临床参数对绝经期伊朗妇女骨质疏松症的诊断价值

Ghazaleh Fazli, Malieheh Arab, Samaneh Saraeian, B. Ghavami, B. Nouri, Tayebeh Jahede Bozorgan
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According to standard curves, bone density of the femur and lumbar spine clarified osteoporosis status for each person. In the first step, univariate analysis with osteoporosis as the main outcome did use the chi-squared test, independent sample t-test. In the next step, factors with a P-value of less than 0.2 were included in the multivariate logistic model, and a predictive model was constructed. The goodness of fit test was applied to assess the model building. The area under the curve (AUC) calculated for the model and the best cut-point for risk of menopause according to the Youden index were determined. The significance level was set at 0.05 for statistical analysis. Statistical analyses did use the program SSPS version 17.0 (SPSS, Inc., Chicago, IL, USA). Results: In 317 cases of the present study, the mean age of the population was 52.46 years old. Ninety-nine (%31.2) of these asymptomatic women revealed osteoporosis on the BMD test. 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引用次数: 0

摘要

背景:骨质疏松症可能导致骨折的风险增加。骨质疏松症的诊断是正确治疗和降低骨折率的结果。目的:本研究旨在建立伊朗妇女骨质疏松症病例发现的预测模型。方法:设计了一项前瞻性诊断价值研究,在伊朗德黑兰的伊玛目侯赛因医学中心招募了317名50岁或以上的无症状妇女进行筛查,为期两年。数据是用普查方法收集的。所有病例均完成包括危险因素在内的问卷调查,并采用双能x线骨密度仪(DXA)测量骨密度。根据标准曲线,股骨和腰椎的骨密度明确了每个人的骨质疏松状况。在第一步中,以骨质疏松为主要结局的单因素分析确实使用了卡方检验、独立样本t检验。下一步,将p值小于0.2的因素纳入多元logistic模型,构建预测模型。采用拟合优度检验评价模型的建立。根据约登指数确定模型计算的曲线下面积(AUC)和绝经风险的最佳切点。显著性水平设为0.05进行统计学分析。统计分析使用SSPS 17.0版程序(SPSS, Inc., Chicago, IL, USA)。结果:本组317例患者的平均年龄为52.46岁。这些无症状的妇女中有99人(31.2%)在骨密度测试中发现骨质疏松症。骨质疏松症的危险因素是年龄和家族史,而BMI、胎次和绝经年龄是骨质疏松症的保护因素。骨质疏松症预测模型构建为:(年龄× 0.149) +(家族史× 0.963) - (BMI × 0.088) -(绝经年龄× 0.097) -胎次× 0.80。选择优登法预测骨质疏松风险的最优截断值= 0.336。结论:伊朗50岁以上人群BMD检测可检出骨质疏松阳性病例至少23.8%。伊朗50岁以上人群骨密度检测可发现骨质疏松阳性病例至少23.8%。本研究建立了基于年龄、家族史、绝经年龄和胎次的骨质疏松概率模型,可以预测女性的骨质疏松风险。
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Diagnostic Value of Clinical Parameters for the Prediction of Osteoporosis in Menopause Iranian Women
Background: Osteoporosis might result in an increased risk of bone fracture. Diagnosis of osteoporosis results in proper treatment and reduction of fracture rate. Objectives: This study aimed to construct a predictive model of osteoporosis case finding in Iranian women. Methods: A prospective diagnostic value study was designed, enrolling 317 asymptomatic women 50 years old or more referred for screening, at the Imam Hossein Medical Center, Tehran, Iran, for two years. The data was collected with the census method. A questionnaire including risk factors was completed, and bone mass densitometry (BMD) was done by the dual-energy X-ray absorptiometry (DXA) method in all cases. According to standard curves, bone density of the femur and lumbar spine clarified osteoporosis status for each person. In the first step, univariate analysis with osteoporosis as the main outcome did use the chi-squared test, independent sample t-test. In the next step, factors with a P-value of less than 0.2 were included in the multivariate logistic model, and a predictive model was constructed. The goodness of fit test was applied to assess the model building. The area under the curve (AUC) calculated for the model and the best cut-point for risk of menopause according to the Youden index were determined. The significance level was set at 0.05 for statistical analysis. Statistical analyses did use the program SSPS version 17.0 (SPSS, Inc., Chicago, IL, USA). Results: In 317 cases of the present study, the mean age of the population was 52.46 years old. Ninety-nine (%31.2) of these asymptomatic women revealed osteoporosis on the BMD test. Age and family history of osteoporosis were risk factors, and BMI, parity, and menopause age were protective factors for osteoporosis. Constructed model of osteoporosis prediction was as follows: (age × 0.149) + (family history × 0.963) - (BMI × 0.088) - (menopause age × 0.097) - (parity × 0.80). Optimal cutoff = 0.336 based on Youden method was chosen to predict the risk of osteoporosis. Conclusions: BMD test in Iran in more than 50 years old might find positive osteoporotic cases in at least 23.8%. BMD test in Iran in more than 50 years old might find positive osteoporotic cases in at least 23.8%. A model of osteoporosis probability constructed based on age, family history, menopause age, and parity in the present study can predict women at risk of osteoporosis.
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