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dc.creatorSimopoulou M., Sfakianoudis K., Antoniou N., Maziotis E., Rapani A., Bakas P., Anifandis G., Kalampokas T., Bolaris S., Pantou A., Pantos K., Koutsilieris M.en
dc.date.accessioned2023-01-31T09:56:18Z
dc.date.available2023-01-31T09:56:18Z
dc.date.issued2018
dc.identifier10.1080/19396368.2018.1504347
dc.identifier.issn19396368
dc.identifier.urihttp://hdl.handle.net/11615/78992
dc.description.abstractAssisted reproductive technology has evolved tremendously since the emergence of in vitro fertilization (IVF). In the course of the recent decade, there have been significant efforts in order to minimize multiple gestations, while improving percentages of singleton pregnancies and offering individualized services in IVF, in line with the trend of personalized medicine. Patients as well as clinicians and the entire IVF team benefit majorly from ‘knowing what to expect’ from an IVF cycle. Hereby, the question that has emerged is to what extent prognosis could facilitate toward the achievement of the above goal. In the current review, we present prediction models based on patients’ characteristics and IVF data, as well as models based on embryo morphology and biomarkers during culture shaping a complication free and cost-effective personalized treatment. The starting point for the implementation of prediction models was initiated by the aspiration of moving toward optimal practice. Thus, prediction models could serve as useful tools that could safely set the expectations involved during this journey guiding and making IVF treatment more effective. The aim and scope of this review is to thoroughly present the evolution and contribution of prediction models toward an efficient IVF treatment. Abbreviations: IVF: In vitro fertilization; ART: assisted reproduction techniques; BMI: body mass index; OHSS: ovarian hyperstimulation syndrome; eSET: elective single embryo transfer; ESHRE: European Society of Human Reproduction and Embryology; mtDNA: mitochondrial DNA; nDNA: nuclear DNA; ICSI: intracytoplasmic sperm injection; MBR: multiple birth rates; LBR: live birth rates; SART: Society for Assisted Reproductive Technology Clinic Outcome Reporting System; AFC: antral follicle count; GnRH: gonadotrophin releasing hormone; FSH: follicle stimulating hormone; LH: luteinizing hormone; AMH: anti-Müllerian hormone; DHEA: dehydroepiandrosterone; PCOS: polycystic ovarian syndrome; NPCOS: non-polycystic ovarian syndrome; CE: cost-effectiveness; CC: clomiphene citrate; ORT: ovarian reserve test; EU: embryo–uterus; DET: double embryo transfer; CES: Cumulative Embryo Score; GES: Graduated Embryo Score; CSS: Combined Scoring System; MSEQ: Mean Score of Embryo Quality; IMC: integrated morphology cleavage; EFNB2: ephrin-B2; CAMK1D: calcium/calmodulin-dependent protein kinase 1D; GSTA4: glutathione S-transferase alpha 4; GSR: glutathione reductase; PGR: progesterone receptor; AMHR2: anti-Müllerian hormone receptor 2; LIF: leukemia inhibitory factor; sHLA-G: soluble human leukocyte antigen G. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.en
dc.language.isoenen
dc.sourceSystems Biology in Reproductive Medicineen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85052064948&doi=10.1080%2f19396368.2018.1504347&partnerID=40&md5=cbfdd19065dc38bd523c41491184a0bf
dc.subjectbody massen
dc.subjectcost effectiveness analysisen
dc.subjectembryo cultureen
dc.subjectembryo developmenten
dc.subjecthumanen
dc.subjectin vitro fertilizationen
dc.subjectmorphological traiten
dc.subjectpriority journalen
dc.subjectreproductive historyen
dc.subjectReviewen
dc.subjectageen
dc.subjectalgorithmen
dc.subjectbiological modelen
dc.subjectblooden
dc.subjectcost benefit analysisen
dc.subjectcytologyen
dc.subjecteconomicsen
dc.subjectfemaleen
dc.subjectgenetic markeren
dc.subjectin vitro fertilizationen
dc.subjectlive birthen
dc.subjectmammalian embryoen
dc.subjectmetabolismen
dc.subjectovary follicleen
dc.subjectpersonalized medicineen
dc.subjectpregnancyen
dc.subjectpregnancy rateen
dc.subjectprognosisen
dc.subjectstandardsen
dc.subjectfollitropinen
dc.subjectgonadorelinen
dc.subjectluteinizing hormoneen
dc.subjectMuellerian inhibiting factoren
dc.subjectAge Factorsen
dc.subjectAlgorithmsen
dc.subjectAnti-Mullerian Hormoneen
dc.subjectBody Mass Indexen
dc.subjectCost-Benefit Analysisen
dc.subjectEmbryo, Mammalianen
dc.subjectFemaleen
dc.subjectFertilization in Vitroen
dc.subjectFollicle Stimulating Hormoneen
dc.subjectGenetic Markersen
dc.subjectGonadotropin-Releasing Hormoneen
dc.subjectHumansen
dc.subjectLive Birthen
dc.subjectLuteinizing Hormoneen
dc.subjectModels, Biologicalen
dc.subjectOvarian Follicleen
dc.subjectPrecision Medicineen
dc.subjectPregnancyen
dc.subjectPregnancy Rateen
dc.subjectPrognosisen
dc.subjectTaylor and Francis Ltden
dc.titleMaking IVF more effective through the evolution of prediction models: is prognosis the missing piece of the puzzle?en
dc.typeotheren


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