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In vitro fertilization (IVF) is a form of assisted reproductive technology where an egg is fertilized with sperm in a controlled laboratory environment before transferring the resulting embryo into the uterus. This process is designed to help individuals and couples experiencing difficulties conceiving.
The IVF process begins with ovarian stimulation, during which reproductive endocrinologists prescribe hormonal medications to stimulate the ovaries to produce multiple eggs instead of the single...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Pretreatment prediction for IVF outcomes: generalized applicable model or centre-specific model?

Jiali Cai1,2, Xiaoming Jiang1,2, Lanlan Liu1,2

  • 1Reproductive Medicine Centre, The Affiliated Chenggong Hospital of Xiamen University, Xiamen, Fujian, China.

Human Reproduction (Oxford, England)
|November 23, 2023
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UK/US IVF pretreatment prediction models show similar discriminatory power in Chinese populations but tend to underestimate predictions. Newer, localized models may offer improved calibration for better clinical application.

Keywords:
IVFlive birthpredictionpretreatmentvalidation

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Area of Science:

  • Reproductive Medicine
  • Biostatistics
  • Health Services Research

Background:

  • Pretreatment prediction models for in vitro fertilization (IVF) are crucial for estimating live birth probabilities.
  • Existing models are primarily based on UK/US populations, limiting their applicability in diverse global regions.
  • The performance of these established models in non-Western populations remains largely unexamined.

Purpose of the Study:

  • To evaluate the performance of established UK/US-based IVF pretreatment prediction models in a Chinese population.
  • To compare the discriminatory power and calibration of external models with newly developed, center-specific models.
  • To identify key predictors for IVF success in the studied cohort.

Main Methods:

  • External validation of four UK/US models (McLernon 2016, Luke, Dhillon, McLernon 2022) using a dataset of 26,382 Chinese patients.
  • Development of center-specific models using XGBoost, Lasso, and generalized linear models.
  • Comparison of model performance using Area Under the Curve (AUC) for discriminatory power and calibration curves.

Main Results:

  • External models demonstrated reasonable discriminatory power, with AUCs ranging from 0.67 to 0.69.
  • A center-specific model achieved a higher AUC of 0.71, identifying age, infertility duration, and endocrine parameters as key predictors.
  • All external models consistently underestimated live birth predictions, with the rescaled McLernon 2022 model showing the best calibration (Slope 1.12, intercept 0.06).

Conclusions:

  • Established UK/US IVF prediction models retain useful discriminatory power in Chinese populations but require calibration adjustments.
  • Center-specific models, particularly those incorporating relevant local predictors, may offer superior calibration and predictive accuracy.
  • Further research with larger, multi-center datasets and inclusion of newer biomarkers like AMH is warranted to refine IVF prediction models globally.