Risk Prediction Models of Natural Menopause Onset: A Systematic Review.

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.

© The Author(s) 2022. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: [email protected].