Objective:

To develop and validate a predictive model for poor ovarian response (POR) in patients undergoing Assisted Reproductive Technology (ART).

Approach:

Key Findings:

• The final model identified AMH, basal FSH, BMI, and antral follicle count as significant predictors of POR.
• The model exhibited high discriminatory ability and good calibration across training, internal validation, and test datasets.
• A nomogram was developed from the model for individualized risk prediction.

Interpretation:

The stepwise logistic regression model provides predictive accuracy and clinical utility for managing ART.

Limitations:

• The study is retrospective and may have inherent biases.
• Only one stimulation cycle per woman was included, which may limit generalizability.

Conclusion:

The study presents a predictive model for POR in ART.

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