Construction and validation of an osteoporosis risk prediction model for middle-aged and elderly healthy physical examination population
10.3760/cma.j.cn115624-20250225-00172
- VernacularTitle:中老年健康体检人群骨质疏松风险预测模型的构建和验证
- Author:
Dongqing HUANG
1
;
Wei LI
1
;
Xiaozhen LI
1
;
Liping CHEN
1
;
Zhang′an WANG
1
;
Jia TAN
1
;
Xiaozhi HUANG
1
;
Yinghua LUO
1
Author Information
1. 广西医学科学院·广西壮族自治区人民医院健康管理中心，南宁 530021
- Publication Type:Journal Article
- Keywords:
Osteoporosis;
Physical examination;
Nomogram;
Prediction;
Osteopenia
- From:
Chinese Journal of Health Management
2025;19(5):355-361
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct and validate the risk prediction model of osteoporosis (OP) in the middle-aged and elderly healthy physical examination population.Methods:In this cross-sectional study, 18 030 middle-aged and elderly people with bone mineral density tested in Health Management Center of Guangxi Zhuang Autonomous Region Hospital from January 2020 to December 2022 were selected. The general data, physical examination index and biochemical blood index were collected. The subjects were divided into training set (12 621 cases) and validation set (5 409 cases) in a ratio of 7∶3 with the simple random sampling method. The variables were screened with minimum LASSO regression and logistic regression and the corresponding nomogram prediction model for the risk of osteoporosis in the middle-aged and elderly health examination population was established. The performance of the nomogram model was evaluated with the area under the receiver operating characteristic curve (ROC AUC), specificity, sensitivity, calibration curve (CAL), and decision curve (DCA).Results:The results of LASSO regression and multivariate logistic regression in training set showed that gender, age, body mass index, hip circumference, waist circumference, systolic blood pressure, total cholesterol, glutamyl transpeptidase and albumin/globulin ratio were the independent best predictors of OP risk in the middle-aged and elderly health examination population (all P<0.05). The ROC AUC-value of the training set was 0.895 (95% CI: 0.886-0.904), with a sensitivity of 87.25% and a specificity of 85.01%. The ROC AUC value of the validation set was 0.892 (95% CI: 0.886-0.898), with a sensitivity of 83.74% and a specificity of 82.46%. The CAL showed a C-index value of 0.790 in the training set and a C-index value of 0.784 in validation set. The CALs all showed deviation correction and obvious curves similar to the ideal line. DCA showed that when the OP risk threshold probability of the training set was 45%-93%, and the OP risk threshold probability of the validation set was 45%-92%, the nomogram model had better efficacy in predicting OP risk in the middle-aged and elderly physical examination population, and the two results were still relatively consistent. Both CAL and DCA showed good performance. Conclusion:This study establishes a practical prediction model for osteoporosis risk in the middle-aged and elderly population, it can provide an early warning for the timely detection of OP risk for the middle-aged and elderly people.
