Hello, everyone. Thank you to all of our fertility and sterility readers and viewers. We're very happy to be here live for Fertility and Sterility Journal Club Global.
We are targeting the European and African time zones. As you can see from my face, it's bright here in America, but for most of our speakers joining us from Europe, it is now evening. So good evening, good morning, wherever you're joining us in the world.
We're very excited to be presenting a paper today called Fertilization Rate as a Novel Indicator for Cumulative Live Birth Rate, a multi-center retrospective cohort of over 9,000 IVF cycles. So with that being said, I would like to hand it over to our hosts for today, Dr. Dom de Ziegler and Paul Portilla from the editorial staff at Fertility and Sterility. Dom, I'll turn it over to you.
Thank, Mike. It's a pleasure to be here and to have you with us at this European time zone. We have a very interesting topic to discuss, and we have a fantastic panel of experts to bring the discussion to a really good point.
I'm going to hand it over to Paul. I, myself, and Paul are working at Hôpital Foge in Paris, and Paul is going to present the different presenters and discussants for this journal club. Welcome, again.
Hello, everyone. So I'm Paul, I'm working in Foge with Dominic, and thank you, Mika, for this invitation. So as you, Mika, mentioned, we are talking a great, we have a great subject tonight to debate about fertilization rate and what's the importance of this indicator in cumulative live births.
And for this, we have the pleasure to have people and experts from all around the world. So I'm going to start with the American, which is in France, Catherine Rakowski, which actually is a professor in Harvard School and also was the past president of ASRM, American Society of Reproduction Medicine, and which, by chance, works with us today. Actually, she's a consultant in our IVF center in Paris.
She's going to have her input on this subject. Then I'm going to present you Marine, which is our director of the lab in Hôpital Foge in the IVF department. Kathleen Hong, which actually is one of the nicest person I've ever met, and she's the director of the lab, which is in Baskerville, RMA, New Jersey, directed by Richard Scott.
And last but not least, actually, the person that has conceived the subject, this article, Andrea Borini, which is now in Bologna in Italy, and he will tell us what he feels about the subject and the importance of long-term and short-term. So please, Marine, I hand it to you to present us the paper, and then we're going to continue with Andrea's comments on his paper. Okay, so thank you for the introduction.
Maybe we can share the slides. Perfect. And for inviting me to present the study we will be discussing today, Fertilization Rate as a Novel Indicator for Cumulative Live Earth Rate, a multicenter retrospective cohort study of 9,394 complete in vitro fertilization cycles.
Next, please. Next slide, please. Thank you.
So, by way of introduction, the objective of this study was to appraise the fertilization rate as a predictive factor for the cumulative live birth rate. And the rational underlying for this objective is that fertilization rate is routinely used as a laboratory key performance indicator for assessment of laboratory performance and gamete competence. And this study extended the routine use of fertilization rate as a KPI to ask, can fertilization rates predict treatment outcome expressed by the clinical live birth rate? Next, please.
So this was a multicenter retrospective analysis of non-donor ICSI cycles reported to the Italian National Registry for the period January 2015 through December 2017. Exclusion criteria included standard IVF, fertility preservation cycles, cycles with any types of PGT, cycles with surgical sperms, and cycles resulting in no embryos. The final data set included ICSI cycles that either resulted in live birth or used all cryopreserved embryos.
And as stated in the paper, in the final analysis, all patients included in the study completed an ICSI cycle, having achieved live birth or having used all cryopreserved embryos. Next, please. So cycles were stratified into three groups based on their fertilization rates, according to the ESRO-VINA consensus, under 65 percent, group 2, 65 to 80 percent, and the group 3 fertilization rates over 80 percent.
So the fertilization rate groups were compared with one-way ANOVA test for cleavage rates and implantation rates, where they're assessed including all embryos transferred, or those only at the cleavage or blastocyst stage. And within and across the three fertilization groups, univariate and multivariate regressions evaluated associations between clinical live birth, cumulative, sorry, cumulative live birth rates and number of oocytes retrieved and patient age. So next, please.
So concerning the demographic characteristics, the study included 9,394 cycles from 7,968 couples from 10 Italian IVF clinics. And as shown above, there were similar numbers of cycle among the three fertilization rates groups, and maternal age was similar as well. So next, please.
So now concerning the relationships between fertilization rates and other KPAs used in clinical embryology, results are shown in this table. And a positive correlation was observed with increasing fertilization rates for all four KPAs, cleavage rates, implantation rates, whether expressed for all embryos, or only those transferred at cleavage stage or blastocyst stage. And as mentioned in the article, the investigators also stratified the data within each fertilization groups, fertilization rates groups, by number of retrieved oocytes, as well as patient age.
So next, please. So regarding the cumulative, regarding the number of oocytes retrieved, so as shown in this article figure, the cumulative libus rates shown on the y-axis, increased with increasing number of oocytes retrieved, shown on the x-axis, and that for each the fertilization rates groups. So the cycles with less than 65% of fertilization rates had cumulative libus rates significantly lower than the average shown in the red line.
And so in the over 80% fertilization rates groups at cumulative libus rates significantly higher than this average. So next, please. So then the data regarding the cumulative libus rates by patient age are shown on this slide.
So next, please. And as you can see, patients were stratified into three groups under 35, 35 to 38, and 39 to 42 years old groups. And regardless of patient age group, the cumulative libus rate increased with increasing fertilization rates.
And the same results were observed when the data was stratified within age group by the number of oocytes retrieved too. So next slide, please. So about the conclusion of this study, firstly, a large cohort from the National Italian Registry.
Secondly, the data reported in this publication indicates a positive association between fertilization rates and cumulative libus rates, and an independent impact of fertilization rates on this cumulative libus rates. And thirdly, together, the results suggest a clinical relevance of this parameter and its adoption as a key performance indicator. So thank you.
Thank you, Maureen, for this presentation. I think it was very clear and very, very the point. So just before we give the chance to Dr. Borini to present his work, we are going to have two questions for the audience.
I will remind you that you can also use the chat to put questions, and we will eventually pass them over to our discussants. So don't hesitate to write on the chat your questions. So now, Tia, I will ask you to just present the two questions for the poll.
So do you monitor fertilization rate in your clinic and share the results with your team? So you have three options, yes, no, I do not know. Please vote. Great! That's 90%, almost 90% they do.
And well, we'll try to make it 100% by the end of the meeting. So let's have the second question. So could fertilization rate be a potential indicator for cumulative pregnancy rates? So you have three options, as always, yes, no, maybe.
Okay, so we have people that are not sure yet of the answer. So now we are going to pass it to Dr. Borini so he can convince us why he believes everyone should do that. And well, hello to Bologna.
Hello, thank you. Thank you, Paul. Thank you all.
Yeah, you see, it's almost the hot water. So everybody uses it. And probably not everybody thinks that this can be just a factor, a KPI for the performance in their clinic.
Anyway, what we know and we knew before was that the pregnancy rate, the cumulative birth rate is dependent from the number of eggs that the patient has in the cycle. I think that almost agree on this. And so what we thought was, of course, the number of the eggs is easy.
But for example, if you have a sperm from the testes, your live birth rate is not increasing in the same way as with a good sperm. So we tried to understand if the fertilization rate was a right indicator. And we had already the Vienna Consensus that was stated that the fertilization rate is important and that it can be an indicator for the lab work.
So in the lab, everybody knows that has to check the fertilization rate for each person that is in the lab. And at the end of the month or at the end of a series, they have to understand if all the data together are in the right range of result. So we thought together, we put together those two things and we look at the data in the huge database that we have in Italy from the register, from the Italian register.
We were 10 clinics, right now we are more than 10, but we were 10 clinics that just send the data per cycle to the register. Usually in Italy, it's still we send the data to the registry, but aggregate way. So the aggregate way doesn't allow you to just do any comparison between cycles and so it's not so usable for research.
In this way, calculating a result in our 10 clinics, we could aggregate a lot of cycle and just look if there were some difference. And what we thought, of course, was to take out all the excluding criteria that Marina has already said, just to avoid any problem in the result. So we know that if we perform ICSI, we just state the first point.
If you are in the bottom as a performance, you got a lower cumulative live birth rate. If you are in the top, probably in your clinic, you got more result and more live birth rate. The second thing was, of course, we look also at what happened in the different centre.
We look at the result in different centres just to see if it was different. And of course, if the centre performed worse or better in the ICSI fertilisation rate, the cumulative live birth rate was lower. So we have both lines that were cumulative birth rate and fertilisation rate were in the same way, dependently on the result of the centre.
So for that, we suggest the fertilisation rate as a QPI indicator for the performance in a centre and to be used just to understand how we are working or not. Great. Thank you for that.
Just keep in mind, we have to convince people. So we have to push the polls from 60% to 100% by the end of the meeting. So we're going to have that question run again.
So now I'm going to pass it to Cathy Hong in Baskin Ridge, New Jersey. So she can have her point of view expressed to the audience. Please, Cathy.
So I think it's great to have some additional affirming data to show that this is an important KPI that we should continue to measure in the lab. And obviously, what we care about, the live birth outcome is that's why people come for care. I think that the information showing that is associated with the number of oocytes retrieved is as we would expect.
All these things are with increasing initial or intermediate outcomes being higher than your overall final outcome being higher. I guess one of the things I would love for Dr. Bururi to be able to kind of explain a little bit is when you're talking about, I mean, it's such a great comprehensive study that you need to look at all these cases. But with the exclusions of no fertilization or having all arrested embryos or NPGT cases, I was just kind of curious if those were specific cases that were excluded for any specific reason, or I almost wonder if including that would have given us even a little bit more information.
Yeah, could be. But you know that usually when you have a different way for inseminating, could be that you have other criteria and other factors that can change the fertilization rate. So when you use ICSI, you should be able to have a right fertilization rate if the performance are in the top one.
Or for example, if I perform an ICSI, of course, those embryos probably have a lower chances to become a baby just because I'm not doing ICSI usually. So for the test is for the PGTA is something that is, of course, you don't transfer embryo that are aneuploidy and you don't know if an aneuploidy depends on what, on the age or on the other stuff. So and also doing the biopsy could impact the developing of the embryo sometimes.
So just because we want to exclude as more confounding factors, we exclude those kind of criteria. Do you think that you get the same? I think that if we were to include all the cases of even embryonic arrest, no for in the case of ICSI, and because I think there have been a number of studies now that I've looked at the impact of biopsy. But I think it's just more just to see if we can get a little bit more data.
And I wonder if not having that information, if there I mean, is there is there any reason not to include those that have no for after ICSI or those that have embryonic arrest afterwards? Because then you're really only looking at the data for people who actually got the transfer. Whereas if you're using this to as a case study, when you're actually... Excuse me, the voice is not clear. I don't know if it's in my... Sorry.
Yeah. So I think just my question was really basically, if we could just get a little bit more information by not excluding those ICSI cases that resulted in no for for embryonic arrest. And ultimately, with the PHT cases, even if you ultimately end up transferring all the embryos in the cohort, like the ones that were included in the study, you can still get a cumulative live birth rate.
Even if you don't, I mean, if you believe that the if you believe in the PGT platform that you're using, right. And so obviously, you know, the goal is never to use technology that that isn't validated. But if it is actually a technology that's showing that the antipoids really are being discarded, then you still have opportunities with the antipoids that you would transfer.
So I just didn't know how much data we were losing and why, you including only five eggs or more, like, are there is this something that is even relevant to even the other cases that didn't get into the analysis? Yeah, the number of the eggs that we put as a limit one was just because we know from the previous study that when you have less than five eggs, your results are lower than if you have more than that. So once again, we try to exclude all the confounding things that can be touched from the reviewer, for example, or for the people that just has to judge the paper and putting too many things together. Sometimes you have too many confounding factors.
So in our paper, it could be that if we add all the other cases, we got the same result or could change. But anyway, you cannot say that there are confounding factors that are trying to differentiate the results. For example, when we talk with Dom about the different fertilization rate in the different centres, that was already was an additional consideration that we did because, of course, we could be at differences.
We know that all the centre has not the same result, not the same competency in doing things. So if we change, if we have the same level of the embryologist to do the ICSI, for example, or the same kind of lab or technology more or less in the lab, can we put all together? I think no. So the confounding factors are always something that doesn't allow you to draw any results, I think.
So I see that things are getting a little bit hotter. So I will give the word to Catherine so she can give us her ideas on the topic. Well, thank you so much, Paul.
And thank you for this invitation to participate in this actually really, I think, very important discussion. So I would first like to congratulate you, Andrea, for your study. I mean, we all know how difficult registry studies are because of the difficulties you just alluded to, to controlling for confounders.
It's a huge challenge and we only have the data that we have to work with and hopefully, you know, do the best we can with what's available. So I have a couple of questions for you, the first of which I think is really poignant to this discussion, and it's to do with the definition of cumulative live birth rate. So as you said in your paper, generally speaking, we think of cumulative live birth rate and you cited three references to this.
We think of it as the sum of all live births from a whole cohort of embryos, whether they're transferred, fresh, or frozen. So I'm just a little bit confused by your definition of cumulative live birth rate in your paper and how you actually calculated it. And the reason I ask this is twofold.
Number one, you state that all the final data set included patients that had completed an ICSI cycle, having achieved a live birth, or having used all their frozen embryos. So immediately that raised a question in my mind, actually, what, what actually, how actually did you calculate cumulative live birth? And then the other question, I think, is really to do with the efficiency of a cycle. I mean, I think we all would agree that the overall efficiency of a cycle is using all the embryos.
So did you actually include cycles that used all their embryos or not? I just was really confused by how you actually calculated cumulative live birth. I couldn't see anywhere in your paper a description as to how you actually performed the calculation. Yeah, I think Marina, Marina, Marine, I'm sorry, Marine just highlighted that we, we calculate the cycle where a pregnancy, a live birth was achieved, either in the fresh or in the frozen, and or all the, if the patient has used all the frozen embryos and she didn't get the live birth.
So we can have a patient that got a live birth and they still have more embryos frozen, but we, of course, we are not talking about the two different ways to calculate the negative or the positive way to calculate the cumulative live birth rate. So it's not the, I don't get the name right now, but anyway, it's just patients that if they still have embryos, we didn't add the possible. So there are no patients that they didn't get the pregnancy, the live birth, I'm sorry, the live birth, and they still have embryos.
Okay, thank you. I think that's a really important clarification. Again, just because my understanding from the literature is the pure definition of cumulative live birth rate is use of all the embryos.
And it gets back to my earlier comments that it's really hard to do these studies. Obviously, to include all cycles where all embryos are used, you have to start with a much earlier time period than you started with December 2015. And then your dataset could also be much smaller.
So this is a real challenge with this sort of work. But I do think it's important for the listeners to understand this distinction between your definition of cumulative live birth rate, and at least what I think most clinical embryologists definition is, which is use of all the embryos. So thank you for clarifying that.
So if you can see the cycle where the patient didn't get the live birth, and you calculate in an enthusiastic way the possible pregnancy rate from the frozen, of course, you could have more higher rates. But in this way, we just calculate what happened. So live birth from the fresh, from the frozen, or no pregnancy at all.
Yeah. Yeah, thank you. Yeah, I understand what you did.
Thank you. So if I may, Paul, can we move on to another point? Yes. So I have, well, thank you for all your opinions.
And now I have a quite interesting question from the audience. So I quote, it is concerning that patients who have not transferred or embryos were removed from the study. So this strongly favors the high responders and high fertilization rate patients.
If they have, for example, five failures and then get discouraged and quit, then none of those failures are counted. So the person proposes that it might be better to use a Cox proportional hazard model or a Kaplan-Meier curve, which would adjust for the dropout and still allow inclusion of all transfer. So the question is, did you consider this approach to calculate the fertilization rate? Yeah, I think that all the papers that were published before, considering the enthusiastic or the negative way to calculate the live birth rate, were criticized because we suppose that there is a live birth dependent on the frozen embryo that you still have.
So we didn't put those cycles just because we cannot state how many babies will be born from frozen embryos. So our way was just to calculate the real live birth and calculate, of course, the negative result when all the patients got all the frozen embryos transferred. So the other ways could be a higher live birth rate, but the worst could be, I don't think that it would be the real cumulative live birth rate.
We cannot hear Dominic. So just one more thing. So do you, is this just a way to say that patients with more embryos have a higher cumulative live birth rate? Yeah, of course, when you have more eggs.
So we know already that the number of eggs is correlated with the cumulative live birth rate. And this is the same when you classify by age. And so that was already stated from other papers and we published also some papers on this topic.
So more embryos you have, more live birth rate you have. But when you calculate the cumulative live birth rate, considering what could happen if you still have embryos, you just suppose the possible live birth rate. It's not the real live birth rate.
So we exclude patients that, as the number of cycles, where they still have embryos to transfer. So I think that is more real than the other way to calculate the cumulative rate. I think I have corrected my audio problem.
We learned through experience. I'm amazed about this paper by one thing. I was given the impression before that the fertilization rate was not a very sensitive parameter.
And actually I'm impressed by the impact it has on cumulative live birth rate. And this is one of the reasons why we had Micah and I decided to put this paper on the journal. My question is, how does it fluctuate from time to time? Paul sent a survey and everybody, almost everybody said that they actually calculate this on a regular basis and share the results with the whole team, clinical and biological.
But how does it fluctuate? And how does the fluctuation that occur from week to week or month to month impact on the results? All the biologists, everyone can answer. Catherine. Yeah, may I weigh in? Thank you.
So it's an excellent question, Dom. So I think most labs will take as an average fertilization rate for IVF around 75% as the target, and for ICSI 80%. And so there is wobble.
Of course, this is biological. So you're going to have variation and variation within 99% or 95% confidence intervals. But generally speaking, in a lab that's performing in a stable way, one should expect those sorts of rates.
And depending upon the size of the lab, it will affect the duration, the time period in which you assess your fertilization rates. So if, for example, it's a very busy lab where you're doing maybe five to seven retrievals a day, then I think very reasonably you can do your calculations on a weekly basis and get a really quite reliable and informative value as to how the lab is performing. I must admit that when I was lab director at Brigham and Women's Hospital, I looked at fertilization rates every single day.
And just by Gersholt, I mean, if you see a patient that had 10% fertilization rate on this cycle that I'm looking at versus 80% on a previous cycle, then that would be a red flag for me. And I would go further deeper into the cycle, look at the stimulation, make sure there weren't any changes in any of the contact materials in the lab that might be of concern. So that's all part of sort of a quality management program.
But nevertheless, I think to your question, you will see variation. I mean, some patients, you know, a young patient with maybe 10, 20 mature eggs may have a low fertilization rate because we must remember the sperm plays into this as well as the oocyte. And there are also oocyte abnormality problems that can actually influence fertilization rates as well.
So I don't know, Kathy, do you want to have anything to add to that? No, I mean, I think I, because we happen to have like a large volume in Basking Ridge, but this is something that we look at all the time. I actually, while you were talking, I pulled up our graphs just to look at them. And, you know, I think obviously there's going to be cases that will play roles like we have, you know, when we have a urology week and a lot of the cases are microcustomers.
And certainly that week there's going to be, we expect there's going to be a little bit of a dip there. But certainly it's something that we follow. And also I think in our lab, because we split all of our cases between two embryologists, the kind of service controls for each other within the same cohort.
But I think that even independent of that, it's good to be able to look at the fertilization rates as an overall metric in the lab. And a question for Andrea, did the work just with all these things said, I mean, we have to offer the chance to to have her go. She presented the articles first, and I would like to hear her opinion on everything that has been discussed and if she has questions for the author.
Marine. Yes, thank you, Paul. No, no, I totally agree with what Catherine just explained two minutes before.
And I think that all clinics are following the fertilization rates, even in France. So it's not it's not a new KPA for embryologist, but it was very interesting to move that about a global procedure evaluation. And that's what's new.
And I was very happy to to present this study because I think that we are calculating in the lab, but maybe not for this use. And it was a really interesting way to another way to to look at this fertilization rates. One question for Andrea.
Thank you, Marie. For Andrea, did your study show any fluctuation from time to time or you could not look at that? No, we did. We did.
I look at it the time to time. So it is difficult. But anyway, I. Still the same problem.
So if you we know that fertilization rate is something that to be checked. I don't know if daily Catherine said Catherine said daily. Yes.
Daily or weekly. Anyway, you need to check which is the performances in your lab. And of course, we know that it's not only a problem for the technical part that the embryologist does.
It's not only a problem of injection or the time or the injection or all the stuff that are important. But we know, of course, that the the the gametes has a different impact as as just because our gametes are all different. So that's why, for example, we didn't put together the sperm from the testes, because we know that they are performing lower than the other the other sperm.
We know that the severe cryptospermia sperm are performing lower than the normal sperm when you perform ICSI. So we know that that point is very important. And but usually nobody, as Marine just said, look at the fertilization rate that is a day by day checking for the lab, the lab job as a global and long term result in terms of life birth rate, cumulative life birth rate.
So that is the, as I said, is is just when you discover the hot water is not something so new, but you just look in a different way and can be a nice point to for everybody in the lab to to look at the result in the long term and and just not thinking that is only a problem of that day or that week, but it's a problem for the patient and for the history of the cycle in the long term. So I think it is very important if everybody should look in the in the future and the accumulation rate for the long term result of the of the center. So I think that everybody has to use this as a KPIs.
Are you happy Paul for that? Yeah, yeah. So the audience really loved your your work, your article, and they have one question. So it's around the fact that you have excluded cases with surgical sperm, ICSI and cases with small numbers of eggs.
Does excluding the more challenging cases will result in some weaknesses in the study? As I said before, of course, if you put more confounded problem and less than five eggs is a confounded topics. And if you use very severe male factor, you you have a gametes that the sperm in the actually that has different performance. So I think that this could be calculated differentially.
Probably we can calculate the cumulative birth rate for all the patients that has sperm from the testis or we are we are looking at that and probably very early we will present that on that. Speaking of KPIs. May I comment, please, Paul? Thank you.
So, yeah, absolutely, Andrea. I mean, I completely agree that this is a new way of looking at fertilization rates. And it's I think it's going to hopefully really prompt people to take fertilization rate more seriously.
I would just like to emphasize for the 5% who don't check their fertilizations and fertilization rates, how important it really is. And it is not just in the long term, as we're discussing with your paper, but in the short term, because this is something that you can act on almost immediately. And if you see one day with five retrievals that you've got less than 10% or 0% fertilization rate, the chances are there really is something toxic in the lab and you can respond immediately.
As compared with, for example, if you use implantation rate as a KPI, you have to wait weeks. So I just wanted to emphasize, at least this is my passion of it, that the critical importance of checking, looking at fertilization rates extremely regularly. And I mentioned what the targets are in the lab at Brigham and Women's Hospital, but each lab is going to have a different target, presumably depending upon their patient population and their own experience.
So every lab should have, they should understand what their target is and use p-charts, performance charts to plot their data and see when there is variation below 99% or 95% confidence interval, whichever they choose to select. So thank you for allowing me just to say that, because I think it is an important point for running an embryology lab. I fully agree, Catherine.
I fully agree. That is very important. And of course, I hope that in every lab in the world, they will do the same.
I hope so. But usually, this is very important for the day-by-day work. And it is important, as our paper probably helped to understand that it's important not only for the day, but for that patient in the day, it's very important for the long-term results.
So I think it's one more point to check. I just, given that the dynamics of IVF today, and the fact that we are all talking about how we should calculate live, cumulative live births, I want to ask your opinion on how should we calculate today live births, cumulative live births, because it's not that clear. As mentioned by Catherine prior, if we would look at all embryos transferred, it will take lots of time, and also many patients will be lost from the statistics.
So what's your opinion on that? We might ask Micah to tell us how SART does it now. So SART does it up until the first live birth, because we're looking at it typically two years after the data is collected. So we can't usefully report it to the clinics 10 years later when it might be available for that entire cohort of embryos.
So to have it be clinically meaningful in any, it's still not real time because it's two years later, but in any meaningful capacity, it has to be including frozen embryo transfers to that first live birth. I think from a research standpoint, the points that Catherine made are very challenging. You either have to model it, or you need a decade-long study, and then the data is out of date because the lab has progressed.
So it's a challenging topic. Catherine. Yeah, so I mean, I think this is actually a matter, honestly, of semantics.
You know, I think with the publications, Andrea, that you referenced in your paper regarding the definition of cumulative live birth rate being the sum of all the births from a whole cohort is fine, and I think that's accurate, because cumulative, by English definition, means together, all together. I think it really is a matter of semantics, and I would suggest that perhaps the way that you use this, you actually calculated your birth rate in your study is not cumulative live birth rate, but it is live birth rate in a realistic clinical setting. That's my two cents about it.
It's incredibly difficult to calculate a cumulative live birth, as we mentioned. Yeah, yeah, I agree with that. You know what? Usually, we use all the statistics just to present patient to the patient our work, and to present patient which are the chances that they got to get a baby, because they ask you 99% one baby, and hopefully it's the first one.
Then if they got the first one with the second or the third embryo frozen, of course, they could have a second one on the other sewing when they want to have the second one, and of course, all the baby that you can get, you understand only when you transfer all. Many times, patient doesn't come for the second baby, and so they don't come for the other transfer from the frozen one. It will be, I think, almost impossible.
In fact, in all the paper that I referred, they try to calculate in the optimistic or realistic way the possible live birth rate, cumulative live birth rate, that of course, we can be critical with that. This is, as you said, just said, is the probably real first live birth rate that the patient can get in a cycle. I think this has been a great discussion on your paper, Andrea, and very provocative, but now that we have this panel of experts, distinguished experts, I would like to hear everybody possibly on how you rank the fertilization rate as a KPI compared to blastulation rate, for example.
Does anyone want to jump in? Catherine, I think you will. I'll try. This gets the whole business of your day three versus day five transfers, selection of patients.
I'll try to answer your first question, your question, which is how do I rank fertilization rate as a KPI versus blastulation rate? So, again, I think that, well, I didn't actually say it, so I won't say it again. Both of them are incredibly, both of them are incredibly important KPIs. Use of blastulation rate, though, I think is not, cannot be applied universally to all cycles, whereas fertilization rate can.
And the reason I say that is because from historical information, we may well, depending upon the program, they will have different policies, but most programs, I think it's fair to say, have a policy to not take the poor responder or the patient who has previously not had a high number of blastocysts formed to a day five, day six or seven transfer in the future. And so you're removing then those patients as potentials in order to apply blastulation as a KPI. So, on balance, I would say that fertilization rate is probably the most important KPI, just because it can be applied to all cycles.
Kathy? If I can just follow up with a question on that, that I think maybe gets to why the poll showed everybody does this as a KPI, but maybe not everyone believes it's an independent predictor of cumulative live birth. Is it just that this is helping us predict the number of good embryos we're going to have to transfer, which it probably does, or is this truly an independent predictor? And Andrea, you can correct me, but I don't think you modeled for the number of usable embryos. In other words, if I get to two good quality embryos and I have a 10% FERT rate versus I get to do two good quality embryos and I have a 90% FERT rate, does the FERT rate still matter in that case, or is this really just the marker of how many good embryos we're going to have to transfer? Yeah, I think it's another matter because, of course, when we look at the blastulation rate, first of all, we talk of a result in lab that, as Katherine said, they culture the embryo up to the blastocyst.
In the world, there are a lot of centers that they transfer on day three and they culture to blastocyst the spare embryos, for example. So, I think it's more and more difficult to look at the cumulative live birth rate. And I think that, of course, you put another problem from the lab, that is how they culture, how is the result of the blastulation.
So, it's important anyway. I think that every lab that culture embryos to blastocyst has to calculate and to check, I don't know if every day, but anyway, they have to check the blastulation rate for sure. But when you look at the cumulative live birth rate, you can use one or the other one, probably in the same way, the same matter, I don't know.
I think that probably requires another study to really investigate that. Another study for you, Andrea. Yeah, for sure, we all need better studies and better understanding of each indicator.
Kathy, do you have something to add? Well, I was just thinking, I mean, these are all such important indicators, all these such important parameters to be measuring, because I think ultimately what we're trying to get at is the efficiency and the ability of a laboratory to make sure that nothing is happening in the laboratory to negatively impact a patient's ability to get a good outcome. I think it's really important to look at all these factors. And I almost wonder, I mean, it's just two different ways of looking at the same question, but at the end, our question is, is the patient, what things should we be looking at to make sure that the patient always has, that we can measure their best, the outcome of their best measure? So I think that even looking at things like if you're going to measure a blastulation rate, then you also need to measure, well, what happens to the embryos that you call blasts? You know, when you measure things like, I mean, fertilization rate is easier because you literally just look at polar bodies and TNs, right? But the thing is, or I guess we can talk about that too, but that's more straightforward of a question than to say like, well, what do you call a blast? What do you call something that's cleaved? Like, yeah, then measure the outcome of that, that you're calling.
So, you know, I thought actually Kate Fisher and Richard Scott put out a paper, I mean, just recently, just about metrics for labs, looking at like, how do you actually measure everything that's useful in a lab? If you're going to measure things like not having something at the end of a cycle and then measure what is the outcome of every embryo that you transfer and then measure what's left over afterwards, if you look at that in an ongoing way, then you can really probably measure the entire efficiency of the lab. But obviously that's like a big picture thing. You need to also be looking at things like fertilization rate and blastulation rate.
So all important measures. Thank you. Thank you, Kathleen.
So just before the last words, I just want to make the poll again, the question. So let's see how many people we convinced with our debate. So Tia, please just pull the poll question.
So could fertilization rate be a potential indicator for community pregnancy rates? I recall you, we had 59 percent in the first question. So now let's see whether our discussion helped. Well, yes, that was useful.
So that's quite incredible. We pushed it by almost that 11 percent. So that's great.
So now I will just take advantage and give everyone the chance to have some closing words. So I'll start with Kathleen. Well, thank you very much.
I didn't expect to have a closing comment, but I appreciate it. So again, Andrea, thank you so much for doing this study. Really challenging, really important study.
And it's really elevated the, I think, the utility of fertilization rate as an important KPI in the lab. And the fact that your data show there's an association with, I'm not going to call it cumulative birth, but I'm going to call it something, if I may, I think is provocative. And I'm certainly going to be working, hopefully, with Marine, who is the lab director at the Foch.
And we can actually ask some of these same questions with the data at the Foch. So thank you again for inviting me and for allowing me to give these final comments. Thank you.
Dominic, do you have something to say at the end? Well, thank you all. And again, this paper was provocative. I felt it when I read it.
I feel it even more tonight, when it was discussed to such an extent. It is simple to calculate. It is far simpler than all other parameters, including the blastivation rate.
And therefore, we need to really learn from your study and put it in practice and follow it. Great study, provocative and fantastic discussion. So indeed, I think we have to learn from your study.
And I hope you'll come soon with a different study, a better one, that will silence all other questions and debates. And I really appreciate for all your work. And I really appreciate for all your time, for each and every one of you who took your time to just debate with us tonight.
And I thank the audience for participating. And I wish you all a great night and better outcomes. Thank you.
Bye-bye. Thank you. Bye-bye.
Good night, everyone. Good night. Bye.
Thank you.
