Oral Presentation Society of Obstetric Medicine of Australia and New Zealand ASM 2018

Use of a multivariable algorithm for the prediction of preterm preeclampsia at midpregnancy: does addition of serum sFlt1 and/or PlGF improve predictive performance? (#23)

Carin Black 1 2 , Ahmed Al-Amin 3 4 , Daniel Rolnik 5 6 , Caroline Stolarek 2 , Stefan C Kane 1 2 3 , Fabricio Costa 4 5 6 , Shaun P Brennecke 2
  1. Department of Obstetrics and Gynaecology, The Royal Women's Hospital, Parkville, VIC, Australia
  2. Department of Maternal-Fetal Medicine, The Royal Women's Hospital, Parkville, VIC, Australia
  3. Pauline Gandel Imaging Centre, The Royal Women's Hospital, Parkville, VIC, Australia
  4. Monash Ultrasound for Women, Clayton, VIC, Australia
  5. Perinatal Services, Monash Medical Centre, Clayton, VIC, Australia
  6. Department of Obstetrics and Gynaecology, Monash University, Clayton, VIC, Australia

AIM Traditionally, preeclampsia screening has been based on maternal factors, including maternal demographic characteristics and medical and obstetric history. These methods give poor detection rates and high false positive rates1. Multivariable prediction algorithms, such as that devised by the Fetal Medicine Foundation2 (FMF) in the United Kingdom, use multiple parameters in attempt to improve overall predictive value3,4. The combination of maternal factors, mean arterial pressure (MAP) and uterine artery Doppler pulsatility index (UAPI) has proven superior to screening by maternal factors alone within our patient population5, with detection rates 100% for preterm preeclampsia and a false positive rate of 12.9%. Can addition of PlGF or sFlt1 to this algorithm at midpregnancy further improve predictive performance?

 

METHODS This prospective study in singleton pregnancies included women attending morphology scans between 19-22 weeks gestation. Maternal factors, MAP, UAPI, maternal PlGF and sFlt1 values were measured and converted to multiples of the median (MoM) for risk assessment. The outcome measured was preterm preeclampsia. Screening performance of the FMF algorithm was determined using receiver operating characteristic (ROC) curves, with calculation of clinical characteristics performed using selected cutoff values.

RESULTS 512 patients were included. When compared with combined screening using the FMF algorithm for preterm preeclampsia using maternal history, MAP and UAPI, addition of PlGF increased AUC from 0.973 to 0.984. Sensitivity and NPV were 100%, specificity 95.04% and PPV 24.24%, translating to detection of all cases of preterm preeclampsia at midpregnancy, with a very low false positive rate at 5%. AUC and clinical characteristics were not improved by addition of sFlt1.

 

CONCLUSIONS Addition of PlGF MoM to the FMF algorithm is superior to screening using maternal history, MAP and UAPI. Though the addition of PlGF does not further increase detection rates, it does significantly reduce false positive rates. Appropriate next steps would be to perform a large scale audit to confirm these findings and a cost-benefit analysis for this combined screening within our patient population.

 

REFERENCES

  1. Gallo DM, Wright D, Casanova C. et al. Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 19-24 weeks' gestation. Am J Obstet Gynecol 2016: 214(5): 619e1-619e17.
  2. Fetal Medicine Foundation (FMF) algorithm available online at fetalmedicine.org
  3. O'Gorman, N, Wright, D, Poon, L. C. et al. Multicenter screening for pre-eclampsia by maternal factors and biomarkers at 11-13 weeks' gestation: comparison with NICE guidelines and ACOG recommendations. Ultrasound Obstet Gynecol 2017: 49(6): 756-60.
  4. Park, F. J, Leung, C. H, Poon, L. C. et al. Clinical evaluation of a first trimester algorithm predicting the risk of hypertensive disease of pregnancy. Aust N Z J Obstet Gynaecol 2013: 53(6): 532-9.
  5. Al-Amin, A, Rolnik, D. L, Black, C. et al. Accuracy of second trimester prediction of preterm preeclampsia by three different screening algorithms. Aust N Z J Obstet Gynaecol 2018: 58(2): 192-6.