Role of First-Trimester Mean Arterial Pressure and Second-Trimester Uterine Artery Pulsatility Index in Prediction of Preeclampsia in High-Risk Pregnant Women
DOI:
https://doi.org/10.51985/Keywords:
Preeclampsia, mean arterial pressure, uterine artery Doppler, pulsatility index, high-risk pregnancyAbstract
Objective: To compare the predictive performance of first-trimester mean arterial pressure (MAP) and second-trimester uterine artery pulsatility index (UtA-PI) for subsequent preeclampsia in high-risk pregnant women.
Study Design and Setting: Validation study at Department of Obstetrics and Gynaecology, Holy Family Hospital, Rawalpindi. Methodology: This validation study included 202 high-risk singleton pregnancies enrolled by non-probability consecutive sampling. MAP was measured in the first trimester and was considered abnormal when >100 mmHg. Uterine artery Doppler was performed at 22+6 to 24+6 weeks of gestation; UtA-PI >1.45 was considered abnormal. Participants were followed after 20 weeks for development of preeclampsia. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy were calculated against the final clinical prediction of preeclampsia.
Result: Preeclampsia developed in 75/202 (37.1%) participants. For MAP, sensitivity was 92.0% (95% CI 83.6–96.3), specificity 89.0% (82.3–93.3), PPV 83.1% (73.7–89.7), NPV 95.0% (89.4–97.7), and overall accuracy 90.1% (85.2–93.5). For second-trimester UtA-PI, sensitivity was 76.0% (65.2–84.2), specificity 79.5% (71.7–85.6), PPV 68.7% (58.1–77.6), NPV 84.9% (77.4–90.2), and overall accuracy 78.2% (72.0–83.4).
Conclusion: In this selected high-risk cohort, first-trimester MAP showed higher predictive accuracy than second-trimester UtA-PI for subsequent preeclampsia. These findings support MAP as a practical component of risk assessment, but they should not be interpreted as establishing MAP as a stand-alone screening strategy for all pregnant women.
Downloads
References
1. Chang KJ, Seow KM, Chen KH. Preeclampsia: recent advances
in predicting, preventing, and managing the maternal and
fetal life-threatening condition. Int J Environ Res Public
Health. 2023;20(4):2994. doi: 10.3390/ijerph20042994.
2. Chaemsaithong P, Sahota DS, Poon LC. First trimester
preeclampsia screening and prediction. Am J Obstet Gynecol.
2022;226(2 Suppl).e2. doi: 10.1016/j.ajog.2020.07.020.
3. Zhu J, Zhang J, Syaza Razali N, Chern B, Tan KH. Mean
arterial pressure for predicting preeclampsia in Asian women:
a longitudinal cohort study. BMJ Open. 2021;11(8). doi:
10.1136/bmjopen-2020-046161.
4. Das E, Singh V, Agrawal S, Pati SK. Prediction of preeclampsia
using first-trimester uterine artery Doppler and pregnancyassociated plasma protein-A (PAPP-A): a prospective study
in Chhattisgarh, India. Cureus. 2022;14(2). doi: 10.7759/
cureus.22026.
5. Rolnik DL, Syngelaki A, O’Gorman N, Wright D, Poon LC,
Nicolaides KH. ASPRE trial: effects of aspirin on mean arterial
blood pressure and uterine artery pulsatility index trajectories
in pregnancy. Ultrasound Obstet Gynecol. 2023;61(6):691-
697. doi: 10.1002/uog.26222.
6. Guerby P, Audibert F, Johnson JA, Okun N, Giguere Y, Forest
JC, et al. Prospective validation of first-trimester screening
for preterm preeclampsia in nulliparous women (PREDICTION
Study). Hypertension. 2024;81(7):1574-1582. doi: 10.1161/
HYPERTENSIONAHA.123.22584.
7. Nguyen-Hoang L, Dinh LT, Tai AST, Nguyen DA, Pooh RK,
Shiozaki A, et al. Implementation of first-trimester screening
and prevention of preeclampsia: a stepped wedge clusterrandomized trial in Asia. Circulation. 2024;150(16):1223-
1235. doi: 10.1161/CIRCULATIONAHA.124.069907.
8. Johnson JM, Walsh JD, Okun NB, Metcalfe A, Pastuck ML,
Maxey CM, et al. The Implementation of Preeclampsia
Screening and Prevention (IMPRESS) Study. Am J Obstet
Gynecol MFM. 2023;5(2):100815. doi: 10.1016/j.ajogmf.
2022.100815.
9. Zwertbroek EF, Groen H, Fontanella F, Maggio L, Marchi L,
Bilardo CM. Performance of the FMF first-trimester
preeclampsia-screening algorithm in a high-risk population
in The Netherlands. Fetal Diagn Ther. 2021;48(2):103-111.
doi: 10.1159/000512335.
10. Okun N, Hoffman B, Johnson JA, Biringer A, Shapiro J, Felix
C, et al. Implementation of multiple marker screening for
preterm preeclampsia in a single tertiary obstetric centre. J
Obstet Gynaecol Can. 2024;46(1):102220. doi: 10.1016/j.
jogc.2023. 102220.
11. Magee LA, Brown MA, Hall DR, Gupte S, Hennessy A,
Karumanchi SA, et al. The 2021 International Society for the
Study of Hypertension in Pregnancy classification, diagnosis
& management recommendations for international practice.
Pregnancy Hypertens. 2022;27:148-169. doi: 10.1016/j.preghy.
2021.09.008.
12. Liu Y, Xie Z, Huang Y, Lu X, Yin F. Uterine arteries pulsatility
index by Doppler ultrasound in the prediction of preeclampsia:
an updated systematic review and meta-analysis. Arch Gynecol
Obstet. 2024;309(2):427-437. doi: 10.1007/s00404-023-
07044-2.
13. Cao L, He B, Zhou Y, Chen T, Gao Y, Yao B. Utility of uterine
artery Doppler ultrasound imaging in predicting preeclampsia
during pregnancy: a meta-analysis. Med Ultrason. 2024;26(2):
197-204. doi: 10.11152/mu-4355.
14. Gil MM, Cuenca-Gomez D, Rolle V, Pertegal M, Diaz C,
Revello R, et al. Validation of machine-learning model for
first-trimester prediction of pre-eclampsia using cohort from
PREVAL study. Ultrasound Obstet Gynecol. 2024;63(1):68-
74. doi: 10.1002/uog.27478.
15. Li T, Xu M, Wang Y, Wang Y, Tang H, Duan H, et al. Prediction
model of preeclampsia using machine learning based methods:
a population based cohort study in China. Front Endocrinol
(Lausanne). 2024;15:1345573. doi: 10.3389/fendo.2024.
1345573.
16. Ansbacher-Feldman Z, Syngelaki A, Meiri H, Cirkin R,
Nicolaides KH, Louzoun Y. Machine-learning-based prediction
of pre-eclampsia using first-trimester maternal characteristics
and biomarkers. Ultrasound Obstet Gynecol. 2022;60(6):739-
745. doi: 10.1002/uog.26105.
17. Liu M, Yang X, Chen G, Ding Y, Shi M, Sun L, et al.
Development of a prediction model on preeclampsia using
machine learning-based method: a retrospective cohort study
in China. Front Physiol. 2022;13:896969. doi: 10.3389/fphys.
2022.896969.
18. MacDonald TM, Walker SP, Hannan NJ, Tong S, Kaitu’uLino TJ. Clinical tools and biomarkers to predict preeclampsia.
EBioMedicine. 2022;75:103780. doi: 10.1016/j.ebiom.2021.
103780.
19. Tarca AL, Taran A, Romero R, Jung E, Paredes C, Bhatti G,
et al. Prediction of preeclampsia throughout gestation with
maternal characteristics and biophysical and biochemical
markers: a longitudinal study. Am J Obstet Gynecol.
2022;226(1):126.e1-126.e22. doi: 10.1016/j.ajog.2021.01.020.
20. Torres-Torres J, Villafan-Bernal JR, Martinez-Portilla RJ,
Hidalgo-Carrera JA, Estrada-Gutierrez G, Adalid-MartinezCisneros R, et al. Performance of a machine-learning approach
for the prediction of pre-eclampsia in a middle-income country.
Ultrasound Obstet Gynecol. 2024;63(3):350-357. doi:
10.1002/uog.27510. PMID: 37796702.
21. Nguyen-Hoang L, Sahota DS, Poon CYL. Effect of aspirin
on biomarker profile in women at high risk for preeclampsia.
Am J Obstet Gynecol. 2025;232(6):e213. doi:
10.1016/j.ajog.2025.01.023.
22. Hodžiæ J, Muraèeviæ B, Graèiæ A, Hodžiæ HS, Bajgoriæ
Škrgo E. First trimester placental growth factor and uterine
artery pulsatility index in the prediction of preeclampsia in
high-risk pregnancy. Med Glas (Zenica). 2025;22(1):71-77.
doi: 10.17392/1864-22-01.
23. Paiboonborirak C, Phupong V. Serum fatty acid binding
protein 4 and Doppler of uterine artery ultrasound in the first
trimester for the prediction of preeclampsia. Hypertens Res.
2024;47(5):1208-1215. doi: 10.1038/s41440-023-01553-y.
24. Wang S, Dong H. Non-linear dose-response relationship
between uterine artery pulsatility index and risk of preeclampsia
in early pregnancy: secondary analysis of a nested cohort
study. PLoS One. 2025;20(1):e0317625. doi:
10.1371/journal.pone.0317625.
25. Clare A, Robertson B, Henry D, Knowles A. Role of second
trimester uterine artery doppler in early prediction of
preeclampsia and intrauterine growth restriction among highrisk women. SAGE Open Med. 2025;13:20503121251378109.
doi: 10.1177/20503121251378109
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Journal of Bahria University Medical & Dental College is an open access journal and is licensed under CC BY-NC 4.0. which permits unrestricted non commercial use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc/4.0
