常熟市城乡居民骨质疏松症预测模型构建及验证

范佳; 王健; 沈耀亮; 何晟; 顾叶; 季立标; 钱骅雯; 朱肇基; 周文军; 叶宏伟

doi:10.16190/j.cnki.45-1211/r.2023.09.019

常熟市城乡居民骨质疏松症预测模型构建及验证

1. 苏州大学附属常熟医院(常熟市第一人民医院), 常熟 215500;
2. 无锡第二老年病医院, 无锡 214174;
3. 常熟市第三人民医院, 常熟 215500

基金项目:

基金课题:江苏省重点研发计划（社会发展）专项资金项目（No.BE2021673）；常熟市卫健委科技计划项目（No.csws 202110；No.csws201906）

详细信息

通讯作者:
叶宏伟，E-mail：yehongwei@foxmail.com

中图分类号: R681
计量
- 文章访问数: 68
- HTML全文浏览量: 2
- PDF下载量: 6
出版历程
- 收稿日期: 2023-03-06
- 网络出版日期: 2024-01-31
- 刊出日期: 2023-09-01

Construction and validation of a prediction model for osteoporosis in urban and rural residents in Changshu City

1. Changshu Hospital Affiliated to Soochow University/Changshu NO.1 People's Hospital, Changshu 215500, China;
2. Wuxi Second Geriatric Hospital, Wuxi 214174, China;
3. Changshu NO.3 People's Hospital, Changshu 215500, China

摘要

摘要 目的：构建常熟市城乡居民骨质疏松症（OP）的预测模型，并对其进行验证。方法：选取2018年4月至2021年3月常熟市第一人民医院影像科双能X线骨密度检测数据库中的2 270例人群作为研究对象，根据T值分为OP组（T≤-2.5，337例），骨量减少组（-2.5< T< -1.0，701例）和骨量正常组（T≥1.0，1 232例），比较3组一般人口学资料、临床特征、握力、血常规指标、肝功能、肾功能、血钙、血磷、25羟基维生素D[25（OH）D3]、甲状旁腺激素（PTH）水平，采用二元logistic回归分析OP的相关影响因素，运用R 语言rms 软件包绘制预测OP 的列线图模型，采用Bootstrap 法进行内部与外部验证，采用受试者工作特征曲线（ROC）分析OP列线图预测模型的预测能力。结果：OP组年龄、女性、糖尿病、认知障碍、睡眠时长≥9 h、碱性磷酸酶、PTH高于骨量减少组，规律补充钙剂、规律摄入含钙奶制品、握力、血钙、25（OH）D3低于骨量减少组（P< 0.05）；骨量减少组年龄、女性、糖尿病、认知障碍、睡眠时长≥9 h、碱性磷酸酶、PTH 高于骨量正常组，规律补充钙剂、规律摄入含钙奶制品、握力、血钙、25（OH）D3低于骨量正常组（P< 0.05）；二元logistic回归分析结果显示：年龄、女性、糖尿病、认知障碍、睡眠时长≥9 h、碱性磷酸酶、PTH 是OP 的相关危险因素，规律补充钙剂、规律摄入含钙奶制品、握力、血钙、25（OH）D3 是OP 的相关保护因素（P< 0.05）；基于以上各影响因素绘制预测OP的列线图模型显示其预测风险能力指数（C-index）为0.944，具有良好的区分度；ROC分析发现，预测OP的列线图模型的ROC下面积（AUC）为0.944（95%CI：0.923～0.960），提示预测OP的列线图模型区分度及预测能力均较好；采用Bootstrap法绘制内部校准图发现，校准曲线贴近标准曲线，提示预测OP的列线图模型与实际观测结果有较好的一致性；外部验证显示其预测死亡风险的AUC为0.950（95%CI：0.945～0.999），外部校准图发现校准曲线仍贴近标准曲线，提示在外部数据中仍具有较高的预测价值。结论：年龄、女性、糖尿病、认知障碍、睡眠时长≥9 h、碱性磷酸酶、PTH、规律补充钙剂、规律摄入含钙奶制品、握力、血钙、25（OH）D3均是OP的影响因素，基于以上因素构建的列线图模型呈现出较高的预测价值，能为本地区早期筛选高风险人群、针对性预防OP等提供参考。
- 骨质疏松症 /
- 影响因素 /
- 预测模型 /
- 预测能力
Abstract Objective:To construct a prediction model for osteoporosis (OP) in urban and rural residents in Changshu City and validate it.Methods:A total of 2, 270 cases from the imaging department of the First People’s Hospital of Changshu with dual-energy X-ray bone density testing database from April 2018 to March 2021 were selected as the study population and divided into OP group (T≤-2.5, 337 cases), bone loss group (-2.5< T< -1.0, 701 cases) and normal bone mass group (T≥-1.0, 1, 232 cases) according to T value, comparing the 3 groups’general demographic data, clinical characteristics, grip strength, routine blood indicators, liver function, renal function, blood calcium, blood phosphorus, 25 hydroxyvitamin D[25(OH)D3]and parathyroid hormone (PTH) levels.Binary logistic regression was used to analyze the influencing factors associated with OP, using the R language rms software package to draw a column line graph model for predicting OP.Bootstrap method was used for internal and external validation, and the receiver operating characteristic (ROC) curve was used to analyze the predictive ability of the OP column line graph prediction model.Results:The OP group had higher age, female, diabetes, cognitive impairment, sleep duration ≥9 h, alkaline phosphatase, and PTH than the bone loss group, and lower regular calcium supplementation, regular intake of calcium-containing dairy products, grip strength, blood calcium, and 25(OH)D3 than the bone loss group(P< 0.05); the bone loss group had higher age, female, diabetes, cognitive impairment, sleep duration ≥9 h, alkaline phosphatase, and PTH than the normal bone mass group, and lower regular calcium supplementation, regular intake of calcium-containing dairy products, grip strength, blood calcium, and 25(OH)D3 than the normal bone mass group(P< 0.05).Binary logistic regression analysis showed that age, female, diabetes, cognitive impairment, sleep duration ≥9 h, alkaline phosphatase, and PTH were risk factors associated with OP, and regular calcium supplementation, regular intake of calcium-containing dairy products, grip strength, blood calcium, and 25(OH)D3 were protective factors associated with OP (P< 0.05); column line graph model predicting OP drawn based on each of these influencing factors showed its predictive risk ability index (C-index) was 0.944, with good discrimination.The ROC analysis found that the area under the ROC (AUC) of the column line graph model for predicting OP was 0.944(95%CI:0.923-0.960), suggesting that the column line graph model for predicting OP had good differentiation and predictive ability; the internal calibration plot using Bootstrap method found that the calibration curve was close to the standard curve, suggesting that the column line graph model for predicting OP was in good agreement with the actual observation.The external validation showed that its AUC for predicting the risk of death was 0.950(95%CI:0.945-0.999), and the external calibration plot found that the calibration curve was still close to the standard curve, suggesting that it still had a high predictive value in the external data.Conclusion:Age, female, diabetes, cognitive impairment, sleep duration ≥9 h, alkaline phosphatase, PTH, regular calcium supplementation, regular intake of calcium-containing dairy products, grip strength, blood calcium, and 25(OH)D3 are all influencing factors of OP, and the column line graph model constructed based on the above factors shows high predictive value, which can provide reference for early screening of high-risk population and targeted prevention of OP in this region.
- osteoporosis /
- influencing factors /
- predictive model /
- predictive ability

HTML全文

参考文献(20)

[1]	LORENTZON M, ABRAHAMSEN B.Osteoporosis epidemiology using international cohorts[J].Current opinion in rheumatology, 2022, 34(5):280-288.
[2]	《中成药治疗优势病种临床应用指南》标准化项目组.中成药治疗骨质疏松症临床应用指南(2021年)[J].中国中西医结合杂志, 2022, 42(4):393-404. STANDARDIZED PROJECT TEAM OF THE CLINICAL APPLICATION GUIDE OF CHINESE PATENT MEDICINE IN THE TREATMENT OF DOMINANT DISEASES.Guidelines for clinical application of Chinese medicine for treatment of osteoporosis (2021)[J].Chinese journal of integrated traditional Chinese and western medicine, 2022, 42(4):393-404.
[3]	KHINDA R, VALECHA S, KUMAR N, et al.Prevalence and predictors of osteoporosis and osteopenia in postmenopausal women of Punjab, India[J].International journal of environmental research and public health, 2022, 19(5):2999.
[4]	BIMAL G, SAHHAR J, SAVANUR M, et al.Screening rates and prevalence of osteoporosis in a real-world, Australian systemic sclerosis cohort[J].International journal of rheumatic diseases, 2022, 25(2):175-181.
[5]	CARPINELLI MAZZI M, IAVARONE A, RUSSO G, et al.Mini-mental state examination: new normative values on subjects in Southern Italy[J].Aging clinical and experimental research, 2020, 32(4):699-702.
[6]	杨弦弦, 唐文革, 汤成, 等.重庆市40岁及以上居民骨质疏松症流行现状及影响因素分析[J].中国慢性病预防与控制, 2021, 29(10):741-745. YANG X X, TANG W G, TANG C, et al.Analysis of osteoporosis prevalence and influencing factors among residents aged 40 years and above in Chongqing[J].Chronic disease prevention and control in China, 2021, 29(10):741-745.
[7]	安苗苗, 金鹰, 赖俊, 等.贵阳市50 岁及以上体检人群骨质疏松症的检出率和影响因素分析[J].贵州医科大学学报, 2022, 47(3):268-272. AN M M, JIN Y, LAI J, et al.Analysis of osteoporosis detection rate and influencing factors in people aged 50 years and above in Guiyang[J].Journal of Guizhou medical university, 2022, 47(3):268-272.
[8]	LITKE R, PUISIEUX F, PACCOU J, et al.A retrospective study on the etiological exploration of osteoporosis in aging men in a French geriatric setting[J].Annales d'endocrinologie, 2022, 83(2):109-113.
[9]	莫惠梅, 肖丹丹, 符蝶, 等.海口市琼山区老年女性骨质疏松影响因素分析[J].华南预防医学, 2020, 46(5):561-563. MO H M, XIAO D D, FU D, et al.Analysis of the influencing factors of osteoporosis in elderly women in Qiongshan district, Haikou City[J].South China preventive medicine, 2020, 46(5):561-563.
[10]	FÖGER-SAMWALD U, KERSCHAN-SCHINDL K, BUTYLINA M, et al.Age related osteoporosis: targeting cellular senescence[J].International journal of molecular sciences, 2022, 23(5):2701.
[11]	MCCLUNG M R, CLARK A L.Osteoanabolic therapy for osteoporosis in women[J].Climacteric, 2022, 25(1):60-66.
[12]	任晓岚, 李明阳, 胡继宏, 等.甘肃省裕固族成年女性骨质疏松患病情况及影响因素分析[J].中国公共卫生, 2022, 38(2):235-240. R EN X L, LI M Y, HU J H, et al.Analysis of osteoporosis disease and influencing factors in Yugu adult women in Gansu Province[J].The Chinese public health, 2022, 38(2):235-240.
[13]	TANG S S, YIN X J, YU W, et al.Prevalence of osteoporosis and related factors in postmenopausal women aged 40 and above in China[J].Chinese journal of epidemiology, 2022, 43(4):509-516.
[14]	曹颖, 喻国, 王意君, 等.探讨老年骨质疏松症与认知功能障碍的相关性[J].中华老年心脑血管病杂志, 2021, 23(3):313-315. CAO Y, YU G, WANG Y J, et al.Exploring the correlation of osteoporosis and cognitive dysfunction in the elderly[J].Chinese journal of elderly cardiovascular and cerebrovascular diseases, 2021, 23(3):313-315.
[15]	谭坦, 孙建岭, 王丽丽, 等.社区中老年人骨质疏松与轻度认知障碍的相关性分析[J].中国慢性病预防与控制, 2022, 30(2):107-111. TAN T, SUN J L, WANG L L, et al.Correlation analysis of osteoporosis and mild cognitive impairment in the elderly in the community[J].Chronic disease prevention and control in China, 2022, 30(2):107-111.
[16]	EBRAHIMPUR M, SHARIFI F, SHADMAN Z, et al.Osteoporosis and cognitive impairment interwoven warning signs: community-based study on older adults-bushehr elderly health (BEH) program[J].Archives of osteoporosis, 2020, 15(1):140.
[17]	WU S, WANG P, GUO X, et al.The associations between different sleep patterns and osteoporosis based on the osteoporosis self-assessment tool for Asians[J].Arch osteoporos, 2020, 15(1):164.
[18]	SWANSON C M, BLATCHFORD P J, ORWOLL E S, et al.Association between objective sleep duration and bone mineral density in older postmenopausal women from the Study of Osteoporotic Fractures (SOF)[J].Osteoporosis international, 2019, 30(10):2087-2098.
[19]	ALONSO N, LARRAZ-PRIETO B, BERG K, et al.Lossof-function mutations in the ALPL gene presenting with adult onset osteoporosis and low serum concentrations of total alkaline phosphatase[J].Journal of bone and mineral research, 2020, 35(4):657-661.
[20]	蒋文艳, 吕静, 闫玉珠, 等.骨质疏松症患者血清骨代谢标志物分析与相关性研究[J].中国骨质疏松杂志, 2020, 26(4):546-549, 609. JIANG W Y, LYU J, YAN Y Z, et al.Analysis and correlation study of serum bone metabolic markers in patients with osteoporosis[J].Chinese journal of osteoporosis, 2020, 26(4):546-549, 609.