Fertility Issues Clinical Trial
Official title:
The Effectiveness of Advanced Decision Support Tool (OPT-IVF) for IVF Treatment
This prospective study is subject to approval of institutional medical research ethics committee. Patient undergoing second cycle IVF will be enrolled into the intervention group. Intervention involved using a clinical decision support tool, Opt-IVF to guide gonadotrophins dosing and trigger dates for a personalized controlled ovarian stimulation cycle based on the distribution of follicle sizes on day 1 and day 5, and hormone dosages given on day 1 to 4. Patients will undergo transvaginal ultrasound exam on day 1 and day 5 of the cycle to determine the number and size of follicles present. The data is used in the Opt-IVF decision support tool to suggest Gonadotropin dosage for D5 and beyond and to recommend the antagonist start day and trigger day. Clinical investigators will not override the Opt-IVF recommended dosage in any patients.
Status | Not yet recruiting |
Enrollment | 30 |
Est. completion date | January 31, 2026 |
Est. primary completion date | January 31, 2026 |
Accepts healthy volunteers | Accepts Healthy Volunteers |
Gender | Female |
Age group | 18 Years to 45 Years |
Eligibility | Inclusion Criteria: - Patient undergoing 2nd cycle of IVF Exclusion Criteria: - Polycystic ovarian syndrome patient |
Country | Name | City | State |
---|---|---|---|
Malaysia | Advanced Reproductive Centre | Kuala Lumpur | Wilayah Persekutuan Kuala Lumpur |
Lead Sponsor | Collaborator |
---|---|
National University of Malaysia |
Malaysia,
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Curchoe CL, Flores-Saiffe Farias A, Mendizabal-Ruiz G, Chavez-Badiola A. Evaluating predictive models in reproductive medicine. Fertil Steril. 2020 Nov;114(5):921-926. doi: 10.1016/j.fertnstert.2020.09.159. — View Citation
Fanton M, Nutting V, Rothman A, Maeder-York P, Hariton E, Barash O, Weckstein L, Sakkas D, Copperman AB, Loewke K. An interpretable machine learning model for individualized gonadotrophin starting dose selection during ovarian stimulation. Reprod Biomed O — View Citation
Hariton E, Chi EA, Chi G, Morris JR, Braatz J, Rajpurkar P, Rosen M. A machine learning algorithm can optimize the day of trigger to improve in vitro fertilization outcomes. Fertil Steril. 2021 Nov;116(5):1227-1235. doi: 10.1016/j.fertnstert.2021.06.018. — View Citation
Leahy BD, Racowsky C, Needleman D. Inferring simple but precise quantitative models of human oocyte and early embryo development. J R Soc Interface. 2021 Sep;18(182):20210475. doi: 10.1098/rsif.2021.0475. Epub 2021 Sep 8. — View Citation
Simopoulou M, Sfakianoudis K, Antoniou N, Maziotis E, Rapani A, Bakas P, Anifandis G, Kalampokas T, Bolaris S, Pantou A, Pantos K, Koutsilieris M. Making IVF more effective through the evolution of prediction models: is prognosis the missing piece of the — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | total cumulative dosage of Gonadotropins | number of total dosage that utilize during one cycle of IVF treatment | for 1 year duration | |
Primary | Total number of matured oocytes | number of matured oocytes following the stimulation protocol | for 1 year duration | |
Primary | total number of good quality blastocyst | total blastocyst achieved after each sti,ulation protocol | for 1 year duration |
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