The Effectiveness of Advanced Decision Support Tool (OPT-IVF) for IVF Treatment
NCT ID: NCT06179420
Last Updated: 2024-03-15
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
30 participants
OBSERVATIONAL
2024-03-13
2026-01-31
Brief Summary
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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.
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Detailed Description
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Conditions
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Study Design
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CASE_CONTROL
PROSPECTIVE
Study Groups
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standard IVF regime protocol based on clinician decision
The dosage of gonadotrophin and time of transvaginal ultrasound decided by clinician
total cumulative gonadotrophin usage
total dosage in IU
clinical decision support tool, Opt-IVF for IVF regime protocol
The dosage of gonadotrophin and time of transvaginal ultrasound decided by a clinical decision support tool ( OPt -IVF)
total cumulative gonadotrophin usage
total dosage in IU
Interventions
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total cumulative gonadotrophin usage
total dosage in IU
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
18 Years
45 Years
FEMALE
Yes
Sponsors
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National University of Malaysia
OTHER
Responsible Party
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Azrai Abu
Associate Prof
Principal Investigators
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Muhammad Azrai Abu, MD
Role: PRINCIPAL_INVESTIGATOR
Department Of Obstetrics And Gynecology, Ukm Medical Centre
Locations
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Advanced Reproductive Centre
Kuala Lumpur, Kuala Lumpur, Malaysia
Countries
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Central Contacts
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References
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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.
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 puzzle? Syst Biol Reprod Med. 2018 Oct;64(5):305-323. doi: 10.1080/19396368.2018.1504347. Epub 2018 Aug 8.
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. Epub 2021 Jul 10.
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 Online. 2022 Dec;45(6):1152-1159. doi: 10.1016/j.rbmo.2022.07.010. Epub 2022 Jul 28.
Correa N, Cerquides J, Arcos JL, Vassena R. Supporting first FSH dosage for ovarian stimulation with machine learning. Reprod Biomed Online. 2022 Nov;45(5):1039-1045. doi: 10.1016/j.rbmo.2022.06.010. Epub 2022 Jun 18.
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.
Other Identifiers
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UKMPPI/111/8/JEP-2023-735
Identifier Type: -
Identifier Source: org_study_id
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