- 學位論文
超音波工具監視骨質疏鬆疾病準確性及預測值研究－以接受者作業特徵曲線方法評估
Accuracy of Ultrasound-Based Bone Mass Density (BMD) for Classification and Yield of Osteoporosis－Evaluation by Recevicer Operating Characteristic (ROC) method
摘要
骨質疏鬆疾病確診的黃金標準工具為雙能量X光吸收測量儀(DXA)所測得骨質密度，當骨質密度T值低於-2.5SD則診斷為骨質疏鬆，但DXA在骨質疏鬆疾病的族群篩檢執行上並非一適合的工具。近年來，定量超音波工具在骨質密度篩檢上為一熱門工具，但討論其檢驗準確性的族群研究缺乏。 本研究蒐集了1999到2004年社區篩檢的骨質密度資料共41245筆，其中39512筆為非骨質疏鬆個案，1733筆為骨質疏鬆個案，得到定量超音波工具在族群資料的骨質密度T值小於等於-2SD時，有敏感度0.58(95%CI: 0.55-0.60)，特異度0.66 (95%CI: 0.64- 0.69)。考慮性別、年齡、BMI≧25、飲酒習慣、吸菸習慣、牛奶攝取頻率、咖啡攝取頻率、更年期狀態等骨質疏鬆的臨床危險因子對骨質密度的影響，帶入回歸模型以預測個人化的檢驗準確率，在此族群中有真陽性率預測範圍16.4%-91.6%，偽陽性率範圍4.7%-88.6%與陽性預測值範圍0.2%- 41.5%。在雙常態分布的假設下以ROC方法分析定量超音波工具在骨質密度範圍-8.0∼+8.0間可能的骨質密度臨界值共23個切點的檢驗準確率，在不同族群分類下畫出ROC曲線，並得到AUC範圍0.59-0.71。遵從族群的ROC分析結果，考慮臨床因子年齡、性別及更年期狀態還有變項與骨質密度間的相關性，發展出個人化的檢驗結果預測值並建構出個人化的定量超音波工具的ROC曲線。利用模擬資料進行骨密度檢測工具ORAI對於骨質疏鬆檢測能力之評估，發現高年齡、體重輕者、無賀爾蒙使用者與停經女性亦為骨質疏鬆之危險因子，此模擬結果與本研究前述發現相符，並得到AUC：0.81。 本研究在骨質密度變動下建構出個人化的定量超音波工具ROC曲線作為思考點，達到提升定量超音波工具在檢驗骨質疏鬆疾病的敏感性，同時降低檢驗偽陽性率，並發展以定量超音波工具作為骨質疏鬆篩檢工具的個人化目標。
並列摘要
The gold standard for osteoporosis diagnosis is bone mass density (BMD) detected by dual energy X-ray absorptiometry (DXA), however DXA is not a suitable tool on population screening. Recently, the quantitative ultrasound (QUS) tool was developing as a BMD screening tool in population screening, but the accuracy of QUS tool has not yet been identified. We collected population BMD screening data from 1999 to 2004, there are 41245 people with 39512 are non-osteoporosis and 1733 are diagnosed to be osteoporosis. Using QUS as BMD screening tool had sensitivity of 0.58(95%CI: 0.55-0.60) and sensitivity of 0.66 (95%CI: 0.64- 0.69) at cut-off point BMD T score≦-2SD.To predict individual accuracy of QUS tool we used regression model to analysis, dichotomous BMD value as dependent variable and risk factors based on FRAX which to be age、gender、BMI above or below 25、milk intake three days or more per week、coffee intake three days or more per week、before or after menopause as independent variables. ROC curve is to analysis the accuracy at possible cut-off point, and AUC range 0.59-0.71 at different population under binormal distribution. After modeling the effect of covariate on test result, we developed the predictive test result of individual and build individual ROC curve at each possible cut-off point of QUS tool. Take individual information in to consideration would elevate the accuracy of QUS tool. Estimated the performance by regression model had AUC 0.81, this result performs similarly well as the simulation result which to be 0.79 in previous study. In this, we develop QUS tool to become a more accurate screening tool in BMD screening.
參考文獻
被引用紀錄
延伸閱讀
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