背景：隨著社會經濟結構和生活型態、飲食方式的大幅改變，以及老齡化人口的大量增加，控制高度流行的心血管代謝疾病(代謝症候群、心血管疾病、慢性腎臟疾病、糖尿病)已成為全世界共同的優先問題。透過行動醫院到點健康篩檢服務已在台灣實施多年，被證明是一個有效的方法，然如何以非實驗室為基礎的風險評估工具來識別並早期介入心血管代謝疾病的高風險受檢者尚缺乏本土性研究調查。
目的：1.探討參與行動醫院篩檢民眾心血管代謝疾病與人口學、身體測量、健康行為之相關因素；2.利用接受者操作特徵曲線(ROC)選擇心血管代謝疾病最佳預測因子及預測臨界值。
方法：研究使用次級資料分析，資料為收集自參加2014年台南市某區行動醫院篩檢活動的社區民眾，資料來源為健康篩檢活動所彙整的民眾心血管代謝疾病的相關實驗室數據及包括身體測量之健康檢查結果及自陳健康問卷兩個部分之個案資料。分析方法包括獨立樣本t檢定及卡方檢定，並以邏輯式回歸分析找出與心血管代謝疾病與多項危險因子的關連性，再以逐步線性回歸分析係數的找出模型參數的最佳線性組合，後以接受者操作特徵曲線(ROC)確定相關危險因素的截止值，判斷共同的危險因素預測疾病的最佳靈敏度和特異性。
結果：共分析270名民眾資料，男性及女性比各為139位(51.48%)、131位(48.52%);平均年齡為58.81歲(標準差+9.99)。邏輯式回歸分析顯示與代謝症候群相關風險因子為BMI (β=0.34, p＜.0001)、收縮壓(β=0.03, p＝.001)；心血管疾病相關風險因子為收縮壓(β=.04, p＜.000)及個人病史 (β=2.18, p＜.000)；腎臟病變相關風險因子為嚼檳榔(β= -1.20, p =. 02)；糖尿病相關風險因子為不足蔬菜攝取量(β=-0.77, p=. 03)；心血管代謝疾病相關風險因子BMI (β=.24, p＜.001)、個人病史(β=-1.21, p＜.001)、長期服藥史(β=-0.84, p＝.01)。
預測代謝症候群傾向分數公式：Y=-18.037+0.337*BMI+0.030*收縮壓+0.052*腰圍>0.2603；心血管疾病傾向分數公式：Y=-6.438+0.037*收縮壓+2.175*個人病史>0.4044；而心血管代謝疾病傾向分數公式: Y=--4.104+0.235* BMI-1.207*個人病史-0.843*長期服藥>0.5670。
結論：本研究結果可應用於台南市行動醫院健康篩檢活動於第一階段時即識別高風險者，避免遺漏未來領取報告參加第二階段活動之高風險的心血管代謝疾病者錯失轉介及進行個別衛教的適當時機。

In high-risk chronic cardiometabolic disease (Cardiovascular Disease [CVD], Ttype 2 diabetes [DM], chronic kidney disease [CKD], Metabolic Syndromes [MS]) have many common risk factors. This study aimed to develop a non-invasive risk factor analysis for identification of people at high risk. Data collected from a cohort participated in a mobile hospital of Tainan of 2014 were secondary analyzed including 139 males (51.48%) and 131 females (48.52%) with the average age of 58.81+9.99 years old. Results of logistic regression analysis indicated that BMI (β=0.34, p＜.0001) and systolic blood pressure (β=0.03, p＝.001) were best predictors for metabolic syndromes; cardiovascular disease-related risk factors were systolic blood pressure(β=.04, p＜.000) and disease history (β=2.18, p＜.000); the only predictor for chronic kidney disease was chewing betel nut (β= -1.20, p =. 02), the only predictor for diabetes was not enough vegetables intake (β=-0.77, p=. 03); and general predictors for cardiometabolic disease were BMI (β=.24, p＜.001), disease history (β=-1.21, p＜.001) and refill medications (β=-0.84, p＝.01). Risk-assessment tool can be used for referring the highest risk individuals to health care for whom might be missed if not showed up for the second phase activity.

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