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A revised prediction model for natural conception.

Alexandra J Bensdorp¹, Jan Willem van der Steeg², Pieternel Steures³

¹Centre for Reproductive Medicine, Academic Medical Centre, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands.

Reproductive Biomedicine Online

|April 25, 2017

Summary

A refined prediction model improves the identification of couples likely to conceive naturally. This updated model incorporates new factors, enhancing its accuracy in reproductive medicine for better fertility assessments.

Keywords:

Natural conception Prediction model Prognosis Subfertility

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Area of Science:

Reproductive Medicine
Clinical Prediction Models
Subfertility Research

Background:

Differentiating couples with favorable natural conception chances is crucial in reproductive medicine.
The characteristics of the subfertile population have evolved since the Hunault prediction model's development.
Existing models may require updates to reflect current subfertility trends.

Purpose of the Study:

To assess if additional predictors can refine the Hunault prediction model.
To extend the applicability of fertility prediction models.
To improve the accuracy of predicting natural conception in subfertile couples.

Main Methods:

Prospective cohort study of consecutive subfertile couples (unexplained and mild male subfertility).
Construction of a multivariable prediction model including Hunault predictors and new potential factors.
Observation of natural conception leading to ongoing pregnancy as the primary outcome.

Main Results:

The revised model incorporated all Hunault predictors plus seven new factors: BMI, cycle length, FSH, tubal status, pregnancy history, semen volume, and morphology.
The revised model demonstrated improved predictive accuracy compared to the Hunault model.
The c-statistic for the revised model was 0.71 (95% CI 0.69–0.73), significantly higher than the Hunault model's 0.59 (95% CI 0.57–0.61).

Conclusions:

The revised prediction model offers enhanced accuracy for predicting natural conception in subfertile couples.
Incorporating additional predictors significantly refines fertility prediction.
The updated model is more applicable to the current subfertile population.