OBJECTIVE: To assess the predictive capacity of male and female characteristics on in vitro fertilization (IVF) outcome in couples with male subfertility and to construct an IVF prediction model. STUDY DESIGN: We performed a cohort study including all couples with male subfertility undergoing IVF. The main outcome measure was an ongoing pregnancy after IVF. The baseline characteristics from a couple including parameters of the semen-analysis were included in a univariable and multivariable analysis to construct a prediction model (model I). The addition of antisperm antibodies (ASA) and post-wash total motile count (TMC) to models I, II and III, respectively, were analyzed. RESULTS: We included 275 couples with male subfertility who underwent 473 IVF cycles with an ongoing pregnancy rate of 19% per cycle. A prediction model containing female age, secondary subfertility, percentage progressively motile sperm, percentage sperm with normal morphology, prewash total motile sperm count, bilateral tubal pathology, history of intrauterine insemination and cycle number was constructed (model I). Prediction with model I resulted in the selection of 95 couples, of whom 55 conceived (pregnancy rate of 28% per cycle). Use of the model with n ASA (Model II) resulted in the selection of 79 couples, of whom still 55 conceived (30% per cycle). CONCLUSION: In couples with male subfertility, the use of a prediction model including ASA improves the efficiency of IVF.
OBJECTIVE: To assess the predictive capacity of male and female characteristics on in vitro fertilization (IVF) outcome in couples with male subfertility and to construct an IVF prediction model. STUDY DESIGN: We performed a cohort study including all couples with male subfertility undergoing IVF. The main outcome measure was an ongoing pregnancy after IVF. The baseline characteristics from a couple including parameters of the semen-analysis were included in a univariable and multivariable analysis to construct a prediction model (model I). The addition of antisperm antibodies (ASA) and post-wash total motile count (TMC) to models I, II and III, respectively, were analyzed. RESULTS: We included 275 couples with male subfertility who underwent 473 IVF cycles with an ongoing pregnancy rate of 19% per cycle. A prediction model containing female age, secondary subfertility, percentage progressively motile sperm, percentage sperm with normal morphology, prewash total motile sperm count, bilateral tubal pathology, history of intrauterine insemination and cycle number was constructed (model I). Prediction with model I resulted in the selection of 95 couples, of whom 55 conceived (pregnancy rate of 28% per cycle). Use of the model with n ASA (Model II) resulted in the selection of 79 couples, of whom still 55 conceived (30% per cycle). CONCLUSION: In couples with male subfertility, the use of a prediction model including ASA improves the efficiency of IVF.
Authors: Maartje Cissen; Madelon van Wely; Irma Scholten; Steven Mansell; Jan Peter de Bruin; Ben Willem Mol; Didi Braat; Sjoerd Repping; Geert Hamer Journal: PLoS One Date: 2016-11-10 Impact factor: 3.240