Prediction of spontaneous conception based on semen parameters

Area of Science:

• Reproductive Medicine
• Andrology
• Urology

Background:

• Accurate male fertility prognosis using semen analysis remains challenging.
• Standard semen parameters and functional tests are crucial for assessing reproductive potential.

Purpose of the Study:

• To identify optimal semen parameters for predicting male fertility.
• To develop a multiple regression model for predicting spontaneous conception using semen analysis.

Main Methods:

• Evaluated 113 fertile and 109 subfertile men.
• Utilized receiver operating characteristic (ROC) curves for individual parameter analysis.
• Employed logistic regression to build predictive models for conception.

Main Results:

• A 12-parameter model achieved 90.3% accuracy in predicting conception and 90.8% for lack of conception.
• A simplified 4-parameter model (sperm concentration, motility, morphology, HOS test) predicted fertility with 84.1% accuracy and subfertility with 88.1% accuracy.
• Semen analysis parameters significantly correlate with in vivo conception rates.

Conclusions:

• Semen analysis, including standard and functional tests, provides valuable insights into male fertility status.
• Developed predictive models offer improved accuracy for male fertility prognosis.
• These findings can aid clinicians in managing male infertility cases more effectively.