OBJECTIVE: 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. DESIGN: Retrospective prediction modeling. SETTING: An outpatient university-affiliated IVF clinic. PATIENT(S): 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. INTERVENTION(S): None. 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. RESULT(S): 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%. CONCLUSION(S): 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.
OBJECTIVE: 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. DESIGN: Retrospective prediction modeling. SETTING: An outpatient university-affiliated IVF clinic. PATIENT(S): 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. INTERVENTION(S): None. 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. RESULT(S): 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%. CONCLUSION(S): We showed that IVFpatients 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.
Authors: Barbara Luke; Morton B Brown; Ethan Wantman; Judy E Stern; Valerie L Baker; Eric Widra; Charles C Coddington; William E Gibbons; Bradley J Van Voorhis; G David Ball Journal: Am J Obstet Gynecol Date: 2015-02-13 Impact factor: 8.661
Authors: Daniel J Kaser; Stacey A Missmer; Katharine F Correia; S Temel Ceyhan; Mark D Hornstein; Catherine Racowsky Journal: J Assist Reprod Genet Date: 2013-07-04 Impact factor: 3.412