APP1135277_Rice_Grant Proposal

GRANT PROPOSAL – 2016 Centres of Research Application ID: 1135277
Excellence funding commencing 2017 CIA Surname: Rice
Peer Recognition and Service
2013 – 2015 Assessor for Singaporean grant funding scheme
2014 – 2015 Assessor for Hong Kong grant funding scheme
2013 UK national research grant funding schemes.
Contributions to NHMRC
2014 NHMRC Research Translation Faculty
2013 - 2015 NHMRC External Assessor for project grants
Top 5 publications in the last 5 years
1: Park F, Russo K, Williams P, Pelosi M, Puddephatt R, Walter M, Leung C, Saaid R, Rawashdeh
H, Ogle R, Hyett J. Prediction and prevention of early onset pre-eclampsia: The impact of aspirin
after first trimester screening. Ultrasound Obstet Gynecol 2015; 46: 419-423.
This prospective cohort study documents the value of prediction (with a 12 week algorithm defining
risk for ePET) and prevention (with low dose Aspirin as a therapeutic intervention) of early onset
pre-eclampsia.
2: Park FJ, Leung CH, Poon LC, Williams PF, Rothwell SJ, Hyett JA. Clinical evaluation of a first
trimester algorithm predicting the risk of hypertensive disease of pregnancy. Aust N Z J Obstet
Gynaecol 2013; 53: 532-9.
A validation study demonstrated the effectiveness of a first trimester algorithm for predicting risk of
early onset pre-eclampsia; demonstrating 90% sensitivity at 90% specificity.
3: Ridding G, Schluter PJ, Hyett JA, McLennan AC. Uterine artery pulsatility index assessment at
11-13 weeks' gestation. Fetal Diagn Ther 2014; 36: 299-304.
This paper defines normal ranges for uterine artery PI at 12 weeks’ gestation in an Australian
population; used in the prediction of risk of early onset pre-eclampsia.
4: Leung C, Saaid R, Pedersen L, Park F, Poon L, Hyett J. Demographic factors that can be used to
predict early-onset pre-eclampsia. J Matern Fetal Neonatal Med. 2014 [Epub ahead of print].
This paper describes demographic factors associated with the risk of developing early onset preeclampsia
in an Australian population. It establishes the baseline for risk algorithms based on
maternal demographic and investigational parameters.
5: E Holanda Moura SB, Park F, Murthi P, Martins WP, Kane SC, Williams P, Hyett J, Silva Costa
Fd. TNF-R1 as a first trimester marker for prediction of pre-eclampsia. J Matern Fetal Neonatal
Med 2016; 29: 897-903.
In collaboration with the group at The Royal Women’s Hospital in Melbourne, this paper explored
the potential of other biomarkers for prediction of early onset pre-eclampsia.
Research relevant to the proposed project
Jon’s primary research interest is the prediction and prevention of adverse obstetric outcomes
through population based screening. His research group has focused on developing predictive
strategies for four major perinatal problems; screening for congenital abnormality, for preeclampsia
and intrauterine growth restriction, for preterm birth and for gestational diabetes. All predictive
testing occurs at 12 weeks’ gestation, the rationale being that this enables application of
preventative interventions that will reduce the prevalence of disease. The group have had significant
success in this area. Jon has lead the integration of some research findings into clinical practice.
One example is the introduction of a pilot program demonstrating the effectiveness of prediction
and prevention of pre-eclampsia; this program involves population-based assessment of maternal
biophysical and biochemical parameters at the 12-week visit. High-risk women are treated with
aspirin to prevent pre-eclampsia. This provides the platform on which women will be recruited to
this study.
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