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

Oleh:

Goldman, Randi H. ; Batsis, Maria ; Petrozza, John C. ; Souter, Irene

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Fertility and Sterility (keterangan: ada di ClinicalKey) vol. 101 no. 06 (Jun. 2014), page 1649–1655.
Topik: Ovulation induction; intrauterine insemination; clinical pregnancy rate; multiple pregnancy; predictive models

Ketersediaan

Perpustakaan FK Nomor Panggil: F02.K.2014.02 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

Lihat Detail Induk

Isi artikel

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.

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