Development of an IVF prediction model for donor oocytes: a retrospective analysis of 10 877 embryo transfers.

STUDY QUESTION: Can we develop a prediction model for the chance of a live birth following the transfer of an embryo created using donated oocytes? SUMMARY ANSWER: Three primary models that included patient, past treatment, and cycle characteristics were developed using Australian data to predict the chance of a live birth following the transfer of an embryo created using donated oocytes; these models were well-calibrated to the population studied, achieved reasonable predictive power and generalizability when tested on New Zealand data. WHAT IS KNOWN ALREADY: Nearly 9% of ART embryo transfer cycles performed globally use embryos created using donated oocytes. This percentage rises to one-quarter and one-half in same-sex couples and women aged over 45 years, respectively. STUDY DESIGN, SIZE, DURATION: This study uses population-based Australian clinical registry data comprising 9384 embryo transfer cycles that occurred between 2015 and 2021 for model development, with an external validation cohort of 1493 New Zealand embryo transfer cycles. PARTICIPANTS/MATERIALS, SETTING, METHODS: Three prediction models were compared that incorporated patient characteristics, but differed in whether they considered use of prior autologous treatment factors and current treatment parameters. We internally validated the models on Australian data using grouped cross-validation and reported several measures of model discrimination and calibration. Variable importance was measured through calculating the change in predictive performance that resulted from variable permutation. The best-performing model was externally validated on data from New Zealand. MAIN RESULTS AND THE ROLE OF CHANCE: The best-performing model had an internal validation AUC-ROC of 0.60 and Brier score of 0.20, and external validation AUC-ROC of 0.61 and Brier score of 0.23. While these results indicate ∼15% less discriminatory ability compared to models assessed on an autologous cohort from the same population the performance of the models was clearly statistically significantly better than random, demonstrated generalizability, and was well-calibrated to the population studied. The most important variables for predicting the chance of a live birth were the oocyte donor age, the number of prior oocyte recipient embryo transfer cycles, whether the transferred embryo was cleavage or blastocyst stage and oocyte recipient age. Of lesser importance were the oocyte-recipient parity, whether donor or partner sperm was used, the number of prior autologous embryo transfer cycles and the number of embryos transferred. LIMITATIONS, REASONS FOR CAUTION: The models had relatively weak discrimination suggesting further features need to be added to improve their predictive power. Variation in donor oocyte cohorts across countries due to differences such as whether anonymous and compensated donation are allowed may necessitate the models be recalibrated prior to application in non-Australian cohorts. WIDER IMPLICATIONS OF THE FINDINGS: These results confirm the well-established importance of oocyte age and ART treatment history as the key prognostic factors in predicting treatment outcomes. One of the developed models has been incorporated into a consumer-facing website (YourIVFSuccess.com.au/Estimator) to allow patients to obtain personalized estimates of their chance of success using donor oocytes. STUDY FUNDING/COMPETING INTEREST(S): This research was funded by the Australian government as part of the Medical Research Future Fund (MRFF) Emerging Priorities and Consumer Driven Research initiative: EPCD000007. L.R. declares personal consulting fees from Abbott and Merck, lecture fees from Abbott, receipt of an educational grant from Merck, past presidency of the Fertility Society of Australia & New Zealand and World Endometriosis Society and being a minor shareholder in Monash IVF Group (ASX:MVF). G.M.C. declares receipt of Australian government grant funding for the research study and the development and maintenance of the YourIVFSuccess website. O.F., J.N., and A.P. report no conflicts of interest. TRIAL REGISTRATION NUMBER: N/A.

How Much Is a Human Life Worth? A Systematic Review.

OBJECTIVES: To systematically review studies eliciting monetary value of a statistical life (VSL) estimates within, and across, different sectors and other contexts; compare the reported estimates; and critically review the elicitation methods used. METHODS: In June 2019, we searched the following databases to identify methodological and empirical studies: Cochrane Library, Compendex, Embase, Environment Complete, Informit, ProQuest, PubMed, Scopus, and Web of Science. We used the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines for reporting and a modified Consolidated Health Economic Evaluation Reporting Standards checklist to assess the quality of included studies. RESULTS: We identified 1455 studies, of which we included 120 in the systematic review. A stated-preference approach was used in 76 articles, with 51%, 41%, and 8% being contingent valuation studies, discrete-choice experiments, or both, respectively. A revealed-preference approach was used in 43 articles, of which 74% were based on compensating-wage differentials. The human capital approach was used in only 1 article. We assessed most publications (87%) as being of high quality. Estimates for VSL varied substantially by context (sector, developed/developing country, socio-economic status, etc), with the median of midpoint purchasing power parity-adjusted estimates of 2019 US$5.7 million ($6.8 million, $8.7 million, and $5.3 million for health, labor market, and transportation safety sectors, respectively). CONCLUSIONS: The large variation observed in published VSLs depends mainly on the context rather than the method used. We found higher median values for labor markets and developed countries. It is important that health economists and policymakers use context-specific VSL estimates. Methodological innovation and standardization are needed to maximize comparability of VSL estimates within, and across, sectors and methods.