Personalized prediction of first cycle in vitro fertilization success

Capsule:
Personalized first-cycle IVF live birth predictions can better identify patients with good-to-excellent prognoses.

Authors:
Bokyung Choi, Ph.D., Ernesto Bosch, M.D., Benjamin M. Lannon, M.D., Marie-Claude Leveille, Ph.D., Wing H. Wong, Ph.D., Arthur Leader, M.D., Antonio Pellicer, M.D., Alan S. Penzias, M.D., Mylene W. M. Yao, M.D.

Volume 99, Issue 7, Pages 1905-1911, June 2013

Abstract:

Objective:
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.

Design:
Retrospective validation of multicenter prediction model.

Setting:
Three 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.

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