[1]王燕,李文静,吕红芝,等.骨质疏松骨折临床预测模型的建立与评价[J].中华老年骨科与康复电子杂志,2022,(06):350-360.[doi:10.3877/cma.j.issn.2096-0263.2022.06.006]
 Wang Yan,Li Wenjing,Lyu Hongzhi,et al.Establishment and Evaluation of Clinical Predictive Model for Osteoporotic fracture[J].Chin J Geriatr Orthop Rehabil(Electronic Edition),2022,(06):350-360.[doi:10.3877/cma.j.issn.2096-0263.2022.06.006]
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骨质疏松骨折临床预测模型的建立与评价()
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中华老年骨科与康复电子杂志[ISSN:1674-3911/CN:11-9292/R]

卷:
期数:
2022年06期
页码:
350-360
栏目:
骨质疏松
出版日期:
2022-12-05

文章信息/Info

Title:
Establishment and Evaluation of Clinical Predictive Model for Osteoporotic fracture
作者:
王燕1李文静1吕红芝1李晶2王娟1任川1张英泽1
050051 石家庄,河北省骨科研究所,河北省骨科生物力学重点实验室,河北医科大学第三医院创伤急救中心1;统计室2
Author(s):
Wang Yan1 Li Wenjing1 Lyu Hongzhi1 Li Jing2 Wang Juan1 Ren Chuan1 Zhang Yingze1.
1Hebei Orthopaedic Research Institute, Department of Hebei Orthopaedic Biomechanics key Laboratory, Trauma first Aid Center of the Third Hospital of Hebei Medical University; 2Statistics Office of the Third Hospital of Hebei Medical University, Shijiazhuang 050051, China
关键词:
骨质疏松 骨折 临床预测模型 列线图
Keywords:
Osteoporosis Fracture Clinical predictive model Nomogram
DOI:
10.3877/cma.j.issn.2096-0263.2022.06.006
文献标志码:
A
摘要:
目的 探讨老年人骨质疏松骨折的影响因素并建立预测模型。方法 回顾性收集2021年1月1日至2021年12月31日河北医科大学第三医院收治的65岁以上骨折患者,以7:3的比例随机分为建模组和内部验证组,另外收集2022年1月1日至2022年3月31日河北医科大学第三医院收治的65岁以上骨折患者作为外部验证组,通过病案查询获取资料。采用多因素logistic回归分析筛选骨质疏松骨折的影响因素,在建模人群中构建骨质疏松骨折临床预测模型,分别进行整体、内部和外部验证,对模型的区分度、校准度和临床有效性进行评价,并绘制相应列线图。结果 共有2 512例骨折患者符合纳入排除标准,年龄(75.2±7.7)岁,男936例(37.3%),女1 576例(62.7%),分为建模组(n=1 751)和内部验证组(n=761)。外部验证组共纳入599例骨折患者。根据多因素logistic回归分析,女性(OR=1.944,95% CI:1.569,2.409)、75岁以上人群(75~84岁,OR=2.150,95% CI:1.718,2.714;85~94岁,OR=4.285,95% CI:2.936,6.198)、BMI<18.5 kg/m2(OR=1.885,95% CI:1.180,3.087)、高血压(OR=1.441,95% CI:1.168,1.779)、低蛋白血症(OR=2.484,95% CI:1.987,3.113)是骨质疏松骨折的独立危险因素,将其纳入到预测模型中并进行评价。模型的AUC值为0.734(95% CI:0.711,0.757),在内部验证组和外部验证组分别为0.717(95% CI:0.681,0.734)和0.717(95% CI:0.656,0.759)。整体、内部及外部验证的校准图均显示模型具有良好的校准度,且H-L检验结果P值均>0.05。DCA曲线提示,阈概率在0.3~0.8时,模型的临床有效性最佳。结论 女性、75岁以上、BMI<18.5 kg/m2、患有高血压和低蛋白血症人群易发生骨质疏松骨折,依据本研究建立的临床预测模型可以较好的预测骨质疏松骨折的发病风险。 【关键词】 骨质疏松; 骨折; 临床预测模型; 列线图
Abstract:
Objective To explore the influencing factors of osteoporotic fracture in the elderly and establish a predictive model. Methods The fracture patients over 65 years old treated in the third Hospital of Hebei Medical University from January 1, 2021 to December 31, 2021 were collected retrospectively and randomly divided into modeling group and internal verification group at the proportion of 7:3. In addition, fracture patients over 65 years old treated in the third Hospital of Hebei Medical University from January 1, 2022 to March 31, 2022 were collected as external verification group, and the data were obtained through medical record inquiry. Multivariate logistic regression analysis was used to screen the influencing factors of osteoporotic fracture. A clinical prediction model of osteoporotic fracture was constructed in the modeling population, which was verified as a whole, internally and externally. The differentiation, calibration and clinical effectiveness of the model were evaluated, and the corresponding diagrams were drawn. Results A total of 2, 512 patients with fracture met the inclusion exclusion criteria, including 936 males (37.3%) and 1, 576 females (62.7%). They were divided into two groups: development group (n=1, 751) and internal validation group (n=761). A total of 599 patients with fracture were included in the external validation group. According to multivariate logistic regression analysis, female (OR=1.944, 95% CI: 1.569, 2.409)、age≥75 years old (75-84 years old, OR=2.150, 95% CI: 1.718, 2.714; 85-94 years old, OR=4.285, 95% CI: 2.936, 6.198)、BMI<18.5 kg/m2(OR=1.885, 95% CI: 1.180, 3.087)、hypertension (OR=1.441, 95% CI: 1.168, 1.779) and hypoproteinemia (OR=2.484, 95% CI: 1.987, 3.113) were independent risk factors for osteoporotic fracture, which were included in the predictive model and evaluated. The AUC value of the model was 0.734 (95% CI: 0.711, 0.757), 0.717 (95% CI: 0.681, 0.734) in the internal verification group and 0.717 (95% CI: 0.656, 0.759) in the external verification group. The calibration maps verified by the development group, internal and external validation group all show that the prediction ability of the model is excellent, and the P value of H-L test are all higher than 0.05. The DCA curve indicated that the clinical validity of the model was the best when the threshold probability was 0.3-0.8. Conclusions Women, over 75 years old, BMI<18.5 kg/m2, people with hypertension and hypoproteinemia are prone to osteoporotic fracture. The clinical prediction model established in this study can better predict the risk of osteoporotic fracture.

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备注/Memo

备注/Memo:
基金项目:河北省2021年度医学科学研究课题(20210552)
更新日期/Last Update: 2023-01-04