Artikel ; Online: Risk Prediction Models of Natural Menopause Onset: A Systematic Review.
The Journal of clinical endocrinology and metabolism
2022 Band 107, Heft 10, Seite(n) 2934–2944
Abstract: 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 ... ...
| Abstract | 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. |
|---|---|
| Mesh-Begriff(e) | Anti-Mullerian Hormone ; Female ; Follicle Stimulating Hormone ; Humans ; Menopause ; Prospective Studies |
| Chemische Substanzen | Anti-Mullerian Hormone (80497-65-0) ; Follicle Stimulating Hormone (9002-68-0) |
| Sprache | Englisch |
| Erscheinungsdatum | 2022-07-31 |
| Erscheinungsland | United States |
| Dokumenttyp | Journal Article ; Systematic Review ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 3029-6 |
| ISSN | 1945-7197 ; 0021-972X |
| ISSN (online) | 1945-7197 |
| ISSN | 0021-972X |
| DOI | 10.1210/clinem/dgac461 |
| Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
Zusatzmaterialien
Kategorien
Verfügbar in ZB MED Köln/Königswinter
| Uh III Zs.134: Hefte anzeigen | Standort: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular ab Jg. 2022: Lesesaal (EG) |
Über subito bestellen
Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.