title: Clinical prediction models to inform individualized decision-making in subfertile couples: a stratified medicine approach.
abstract: Infertility is defined as failure to conceive after 1 year of unprotected intercourse. This dichotomization into fertile versus infertile, based on lack of conception over 12-month period, is fundamentally flawed. Time to conception is strongly influenced by factors such as female age and whilst a minority of couples have absolute infertility (sterility), many are able to conceive without intervention but may take longer to do so, reflecting the degree of subfertility. This natural variability in time to conception means that subfertility reflects a prognosis rather than a diagnosis. Current clinical prediction models in fertility only provide individualized estimates of the probability of either treatment-independent pregnancy or treatment-dependent pregnancy, but do not take account of both. Together, prognostic factors which are able to predict natural pregnancy and predictive factors of response to treatment would be required to estimate the absolute increase in pregnancy chances with treatment. This stratified medicine approach would be appropriate for facilitating personalized decision-making concerning whether or not to treat subfertile patients. Published models are thus far of little value for decisions regarding when to initiate treatment in patients who undergo a period of, ultimately unsuccessful, expectant management. We submit that a dynamic prediction approach, which estimates the change in subfertility prognosis over the course of follow-up, would be ideally suited to inform when the commencement of treatment would be most beneficial in those undergoing expectant management. Further research needs to be undertaken to identify treatment predictive factors and to identify or create databases to allow these approaches to be explored. In the interim, the most feasible approach is to use a combination of previously published clinical prediction models.
authors: Medical Statistics Team, Division of Applied Health Sciences, University of Aberdeen, Polwarth Building, Foresterhill, Aberdeen, UK d.mclernon@abdn.ac.uk.
authors: D J
authors: DJ
authors: McLernon
authors: Department of Public Health, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands.
authors: E R
authors: ER
authors: te Velde
authors: Department of Public Health, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands.
authors: E W
authors: EW
authors: Steyerberg
authors: The Robinson Institute, School of Paediatrics and Reproductive Health, University of Adelaide, Adelaide, Australia.
authors: B W J
authors: BW
authors: Mol
authors: Division of Applied Health Sciences, University of Aberdeen, Polwarth Building, Foresterhill, Aberdeen, UK.
authors: S
authors: S
authors: Bhattacharya
mesh_terms: D003661:Decision Support Techniques; D006801:Humans; D007246:Infertility / Q000628:therapy*; D008962:Models, Theoretical; D027724:Reproductive Techniques, Assisted
keywords: epidemiology; female infertility; pregnancy; statistics