scholarly journals The novel POSEIDON stratification of ‘Low prognosis patients in Assisted Reproductive Technology’ and its proposed marker of successful outcome

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2911 ◽  
Author(s):  
Peter Humaidan ◽  
Carlo Alviggi ◽  
Robert Fischer ◽  
Sandro C. Esteves
Keyword(s):  
Assisted Reproductive Technology ◽  
Ovarian Reserve ◽  
Prediction Models ◽  
Live Birth Rate ◽  
Ovarian Response ◽  
Antral Follicle ◽  
Antral Follicle Count ◽  
Reproductive Technology ◽  
Successful Outcome ◽  
Specific Patient

In reproductive medicine little progress has been achieved regarding the clinical management of patients with a reduced ovarian reserve or poor ovarian response (POR) to stimulation with exogenous gonadotropins -a frustrating experience for clinicians as well as patients. Despite the efforts to optimize the definition of this subgroup of patients, the existing POR criteria unfortunately comprise a heterogeneous population and, importantly, do not offer any recommendations for clinical handling. Recently, the POSEIDON group (Patient-Oriented Strategies Encompassing IndividualizeD Oocyte Number) proposed a new stratification of assisted reproductive technology (ART) in patients with a reduced ovarian reserve or unexpected inappropriate ovarian response to exogenous gonadotropins. In brief, four subgroups have been suggested based on quantitative and qualitative parameters, namely, i. Age and the expected aneuploidy rate; ii. Ovarian biomarkers (i.e. antral follicle count [AFC] and anti-Müllerian hormone [AMH]), and iii. Ovarian response - provided a previous stimulation cycle was performed. The new classification introduces a more nuanced picture of the “low prognosis patient” in ART, using clinically relevant criteria to guide the physician to most optimally manage this group of patients. The POSEIDON group also introduced a new measure for successful ART treatment, namely, the ability to retrieve the number of oocytes needed for the specific patient to obtain at least one euploid embryo for transfer. This feature represents a pragmatic endpoint to clinicians and enables the development of prediction models aiming to reduce the time-to-pregnancy (TTP). Consequently, the POSEIDON stratification should not be applied for retrospective analyses having live birth rate (LBR) as endpoint. Such an approach would fail as the attribution of patients to each Poseidon group is related to specific requirements and could only be made prospectively. On the other hand, any prospective approach (i.e. RCT) should be performed separately in each specific group.

Antral Follicle Diameter Variance Within Each Ovary May Be A Predictor For Poor Response In Cases With Normal Ovarian Reserve

2018 ◽  
Vol 126 (08) ◽  
pp. 521-527
Author(s):  
Ilhan Sanverdi ◽  
Enis Ozkaya ◽  
Suna Kucur ◽  
Dilsat Bilen ◽  
Meryem Eken ◽  
...  
Keyword(s):  
Ovarian Reserve ◽  
Poor Response ◽  
Ovarian Response ◽  
Antral Follicle ◽  
Multivariate Regression Analysis ◽  
Antral Follicle Count ◽  
Estradiol Level ◽  
Poor Ovarian Response ◽  
Follicle Diameter ◽  
The Right

Abstract Objectives To determine the predictive value of antral follicle diameter variance within each ovary for ovarian response in cases with normal ovarian reserve tests. Methods This is a prospective observational study. One hundred and thirty nine infertile women who underwent ART in IVF-ICSI unit of Zeynep Kamil women and children’s Health Training and research hospital between January 2017 to June 2017 were recruited. Blood samples were collected on day 2/day 3 for assessment of serum FSH and estradiol. Trans-vaginal sonography was done for antral follicle count. During antral follicle count, in order to determine antral follicle diameter variance, diameters of the largest and smallest follicles were recorded. Variance was calculated by subtracting the smallest diameter from the largest one. Following ovarian stimulation with antagonist protocol, poor response was determined in cases with total oocyte number≤3. Ovarian reserve tests and antral follicle diameter variance were utilized to predict cases with poor response in women with normal ovarian reserve. Results Antral follicle diameter variance both in right (AUC=0.737, P<0.001) and left (AUC=0.651, P<0.05) ovaries significantly predicted poor ovarian response. Variance>3.5 mm was found to have 75% sensitivity to predict poor response. Basal serum FSH with estradiol levels and AFC failed to predict poor response (P>0.05). Other significant predictors for poor response were day 5 estradiol level and estradiol level at trigger day (P<0.05). In multivariate regression analysis, both AFC and antral follicle diameter variance in the right ovary were found to be significantly associated with clinical pregnancy, on the other hand peak estradiol concentration and antral follicle diameter variance in the right ovary were significantly associated with poor response. Conclusion Antral follicle diameter variance may be utilized to predict poor ovarian response in cases with normal ovarian reserve.

scholarly journals Diminished Ovarian Reserve, Causes, Assessment and Management

2013 ◽  
Vol 4 (2) ◽  
pp. 45-55 ◽  
Author(s):  
Mala Arora ◽  
Mandeep Kaur
Keyword(s):  
Ovarian Reserve ◽  
Treatment Options ◽  
Poor Response ◽  
Ovarian Response ◽  
Antral Follicle ◽  
Antral Follicle Count ◽  
Diminished Ovarian Reserve ◽  
Assisted Hatching ◽  
Ovum Donation ◽  
Antagonist Protocol

ABSTRACT Diminished ovarian reserve predicts diminished ovarian response to stimulation but does not predict cycle fecundity. It has been recently defined by ESHRE, the Bologna's criteria, according to which at least two of the following three features should be present: (1) Age >40 years/any other risk factor for DOR, (2) abnormal ovarian reserve test, i.e. antral follicle count, AMH, (3) poor ovarian response in a previous stimulated cycle, i.e. less than three follicles after standard gonadotropin stimulation. Poor response to maximal stimulation on two previous occasions also defines DOR. The treatment options are limited. Avoiding the GnRH agonist long protocol and stimulation with microdose flare or antagonist protocol yields better results. Adjuvant therapy with LH, DHEAS and growth hormone shows some benefit in improving the oocyte yield. It is advisable to perform ICSI for all obtained oocytes and some advocate assisted hatching. Pregnancy rates are, however, poor and often these patients require ovum donation. Developing tests that will diagnose DOR in a low-risk population will allow women to plan their reproductive careers early. How to cite this article Kaur M, Arora M. Diminished Ovarian Reserve, Causes, Assessment and Management. Int J Infertility Fetal Med 2013;4(2):45-55.

scholarly journals Clinical Application of AMH Measurement in Assisted Reproduction

2020 ◽  
Vol 11 ◽  
Author(s):  
Hang Wun Raymond Li ◽  
Scott M. Nelson
Keyword(s):  
Decision Making ◽  
Ovarian Reserve ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Ovarian Response ◽  
Antral Follicle ◽  
Clinical Decision ◽  
Treatment Protocols ◽  
Treatment Pathways ◽  
Individualize Treatment

Anti-Müllerian hormone reflects the continuum of the functional ovarian reserve, and as such can predict ovarian response to gonadotropin stimulation and be used to individualize treatment pathways to improve efficacy and safety. However, consistent with other biomarkers and age-based prediction models it has limited ability to predict live birth and should not be used to refuse treatment, but rather to inform counselling and shared decision making. The use of absolute clinical thresholds to stratify patient phenotypes, assess discordance and individualize treatment protocols in non-validated algorithms combined with the lack of standardization of assays may result in inappropriate classification and sub-optimal clinical decision making. We propose that holistic baseline phenotyping, incorporating antral follicle count and other patient characteristics is critical. Treatment decisions driven by validated algorithms that use ovarian reserve biomarkers as continuous measures, reducing the risk of misclassification, are likely to improve overall outcomes for our patients.

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