| Objective To explore the relationship between reproductive characteristics and osteoporosis in postmenopausal women, to identify the influencing factors, and to establish an individualized prediction model. Methods The general data, OSTA score, reproductive characteristics, and bone mineral density data of 350 postmenopausal women were retrospectively analyzed. The independent sample t test and binary logistic regression analysis were used to determine the predictors. The predictors were used to construct the prevalence of postmenopausal osteoporosis and the nomogram. The internal risk was validated. Results Age, BMI, age of menarche, age of menopause, and number of pregnancies were correlated with the occurrence of postmenopausal osteoporosis. The binary logistic regression analysis showed that the accuracy of the incidence of osteoporosis predicted with age, BMI, age of menarche, age of menopause, and number of pregnancies was 72.6%, which was higher than the accuracy of predicting the incidence of osteoporosis by OSTA score (69.4%). Five factors as independent predictors of postmenopausal osteoporosis in women were merged into a nomogram. The constructed nomogram showed good calibration and discrimination. The AUC was 0.8141. Conclusion We have designed a prediction model for the risk of osteoporosis in postmenopausal women. By combining age, BMI, age of menarche, age of menopause, and number of pregnancies in postmenopausal women, we can predict the disease of individuals, which can be used conveniently and rapidly for postmenopausal women. The model helps to predict the risk of osteoporosis in postmenopausal women and to decide whether patients need further osteoporosis related tests.The data collected in the study show that this model has higher accuracy. |