BMC Geriatrics · 12 authors, 14 centres
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This retrospective multicenter study of 21,070 elderly patients with T2DM across six Chinese hospitals developed a seven-variable logistic regression model to predict osteoporosis risk. The model achieved an AUROC of 0.713 in the training set and 0.694 in external validation, incorporating age, sex, hypertension, coronary heart disease, cerebral infarction, hyperlipidemia, and surgical history.
A retrospective multicenter study enrolled 21,070 elderly patients with T2DM from six tertiary hospitals in Southwest China between 2012 and 2022. Univariate logistic regression, LASSO regression, and multivariate logistic regression were used to select predictors for an osteoporosis risk model. The incidence of osteoporosis was 7.01% (1,476/21,070). Seven predictors were selected: age, sex, hypertension, coronary heart disease, cerebral infarction, hyperlipidemia, and prior surgical history. The model yielded an AUROC of 0.713 in the training set, 0.716 in internal validation, and 0.694 in external validation. Calibration was assessed via bootstrap with 1,000 resamples, and decision curve analysis and clinical impact curve demonstrated clinical utility. Female sex carried the strongest association (OR=3.138). Limitations include the retrospective design, reliance on baseline clinical data alone, and potential unmeasured confounders such as medication use and disability status. The model is intended as a screening tool rather than a diagnostic device.
