Fetal Medicine Foundation prediction model can be implemented as part of routine prenatal care through the use of the existing infrastructure of routine prenatal care.

Adjusting antimüllerian hormone levels for age and body mass index improves detection of polycystic ovary syndrome.

OBJECTIVE: To examine whether accounting for a woman's age and body mass index (BMI) would improve the ability of antimüllerian hormone (AMH) to distinguish between women with (cases) and without (controls) polycystic ovarian syndrome (PCOS). DESIGN: An opportunistic case-control dataset of reproductive age women having evaluations for PCOS as defined by National Institutes of Health criteria. SETTING: Two medical centers in the United States enrolled women. Serum samples were analyzed for relevant analytes. PATIENTS: Women were between 18 and 39 years of age when samples and clinical information were collected. Residual samples had been stored for 2-17 years. AMH was measured via immunoassay. INTERVENTIONS: None; this was an observational study. MAIN OUTCOME MEASURES: Detection and false-positive rates for PCOS were computed for AMH results expressed as multiples of the median (MoM) both before and after adjustment for the woman's age and BMI. RESULTS: Using unadjusted AMH MoM results, 168 cases (78%) cases were at or beyond the 90th centile of controls (2.47 MoM). After accounting for each woman's age and BMI, 188 (87%) of those women were beyond the 90th centile of controls (2.20 MoM), a significant increase (P = .015). The adjusted AMH MoM levels fitted logarithmic normal distributions well (mean, standard deviation for controls and cases of 0.0000, 0.2765 and 0.6884, 0.2874, respectively) and this allowed for computation of patient-specific PCOS risks. CONCLUSIONS: Accounting for the woman's age and BMI resulted in significantly higher AMH-based detection rates for PCOS at a 10% false-positive rate, and patient-specific PCOS risks could be computed.