Pregnancy prediction models and eSET criteria for IVF patients--do we need more information?
OBJECTIVE
The purpose of the present study was to evaluate statistical prediction models and simple allocation criteria, based on predictors for pregnancy, as tools to identify a good prognosis group in a possible eSET setting.


METHODS
A pregnancy prediction model based on logistic regression models was generated by analysis of 1675 DET treatment cycles. The model was evaluated and compared to simple eSET allocation criteria.


RESULTS
Embryo quality, patient age, and basal FSH were identified as significant predictors (at 5% significance level) of pregnancy. Although comparable to previously generated models, the predictive ability of the present model was relatively poor and practically similar to simple allocation criteria based on age and embryo quality.


CONCLUSIONS
Existing prediction models, or simple allocation criteria, are limited in identifying good prognosis patients. Future studies of the applicability of improved pregnancy prediction models will need very comprehensive and detailed patient and embryo information.
Ottosen
Lars D M
LD
The Fertility Clinic, Department of Obstetrics and Gynaecology, Aarhus University Hospital, Skejby Sygehus, Brendstrupgaardsvej, DK-8200, Arhus N, Denmark. Lars.ottosen@ki.au.dk
Kesmodel
Ulrik
U
Hindkjaer
Johnny
J
Ingerslev
Hans Jakob
HJ
D000328:Adult; D000367:Age Factors; D004624:Embryo Transfer; D004622:Embryo, Mammalian / Q000033:anatomy & histology; D005260:Female; D005307:Fertilization in Vitro; D005640:Follicle Stimulating Hormone / Q000097:blood; D005544:Forecasting; D006801:Humans; D016015:Logistic Models; D008954:Models, Biological; D011247:Pregnancy; D018873:Pregnancy Rate
D005640:Follicle Stimulating Hormone