Making IVF more effective through the evolution of prediction models: is prognosis the missing piece of the puzzle?
| Autor: | Agni Pantou, Michael Koutsilieris, Anna Rapani, Konstantinos Sfakianoudis, Panagiotis Bakas, Theodoros Kalampokas, Konstantinos Pantos, Stamatis Bolaris, Evangelos Maziotis, George Anifandis, Mara Simopoulou, N. G. Antoniou |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Anti-Mullerian Hormone
Genetic Markers 0301 basic medicine Pregnancy Rate Computer science Cost-Benefit Analysis Urology medicine.medical_treatment Fertilization in Vitro Machine learning computer.software_genre Models Biological Body Mass Index Gonadotropin-Releasing Hormone 03 medical and health sciences 0302 clinical medicine Ovarian Follicle Pregnancy medicine Humans Precision Medicine Ivf outcome 030219 obstetrics & reproductive medicine Assisted reproductive technology business.industry Age Factors Luteinizing Hormone Embryo Mammalian Prognosis 030104 developmental biology Reproductive Medicine Order (business) Female Artificial intelligence Follicle Stimulating Hormone business Live Birth computer Algorithms Predictive modelling |
| Zdroj: | Systems Biology in Reproductive Medicine. 64:305-323 |
| ISSN: | 1939-6376 1939-6368 |
| DOI: | 10.1080/19396368.2018.1504347 |
| Popis: | Assisted 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.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. |
| Databáze: | OpenAIRE |
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