Developing & Evaluating Models for Early Predicting Obstetrical Diseases in Pregnant Women by Non-invasive Prenatal Test

Preterm Birth Clinical Trial

Official title:

Developing and Evaluating Models for Early Prediction of Obstetrical Diseases: Preeclampsia, Spontaneous Preterm Birth, and Gestational Diabetes in The Pregnant Women Performed Non-invasive Prenatal Screening (NIPT)

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06385366
• Other study ID #: GS_NP1
• Status: Not yet recruiting
• Start date: May 15, 2024
• Est. completion date: June 6, 2025

Study information

• Verified date: April 2024
• Source: Gene Solutions
• Contact: Sang H Sang, MSc, MD
• Phone: +84932198107
• Email: sangtang[@]genesolutions.vn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

This is Observational study, aiming to investigate the potentiality of cffDNA and cfRNA by a non-invasive test, in combination with clinical characteristics, to establish models for early screening and predicting high-risk pregnancy of PE, SPB, and GDM in Vietnam.

Description:

This study is estimated to enroll 663 pregnant women with adverse pregnancy complications, including 221 cases of PE/eclampsia, 221 cases of SPB due to Preterm premature rupture of membranes (PPROM) or preterm labor, and 221 cases of GDM. Furthermore, the control group will enroll 442 participants, who are healthy pregnancies, ≥ 37 weeks of gestation. Study subjects who participate should meet the study inclusion and exclusion criteria:

As part of the protocol, demographic data, medical and family history, outcomes at delivery, and any relevant prior concomitant medication data will be recorded during follow-up visits. All participants are to be followed until birth delivery.

SAMPLE COLLECTION

• At recruitment, 10 mL of peripheral blood is collected for cffDNA and cfRNA analyses.

• An available NIPT sample at 1st trimester is processed for cffDNA and cfRNA analyses.

• A case report forms (CRF-1 and CRF-2) are used to collect demographic data, medical and family history, any relevant prior concomitant medication data, and outcomes at delivery.

The study end date of a participant is estimated within 7 months since her enrollment date.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 1105
• Est. completion date: June 6, 2025
• Est. primary completion date: May 6, 2025
• Gender: Female
• Age group: 18 Years to 80 Years

Eligibility

Inclusion Criteria:

1. At recruitment, women with singleton pregnancies must fulfill the conditions:

Cases: diagnosis of Preeclampsia/eclampsia, Preterm premature rupture of membranes (PPROM)/preterm labor leading to SPB, and/or gestational diabetes mellitus.

Controls: healthy pregnancy at = 37 weeks of gestation

2. History of undergoing non-invasive prenatal testing (NIPT) at 9-13 weeks 6 days of gestation at Gene Solutions Lab. NIPT report was at low-risk. No abnormal fetal and maternal conditions were confirmed at NIPT time.

3. NIPT blood sample is available according to post-test sample storage procedures at Gene Solutions Lab.

4. Consent to voluntarily participate in the study

Exclusion Criteria:

1. Multiple pregnancies

2. Pregnancy with any genetic abnormality

3. Pregnancy with any fetal structural abnormality

4. Pregnancy with indications for termination, miscarriage, or stillbirth due to other complications

5. Maternal medical history of diabetes mellitus type 1/ type 2, chronic hypertension, and chronic kidney disease. Maternal abnormal uterus anatomy and history of cervical cone biopsy sample or loop electrocautery excision procedures (LEEP).

Related Conditions & MeSH terms

• Diabetes, Gestational
• Gestational Diabetes
• Pre-Eclampsia
• Preeclampsia
• Pregnancy Complications
• Pregnant With Complication
• Premature Birth
• Preterm Birth

Locations

Country	Name	City	State
n/a

Sponsors (2)

Lead Sponsor	Collaborator
Gene Solutions	Medical Genetics Institute (MGI)

References & Publications (19)

Becking EC, Scheffer PG, Henrichs J, Bax CJ, Crombag NMTH, Weiss MM, Macville MVE, Van Opstal D, Boon EMJ, Sistermans EA, Henneman L, Schuit E, Bekker MN. Fetal fraction of cell-free DNA in noninvasive prenatal testing and adverse pregnancy outcomes: a nationwide retrospective cohort study of 56,110 pregnant women. Am J Obstet Gynecol. 2023 Dec 12:S0002-9378(23)02128-2. doi: 10.1016/j.ajog.2023.12.008. Online ahead of print. — View Citation

Brosens I, Pijnenborg R, Vercruysse L, Romero R. The "Great Obstetrical Syndromes" are associated with disorders of deep placentation. Am J Obstet Gynecol. 2011 Mar;204(3):193-201. doi: 10.1016/j.ajog.2010.08.009. Epub 2010 Nov 20. — View Citation

Camunas-Soler J, Gee EPS, Reddy M, Mi JD, Thao M, Brundage T, Siddiqui F, Hezelgrave NL, Shennan AH, Namsaraev E, Haverty C, Jain M, Elovitz MA, Rasmussen M, Tribe RM. Predictive RNA profiles for early and very early spontaneous preterm birth. Am J Obstet Gynecol. 2022 Jul;227(1):72.e1-72.e16. doi: 10.1016/j.ajog.2022.04.002. Epub 2022 Apr 6. — View Citation

Chaemsaithong P, Sahota DS, Poon LC. First trimester preeclampsia screening and prediction. Am J Obstet Gynecol. 2022 Feb;226(2S):S1071-S1097.e2. doi: 10.1016/j.ajog.2020.07.020. Epub 2020 Jul 16. — View Citation

Darghahi R, Mobaraki-Asl N, Ghavami Z, Pourfarzi F, Hosseini-Asl S, Jalilvand F. Effect of cell-free fetal DNA on spontaneous preterm labor. J Adv Pharm Technol Res. 2019 Jul-Sep;10(3):117-120. doi: 10.4103/japtr.JAPTR_371_18. — View Citation

Del Vecchio G, Li Q, Li W, Thamotharan S, Tosevska A, Morselli M, Sung K, Janzen C, Zhou X, Pellegrini M, Devaskar SU. Cell-free DNA Methylation and Transcriptomic Signature Prediction of Pregnancies with Adverse Outcomes. Epigenetics. 2021 Jun;16(6):642-661. doi: 10.1080/15592294.2020.1816774. Epub 2020 Oct 13. — View Citation

Dugoff L, Barberio A, Whittaker PG, Schwartz N, Sehdev H, Bastek JA. Cell-free DNA fetal fraction and preterm birth. Am J Obstet Gynecol. 2016 Aug;215(2):231.e1-7. doi: 10.1016/j.ajog.2016.02.009. Epub 2016 Feb 11. — View Citation

Fonseca EB, Celik E, Parra M, Singh M, Nicolaides KH; Fetal Medicine Foundation Second Trimester Screening Group. Progesterone and the risk of preterm birth among women with a short cervix. N Engl J Med. 2007 Aug 2;357(5):462-9. doi: 10.1056/NEJMoa067815. — View Citation

Gabbay-Benziv R, Baschat AA. Gestational diabetes as one of the "great obstetrical syndromes"--the maternal, placental, and fetal dialog. Best Pract Res Clin Obstet Gynaecol. 2015 Feb;29(2):150-5. doi: 10.1016/j.bpobgyn.2014.04.025. Epub 2014 Aug 20. — View Citation

Guo Z, Yang F, Zhang J, Zhang Z, Li K, Tian Q, Hou H, Xu C, Lu Q, Ren Z, Yang X, Lv Z, Wang K, Yang X, Wu Y, Yang X. Whole-Genome Promoter Profiling of Plasma DNA Exhibits Diagnostic Value for Placenta-Origin Pregnancy Complications. Adv Sci (Weinh). 2020 Feb 18;7(7):1901819. doi: 10.1002/advs.201901819. eCollection 2020 Apr. — View Citation

Hajian-Tilaki K. Sample size estimation in diagnostic test studies of biomedical informatics. J Biomed Inform. 2014 Apr;48:193-204. doi: 10.1016/j.jbi.2014.02.013. Epub 2014 Feb 26. — View Citation

Hod M, Lieberman N. Maternal-fetal medicine--how can we practically connect the "M" to the "F"? Best Pract Res Clin Obstet Gynaecol. 2015 Feb;29(2):270-83. doi: 10.1016/j.bpobgyn.2014.06.008. Epub 2014 Aug 21. — View Citation

Karapetian capital A, Cyrilliccapital O, Cyrillic, Baev capital O, CyrillicR, Sadekova capital A, Cyrilliccapital A, Cyrillic, Krasnyi capital A, Cyrilliccapital EM, Cyrillic, Sukhikh GT. Cell-Free Foetal DNA as a Useful Marker for Preeclampsia Prediction. Reprod Sci. 2021 May;28(5):1563-1569. doi: 10.1007/s43032-021-00466-w. Epub 2021 Jan 21. — View Citation

Munchel S, Rohrback S, Randise-Hinchliff C, Kinnings S, Deshmukh S, Alla N, Tan C, Kia A, Greene G, Leety L, Rhoa M, Yeats S, Saul M, Chou J, Bianco K, O'Shea K, Bujold E, Norwitz E, Wapner R, Saade G, Kaper F. Circulating transcripts in maternal blood reflect a molecular signature of early-onset preeclampsia. Sci Transl Med. 2020 Jul 1;12(550):eaaz0131. doi: 10.1126/scitranslmed.aaz0131. — View Citation

Rani PR, Begum J. Screening and Diagnosis of Gestational Diabetes Mellitus, Where Do We Stand. J Clin Diagn Res. 2016 Apr;10(4):QE01-4. doi: 10.7860/JCDR/2016/17588.7689. Epub 2016 Apr 1. — View Citation

Reicher L, Fouks Y, Yogev Y. Cervical Assessment for Predicting Preterm Birth-Cervical Length and Beyond. J Clin Med. 2021 Feb 7;10(4):627. doi: 10.3390/jcm10040627. — View Citation

Roberge S, Nicolaides K, Demers S, Hyett J, Chaillet N, Bujold E. The role of aspirin dose on the prevention of preeclampsia and fetal growth restriction: systematic review and meta-analysis. Am J Obstet Gynecol. 2017 Feb;216(2):110-120.e6. doi: 10.1016/j.ajog.2016.09.076. Epub 2016 Sep 15. — View Citation

Weiner CP, Cuckle H, Weiss ML, Buhimschi IA, Dong Y, Zhou H, Ramsey R, Egerman R, Buhimschi CS. Evaluation of a Maternal Plasma RNA Panel Predicting Spontaneous Preterm Birth and Its Expansion to the Prediction of Preeclampsia. Diagnostics (Basel). 2022 May 27;12(6):1327. doi: 10.3390/diagnostics12061327. — View Citation

Zhou S, Li J, Yang W, Xue P, Yin Y, Wang Y, Tian P, Peng H, Jiang H, Xu W, Huang S, Zhang R, Wei F, Sun HX, Zhang J, Zhao L. Noninvasive preeclampsia prediction using plasma cell-free RNA signatures. Am J Obstet Gynecol. 2023 Nov;229(5):553.e1-553.e16. doi: 10.1016/j.ajog.2023.05.015. Epub 2023 May 19. — View Citation

* Note: There are 19 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Characteristics of pregnant women at 1st trimester (9-13 weeks 6 days of gestation)	Observe the characteristics of pregnant women at 1st trimester (9-13 weeks 6 days of gestation): clinical features, cffDNA, cfRNA	12 months
Primary	Characteristics of pregnant women at recruitment	Characteristics of pregnant women at recruitment: clinical features, cffDNA, cfRNA	12 months
Primary	Define the significant differences between cases and controls	Comparison between clinical features, cffDNA, and cfRNA of early pregnancy and at recruitment, then defines the significant differences between cases and controls	12 months
Primary	The development of learning machine models	The development of learning machine models involved potential factors that help predict events of interest (PE, SPB, and GDM). From cfRNA and cfDNA data, factors that differ between the two groups will be identified and evaluated for their potentiality in predicting high-risk individuals. The Receiver Operating Characteristic (ROC) curve and values of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were used to determine the validity of the constructed model.	12 months
Primary	Evaluation of the developed models	Evaluation of the developed models by determining their sensitivity, specificity, area under the ROC Curve (AUC), positive predictive value (PPV), negative predictive value (NPV), and accuracy.	12 months

External Links

•   Mastrolia SA, Cetin I. The "Great Obstetrical Syndromes". In: Petraglia F, Fauser BC, editors. Female Reproductive Dysfunction. Cham: Springer International Publishing; 2020. p. 411-30. Available from: https://doi.org/10.1007/978-3-030-14782-2_21.