Predictors of live birth in women with a history of biochemical pregnancies after assisted reproduction treatment. (May 2020) - Tarín, Juan J.; Pascual, Eva; Gómez, Raúl; García-Pérez, Miguel A.; Cano, Antonio
Highlights: A history of biochemical pregnancies decreases live birth chances in IVF/ICSI cycles. The proportion of these women increases as number of IVF/ICSI cycles rises. Women that have a live birth do not exhibit a high assisted fecundity. A predictive model of live birth shows excellent discriminatory capacity. Abstract: Objectives: To ascertain whether women with a history of biochemical pregnancies (BPs) in in-vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) treatment cycles have decreased chances of live birth (LB); and (2) to build a predictive model for LB in this particular population of infertile women. Methods: In order to achieve the first objective, data from 1536 women that had a LB using autologous fresh or frozen embryos, or dropped out of further IVF/ICSI treatments after completing one to three unsuccessful treatment cycles were retrospectively analyzed. A subpopulation of 90 women that experienced one or more BPs in our assisted reproduction unit were selected to build a predictive logistic regression model for LB. Results: LB percentages significantly decreased from a value of 55.3 % in women with no history of previous BPs to 30.9 % and 11.1 % in women that displayed a history of one or more than one BP, respectively. Three out of 35 selected potential predictors were finally included into the model: "number of the last embryo transfer cycle resulting in a BP", "women's age", and "oligo-, astheno-, and/or teratozoospermia". The value of the c-statistic was 0.819 (asymptotic 95 % CI: 0.724-0.913). The model adequately fitted the data with no significant over or underestimation of predictor effects. Conclusion: (1) A history of BPs is negatively associated with later chance of LB in women undergoing a series of IVF/ICSI treatment cycles; and (2) LB probability of women with a history of BPs can be predicted using a model with excellent discriminatory capacity.