Ovarian Response Prediction Index (ORPI) as a Predictor Tool for Ovarian Response and Clinical Pregnancy in IVF/ICSI Cycle: A Retrospective Cohort Study

Objectives In vitro fertilisation (IVF) cycles employ different ovarian stimulation protocols to promote follicle development and boost the number of embryos. Anticipating ovarian response is crucial for maximising treatment effectiveness and minimising complications from under- or over-stimulation. Age, anti-Müllerian hormone (AMH), and antral follicle count (AFC) are well-known assessors of ovarian response, which makes them established predictors of ovarian response. The Ovarian Response Prediction Index (ORPI) combines these factors to provide a more tailored approach to stimulation protocols, potentially enhancing IVF success rates. Material and Methods It was a retrospective cohort study that included 302 patients undergoing IVF/ICSI cycles between March 2021 and March 2023. Patients aged < 39 years, with a body mass index (BMI) of 20–30 kg/m², regular menstrual cycles, and no history of ovarian surgery or severe endometriosis were included. AMH levels were measured using chemiluminescent immunoassay, and AFC was assessed by transvaginal ultrasound. ORPI was calculated as (AMH × AFC)/age. Outcomes included total retrieved oocytes, metaphase II (MII) oocytes, and clinical pregnancy rates. Results Strong positive correlations were found between ORPI and both total oocytes (r = 0.714, p < 0.0001) and MII oocytes (r = 0.746, p < 0.0001). Univariate logistic regression indicated that age, AMH, AFC, and ORPI were significant predictors of obtaining ≥ 4 oocytes and MII oocytes (p < 0.05). Receiver operating characteristic curve analysis demonstrated that ORPI has excellent discriminative ability for predicting ≥4 oocytes (AUC = 0.907), ≥4 MII oocytes (AUC = 0.937), and clinical pregnancy (AUC = 0.822), with optimal cutoff values established. Conclusion ORPI, which combines age, AMH, and AFC, strongly predicts ovarian response and clinical pregnancy in IVF/ICSI cycles. It can help formulate personalised ovarian stimulation protocols, potentially enhancing patient counselling and treatment outcomes.