Since 1992, all clinics performing ART in the United States have been required to submit annual success rates to the Centers for Disease Control and Prevention (379). Success rates of IVF vary from program to program. The most comprehensive assessment of the efficacy of ART programs in North America comes from the database of the Society for Assisted Reproductive Technology (SART). Information on SART and registered ART clinics are accessible by the public online. Because ICSI and IVF rates are similar, statistical analyses combine them for the annual SART reports. IVF success rates are largely dependent on maternal age, and data from recent studies, including those from SART, are summarized in Table 32.10 (380). SART statistics should not be used to compare IVF clinics since success rates are largely dependent on patient demographics, including the cause of infertility, which will be different for each individual clinic. Lifestyle factors affecting IVF success are discussed under “Causes of Infertility.” Since SART does not publish success rates for non-ART cycles, it can often be helpful for physicians to assist patients in comparing the success rates for clomiphene, gonadotropins, and IVF, although rates vary widely between studies (Table 32.11).
A new approach to predicting live birth probabilities focuses on the use of machine learning or the mining of clinic-specific IVF outcomes data to provide a personalized per-cycle prognosis that pertains to the clinical scenario of each patient (381). Brieﬂy, a live birth prediction model was developed by boosted tree analysis of baseline clinical data, uterine response to a patient’s first IVF treatment, and embryo developmental parameters, with no preselection of prognostic factors. The model predicted live birth outcomes in a subsequent IVF treatment cycle. Validation by an independent dataset and comparison with a control model that is based on chronological age alone showed that the boosted tree model was more than 1,000 times better in fitting new data and improved discrimination (i.e., the ability to discern among patients with different prognoses) by receiver–operator curve analysis. Approximately 60% of patients were found to have significantly different predicted live birth probabilities when compared to the use of age categories. Further testing across different clinics will be required to determine whether this approach may be generally valid and applicable. Nevertheless, the nonredundant and unique
Table 32.10 In Vitro Fertilization Success Rates
CPR, clinical pregnancy rate; Birth, live birth rate; SAB, spontaneous abortion, N/A, not available.. From refs. 379,380,420,421,423.
Table 32.11 Fertility Treatment Success Rates
CC, clomiphene citrate; TI, timed intercourse; IUI, intrauterine insemination; INJ, injectable gonadotropins; IVF, in vitro fertilization. From refs. 313, 379, 380, 418–422. (looks like these are the correct refs) contribution of the patient’s age to predicting outcomes was found to be limited if data pertaining to the response to gonadotropins and embryo development in the first IVF treatment were available. These findings may move prognostic counseling away from an age-centric paradigm and toward a more objective extraction of predictive value from simultaneous analysis of a wide array clinical factors.
Cessation of Therapy Patients must be accurately informed of estimated success rates and reasonable expectations for all therapeutic interventions. Patients with a very poor prognosis have a 2% to 5% chance of achieving a live birth with fertility therapy, and those with a futile prognosis have a 1% or less chance. Refusing or limiting therapy may be justified if the risk of intervention outweighs the potential benefits (382).