Predicting the outcomes of assisted reproductive technology treatments: A systematic review and quality assessment of prediction models

Ian Henderson, Michael Rimmer, Stephen Keay, Paul Sutcliffe, Khalid Khan, Ephiya Yasmin, Bassel H Al Wattar

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Predicting the outcomes of assisted reproductive technology (ART) treatments is desirable, but adopting prediction models into clinical practice remains limited. We aimed to review available prediction models for ART treatments by conducting a systematic review of the literature to identify the best performing models for their accuracy, generalisability and applicability.

Evidence review
We searched electronic databases (MEDLINE, EMBASE, and CENTRAL) until June 2020. We included studies reporting on the development or evaluation of models predicting the reproductive outcomes before (pre-ART) or after starting (Intra-ART) treatment in couples undergoing any ART treatment. We evaluated the models’ discrimination, calibration, type of validation, and any implementation tools for clinical practice.

We included 69 cohort studies reporting on 120 unique prediction models. Half the studies reported on pre-ART (48%) and half on intra-ART (56%) prediction models. The commonest predictors used were maternal age (90%), tubal factor subfertility (50%), and embryo quality (60%).

Only fourteen models were externally-validated (14/120, 12%) including eight pre-ART models (Templeton, Nelson, LaMarca, McLernon, Arvis, and the Stolwijk A/I,C,II models), and five intra-ART models (Cai, Hunault, van Loendersloot, Meijerink, Stolwijk B, and the McLernon post-treatment model) with a reported c-statistics ranging from 0.50 to 0.78. Ten of these models provided implementation tools for clinical practice with only two reported online calculators.

We identified externally validated prediction models that could be used to advise couples undergoing ART treatments on their reproductive outcomes. The quality of available models remains limited and more research is needed to improve their generalizability and applicability into clinical practice.
Original languageEnglish
JournalFertility and Sterility
Issue number1
Early online date3 Dec 2020
Publication statusPublished - Jan 2021


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