Literature DB >> 24690238

Patient-specific predictions of outcome after gonadotropin ovulation induction/intrauterine insemination.

Randi H Goldman1, Maria Batsis2, John C Petrozza3, Irene Souter3.   

Abstract

OBJECTIVE: To use patient-specific and cycle-specific characteristics to predict clinical pregnancy, multiple pregnancy, and spontaneous abortion rates after gonadotropin ovulation induction (OI)/IUI.
DESIGN: Retrospective chart review.
SETTING: Academic fertility center. PATIENT(S): A total of 1,438 women who underwent 3,375 gonadotropin OI/IUI cycles. INTERVENTION(S): Individual and cycle-specific characteristics were evaluated to determine predictors of the rates of clinical pregnancy, multiple pregnancy, and spontaneous abortion. Logistic regression using individual parameters was used to create predictive models. MAIN OUTCOME MEASURE(S): Clinical pregnancy (CPR), multiple pregnancy (MPR), and spontaneous abortion rates (SABR). RESULT(S): Multiple predictors were identified for CPR, MPR, and SABR. The presence of at least two follicles ≥ 13 mm at ovulation trigger significantly increased CPR (odds ratio [OR], 95% confidence interval [CI] = 1.45, 1.18-1.78) and MPR (OR, 95% CI = 5.17, 2.16-12.41). An E2 level >400 pg/mL significantly increased MPR (OR, 95% CI = 9.54, 2.31-39.42). Logistic regression models were developed for individualized predictions of outcome. CONCLUSION(S): Regression analysis reveals the patient and cycle-specific characteristics that are significant predictors of CPR, MPR, and SABR after OI/IUI. Logistic models using significant or nearly significant predictors for CPR, MPR, and SABR offer improved predictive power relative to simpler models, and allow for the development of a risk calculator for personalized patient counseling.
Copyright © 2014 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Ovulation induction; clinical pregnancy rate; intrauterine insemination; multiple pregnancy; predictive models

Mesh:

Substances:

Year:  2014        PMID: 24690238     DOI: 10.1016/j.fertnstert.2014.02.028

Source DB:  PubMed          Journal:  Fertil Steril        ISSN: 0015-0282            Impact factor:   7.329


  2 in total

1.  CNFE-SE: a novel approach combining complex network-based feature engineering and stacked ensemble to predict the success of intrauterine insemination and ranking the features.

Authors:  Sima Ranjbari; Toktam Khatibi; Ahmad Vosough Dizaji; Hesamoddin Sajadi; Mehdi Totonchi; Firouzeh Ghaffari
Journal:  BMC Med Inform Decis Mak       Date:  2021-01-02       Impact factor: 2.796

2.  A personalized medicine approach to ovulation induction/ovarian stimulation: development of a predictive model and online calculator from level-I evidence.

Authors:  Irene Souter; Fangbai Sun; Heping Zhang; Michael P Diamond; Richard S Legro; Robert A Wild; Karl R Hansen; Nanette Santoro
Journal:  Fertil Steril       Date:  2022-02       Impact factor: 7.329

  2 in total

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