TY - JOUR
T1 - Risk Prediction Models of Natural Menopause Onset
T2 - A Systematic Review
AU - Raeisi-Dehkordi, Hamidreza
AU - Kummer, Stefanie
AU - Francis Raguindin, Peter
AU - Dejanovic, Gordana
AU - Eylul Taneri, Petek
AU - Cardona, Isabel
AU - Kastrati, Lum
AU - Minder, Beatrice
AU - Voortman, Trudy
AU - Marques-Vidal, Pedro
AU - Dhana, Klodian
AU - Glisic, Marija
AU - Muka, Taulant
N1 - Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved.
PY - 2022/10/1
Y1 - 2022/10/1
N2 - Context: Predicting the onset of menopause is important for family planning and to ensure prompt intervention in women at risk of developing menopause-related diseases. Objective: We aimed to summarize risk prediction models of natural menopause onset and their performance. Methods: Five bibliographic databases were searched up to March 2022. We included prospective studies on perimenopausal women or women in menopausal transition that reported either a univariable or multivariable model for risk prediction of natural menopause onset. Two authors independently extracted data according to the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist. Risk of bias was assessed using a prediction model risk of bias assessment tool (PROBAST). Results: Of 8132 references identified, we included 14 articles based on 8 unique studies comprising 9588 women (mainly Caucasian) and 3289 natural menopause events. All included studies used onset of natural menopause (ONM) as outcome, while 4 studies also predicted early ONM. Overall, there were 180 risk prediction models investigated, with age, anti-Müllerian hormone, and follicle-stimulating hormone being the most investigated predictors. Estimated C-statistic for the prediction models ranged from 0.62 to 0.95. Although all studies were rated at high risk of bias mainly due to the methodological concerns related to the statistical analysis, their applicability was satisfactory. Conclusion: Predictive performance and generalizability of current prediction models on ONM is limited given that these models were generated from studies at high risk of bias and from specific populations/ethnicities. Although in certain settings such models may be useful, efforts to improve their performance are needed as use becomes more widespread.
AB - Context: Predicting the onset of menopause is important for family planning and to ensure prompt intervention in women at risk of developing menopause-related diseases. Objective: We aimed to summarize risk prediction models of natural menopause onset and their performance. Methods: Five bibliographic databases were searched up to March 2022. We included prospective studies on perimenopausal women or women in menopausal transition that reported either a univariable or multivariable model for risk prediction of natural menopause onset. Two authors independently extracted data according to the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist. Risk of bias was assessed using a prediction model risk of bias assessment tool (PROBAST). Results: Of 8132 references identified, we included 14 articles based on 8 unique studies comprising 9588 women (mainly Caucasian) and 3289 natural menopause events. All included studies used onset of natural menopause (ONM) as outcome, while 4 studies also predicted early ONM. Overall, there were 180 risk prediction models investigated, with age, anti-Müllerian hormone, and follicle-stimulating hormone being the most investigated predictors. Estimated C-statistic for the prediction models ranged from 0.62 to 0.95. Although all studies were rated at high risk of bias mainly due to the methodological concerns related to the statistical analysis, their applicability was satisfactory. Conclusion: Predictive performance and generalizability of current prediction models on ONM is limited given that these models were generated from studies at high risk of bias and from specific populations/ethnicities. Although in certain settings such models may be useful, efforts to improve their performance are needed as use becomes more widespread.
KW - onset of menopause
KW - perimenopause
KW - premenopausal women
KW - risk prediction model
UR - http://www.scopus.com/inward/record.url?scp=85139375071&partnerID=8YFLogxK
U2 - 10.1210/clinem/dgac461
DO - 10.1210/clinem/dgac461
M3 - Review article
C2 - 35908226
AN - SCOPUS:85139375071
SN - 0021-972X
VL - 107
SP - 2934
EP - 2944
JO - Journal of Clinical Endocrinology and Metabolism
JF - Journal of Clinical Endocrinology and Metabolism
IS - 10
ER -