研究背景
近年來，許多疾病的預測模型(prediction models)已被廣泛應用在預測病人健康結果及醫療花費的評估。目前，針對第二型糖尿病病人的併發症以及醫療花費的模擬預測模型中，已有英國糖尿病前瞻性研究危險引擎(UK Prospective Diabetes Study risk engine, UKPDS risk engine)等風險預測式可以預測糖尿病共病症的罹病風險；然而，若要應用現有的模型於台灣時，仍有許多不足之處。因此，本研究將利用本土資料進行第二型糖尿病與中風的風險預測式建立，並利用內部資料驗證其預測能力，以期能提供國人一個更佳的風險預測，進而評估風險後進行疾病預防措施。
研究目的
1. 評估現有中風之風險預測式(risk equations)(e.g., UKPDS risk engine, RECODe prediction model) 於台灣第二型糖尿病預測中風之能力。
2. 建構台灣第二型糖尿病中風之風險預測式(risk equations)，以進一步未來建立糖尿病疾病演進與中風的模擬模型(simulation models)。
3. 針對建立的風險評估式(risk equations)進行內部確效(internal validation)。
研究方法
本研究採用回溯性世代 (retrospective cohort)進行研究。研究族群為2011年起至在成大醫院就診之第二型糖尿病個案，以就診第一年的就醫資料為弊病人基本資料，將族群隨機分成4:1的模型訓練組(Training set)及模型驗證組(validation set)，以cox-regression的方式進行預測式的建立，並以向後式選取(backward selection)進行變數篩選。
模型驗證組除針對本研究所建立的模型進行模型比較外，亦進行已知UKPDS、RECODe等第二型糖尿病中風險預測式進行外部模型比較，以確立現有預測式的適用性。
研究結果
觀察期間共6,393名第二型糖尿病病人，共有59名發生中風。利用5,114名成大醫院第二型糖尿病病人訓練組風險預測式，最終模型中風預測模型的變數包含：年齡(age)、性別(sex)、抽菸(smoking)、心律不整病史(history of atrial fibrillation)、收縮壓(systolic blood pressure)以及總膽固醇(total cholesterol)、高密度膽固醇(high-density lipoprotein)、心血管病史(CVD)、降血壓藥物治療(anti-hypertensive treatment)、降膽固醇藥物治療(statins)、抗凝血劑(anti-coagulants)、糖化血色素(HbA1c)、腎功能(serum creatinine)、以及白蛋白-肌酐酸比值(albumin-creatinine ratio)、尿糖(urine glucose)、平均紅血球體積(mean corpuscular volume)、 平均紅血球血紅素濃度(mean corpuscular hemoglobin concentration)等；訓練組的中風風險預測式的預測能力c-statistics為0.7364，驗證組的預測能力為0.6237，優於UKPDS stroke risk equation 及RECODe stroke risk equation對於驗證組的c-statisitcs預測能力0.5757及0.5867；而net reclassification improvement則發現在不同的條件下新建立的模型優於或不劣於UKPDS及RECODe的風險預測式。
研究結論
糖尿病與中風因其高發病率、高致死率，成為全世界甚大的疾病負擔。比較目前常用的心血管病風險評估工具如一般族群的Framingham風險評估、針對第二型糖尿病的英國UKPDS風險評估，及較新利用Accord等前瞻性糖尿病研究資料建立的RECODe風險評估，發現CVD風險評估模型隨著風險因素的不斷發現而不斷完善，同時也存在不同程度的局限性。我國尚缺乏對CVD進行全面系統、綜合傳統風險因素和治療藥品的有效風險評估研究，因此藉由本研究提出將中風相關風險因子與治療藥品暴露等客觀參數運用到Stroke風險因素評估中,以期為我國疾病風險評估研究提供新的方向。

SUMMARY
Abstract: Objective To establish a stroke risk equation to evaluate the risks of stroke among Taiwanese type 2 diabetes population.
Methods A total of 6,393 type II diabetic people who underwent out-patient clinic at the National Cheng Kung University Hospital during Jan. 2011 and Dec. 2013 were enrolled in the study. These people were randomly divided into the training group (n=5,114, 80%) and testing group (n=1,279, 20%). Cox regression was used to construct a simple risk model among the training group by backward selection method, and risk classification was drawn up according to the prognostic index by p value <0.05. Internal validation was used to test the stability of the model in the testing group. Discriminatory ability was determined by the area under the ROC curve.
Results Altogether 59 new stroke cases were observed during follow-up period. The risk factors included age, sex, smoking, systolic blood pressure,CVD, statins, anti-coagulants, anti-hypertensive agents, HbA1c, total cholesterol, high-density lipoprotein, serum creatinine, albumin-creatinine ratio, urine glucose, mean corpuscular volume and mean corpuscular hemoglobin concentration. The estimated AUC for the model was 0.7364 in the training group and 0.6237 in the testing group. Conclusion We have constructed a risk model that could be useful for identifying individuals at high risk of stroke in type II diabetes population.

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