Risk prediction models for postmenopausal osteoporosis: a systematic review and meta-analysis study
Abstract
BACKGROUND: Postmenopausal osteoporosis usually happens 5 ~ 10 years after menopause. Low awareness, low detection rates and high morbidity have prevented the possibility of early or preventive interventions, thus increasing the social and economic burden on families and societies. A reliable prediction model for postmenopausal osteoporosis has the potential to guide the prevention, but regarding the early prediction of postmenopausal osteoporosis without fracture, this field has not been sufficiently studied. Although many scholars have developed several prediction models to estimate the risk of postmenopausal osteoporosis without fractures, the evidence about the model quality and clinical applicability is scarce. METHOD: Nine databases (Medline, Embase, Web of science, CINAHL, The Cochrane Library, CNKI, SinoMed, Wanfang, VIP data) were systematically searched from 1 January 2014 to 1 May 2024. Two researchers independently extracted data using the CHARMS checklist and assessed bias using the PROBAST tool. The primary outcomes of interest were related to the model’s discriminative ability (assessed by pooled AUC values) and calibration performance (evaluated using calibration curves or the calibration intercept and slope). We performed meta-regression and sensitivity analyses to explore the influence of important factors, such as data sources, machine learning methods, and types of predictor variables, on the aforementioned. results. Additionally, subgroup analyses were…
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Evidence Hub reads this as a meta-analysis, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 4,417 participants or records.
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