Wednesday, May 21, 2008

Helping patients to decide how many embryos to transfer !

Making embryos is something the IVF lab can guarantee, since it’s a procedure which is done in the lab. However, after we transfer the embryos back into the uterus, whether or not they will implant to become a baby is not in anyone’s control. This is because nature is not very efficient in producing babies. We know that transferring more embryos does improve pregnancy rates – but it also increases the risk of a multiple pregnancy.

The key question is – how many embryos should we transfer , in order to maximise the chances of success, without increasing the risk of a high order multiple ? This is a difficult question to answer.

In many countries, such as the UK, the decision is taken out of the hands of the patient and the doctor. HFEA rules allow the transfer of only 2 embryos. While this may be fine for a 30 year old, this is not sensible for a 42 year old, who eggs are of poorer quality and who has a much lower chance of getting pregnant. Using a "one size-fits all" approach is not in the patient's best interests ( but is all the bureaucrat can offer !)

This clever tool, which uses basic probability principles, can help patients to maximise their chances of success, by helping them to figure out how many embryos they should transfer ! It can also be use for patients who want to maximise their chances of having twins - an "instant family" !


Binomial ( which, as the word suggests, comes from bi = two) probability deals with the probability of decisions which have two possible outcomes. If an event can have 2 outcomes
( the probability of which is p and q) , then its probability can be expressed as a binomial probability , if p and q are complementary (i.e. p + q = 1) For example, the outcome of
tossing a coin can be either heads or tails, each which has a (theoretical) probability of 0.5. Rolling a four on a six-sided die can be expressed as the probability (1/6) of getting a 4
or the probability (5/6) of rolling something else.

(Source: Wikipedia - http://en.wikipedia.org/wiki/Binomial_probability)

In our case, the two probabilities are: getting pregnant (p) or not getting pregnant (q). No other option is possible.

If you roll a die once, the probability of getting a specific number say 6 is clearly 1/6. However, the problem becomes a little more complex, once we roll the die several times and want to know the probability of getting at least one six. In our case, this will be the equivalent of transferring multiple embryos together and wanting to know the probability of conceiving at
least one baby.

For people who could keep awake during their statistics classes, the formula is:




The binomial model we are proposing to use is a
simplistic model which makes several assumptions:

1. The success probability of each embryo (becoming a baby) is the same. In reality the success probability of individual embryos will vary based on their quality.


2.
That the success probability of each embryo is not impacted by the presence of other embryos in a process where multiple embryos are transferred simultaneously.

The success probability number at best is a guess – there is no scientific way to calculate this precisely. It depends on factors dependent not just on the quality of the embryo but also the specific physiology of the individual mother in question. In medical terms, this is called the implantation rate (the chances of a single embryo becoming a baby). This varies from clinic to clinic; and from patient to patient.

All you need to do is enter the number of embryos being transferred in cell C2 (for simplicity the number of embryos in this spreadsheet is restricted to 10) and the success probability of each embryo in cell C3 (the success probability has to be a number between 0 and 1 or in percentage terms between 0 and 100%).

The results will be immediately available below.

Let us illustrate the tool with an example where success probability ( implantation rate) for each embryo is 10% and 3 embryos are transferred. This is what the probability chart will look like:

Number of embryos being transferred 3

Success probability of each embryo transferred 10%





Probability of having 3 babies 0%

Probability of having 2 babies 3%

Probability of having 1 babies 24%

Probability of having 0 babies 73%





Total 100.0%
-




How do you interpret this ? This means: the chances of having triplets is practically zero;
twins is also very low probability at 3%. There is a good chance of conceiving a single baby at 24%. There is a high chance (73%) that the IVF will not succeed.

Now see what happens when you transfer 4 embryos ? or 5 ? or 2 ?

Which will give you the best risk-benefit ratio ? This is a very personal decision - and it's one you should make for yourself !

The way to improve the chances of success will be to increase the number of embryos transferred : or to improve the quality of embryos (perhaps by selecting a good IVF clinic which has a high success rate).

Go ahead and play around with the tool till you get a good feel of the probability. You can use this tool to figure out how many embryos should you transfer in your IVF cycle.



This is the great advantage of having smart friends who know much more mathematics than I do ! This spreadsheet was developed by my friend, Mr Rajiv Rai - and will help mathematically challenged doctors ( and their patients ! ) like me

Feedback is welcome ! Please email me at [email protected] !

3 comments:

  1. Anonymous11:07 AM

    Dr Malpani,

    This seems to be an excellent tool. In my case, my cavity is sufficient to hold only one baby. Is there any way you could transfer multiple embryos and if more than one implants, is foetul reduction an option that you provide at your clinic?

    Thanks,
    DK

    ReplyDelete
  2. Anonymous7:02 AM

    Dr Malpani

    I would like to use your tool to play around with probability in deciding how many embryos to transfer, but I am unable to plug in any information on the spreadsheet. I would like to find probabilities when implanting 2 embryos.

    Thanks.

    ReplyDelete
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