Raeisi-Dehkordi, Hamidreza; Kummer, Stefanie; Raguindin, Peter Francis; Dejanovic, Gordana; Taneri, Petek Eylul; Cardona, Isabel; Kastrati, Lum; Minder, Beatrice; Voortman, Trudy; Marques-Vidal, Pedro; Dhana, Klodian; Glisic, Marija; Muka, Taulant (2022). Risk prediction models of natural menopause onset: a systematic review. The journal of clinical endocrinology and metabolism, 107(10), pp. 2934-2944. Oxford University Press 10.1210/clinem/dgac461
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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
To summarize risk prediction models of natural menopause onset and their performance.
DATA SOURCES AND STUDY SELECTION
Five bibliographic databases were searched up to March 2022. We included prospective studies on perimenopausal women or women in menopausal transition, that reported either the univariable or multivariable model for risk prediction of natural menopause onset.
DATA EXTRACTION
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 PROBAST (prediction model risk of bias assessment tool).
DATA SYNTHESIS
Of 8'132 references identified, we included 14 articles based on 8 unique studies comprising 9'588 women (mainly Caucasian) and 3'289 natural menopause events. All the included studies used onset of natural menopause (ONM) as outcome, while four studies predicted early ONM as well. Overall, there were 180 risk prediction models investigated, with age, anti-Müllerian hormone (AMH) and follicle-stimulating hormone (FSH) 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.
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