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Fertil Steril. 2013 Jun;99(7):1905-11. doi: 10.1016/j.fertnstert.2013.02.016. Epub 2013 Mar 21.

Personalized prediction of first-cycle in vitro fertilization success.

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1
Univfy, Los Altos, California 94022, USA.

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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