An algorithm to personalise the diagnosis of recurrent implantation failure based on theoretical cumulative implantation rate

By Genia Rozen, Peter Rogers, Wan Tinn Teh, Catheryn J Stern, and Alex Polyakov


Recurrent implantation failure (RIF) is an imprecisely defined disorder lacking a robust scientific basis. The incomplete understanding of RIF provides significant diagnostic and therapeutic challenges, and a better understanding of the underlying issues is necessary to improve outcomes. We propose a novel concept termed ‘Theoretical Cumulative Implantation Rate’, the calculation of which is based on objective data, to define whether a patient should be diagnosed with RIF. An updated definition to assist with patient counselling and planning research studies, which is more precise and standardised, is well overdue.

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Q&A with Dr Genia Rozen

What is the current definition of recurrent implantation failure (RIF)?

Recurrent implantation failure (RIF) is an imprecisely defined and disputed disorder. It is characterised by the failure to achieve pregnancy after repeated embryo transfers.  

What inspired this research to come about?

Recurrent failures to achieve pregnancy are stressful and overwhelming for all involved. Experimental therapies for unexplained RIF are sometimes prescribed due to pressure from patients to investigate and offer non evidence-based treatments.

Our idea stemmed from a simple but critical question: ‘when during treatment does the failure of a given number of embryos to implant become more likely due to an unrecognised, underlying pathology and not due to chance alone?’ In other words, how many embryo transfers without a positive pregnancy should a patient have before additional investigations are considered?

We hope to address the shortcomings in the widely used definitions of RIF (recurrent implantation failure), in order to provide a better understanding for when additional investigations should be considered by the treating specialist. This can also be a tool for counselling patients, as well as in designing research studies.

Once more established factors are excluded, the remaining points are controversial, their role in the pathogenesis of RIF is debated or there is no demonstrated effective treatment (Shaulov et al.,2020). An individualised and scientific approach towards initiation of investigations will assist clinical decision making.

Many RIF investigations will not yield a definitive aetiology, and only some of the pathologies will have evidence-based management options. The quality of evidence is generally low and due to the lack of a sound scientific basis, an empirical approach to testing and treatment may be undertaken (Polanski et al.,2014). Over-inclusive and insufficiently stringent definitions of RIF make it difficult to uncover real pathology, hampering research in this field.

Incorporating all the factors which may impact the chance of pregnancy, based on logical and consistent criteria, which can be flexibly tailored to an individual patient, is an attractive prospect.

What is new or significant about this research?

In our research, we propose a novel concept called ‘Theoretical Cumulative Implantation Rate’ (TCIR), which can be calculated based on objective data. Each embryo transferred following an IVF cycle has a theoretical implantation rate (TIR) based on a number of factors. For example, female age, embryo quality, embryo grading, cycle details, chromosomal variation (if known) and inter-laboratory variability.

We propose that an individual’s theoretical cumulative implantation rate can be calculated by combining the implantation rate for each embryo transfer attempt. Clinicians can use a table which is included in the paper, to work out their patient’s individual theoretical implantation rate. More details on this approach can be viewed within the paper here.

What are the benefits of calculating a personalised TCIR?

Calculating a personalised Theoretical Cumulative Implantation Rate would be an improvement on existing clinical practice due to incorporating all of the factors which may impact the chance of pregnancy. This can in turn influence areas such as patient counselling, by for example, reassuring a patient that pregnancy is still likely. It would help with more consistent timing of initiating investigations, as well as assist in the planning of research studies on RIF.

What messages do you want to give to patients about RIF?

RIF is not a diagnosis in itself and we hope our algorithm can reduce unwarranted testing and therapeutic interventions, as many of these lack evidence of safety and effectiveness.

What are the next steps for this research?

Expert opinion and pilot studies to refine and utilise this definition could be pursued to test its utility in clinical practice.