Time to pregnancy: a computational method for using the duration of non-conception for predicting conception.

Area of Science:

• Reproductive Medicine
• Biostatistics
• Computational Biology

Background:

• Determining the optimal time for fertility investigation and treatment after conception failure is a significant clinical challenge.
• Couple fertility is inherently uncertain, necessitating probabilistic modeling for accurate prediction.

Purpose of the Study:

• To develop and validate a computational approach for assessing future pregnancy chances based on attempted conception cycles.
• To evaluate the robustness of the traditional 12-cycle infertility threshold and explore its adaptability across different populations.
• To model the impact of female reproductive aging on fertility timelines and intervention timing.

Main Methods:

• Utilized a computational method employing Bayes' theorem to model fertility uncertainty as a probability distribution.
• Generated posterior distributions for conception probability conditional on the number of previous cycles of attempted conception.
• Applied the computational method to analyze time-to-pregnancy data and model female reproductive aging effects.

Main Results:

• The computational model demonstrated good fits with observed time-to-pregnancy data across various populations.
• The 12-cycle non-conception standard for subfertility was found to be reasonably robust, with potential adjustments based on population and specific fertility metrics.
• For women over 35, earlier investigation and treatment are indicated; ignoring age-related decline can delay subfertility assessment by up to two cycles.

Conclusions:

• The developed computational method provides a flexible framework for evaluating fertility and informing clinical decisions.
• The timing for initiating fertility investigations may need to be individualized based on demographic factors and specific fertility goals.
• Reproductive aging significantly impacts fertility timelines, underscoring the need for timely intervention in older women.