Fertility and Sterility On Air - TOC: December 2025

Take a sneak peek at this month's Fertility and Sterility! Articles discussed this month are:  

Articles:

1. The reproductive journey of women with obesity undergoing assisted reproductive technology: an analysis of 48,595 in vitro fertilization cycles in 31,829 women
2. The reproductive endocrinology and infertility subspecialist: definition, training, and scope of practice in the United States
3. Clinical outcomes in patient-oriented strategies encompassing individualized oocyte number (POSEIDON) low-prognosis patients receiving in vitro fertilization / intracytoplasmic sperm injection treatment: a multi-center retrospective cohort study\
4. Gonadotropin-releasing hormone antagonist protocol is associated with higher oocyte yield in young women at high risk for low oocyte retrieval: a retrospective study using three statistical methods
5. Outcomes of in vitro fertilization cycles with embryo donation compared with double gamete donation with cryopreserved donor oocytes
6. Leveraging anti-Müllerian hormone and metaphase II oocyte yield to improve counseling for oocyte cryopreservation outcomes
7. Effect of a one-step fast warming protocol on reproductive outcomes of vitrified-warmed blastocysts

Welcome to Fertility and Sterility On Air, the podcast where you can stay current on the latest global research in the field of reproductive medicine. This podcast brings you an overview of this month's journal, in-depth discussions with authors and other special features. F&S On Air is brought to you by the Fertility and Sterility family of journals, in conjunction with the American Society for Reproductive Medicine, and is hosted by Dr. Kurt Barnhart, Editor-in-Chief, Dr. Eve Feinberg, Editorial Editor, Dr. Micah Hill, Media Editor, Dr. Pietro Bortoletto, Interactive Associate-in-Chief, and Associate Editor, Dr. Kate Devine.

Hi everyone, and welcome back to another episode of F&S On Air. I'm your co-host Pietro Bortoletto, and joined with me, Dr. Kurt Barnhart, Dr. Kate Devine, and Dr. Eve Feinberg. How are you guys? Hi Pietro.

Good morning, Pietro. Good morning from a very snowy Chicago. I've been following that in the news.

I'm not jealous at all, but I think it's coming our way up in New England. Have fun with that one. We had some snow.

We had some already. Didn't stick. Definitely wreaked havoc on the travel and flight situation yesterday, so at least you weren't traveling.

It does make me wonder why the RSNA continues to be held the first week of December in Chicago every year, watching along with my radiology buddies who are posting photos from downtown Chicago and McCormick Place. Woof. Yeah, it was bad.

My daughter's flight got canceled going back to college, and it was just a mess. So glad that all the travel is behind everyone. We're here, we're indoors, we're warm, and we're coming off the heels of a really amazing annual meeting in San Antonio.

I don't know about you guys, but I recorded some really wonderful podcast content from the meeting. I was really excited to see it packaged up and come out as an episode. Hopefully this, by the time you're hearing this, that will have already been published.

Eve, Kate, Kurt, did you guys have any favorite interviews from the annual meeting? Well, I don't want to spoil everyone's listening enjoyment, but yes, there were some good ones that I recorded. I think I'm in episode two, so I really enjoyed the talk on transfer of segmental mosaics and outcomes from that, so more to follow. There was a good vibe at the meeting.

I think that the annual meeting is back. There was good buzz in the hallways, the science was good, the meeting was great. Totally agree.

There's a lot of really fascinating science coming down the pike. I was really excited to interview those folks that are looking at AMH as a potential therapeutic target, so I think we'll see hopefully some good research coming out there, and that's covered as well in the on-air live from San Antonio. Well, in addition to good meeting content, we also have some really wonderful content in our December table of contents coming to you from fertility and sterility.

As a reminder for our listeners, you're probably noticing that there's a third episode in there each month. That's our new F&S roundtable discussion that makes sure that we highlight our fertile battle and views and reviews section of the journal, which unfortunately just didn't get enough playtime here on our on-air podcast, so if you haven't listened to that episode, co-hosted by Dr. Emily Barnard and Dr. Ben Pipert. In this month's fertility and sterility table of contents, we have a seminal contribution entitled The Reproductive Journey of Obese Women Undergoing ART, an analysis of 48,000 IVF cycles in 31,000 women.

Kate, tell us a little bit more about the seminal contribution this month. Yeah, I was really excited to read this. The authors evaluated women's WHO category BMI, so that's just as a reminder, underweight, normal weight, overweight, or obese BMI categories, and associated it with the cumulative probability of achieving a live birth.

And they looked at it in a slightly different way than authors have previously. They looked at it on a per egg, per embryo, and per embryo transfer basis. And they did follow these women all the way out to either a live birth or drop out from treatment.

They looked at about 49,000 ART cycles, and these were cycles, all of which had a completed egg retrieval. So not per cycle start, but really per egg retrieval. And these were completed among 32,000 or so patients at IVRMA in Spain.

So it was multi-center, but all Spanish IVRMA centers from 2017 to 2023. And so outcomes were adjusted only by male and female age at treatment start, so they didn't have their age or BMI advance cumulatively as they progressed through treatment, but just used their starting age in BMI. And they looked at a mixture of cleavage and blast transfers, fresh and frozen transfers, single and double transfers, and with and without PGTA.

So quite a heterogeneous group of treatment journeys. Again, the authors did not adjust for those other differences, only for male and female age, and they did not adjust or report parity or race. They did report infertility diagnosis, but they did not adjust for it.

Not surprisingly, they did note that there was a higher rate of ovulatory dysfunction in the obese cohort. Despite not adjusting for some of these other potential covariates, they did conduct a robust group of sub-analyses looking at single embryo transfer only, day five transfer only, frozen embryo transfer only, and only those embryos that were PGTA tested. So in terms of their findings, in this large cohort, the authors found a small but statistically significant decrease in success as, again, defined as cumulative probability of achieving a first live birth by each of their defined metrics.

So again, per egg, per embryo, and per embryo transfer. And they found this decreased chance of achieving this outcome in both the overweight and obese cohorts in both the unadjusted analysis and the analysis adjusted for male and female age. Of note in their population, somewhat different from I think all of us on this podcast, only 18% of their cohort were overweight and only 8% were obese.

So while not the main topic for these authors, I think that this study for me is in large part most fascinating for highlighting the differences between US and European populations and practices, even at a place like IVY, RMA in Spain, race not having been a significant factor even to report. And also even just looking at the fact that the average AMH was about three and the average number of eggs retrieved was about nine. Another notable difference was that there was almost no uterine factor infertility noted as a primary diagnosis in this population.

And less than 1% of patients had a primary diagnosis of uterine factor in any of their four BMI cohorts. I do think that it's a really helpful and overall novel way of looking at BMI as a potential predictive factor in terms of success from ART. That said, my gut is that we need to have a very cautious interpretation of these data.

At the end of the day, one perspective here is that in a way these data are reassuring given that in the adjusted analysis, the reduction in live birth was relatively modest for all the metrics tested, especially in the overweight group. So in the overweight group, the unadjusted hazard ratio for arguably was their primary outcome of per egg probability of live birth was 0.94 with a 95% confidence interval with the upper limit of 0.99. So almost one. And even in the obese group, the hazard ratio was 0.85 for the unadjusted and 0.94 for the adjusted.

And so, you know, I do think that of course, obesity and overweight status is an important factor to consider and to counsel patients upon. But I think we need to be very careful about, for example, counseling patients to take extreme measures to reduce their weight prior to ART treatment, especially in light of other recent data that have shown or that have been at least inconclusive in terms of whether weight loss prior to ART improves outcomes or even really obstetrical outcomes. And so I commend these authors.

I think that the data are extremely helpful. They have supplemental tables, one, two, and three, which they feel would be very helpful for counseling overweight and obese patients. And I agree with that.

I think they're helpful in letting patients know what they may experience over, you know, really up to six transfers. But again, you know, reassuring data in that 84% of obese women who were able to continue up to six transfers achieved a live birth. The other thing I think is really notable about this paper is how I think probably the substantial review process and really high quality reviewers helped make it the excellent final manuscript that it is with over 20 tables and figures represented among the four in the main manuscript and all those in the supplementary data.

So definitely encourage folks to read this really interesting way to look at how overweight and obesity impact ART. That said, overall, these are patients who can expect to have a really high chance of success. I'm interested to hear what all of you thought and how this might impact your patient counseling.

Yeah. I mean, I largely agree with you. I think the one point where I have a slightly different bend on it is that despite six transfers, the obese group never quite achieved the same success as the normal weight group.

And so I think that's most elegantly shown in their figures and specifically figure one. If you look at the stair-step diagram showing the incremental increase in success over transfers, there's a very clear stepwise increase, but you just never reach that same level of success. So while I think that the adjusted ratios make it closer, you just don't quite get the same overall outcome.

And so I agree that probably the answer to all of this is not rapid weight loss prior to attempting ART. There was a recent JAMA paper that showed higher risk of preeclampsia in those patients. But I think that in the years leading up to conception and that preconception period, a focus on health and weight loss and optimization is probably needed much more than we are currently doing in counseling patients.

I agree with you guys that this might be the theme of our podcast here is clearly this is a factor. And you can look at this as a glass half full saying, look how well you can do if you continue and you can go up to six transfers, you can get a good pregnancy rate. But the half empty is this is not really great pregnancy rates and not many people have this much energy time to persist this long.

So I think what it tells me is I like the title of the paper that it really is a journey for some people. And I don't know that there's any causality or scientific new information here. It's just that it can work.

It just is going to take some effort. And it clearly is a factor that's lowering success rate. One interesting, I think, intermediate endpoint here that is kind of buried in the leading table three is that they looked at the underweight, normal weight, overweight and obese groups and looked at some of the laboratory parameters that we have kind of always struggled with, like, are they actually different? Does obesity drive any of these differences? And I think the important thing to highlight the number of oocytes, the maturity rate, fertilization rate and blast quality all appear to be essentially the same across all four groups, which I think is helpful to see at scale and across a variety of different kind of treatment cycle types, both fresh and frozen transfers, day three and day five transfers.

So kudos to the authors on nice figures, figure one, but also a nice helpful table, table three. Yeah. And there's so much other data that could be pulled out of this analysis.

They did it as a Kaplan-Meier survival analysis by event. So by number of transfer, number of eggs, number of members. I'd also love to see it by time.

I'd also love to see it by actual proportion that drop out and when, because we know that obesity can impact that for folks and make, as Kurt eloquently said, the journey and the authors in their title, much more arduous. So great data. I even think that as many as these 20 tables and figures are there, there's even more there.

Great discussion. Lots of rich content in this article. And like Kate said, lots of supplemental materials if you're wonky and really want to dive into the weeds in this article.

We're going to move on to our next article from the ASRM pages section and just wanted to point out that you're probably missing the sweet tones of Dr. Micah Hill today on the podcast. He's a bit under the weather and our thoughts are out to him if he's listening after the fact. Hopefully he is feeling better in the month of December, but pitch hitting for Micah coming off of the injury reserve list, Dr. Eve Feinberg.

Tell us a little bit about this ASRM pages article, Eve. Yeah, I actually love this and I pulled it out over Thanksgiving as my nephew's girlfriend was studying for the MCAT and was prepping for the podcast. And she said, what exactly do you do as a reproductive endocrinologist? And I think this paper really elegantly describes exactly what we do and what our training is.

So if I were to distill it down into 10 points, this paper has a crisp definition of what an REI is. This whole audience knows that we are physicians trained to diagnose and treat complex reproductive and endocrine disorders, infertility, and provide advanced surgical and ART services. It defines our training pathway, which is quite rigorous, four years of medical school, four years of OBGYN residency, and a three-year ACGME accredited REI fellowship with rotations, research electives.

And the next point is board certification. There are two pathways to board certification with most people following the ABOG pathway, which is for examinations to written to oral for both residency and fellowship. So clinical scope of what we do, endocrine disorders, menstrual irregularities, endometriosis, fibroids, complex gynecological conditions.

I really like how it talks about how we are highly trained in transvaginal ultrasonography and advanced diagnostic testing. And that because of the sheer volume of ultrasounds that we do in our training, that our societies do not believe that additional ultrasound training is needed. We perform ovulation induction and ART.

We perform advanced surgery. Genetics and counseling has become a huge part of what we do. Fertility preservation for cancer patients, transgender individuals, and those at risk for premature ovarian insufficiency.

And I think finally that 10th point, leadership and research. REIs lead multidisciplinary ART programs. We ensure regulatory compliance, which is no small feat.

And I think perhaps most important is we advance evidence-based practice through research and scholarly activity. And this is a highly scholarly field, as I think everyone on this podcast can appreciate. We are all about the data and we are all about practicing in accordance with the data and trying to advance what we do, how we do it, and how well we do it with data.

So I thought this was a really nice article that lays out exactly what we do, what our training looks like, and what our certification looks like. Make no mistake, we are reproductive experts. And I think that as other fields try to dabble a little bit in reproductive medicine and cycle tracking, I think the expertise clearly lies within our board-certified subspecialties.

So this was a great piece that just spells it all out. I want to call out that this document also very nicely expanded on complex reproductive and surgical procedures, things that there is still a small but mighty group of reproductive endocrinologists that do high-volume work in, and a lot of the rest of the field collaborates with minimally invasive surgeons to do. So it's nice to see that still kind of being predominantly featured in our scope of practice as reproductive endocrinologists.

Kurt, when ASRM publishes a document like this in Fertility and Sterility, what's the goal of having this paper published? Who's the audience? I think the audience is everybody. I think that my takeaway in this document is that given our highly specialized training, and we are clearly subspecialists, it's a big tent. There are a lot of skills in reproductive medicine and what we perform.

Yeah, I've heard the argument that we're getting more and more focused on technology, but I think this is a paper to remind us that there's really a really big scope of what needs to be done. Just because we've become experts in embryology and IVF and genetics doesn't take away from the underpinning of the training that got us there. And I think that's what we periodically need to be reminded about, both for our practice, but also for the patients that come to see us.

There really is, I hate to take a loaded political term, a big tent. Well said. Thanks, Kate, for that nice description of the seminal contribution article.

We're going to jump straight into the ART section. Kurt, you have a pair of articles today that I think is interesting. Some old questions, but using some novel methods to be able to dig a little bit deeper.

Tell us a little bit about the Poseidon criteria in this multicentered retrospective cohort study. Sure. There were some nice articles that teach us both about some old concepts that we've been grappling with for a long time, but perhaps, as you said, in a new light.

So I'm talking about an article by Zhao and colleagues titled, Clinical Outcomes and Patient-Oriented Strategies Encompassing Individualized Oocyte Number, Low Prognosis Patients Receiving In-Vitro Fertilization, a Multicenter Retrospective Cohort Study. That was a mouthful. And it is a very complex article.

But let me see if I can make it really straightforward and we can talk about what it really means in terms of not only the statistics in the paper, but what it means for our patients. So this paper is looking at a very select subgroup of women using the Poseidon criteria and trying to find basically cumulative pregnancy rates based on a very specific definition in Poseidon. Let me go through real quickly.

So we all probably know the European Society of Human Reproduction Embryology, our favorite colleagues at ESHRA, worked very hard to come up with a definition for poor ovarian reserve and IVF with the Bologna criteria. And it very quickly became identified that Bologna criteria had a very wide range of prognosises, primarily based on age, and that perhaps was a better criteria. And that's where the patient-oriented strategies encompassing individualized oocyte number or Poseidon, how they got to that is beyond me.

I have to admit, that's the first time I learned that it was actually stood for something. I just thought it was a really cool name that they chose for the criteria. Reminds me of Aquaman a little bit, but it does have a true name.

And it really is focused on poor ovarian reserve patients or low prognosis patients focused on human proclaimant of live birth. Now, I had to go up and look real quickly because it doesn't say in the paper, and I don't know if that's top of my head, but let's just put this in terms of context here. So group one in Poseidon are basically young good ovarian reserve patients, but have poor suboptimal response.

I know we can get into specifics, but we can do that. Group two is basically older, but good ovarian reserve patients, but had poor optimal response. Group three is young patients with diminished ovarian reserve, and group four are older patients with diminished ovarian reserve.

And for the purposes of this paper, they added a group five, which are basically normal patients. And what they did with a large cohort is they tried to figure out what the cumulative pregnancy rates would be in each of these groups, because that's really the question of the day. It's not what the pregnancy rate is after one cycle.

It really is what if somebody persisted and what if somebody continued on, what would those curves look like? Because I think our field is moving towards, it really is a journey, as we said before. And I don't know if it's self-serving or scientific, we can talk about, but if you can continue doing IVF, there's a good chance you're going to have a baby ultimately. And this paper is trying to decide like what is the trajectory of that, and is there a plateau? So I'm amazed with the group that comes out of China sometimes, they're looking at a mere 100,000 people in cohorts over five cities across China.

And they basically broke these patients into the different groups that I just mentioned with Poseidon, but they also have an A and a B about expected and unexpected in age, so it gets a little bit complex. And basically they're saying that the young group of Poseidon, I'm giving you the answer first, seems to have a cumulative pregnancy rate that rivals or gets pretty close to people that are normal responders. So it's the young people within the Poseidon categories that really should continue on and ultimately will have a pretty good curve for cumulative pregnancy rate.

Whereas the advanced age or low prognosis patients, particularly those greater than 40, really don't benefit from additional cycles, and repeated ovarian simulation doesn't seem to compensate for reduced ovarian reserve. So that's the take-home message. Let's look at a little bit more closely to see really what it means.

So one of the difficulties with the articles out of China is they don't always practice the same way in the United States. So let me just put some of the issues up front. These patients were not aggressively treated.

They start with a dose of 150 or 300 international units a day, and they still do some day three transfers, but they restricted this group to no more than two embryos transferred. So already there, my practice in decreased ovarian reserve might be a little different than that, but just keep that in mind. So the outcome basically was a clinical live birth rate up to six or even more cycles.

And they made two, I don't know, I want to describe this. They made two important categorizations. They said something called a conservative estimate, which means that obviously not every patient went to six cycles.

So what they did with the conservative estimate was they basically said, if you had gone to six cycles conservatively, your chance of pregnancy would have been the same as I saw in the third cycle or the fourth cycle or the fifth cycle or the sixth cycle. So you could just, in a sense, project it out. That was the optimal message.

So basically they're saying the best you can do is if you continued on your cycles, you would have the same success rate as people that were in your cohort. So if you stopped at two cycles, you can project out what would have happened in your third, fourth, fifth, and sixth. Whereas the conservative estimate was if you stopped at two cycles, you wouldn't get any, you wouldn't achieve a pregnancy.

So there's a big difference between projecting out the same success rate for six cycles and stopping where you are. And I'm glad they did both. I would argue neither are probably correct, but maybe somewhere in between.

So the baseline was that if you take the normal responders, you're getting a cumulative live birth rate up to 92%. So that's what they're calling their baseline. And they're saying that young, unexpected suboptimal responders actually can rival that.

And they can get up to, you know, close to what would be a normal responder that wasn't classified as Poseidon. They also noticed that there was at least a 5% increase with subsequent cycles. And they call that worth continuing, so to speak.

Whereas some of the other cycles had really horrible success rates. And there's lovely tables in here that you can look at. But I think the most telling cycle is not that the young Poseidon people can get close to normal group, was that, in my mind, how horrible some of the cycles were in people that had real discussions of Poseidon.

For example, the group four people, which are older, with decreased ovarian reserve, by the way, had a 10% chance of getting pregnant in one cycle. And after six cycles, it was 25%. And that's just, you know, that's the optimal estimate.

In the less than optimal estimate, it went from 10% to 15%. So that really begs the question of how much should someone put into it when your prognosis really is that bad. So if your prognosis is good in the group 1A cycle, you know, you go from about a 25% pregnancy rate, by the way, that may or may not be different than the United States practices.

But after six groups, you're rivaling, you know, 92% or something like that. Now, this is different from the often quoted Pietro's article in Richard Scott about you can get cumulative pregnancy rate of 94%, I think it is, with three euploid embryos. This is untested.

This is just saying continue and move on. But you can see how they're related, whereas this is, in a sense, real-world practice in China, as opposed to the PGTA here. That really is the take-home message of this paper, eloquently done, based on a lot of people, but ultimately still a projection.

And really what it's focusing on is the young, unexpected people with decreased ovarian reserve and telling them, I think it's worth continuing, whereas it's really confirming, but with really good data to say, wow, how many cycles should you really go through if you really are repetitively having decreased ovarian reserve or having poor response, that there's only so much you can make up with repetitive cycles. Now, I was a little bit less optimistic about this paper than others, even the editorial, I'm sorry, the inkling, which is, you should also read too, a lovely inkling by Miriam Aldasser and Phil Romanowski. And they basically, and their great discussion of this is that endurance is the key to crossing the finish line.

My argument, and I want to talk a little bit about is, I think we need to really talk about what's the finish line for some of these people, and that there's really, I'm hearing it a lot in the papers I see and our discussions that if you just hang with it, it's going to work, but there's another way to look at this. You hang with it, it's not going to work. And I don't know where that is.

And that's the hardest thing to tell somebody. Obviously people choose with their feet and don't come back, but is it best for us as a field to keep saying, yeah, all you need is another cycle. Is that self-serving or is that really based on science? So what do you guys think? I think the tricky part is, and I'll say this from the lens of someone who practices in a mandated state, it's really hard to tell the patient who has coverage for six cycles before their insurance says no more to abandon hope or stop cycling after the third cycle, based on the study from this paper.

I think people who don't have the big financial tax of continuing a third, fourth, fifth, or sixth cycle are going to be hard pressed to not take a couple more shots on goal. And I imagine Eve is probably agreeing with me coming from Illinois, where she sees a high volume of mandated patients, but Kurt and Kate, you guys might have different opinions based on patients paying out of pocket for treatment in your centers. Yeah, I think both Kurt and Pietra, you hit the nail on the head in that it's really an ROI question for a lot of these patients who, and beyond that, who's ROI? Is it the insurance company or is it theirs? And so it really is our responsibility as REIs to counsel these patients as to, it's not just about cost and it's not just about time, it's also about risk.

And absolutely, if the risk of complication is higher than the probability of a baby, which in some of these, in the conservative estimate and the group 4B or what have you from this paper, some of those patients, that's true. And I think that needs to be a bright line for all of us in counseling patients. I do commend these authors in terms of doing such a robust sensitivity analysis.

I think even the last paper that I reviewed in their survival analysis, it would have been nice to see kind of some more fleshing out of patients if they do versus if they don't continue, which is really, really what they're getting at here. But I think it's super helpful and a good reminder for us to think about the cost of the healthcare system and the potential risk that our treatments can have. I think a lot of this depends on what do you do next, because it's really hard to tell someone to give up that doesn't want to give up.

And I'm not advocating that we should be rationing treatment by any means. Please don't take my comments that way. But remember back in the day, we used to argue how many IUIs was worth it.

And I used to kind of make up data to say, you know, if you haven't gotten pregnant by three, you know, it's worth it maybe to do three more, but after six, it's diminishing returns. I think we're going to face the same question for IVF, because if you look at these curves, there really is a plateau after two and three cycles in most of these groups. So again, counseling people about stopping is a very difficult topic, but we should know as scientists and physicians that there are diminishing returns at a certain point.

And yes, it varies a little bit by your age and by your criteria. Yeah. And I think like while we shouldn't be rationing, I think that we spend a lot of time talking about capacity, right? Like what is the capacity of each of our labs? It's not trivial to take somebody through an IVF cycle.

And even though the patient may not pay the cost of it, I think we as physicians pay the cost of it. Our laboratory technicians and embryologists pay the cost of it. And I think there is a real cost to doing too many cycles.

And so, you know, we have sort of a policy in our group where if somebody hits their sixth cycle, we bring it to our group and we sort of decide as a group whether or not we would recommend additional attempts. And I think on the policy side of things, it kills me that some young patients are cut off at four cycles, whereas some older patients are not cut off at all. And so I think that while I don't want to restrict access, I do want it to be somewhat graduated.

And those who have a better prognosis should have more tries, so to speak, from an insurance standpoint than somebody who has a poor prognosis. And how do you navigate that? It's incredibly difficult. I want to make a shout out to a wonderful lecture by my partner, Kate O'Neill at ASRM as a plenary talk with her uterine transplant talk.

And she laid out the uterine transplant nicely, but she anticipated the question, it's not cost effective. And in her lecture, she gave the counter argument to that. If you just look at IVF for women, 841 or over across the country, you're spending hundreds of thousands of dollars per pregnancy, which is far more expensive and less cost effective than doing a uterine transplant.

Now, I know they're different population, but she really called out the field to say how much money we're wasting in treating people that really have really no success. I will say Kate's lecture was phenomenal. Congrats, Kate, if you're listening.

But I will say that I think that's true in many fields of medicine. How much money are we spending on end of life care? It's a huge resource utilization for ICUs and hospitals, and yet we are not doing a good job. And I think similarly, at end of prognosis or minimal prognosis with IVF, as a field, I think we are not doing as good of a job as we could.

And I'm party to that as well. It's very difficult when you have a patient who, for religious reasons or personal reasons or other reasons, financial reasons, cannot afford to move on to donor egg or embryo donation or foster parenting or adoption or cannot wrap their head around living child free. We do.

We treat their emotional needs just as much as we treat their physical needs. And I think that this paper really, especially those survival curves, again, that stair step is zero in group four beyond that third cycle. And so I think we really need to be more focused in our counseling.

Yeah, I agree. This is why we're bringing up, this is why we're talking about it, and this is why I think the data will lead us in that direction since that's what we should be looking at, the data. Pietro, I'm going to go on to another one.

And if you don't mind hearing my voice too much, I'm going to be a little bit professorially because this one is worth, not necessarily in the findings, but in learning a little bit about how they did it. This is, again, an older question, a question you might say is not novel. And this is gonadotropin-releasing hormone antagonist protocols is associated with higher oocyte yield in young women at high risk for low oocyte.

The rest of the title is a retrospective study using three statistical methods. So we hear a lot about the GENERIDGE protocols versus progesterone priming protocols, which is what this is looking at. And this is looking at it in a very particular question, which is young women who are at risk for low yield.

So again, it's not everybody. This is a very specific population. And again, this study out of China has a very large amount of patients.

And I'll give you the quick hit about what it finds, but it is interesting to how they got there. So it's a total of 2,000 women aged less than 35. And they used a nomogram, which I kind of looked up and didn't know existed, but I guess makes sense.

Although again, a nomogram can only go so far. But it basically, a nomogram predicted with age, anti-malarian hormone, antral follicle count, basal FSH, and an FSH-LH ratio, whether you're going to have a good response or not. So they're basically saying, by this nomogram, we predict this woman is going to have a low response, but she's young.

Should we use a progesterone suppressed protocol, or should we use GENERIDGE? And they're looking at the incidence of low oocyte yield, which they define as less than 10 oocytes, and then number of oocytes retrieved. But what caught my eye, basically, the conclusion of the study is that the GENERIDGE protocol is superior to the progesterone-primed ovarian simulation suppression protocol in retrieving oocytes in this particular subgroup. So they're saying that there is a lower number of eggs and a greater chance that you'll get less than 10 eggs if you use progesterone as opposed to GENERIDGE agonist.

But what they describe are three statistical methods that I'm just going to go through pretty quickly with you just to describe what it means, and it's kind of a new way of thinking. So it's not as complex as it thinks. What they used first was something called Bayesian logistic regression as opposed to standard logistic regression.

The second concept they used was, well, what if we do propensity score matching first because it's a cohort study and then use Bayesian logistic regression? And then the third one was, let's look at these women in age-specific strata to look to see if stratifying the groups makes a difference. So let's give you a quick tutorial on what Bayesian is. Bayesian logistic regression differs from standard logistic regression in that it treats the parameters that you're going to study as probability distributions instead of fixed values.

So basically, it incorporates the prior information which you can put into your model, the information in your study, and then gives you a posterior probability what they call the end of the study. This is what we think it has. So Bayes' theorem tells you that you can update your belief about something after observing new evidence.

So I'm not sure it makes a big difference, but let me see if I can explain that a little bit. So regular logistic regression is sometimes called frequentist logistic regression. It basically just looks at your data, cuts it multiple, multiple, multiple times, and says, what's the most likely outcome that you're going to see in this data set? Whereas Bayesian analysis allows you to make some assumptions first.

For example, in this particular case, you can tell the Bayesian analysis model that we understand that there's a correlation between AMH and oocyte yield, that we believe that the higher your AMH, the more eggs you are. So in a sense, the Bayesian analysis can say, when you see somebody that has an AMH of 0.1 that got 30 eggs, that's an anomaly, whereas logistic regression would treat that as true data. So that's one example that you can have it.

The output is similar in that it's an odds ratio. But remember, we all interpreted 95% confidence intervals, and you can be a smart ass by telling somebody you're interpreting it improperly. But the Bayesian analysis is different.

So the interpretation of the 95% confidence interval for a normal logistic regression is that if I repeated this analysis 95 times, the answer I would get would be 95% of the time would be in that confidence range. Whereas the Bayesian analysis is saying, since it's probability that it's predicting, it's saying, no, the answer is 95% chance the answer is within this range. So I'm not sure mathematically that's a huge difference, but in your mind, it's a little bit of a difference.

And then you can also go one step farther, and the Bayesian analysis can say, what's the probability of a meaningful effect? So if the odds ratio is 1.3, the Bayesian analysis can say there's a 45% chance or something like that, that it's at least 1.5 or greater. So it gives you more of a way of thinking about it logistically. So I did a little bit of a deep dive to say, well, what if your analysis that you put in is wrong? What if the assumptions you think you know are not correct, and you're telling the analysis that these variables are correlated? And so maybe it doesn't work as well for discovery.

But the short answer is that the Bayesian analysis the data will trump the assumption. So if it sees that your assumptions are wrong, it'll tell you you're wrong and still give you, theoretically, the right answer. So let's go back, knowing that a little bit, to what the paper found.

So if you look at the tables in this, and by the way, you should just to understand a little bit about Bayesian analysis, it basically can tell you what variables are associated, just like a logistic regression can be that age is associated with outcome or AMH associated outcome. But then it can basically look at the two groups, and it can give you what's called a probability. And you don't read it like a p-value.

You read it like a probability, meaning if the probability is close to 1, it's a null effect. If it's only in about 100, then it's a weak effect. But if it gets to be 300, 400, or 10 to the 30th power or something like that, which is some of the answers here, then you can be pretty sure that that's a true statement.

And it's basically saying, before propensity scoring, the probability of getting low number of eggs, less than 10, is 58% for the GnRH group versus 77% for the progesterone prime group. So that's a reasonable difference. And then the number of eggs actually is about an extra one or two eggs.

They go on one step further to say, again, this is a cohort. What about if we match the population first? And that's called propensity score matching. Very briefly, propensity score matching is saying, I'm going to create a score on all the patients in my cohort of what's the propensity that they're going to get a certain score that's predicting my outcome.

It's not telling you what happened after the outcome. It's saying, how do I get people's propensity to having this particular prognosis, if you will? So you can put in the age and AMH and things like that. And you can take the outliers and remove them and really match your cohorts that way.

And then, because the cohorts are now group A and group B are much more matched, you can do the same Bayesian analysis, even though you control for these factors, and get a closer estimate by removing some of this measured and hopefully unmeasured confounding. In this case, it shows that after propensity score matching, the chance of having the same number of eggs is about the same. So there's still some confounding factor in this, even though that as you get the population closer to the same, the effect gets less and less and less.

So after propensity score matching, they get 68% versus 60%, which is obviously not significant. And then the final thing they do in the third statistical analysis is they look at, by the way, the number of eggs is 9.6 versus 4.3 before the propensity score. And after the propensity score, it's 8.3 versus 5.3. So there's perhaps a couple of egg differences, but not in the risk of getting what's called the low number of eggs, which they defined as, again, as 10.

And then the third risk, a certain analysis they did was they looked at it by strata. And they basically looked at it a decade of life. Basically, 9 out of 10 of their strata favors the antagonist protocol.

But interestingly, the highest strata actually shows a benefit statistically towards the progesterone. So what that tells me is there's just a lot of cutting of this data, and not everything is going to turn out exactly the way you want. So I hope I was mildly eloquent in telling you why they did this in terms of their statistics.

The short answer is it adds to the literature for clinic message that perhaps young women where you're expecting a low number of eggs, there might be something variable about the progesterone limiting the follicle growth. By the way, they see no difference in fertilization rate. They see no difference in pregnancy rate.

It's just they think that it might be bringing one of those antral follicles forward or pre-antral follicles forward. But again, it's one of these things where I'm not really sure clinically it's going to drive me to use one protocol or another. I may be questioning it in this one group, but we'll leave it at that.

But it was an interesting way to talk about the stats and saying the take-home messages are when you have a large cohort and physicians are deciding which group to put them in, therefore it's not randomized, there's a ton of confounding in there. Physicians, it's called confounding by indication or confounding by physicians. We have little AI brains ourselves, and we made a decision that might be correct to treat one group this way and another group this way.

And to get all of that confounding out, even Bayesian versus logistic regression doesn't remove it, and even doing propensity score and then Bayesian doesn't remove it all in my opinion. So the reason I'm spending so much time on this is this is really a nicer way and a much more robust way of doing statistics for cohort analysis than just comparing the results. So paper infertility really is not going to be accepted if you just give me crude results.

It might not be accepted if it doesn't have at least more than one ways of controlling for confounding. And this is a kind of roadmap of ways that perhaps we might be even doing more controlling for confounding or simulated trials as we talked about at ASRM this month to really get closer and closer to the truth. Sorry about that.

Professor Lee, what do you think? No, I love the Professor Lee, and I actually learned a lot. So I think when I read the article, what jumped off the page at me, and I think this is where propensity score matching adjusts for this, so to speak, is the patients who were in the progesterone group had a higher FSH, a lower AMH, and were older. So they were at like a priori, I would have looked at that group and said that they are going to do worse.

And so to me, it was not at all surprising that that group did worse. And so I think where propensity score matching comes in is it matches them far more elegantly than I'm probably describing, but it matches them in such a way that you're normalizing those factors. So it's almost like you are readjusting who's in what group and then doing more of a fair comparison because the raw data in this is not a fair comparison.

And so these progesterone priming group was destined to do worse just based on their like ovarian reserve and their age. So I think if you look at the paper in light of the adjustment from the propensity score matching, which really does account for the marked difference at baseline between the two groups, you see it doesn't have statistical significance in terms of the differences between the two groups. And so I think when I read this, the interpretation that I took home with was, yes, if you have lower ovarian reserve, you're going to do worse.

But if we're comparing apples to apples, which is what propensity score matching tries to do, is you're really not seeing that market of a difference. Although admittedly, it didn't reach statistical significance, but I think clinically, maybe it's meaningful. And I think maybe it will give me pause in that group of young, less than favorable ovarian reserve.

Maybe the answer is to do an antagonist, whereas more recently, I've been doing more progesterone priming for its ease and simplicity and cost. And just to be clear to go to clinical message, they're claiming the reason that the antagonist protocol might be better is that you don't have any suppression at all for the first couple of days in the protocol and you're starting it later. Whereas the progesterone, you know, deeper purveyor is given at the start of the cycle and theoretically might suppress from the very beginning.

So the antagonist cycle is more flexible. Now, whether that's true or not, I don't know. I just wanted to give people the clinical context of what you're talking about.

Yeah, I didn't buy their biological plausibility. But what I did like about the paper and the message I'm giving is that whenever you have a cohort studies, and there's a lot of cohort studies in our literature, you really, really need to pay attention to how you're going to control for this confounding that you know about and the confounding you don't know about. And I like the way that they stepped up in terms of using more sophisticated analyses.

And by the way, you shouldn't just use one, you should use multiple analyses. And the best paper presents them both or all three of them. And then you can actually look yourself to see, you know, how robustly you believe the finding.

If it's all over the place with different methodology, not very robust. If you get the same answer doing it three different ways, that becomes a lot more believable. Yeah, thank you for that review, Kurt.

I can't wait to bring this back to my research team and review this paper together and think about how we can use it to help step up some of our analyses. And I think the jury's still out on PPOS or just-in-primed protocols versus antagonists. The data are very mixed.

There's also a very nice paper in F&S reports came out in the last couple of months on the same topic with slightly different findings. So TBD, but great, great primer in excellent analysis. Thanks, Kurt.

Really nicely described use of these statistical methods. And if you're a fellow listening to this, this is one that may be worth printing out and diving a little deeper into. And if you have the stomach for it, potentially even journal clubbing with your local statistician.

Rich fodder for discussion here. Kate, let's get back to the clinical side. We have some information about outcomes from embryo donation cycles.

Tell us a little bit more about this paper. Yeah, so here the authors use the SART database in an attempt to answer the really clinically relevant question of whether embryo donation or egg and sperm donation with de novo embryo creation will result in a better outcome. And they evaluated about 3,500 frozen donor embryo transfers and only 439 cycles where the embryos were derived from a frozen donor egg and donor sperm.

And the patients that were included, the cycles that were included in this analysis occurred between 2016 and 2019. There were some differences in the group. So those using donated embryos were about four years younger on average.

And though the authors didn't too much on this, they were also more likely to be white. So 87 and a half percent of those using donated embryos were white versus 71.5% white in the combined gamete donation group. As expected, the age of the egg source was higher among donated embryos at 29 years old versus 26 years old for those using donated eggs.

However, the egg source age was really missing quite a bit among donated embryos as we might expect. So 75% of the cycles from the donated embryos, the age of the egg source was unknown. Overall and reassuringly live birth rates were really good.

So 44.1% versus 45.1% and were not statistically significantly different. And overall, there were no differences between the two groups in any of the outcomes that the authors assess. So in addition to live birth as their primary outcome per transfer, they looked at clinical pregnancy, miscarriage, prematurity, low birth weight, and overall good perinatal outcome, which they defined as a singleton term live birth of normal weight.

And this was true both in their unadjusted analysis that they found no difference, as well as after adjusting for age, BMI, smoking, morbidity, parity, race, infertility diagnosis, the number transferred and the embryo stage at transfer. They did not include any PGT tested embryos. So, you know, in conclusion, I think this is a pretty straightforward and helpful analysis.

While I was initially surprised by the relatively small number of double gamete donation cycles. And the reason I was surprised is we have a pretty robust donated embryo transfer program at my center. And yet, even many of my single moms by choice will choose donor eggs, as opposed to donated embryos, given the overall wider, you know, selection that they have when using donor eggs relative to donated embryos.

That said, when I thought more about it, I think it makes sense considering that the study period was 2016 to 2019. And the limitation to only frozen donor eggs. I think as a field, we've seen egg donation cycles largely shift in favor of using frozen eggs in the contemporary timeframe subsequent to 2019.

I think there's just been a huge shift. So, though not the age of this study, I would have been interested to see whether the outcomes varied between those cycles using fresh donor eggs and donor sperm. We all know of the very nice paper last year from Jen Kawas and colleagues looking at the differences in outcomes between fresh and frozen donor eggs.

And I think that's a story that continues to unfold. That said, overall, great analysis that helps us reassure patients that the outcomes are very good and comparable if choosing frozen donor eggs versus frozen embryos. And, you know, though not the hypothesis that the authors were aiming to evaluate, I think the data suggests a lower availability of racial diversity among donated embryos given that significantly more women of color chose double gamete donation over embryo donation.

And I've found that fascinating over time, the differences among patients who do choose to donate their embryos and what might be driving that. I'm so interested to hear your thoughts on this and how it kind of rings true to your individual practices. Yeah, I mean, I think if you have a cohort of embryo donors that are under 30, then these results are not surprising at all to me.

But I think in reality, when we look at donated embryos, we're seeing far more age of the female partner from the embryo cohort being 34, 36. I had somebody that was trying to, you know, they had PGT tested embryos, but the embryo source was 40. And so I think if you restrict it to a very young population of egg donors and a young population of embryo donors, I'm not surprised that the results are the same.

And I know it's SART data and it's hard to get so nuanced, but that didn't surprise me. It surprised me more that the average age was so low. And so I think the selection bias.

Yeah. And again, 75% of them had no data on age. So who knows, maybe, you know, it's hard to know whether 29 is really representative, but I couldn't agree more with what you said.

There's no difference between 29 and 26, thankfully. Right. So that just didn't surprise me.

I still don't, my take home from this is not that embryo donation is the same as double donor. I think my take home is that age matters. And when looking at your egg donor, when looking at your embryo donor, you want to evaluate the age of the egg source.

That is the take home that I read into this. Okay. If I asked you to take a look at table two and just talk out loud a little bit with the reader, what do you make of the live birth rate and the miscarriage rate in this group of patients that are utilizing either donor embryos or donor gametes? Does this kind of line up with what you counsel patients and what you typically see in your practice? Yeah.

And there's, this is covered very nicely in the inkling as well that, wow, have things changed in such a short amount of time. You know, they, there was, I think somewhere in the range of about 60% single embryo transfer in both groups. That is way, way, way too low, especially when we're talking about using donor eggs.

And so again, really when interpreting these data, we need to consider that this is 2016 to 2019 and things have changed. And those live birth rates of 44, 45%, those should be for a singleton, for a single embryo transfer. Thanks, Kate.

I wanted to make sure we highlighted that to the listeners, particularly with your SART QA committee hat on. And I'd be remiss if I didn't put my, my wife is an MFM hat on, a prematurity rate of 25%. That is crazy high.

I think we need to understand that a little bit more in addition to just why the low birth weight is as high as it is and this good perinatal outcome is as low as it is. So more, more stuff to understand things that are limited by the nature of what's collected in SART. And you'd have to look at this at an institutional level rather than a national database level, but definitely more food for thought for us.

Well, I sure would imagine that it's correlated with the 40% double transfer and the high rate of twins that are going to obviously result in this population. But yes, thank you to SART for all the work that you do to collect these data and to really continue to encourage single embryo transfer. Eve, we're going to dive into the research letters section.

As a reminder to the listener, the research letter section is for smaller, shorter, tighter articles with a very unique singular finding that could benefit from being published, but doesn't need the full thousands and thousands of words of a manuscript. Eve, you have a great article looking at AMH in the research letters. Tell us more about it.

Thanks Pietro. I really liked this one and the title is Leveraging AMH and M2 oocyte yield to improve counseling for oocyte cryopreservation outcomes. And just to drill it down, I think that most of the data that are out there look at age or AMH, or we did a calculator that looks at age, AMH, race, BMI, but these are actual real world data from a single program.

And they drill it down into what is the AMH and what is the age of the patient, and have some really good data on how many eggs these patients actually got, and then what is the range of what they saw in those egg numbers. And shout out to Kate, you looked at this in baby budgeting, and we looked at this in our SART analysis looking at cost effectiveness and number of eggs that can be retrieved. This is just another paper that looks at it in another way.

So it was a retrospective cohort study at a single high volume center. It was extend fertility medical practice. They had 8,000 women who were ages 18 to 45 coming in for egg freezing, who had AMH levels drawn between 2016 and 2023.

Their final analysis was restricted to 5,900 presumed fertile individuals who came in for egg freezing. And then of these, 3,094 pursued their first OC cycle within one year of AMH testing. So what I really liked is there were two tables.

One, they stratified age specific percentiles for AMH, and they looked at these stratified profiles based on age from less than 30 to age 45. And they looked at the 5th, 10th, 50th, 75th, and 95th percentiles. So I thought that was really nice.

And this is kind of an update of that CIFR paper that looked at 17,000 all comers to fertility clinics by age. And so it gives us a real idea of what the median AMH levels should be by age. And then what they did was they calculated the percentile distributions and median M2 oocyte counts by age and AMH.

And this is table two. And I will say I've already copied and pasted this into my epic dot phrase for oocyte vitrification counseling. I think it's a really excellent table, and I encourage everyone to take a look.

And so what did they actually see? I mean, this part is not surprising. AMH declines with age. There is a wide variability within age groups.

And so, for example, median AMH at age 30 is 2.96. And then in this group, patients will get a median of 14 eggs retrieved with an interquartile range of 10 to 20. And then at age 40, they found the median AMH was 1.63 nanograms per ml, which I was a little high. And the median egg number was 8 with a range of 5 to 12, which feels about right to me.

The majority of the cycles in the paper were ages 30 to 37. And not surprisingly, there was a strong positive correlation between AMH and M2 yield across all ages. So in the very low, ultra low AMH group, AMH less than 0.5, there was a median at age 35 of 2 oocytes, whereas AMH greater than 5, there was a median of 20 oocytes, which actually I thought seemed kind of low.

And I'm curious about the gonadotropin dosing practices and what total dose of gonadotropins were used. But in this very short, concise research letter, they didn't dive into that. And so I can't help but wonder how much of this could differ based on gonadotropin dosing.

And so overall, AMH are the most critical predictors of oocyte yield, which I think is similar to what we found when we devised our calculator. We did a multivariable logistic regression using those five factors of age and race and BMI. But no doubt, crudely, you can look at age and AMH to get the best predictor.

And I really like how it is a large cohort from a single center, presumably fertile women, and you can actually look and plot your patients on this table and give them a more nuanced expectation of what they could get. So I think it's really nice. I love this paper.

I hate the typesetting of Table 2. I wish it had been formatted a little bit differently because it's such a nice, helpful table for counseling and including in talks, but really nice use of the research letter to kind of clarify a nice singular finding that's important to share. Thanks, Eve. We'll have to reformat it.

And I couldn't agree more with wanting to know more about the gonadotropin dosing for these patients. This is a real area of fascination for me recently is how do we dose to get the most ROI per unit of gonadotropin? And I just think we still have no idea. So would love to see that enrichment.

The last article of the podcast is by Kara Jani et al. entitled The Effect of a One-Step Fast Warming Protocol on Reproductive Outcomes of Vitrified Warmed Blastocysts. So when we think about vitrification, we always talk about the shift from slow freezing to rapid freezing, but not nearly as much attention has been paid to warming rates, often overlooked and underappreciated.

There's been some foundational cryobiology work to show that warming rate is just as important as cooling rate for cell survival. And it's primarily because slow warming allows ice crystal formation during the re-warming process. This Greek study asks a pretty practical question.

Can we simplify and accelerate the warming process while maintaining excellent embryo survival and pregnancy outcomes? Their primary objective was to compare novel one-step fast warming protocol against the standard multi-step. And again, the multi-step process takes on average eight to 10 minutes, whereas the rapid warming is about a minute to a minute and a half. The study evaluated both laboratory outcomes as well as clinical outcomes.

This was a prospective cohort study done between 23 and 2024 at a single center in Thessaloniki, Greece. And a control group was retrospectively selected from the same time period to have kind temporal parameters within the same clinic. So in total, they had 802 frozen embryo transfer cycles with 401 transfers in each group, which is a nice size for this kind of study.

And what they did was they looked at these laboratory outcomes. And I won't bore you with the statistical methods, they're pretty straightforward and simple in the paper. But the big take-home was that the survival rate was 100% in both groups, meaning every single embryo survived the procedure regardless of it being slow or rapidly warmed back to temperature.

This perfect survival rate reflects, I think, high quality lab, high quality vitrification techniques in selection of largely good quality expanded blastocysts for cryopreservation. The primary findings when it came from the clinical side, this is where it really matters, pregnancy rates were 72.8% in the fast warming group versus 69.6% in the controls. That was a non-statistical difference between both groups.

Clinical pregnancy rates, again, continued to be non-statistically different as well as implantation rates. And if you march this out to ongoing pregnancy rates and live birth, there continued to be no statistical difference between either group. I think the clinical implications for this is that at least in this single center study in Greece, the fast warming protocol appeared to be at very least non-inferior to the standard approach.

100% survival rate in both groups is very high. I think most centers in the US would probably quote in the 95 plus range. But I think the reduction in time from eight minutes to one minute represents an 87.5% time savings per patient.

And in a high volume IVF laboratory, you can imagine how this will add up over the course of a day, a week, a month, and a year. So love to see this kind of discrete finding being presented, I think adds to the body of evidence that we should pay a little bit more attention to how we warm our frozen blastocysts for transfer. But curious to see how our practice evolves in the next five to 10 years as it relates to warming of blastocysts.

Kate, what does Shady Grove do now in terms of their protocol for warming blastocysts? Yeah, we do use a quick warming protocol as well. I have to, you know, kibitz with our lab director and actually lab directors because some of our centers use slightly different protocols. See if it's exactly this one that was 100% successful in Greece, you know, and it's impressive.

I mean, this is not a very small cohort of embryos. It's not that 100% survived out of 100 embryos. So I am interested in learning more about how we can apply these data in our center.

Thanks, Kate. And thanks to the authors for this very nice research letter. Guys, thanks for another rich discussion.

So much great content coming out in the December issue of Fertility and Sterility. Again, a big shout out to Micah, who's at home and hopefully listening, but will be back hopefully on the podcast in the new year. That's all the time we have for today.

Thank you, Kate. Thank you, Kurt. And thank you, Eve.

Happy holidays, everyone. Happy holidays. Happy holidays until 2026.

This concludes our episode of Fertility and Sterility On Air, brought to you by Fertility and Sterility in conjunction with the American Society for Reproductive Medicine. This podcast is produced by Dr. Molly Kornfield, Dr. Adriana Wong, Dr. Elena HogenEsch, Dr. Selina Park, Dr. Carissa Pekny, and Dr. Nicholas Raja.