title: Pregnancy is predictable: a large-scale prospective external validation of the prediction of spontaneous pregnancy in subfertile couples.
abstract: BACKGROUND
  Prediction models for spontaneous pregnancy may be useful tools to select subfertile couples that have good fertility prospects and should therefore be counselled for expectant management. We assessed the accuracy of a recently published prediction model for spontaneous pregnancy in a large prospective validation study.
  
  
  METHODS
  In 38 centres, we studied a consecutive cohort of subfertile couples, referred for an infertility work-up. Patients had a regular menstrual cycle, patent tubes and a total motile sperm count (TMC) >3 x 10(6). After the infertility work-up had been completed, we used a prediction model to calculate the chance of a spontaneous ongoing pregnancy (www.freya.nl/probability.php). The primary end-point was time until the occurrence of a spontaneous ongoing pregnancy within 1 year. The performance of the pregnancy prediction model was assessed with calibration, which is the comparison of predicted and observed ongoing pregnancy rates for groups of patients and discrimination.
  
  
  RESULTS
  We included 3021 couples of whom 543 (18%) had a spontaneous ongoing pregnancy, 57 (2%) a non-successful pregnancy, 1316 (44%) started treatment, 825 (27%) neither started treatment nor became pregnant and 280 (9%) were lost to follow-up. Calibration of the prediction model was almost perfect. In the 977 couples (32%) with a calculated probability between 30 and 40%, the observed cumulative pregnancy rate at 12 months was 30%, and in 611 couples (20%) with a probability of >or=40%, this was 46%. The discriminative capacity was similar to the one in which the model was developed (c-statistic 0.59).
  
  
  CONCLUSIONS
  As the chance of a spontaneous ongoing pregnancy among subfertile couples can be accurately calculated, this prediction model can be used as an essential tool for clinical decision-making and in counselling patients. The use of the prediction model may help to prevent unnecessary treatment.
authors.affiliation: Department of Public Health, Erasmus MC, University Medical Center, Rotterdam, The Netherlands. j.w.vandersteeg@amc.uva.nl
authors.forename: J W
authors.initials: JW
authors.lastname: van der Steeg
authors.forename: P
authors.initials: P
authors.lastname: Steures
authors.forename: M J C
authors.initials: MJ
authors.lastname: Eijkemans
authors.forename: J D F
authors.initials: JD
authors.lastname: Habbema
authors.forename: P G A
authors.initials: PG
authors.lastname: Hompes
authors.forename: F J
authors.initials: FJ
authors.lastname: Broekmans
authors.forename: H J H M
authors.initials: HJ
authors.lastname: van Dessel
authors.forename: P M M
authors.initials: PM
authors.lastname: Bossuyt
authors.forename: F
authors.initials: F
authors.lastname: van der Veen
authors.forename: B W J
authors.initials: BW
authors.lastname: Mol
mesh_terms: D000328:Adult; D015331:Cohort Studies; D003944:Diagnostic Techniques, Obstetrical and Gynecological; D005260:Female; D006801:Humans; D007246:Infertility / Q000503:physiopathology*; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D011247:Pregnancy; D011336:Probability; D011446:Prospective Studies