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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
A model for predicting age at menopause in white women
Lukas A Hefler1, Christoph Grimm, Eva-Katrin Bentz
1Department of Obstetrics and Gynecology, Medical University of Vienna, Vienna, Austria. lukas.hefler@meduniwien.ac.at
Objective:
To develop a model to predict the age at natural menopause and the risk for premenopausal hysterectomy.
Design:
Cross-sectional study.
Setting:
Multicenter study.
Patient(S):
A total of 1,345 white women.
Intervention(S):
Ten single nucleotide polymorphisms (SNPs) of seven estrogen (E)-metabolizing genes (i.e., catechol-O-methyltransferase, 17-beta-hydroxysteroid dehydrogenase type 1, cytochrome P-450 [CYP] 17, CYP1A1, CYP1B1, CYP19, and E receptor [ER] alpha) were analyzed by sequencing-on-chip-technology.
Main Outcome Measure(S):
Patients' reproductive and medical histories were ascertained and correlated to genotypes.
Result(S):
The model incorporates the number of full term pregnancies, the body mass index (BMI), a history of breast surgery, and the presence of CYP17 and CYP1B1-4 polymorphisms as well as the BMI to predict age at natural menopause and the risk for undergoing premenopausal hysterectomy.
Conclusion(S):
We present the first model to date, which can predict age at natural menopause and the risk for undergoing premenopausal hysterectomy based on genotype information and personal history.
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