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| 骨质疏松风险预测模型的构建：利用IOF骨质疏松一分钟测试 |
| Construction of an osteoporosis risk prediction model: utilizing the IOF one-minute osteoporosis risk test |
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| DOI：10.3969/j.issn.1006-7108.2024.04.004 |
| 中文关键词: 骨质疏松 骨质疏松症风险1 min测试 风险预测模型 |
| 英文关键词:osteoporosis one-minute osteoporosis risk test risk prediction model |
| 基金项目:深圳市卫健委临床研究项目（SZLY2018012） |
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| 中文摘要: |
| 目的 利用IOF骨质疏松症风险1 min测试，构建临床预测模型来作为骨质疏松的评估工具。方法 通过收集在本院骨科骨质疏松专病门诊就诊的470名对象的骨质疏松症风险1 min测试结果和骨质疏松患病数据，利用LASSO回归模型和多因素Logtisic回归分析患者发生骨质疏松的风险因素，并建立骨质疏松风险预测模型。结果 LASSO回归结果显示，父母是否有过骨折或者被诊断为骨质疏松症，父母曾经有人驼背，患者实际年龄超过40岁或BMI≤18.5 kg/m2等9个问题，被定义为发生骨质疏松风险的特征变量。多因素Logtisic回归结果表明，年龄（OR：1.12，95 % CI：1.09~1.15）、性别（OR：6.81，95 %CI：3.95~12.1）、是否在成年之后因轻摔发生骨折（OR：2.87，95 %CI：1.82~4.26）、每天运动量少于30 min（OR：1.59，95 %CI：1.29~2.18）与骨质疏松的高风险有关（P＜0.05）。利用Logtisic回归构建列线图模型，其预测骨质疏松发生的的ROC曲线下面积为值为0.856。通过校准曲线研究表明，其优势在于列线图模型的理想曲线与偏差校正曲线能够保持较好的一致性。从临床适用性来说，与“全干预”和“不干预”方案相比较，列线图模型的临床净收益率均表现出一定优势。结论 利用IOF提供的骨质疏松症风险1 min测试，构建出的风险预测模型有良好的预测效果，可以为临床医生提供参考和帮助。 |
| 英文摘要: |
| Objective To construct a clinical prediction nomogram model using the one-minute IOF osteoporosis risk test as an evaluation tool for osteoporosis. Methods The one-minute test results and the incidence of osteoporosis were collected from 470 patients in the osteoporotic clinic of our hospital. LASSO regression model and multifactor Logtisic regression were used to analyze the risk factors of osteoporosis, and the risk prediction model of osteoporosis was established. Results LASSO regression results showed that the characteristic variables of osteoporosis risk were when a parent had been diagnosed with osteoporosis or had a bone fracture after a light fall, one parent had a humpback, actual age was over 40 years old, whether a bone fracture occurred as an adult due to a light fall, and BMI≤18.5 kg/m2. Multivariate Logtisic regression results showed that age (OR:1.12, 95 %CI: 1.09-1.15), sex (OR:6.81, 95 %CI: 3.95-12.1), whether adult fractures occurred due to light falls (OR:2.87, 95 %CI: 1.82-4.26) and daily exercise less than 30 minutes (OR:1.59, 95 %CI: 1.29-2.18) were associated with a higher risk of osteoporosis (P< 0.05). logtisic regression was used to construct a nomogram model, and the area under ROC curve for predicting osteoporosis was 0.856. The calibration curve indicates that the deviation correction curve of the nomogram model is in good agreement with the ideal curve. The clinical net return rate of the nomogram model is higher than that of the "full intervention" and "no intervention" schemes, suggesting that the nomogram model has good clinical applicability. Conclusion By using the one-minute test of osteoporosis risk provided by IOF, the risk prediction model constructed has good prediction effect, which can provide greater reference and help for clinicians. |
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