Benjamin M. Lannon, M.D., Bokyung Choi, M.Sc., Michele R. Hacker, Sc.D., Laura E. Dodge, M.P.H., Beth A. Malizia, M.D., C. Brent Barrett, Ph.D., Wing H. Wong, Ph.D., Mylene W.M. Yao, M.D., Alan S. Penzias, M.D.
Vol 98, Issue 1 , Pages 69-76
To report and evaluate the performance and utility of an approach to predicting IVF–double embryo transfer (DET) multiple birth risks that is evidence-based, clinic-specific, and considers each patient's clinical profile.
Retrospective prediction modeling.
An outpatient university-affiliated IVF clinic.
We used boosted tree methods to analyze 2,413 independent IVF-DET treatment cycles that resulted in live births. The IVF cycles were retrieved from a database that comprised more than 33,000 IVF cycles.
Main Outcome Measure(s):
The performance of this prediction model, MBP-BIVF, was validated by an independent data set, to evaluate predictive power, discrimination, dynamic range, and reclassification.
Multiple birth probabilities ranging from 11.8% to 54.8% were predicted by the model and were significantly different from control predictions in more than half of the patients. The prediction model showed an improvement of 146% in predictive power and 16.0% in discrimination over control. The population standard error was 1.8%.
We showed that IVF patients have inherently different risks of multiple birth, even when DET is specified, and this risk can be predicted before ET. The use of clinic-specific prediction models provides an evidence-based and personalized method to counsel patients.
Read the full text at: http://www.fertstert.org/article/S0015-0282(12)00443-8/fulltext