Pretreatment antimüllerian hormone levels and outcomes of ovarian stimulation with gonadotropins/intrauterine insemination cycles

Vagios, Stylianos; Hsu, Jennifer Y.J.; Sacha, Caitlin R.; Dimitriadis, Irene; Christou, G.; James, Kaitlyn; Bormann, Charles L.; Souter, Irene

doi:10.1016/j.fertnstert.2021.02.047

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…However, studies examining low cut‐offs have shown that low AMH levels may be a predictor of treatment failure. Vagios et al reported that the hazard ratios for pregnancy after three cycles and six cycles of IUI corresponding to <0.7 versus 0.7–4.4 versus >4.5 ng/mL of AMH levels were Ref versus 1.45 (0.99–2.16, p = 0.065) versus 1.71 (1.06–2.74, p = 0.027) and Ref versus 1.56 (1.06–2.28, p = 0.022) versus 1.73 (1.10–2.74, p = 0.018), respectively 30 . Koo et al reported the significant increase in time to pregnancy in very low AMH level groups (<1.19 ng/mL for ≤31 years and <0.60 ng/mL for 32–34 years) 33 .…”

Section: Resultsmentioning

confidence: 95%

“…Vagios et al reported that the hazard ratios for pregnancy after three cycles and six cycles of IUI corresponding to <0.7 versus 0.7-4.4 versus >4.5 ng/mL of AMH levels were Ref versus 1.45 (0.99-2.16, p = 0.065) versus 1.71 (1.06-2.74, p = 0.027) and Ref versus 1.56 (1.06-2.28, p = 0.022) versus 1.73 (1.10-2.74, p = 0.018), respectively. 30 Koo et al reported the significant increase in time to pregnancy in very low AMH level groups (<1.19 ng/mL for ≤31 years and <0.60 ng/mL for 32-34 years). 33 Overall, the utility of AMH in predicting success of non-ART treatments may be useful only in cases with very low AMH levels.…”

Section: Resultsmentioning

confidence: 99%

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Anti‐Müllerian hormone for screening, diagnosis, evaluation, and prediction: A systematic review and expert opinions

Iwase,

Asada,

Sugishita

et al. 2023

J of Obstet and Gynaecol

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AimTo present evidence‐based recommendations for anti‐Müllerian hormone (AMH) measurement as an ovarian reserve test.MethodsA systematic literature search for the clinical utility of AMH was conducted in PubMed from its inception to August 2022 to identify studies, including meta‐analyses, reviews, randomized controlled trials, and clinical trials, followed by an additional systematic search using keywords. Based on this evidence, an expert panel developed clinical questions (CQs).ResultsA total of 1895 studies were identified and 95 articles were included to establish expert opinions subdivided into general population, infertility treatment, primary ovarian insufficiency, polycystic ovary syndrome, surgery, and oncofertility. We developed 13 CQs and 1 future research question with levels of evidence and recommendations.ConclusionThe findings of the current systematic review covered the clinical utility of AMH including its screening, diagnosis, evaluation, and prediction. Although some clinical implications of AMH remain debatable, these expert opinions may help promote a better understanding of AMH and establish its clinical significance.

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Section: Resultsmentioning

confidence: 95%

Section: Resultsmentioning

confidence: 99%

Anti‐Müllerian hormone for screening, diagnosis, evaluation, and prediction: A systematic review and expert opinions

Iwase,

Asada,

Sugishita

et al. 2023

J of Obstet and Gynaecol

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“…In an ART population, a low AMH level was not associated with blastulation rate per fertilized oocyte or with live birth per embryo transfer (11). On the other hand, a low AMH level was associated with a lower cumulative probability of achieving clinical pregnancy and a higher rate of miscarriage in subjects undergoing ovarian stimulation with gonadotropins for intrauterine insemination (12). This question remains a subject of intense debate and inquiry in the field and is beyond the scope of this review.…”

Section: Determination Of Ovarian Aging: a Historical Perspectivementioning

confidence: 88%

Antimüllerian hormone use and misuse in current reproductive medicine practice: a clinically oriented review

Quinn

Cedars²,

Huddleston³

et al. 2022

F&S Reviews

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Antim€ ullerian hormone (AMH) was originally discovered because of its role in suppressing the uterine and tubal structures during male sexual development. It has since become a valuable adjunct to the practice of reproductive endocrinology in several avenues. The ability of AMH to provide useful, albeit indirect, information regarding the size of the ovarian follicle pool has been used successfully for predicting ovarian reserve, forecasting the time to menopause, supporting the diagnosis of polycystic ovary syndrome and predicting the response to treatment, and assisting in dose selection for women undergoing assisted reproductive technology. However, the enthusiasm for AMH as a relatively new tool in the armamentarium of the reproductive endocrinologist must be tempered by its limitations. Although AMH is helpful in ascertaining the quantity of remaining ovarian follicles, it does not provide information about follicle quality. Therefore, using AMH to forecast fertility potential can be fraught with error and can drive unwarranted medical treatment. Certain conditions and medications can also result in falsely low AMH determinations, which can again lead to inappropriate treatment recommendations. The knowledge of the proven usefulness of AMH and its limitations is therefore critical for optimal clinical practice.

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“…To enhance efficacy for infertility patients, use of non-invasive prognostic factors has been recommended, e.g., ages, types of infertility, and durations of infertility (8)(9)(10).Artificial intelligence (AI), for example, including convolutional neural network (CNN), has already been widely used in enhancing diagnosis and treatment for various diseases (11)(12)(13)(14); and random forest model and artificial neural network (ANN) have been used for improving accuracy in grading collected data (15)(16)(17).…”

Section: Introductionmentioning

confidence: 99%

Development of a Diagnosis Grading System for Patients Undergoing Intrauterine Inseminations: A Machine-learning Perspective

Jin

zong

Xue

2022

Preprint

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ObjectiveTo develop an innovative, non-invasive and objective grading system for enhancing clinicians’ assessment of intrauterine insemination (IUI) patients.DesignPatients who had undergone IUI treatments previously were divided into pregnant (N=4618) and non-pregnant(N=20974) groups. An evaluation index method was developed using collected clinical data from the two groups, particularly on indications of considerable differences between the two groups. The weight of each indicator was determined using random forest machine learning, and the indicators and patients’ conditions were classified using an entropy-based feature discretization technique. The indices for each indicator were further divided into five grades, from A to E, and given five points to one point, respectively. Effectiveness of the system was tested using the ten-fold cross-validation method.SettingReproductive medicine center, Shanghai, ChinaPatientsInfertile couples who had undergone IUI treatment.InterventionNone.Main Outcome MeasuresWeight of each indicator and grades of infertile patients.ResultsFrom the 25,592 medical records of infertile couples who had IUI, 4618 women were pregnant subsequently, with a mean age of 28.69±3.34 years. From the collected records, 18 indicators (e.g., body mass indices [BMI], endometrial thicknesses, couples’ ages, IUI cycle days, and semen situations) were selected to construct our diagnosis scoring system. Among the 18 indicators, BMI (weight, 12.49%), endometrial thickness (11.99%), female age (11.88%), semen density (10.41%), semen volume (8.92%), cycle day (7.38%) and male age (6.96%) were closely related to the pregnancy rates. Among patients with the final scores for > 75.29 individually, the pregnancy rates for them was > 56.35%. The system’s stability was 95.1% (95%CI,94.5%-95.7%) according to cross-validation data.ConclusionThis quick and objective machine learning-based approach can be used to simplify and enhance the decision-making process among clinicians, especially to advise and to select patients for better IUI outcomes.

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Pretreatment antimüllerian hormone levels and outcomes of ovarian stimulation with gonadotropins/intrauterine insemination cycles

Cited by 7 publications

References 25 publications

Anti‐Müllerian hormone for screening, diagnosis, evaluation, and prediction: A systematic review and expert opinions

Anti‐Müllerian hormone for screening, diagnosis, evaluation, and prediction: A systematic review and expert opinions

Antimüllerian hormone use and misuse in current reproductive medicine practice: a clinically oriented review

Development of a Diagnosis Grading System for Patients Undergoing Intrauterine Inseminations: A Machine-learning Perspective

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