International journal of fertility & sterility · 2026
Modeling Diagnosis and Clinical Outcomes in Infertility Couples Considering Multiple Factors: A Retrospective Study.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ghasemabadi Atena, Ghiasi Hafazi Somayeh, Effati Sohrab, Namakin Kosar, Ahmadi Zand Mohammadreza, Ghamsary Mark
Аннотация
OBJECTIVE: Infertility arises from diverse etiologies, with polycystic ovarian syndrome (PCOS) being a leading cause among women, while male factors account for approximately 50% of cases. Selecting the most appropriate assisted reproductive technology (ART) for each couple is crucial. This study aimed to develop a dynamic mathematical model to predict ART success rates based on infertility etiology, laboratory tests, and clinical findings.
MATERIALS AND METHODS: In this retrospective study, data from 1,374 cases were reviewed, and a sample of 502 couples from the Yazd Research and Clinical Center for Infertility, Yazd, Iran, was analyzed (April 2016-February 2017). Participants were evaluated according to body mass index (BMI), anti-Müllerian hormone (AMH) levels, number of transferred embryos, and infertility etiology of (PCOS, male factor, or both). Couples were categorized into eight classes based on infertility susceptibility, embryo quality (A, B, C), positive beta human chorionic gonadotropin (β-hCG), clinical pregnancy outcomes, and fertility status.
RESULTS: The model estimated ART success rates within subgroups considering all relevant factors. Higher BMI (>30 kg/m²) and lower AMH (<3.5 ng/mL) were associated with reduced success rates. Intracytoplasmic sperm injection (ICSI) showed higher predicted success across all etiologies. Mathematical analysis indicated system stability, and backward bifurcation highlighted the importance of increasing recovery rates (positive clinical pregnancy) to reduce the primary reproduction number (R₀) below one and control infertility. No Hopf bifurcation was observed, indicating the absence of periodic fluctuations in treatment outcomes.
CONCLUSION: This dynamic model provides a framework to predict ART success and understand the interactions between clinical and biological factors in infertile couples. It may assist clinicians in optimizing individualized treatment strategies based on patient-specific characteristics.
MATERIALS AND METHODS: In this retrospective study, data from 1,374 cases were reviewed, and a sample of 502 couples from the Yazd Research and Clinical Center for Infertility, Yazd, Iran, was analyzed (April 2016-February 2017). Participants were evaluated according to body mass index (BMI), anti-Müllerian hormone (AMH) levels, number of transferred embryos, and infertility etiology of (PCOS, male factor, or both). Couples were categorized into eight classes based on infertility susceptibility, embryo quality (A, B, C), positive beta human chorionic gonadotropin (β-hCG), clinical pregnancy outcomes, and fertility status.
RESULTS: The model estimated ART success rates within subgroups considering all relevant factors. Higher BMI (>30 kg/m²) and lower AMH (<3.5 ng/mL) were associated with reduced success rates. Intracytoplasmic sperm injection (ICSI) showed higher predicted success across all etiologies. Mathematical analysis indicated system stability, and backward bifurcation highlighted the importance of increasing recovery rates (positive clinical pregnancy) to reduce the primary reproduction number (R₀) below one and control infertility. No Hopf bifurcation was observed, indicating the absence of periodic fluctuations in treatment outcomes.
CONCLUSION: This dynamic model provides a framework to predict ART success and understand the interactions between clinical and biological factors in infertile couples. It may assist clinicians in optimizing individualized treatment strategies based on patient-specific characteristics.