Personalized prediction of first-cycle in vitro fertilization success

ObjectiveTo test whether the probability of having a live birth (LB) with the first IVF cycle (C1) can be predicted and personalized for patients in diverse environments.DesignRetrospective validation of multicenter prediction model.SettingThree university-affiliated outpatient IVF clinics located in different countries.Patient(s)Using primary models aggregated from >13,000 C1s, we applied the boosted tree method to train a preIVF-diversity model (PreIVF-D) with 1,061 C1s from 2008 to 2009, and validated predicted LB probabilities with an independent dataset comprising 1,058 C1s from 2008 to 2009.Intervention(s)None.Main Outcome Measure(s)Predictive power, reclassification, receiver operator characteristic analysis, calibration, dynamic range.Result(s)Overall, with PreIVF-D, 86% of cases had significantly different LB probabilities compared with age control, and more than one-half had higher LB probabilities. Specifically, 42% of patients could have been identified by PreIVF-D to have a personalized predicted success rate >45%, whereas an age-control model could not differentiate them from others. Furthermore, PreIVF-D showed improved predictive power, with 36% improved log-likelihood (or 9.0-fold by log-scale; >1,000-fold linear scale), and prediction errors for subgroups ranged from 0.9% to 3.7%.Conclusion(s)Validated prediction of personalized LB probabilities from diverse multiple sources identify excellent prognoses in more than one-half of patients. To test whether the probability of having a live birth (LB) with the first IVF cycle (C1) can be predicted and personalized for patients in diverse environments. Retrospective validation of multicenter prediction model. Three university-affiliated outpatient IVF clinics located in different countries. Using primary models aggregated from >13,000 C1s, we applied the boosted tree method to train a preIVF-diversity model (PreIVF-D) with 1,061 C1s from 2008 to 2009, and validated predicted LB probabilities with an independent dataset comprising 1,058 C1s from 2008 to 2009. None. Predictive power, reclassification, receiver operator characteristic analysis, calibration, dynamic range. Overall, with PreIVF-D, 86% of cases had significantly different LB probabilities compared with age control, and more than one-half had higher LB probabilities. Specifically, 42% of patients could have been identified by PreIVF-D to have a personalized predicted success rate >45%, whereas an age-control model could not differentiate them from others. Furthermore, PreIVF-D showed improved predictive power, with 36% improved log-likelihood (or 9.0-fold by log-scale; >1,000-fold linear scale), and prediction errors for subgroups ranged from 0.9% to 3.7%. Validated prediction of personalized LB probabilities from diverse multiple sources identify excellent prognoses in more than one-half of patients.