Predictive model for live birth at 12 months after starting in-vitro fertilization treatment

Another potential limitation is that the live birth rate may be affected by confounding factors that potentially arise as the pregnancy progresses. For example, prematurity and pregnancy complications, such as hypertension, gestational diabetes, and intrauterine growth retardation could reduce the probability of live birth. Lastly, as with any retrospective study designed to analyse pre-existing data, the presence of biases cannot be excluded [36], To mitigate random error and statistical bias, we used a relatively large sample size (n = 2600) to construct this model. It is important for clinicians to manage patient expectations about then chances for successful outcomes at different stages of the r\T cycle. The main function of our model is to create a patient counselling tool that accounts for all relevant predictors of live birth starting from the tune of presentation to the day of embryo transfer. By collectively assessing these factors, clinicians will be able to provide patients with a more accurate prognosis than with pre¬treatment information alone. In places where access to I AT treatment is regulated by insurance reimbursement or legislative policies, patients with poor pre-treatment factors (e.g. advanced age or low ovarian reserve) are often demed r\T treatment due to then- low chance of achieving Eve birth and advised to consider donor IAT or adoption instead. Tins IS often contrary to the patients’ desire to have their own genetic offspring. From the clinician’s perspective, predicting reproductive outcomes is a dynamic process. For example, 35/2600 patients (1.3%) in our development cohort were aged >38 years and had AMH <1.25 ng/mL, but 22/35 (62.9%) had at least 1 good embryo for transfer. Therefore, the probability of live birth should be adjusted according to the patient’s response to IAT treatment up to the day of embryo transfer.