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研究生： 阮垂蓉
Ruan, Chuei-Rong
論文名稱： 運用台灣第二型糖尿病患者建立與驗證糖尿病腎病變之健康與經濟評估模型
Development and validation of a simulation model for diabetic kidney disease among patients with type 2 diabetes for health and economic evaluations in Taiwan
指導教授： 歐凰姿
Ou, Huang-Tz
共同指導教授: 郭士禎
Kuo, Shih-Chen
林威宏
Lin, Wei-Hung
黃千惠
Huang, Chien-Huei
學位類別： 碩士
Master
系所名稱： 醫學院 - 臨床藥學與藥物科技研究所
Institute of Clinical Pharmacy and Pharmaceutical sciences
論文出版年： 2023
畢業學年度： 111
語文別： 中文
論文頁數： 177
中文關鍵詞： 糖尿病腎病變 、多狀態轉換模型 、風險預測模型 、模型驗證 、模型更新
外文關鍵詞： diabetic kidney disease, multi-state transition model, risk prediction model, model validation, model updating
相關次數： 點閱：156  下載：0 
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    • 研究背景與目的
    糖尿病腎病變 (diabetic kidney disease, DKD) 是導致糖尿病患者末期腎病變 (end stage renal disease, ESRD) 最主要的原因，為個人和整體社會經濟都帶來嚴重的負面影響。國際針對糖尿病腎病變的疾病進程已發展出各類型疾病預測模型 (disease progression model)；其中基於風險方程式的多狀態轉換模型 (risk equation-based, multi-state transition model) 在醫療決策中扮演重要的角色。目前臺灣仍缺乏以風險方程式建構的多狀態轉換模型 (risk equation-based, multi-state transition model) 來模擬糖尿病腎病變疾病進程。現有的風險方程式主要來自於白人族群，包含：美國 RECODe 模型；英國 UKPDS-OM2 模型。而由華人族群建立的 CHIME 模型，在華人或亞洲族群的風險預測能力表現較 UKPDS-OM2 模型及 RECODe 模型佳，然而上述這些風險方程式於臺灣人適用性尚待驗證。
    本研究旨在驗證及更新現有風險方程式，並基於更新後的風險方程式建立臺灣第二型糖尿病族群的糖尿病腎病變預測模型，用於未來臨床照護以及藥物經濟學評估，進一步提供醫療政策參酌。

    研究方法
    首先，根據文獻回顧結果，建立模擬糖尿病腎病變疾病進程之多狀態轉換模型 (multi-state transition model)。本研究所建立的模型包含五個狀態，分別是正常白蛋白尿、微量白蛋白尿、巨量白蛋白尿、腎衰竭和死亡。接著尋找相關的風險預測模型，以套用於多狀態轉換模型。對於正常白蛋白尿轉為微量白蛋白尿/巨量白蛋白尿的轉移機率，本研究套用 RECODe 模型。而針對正常白蛋白尿轉為腎衰竭的轉移機率，本研究進行 RECODe、UKPDS-OM2 和 CHIME 三個模型的比較。最後，利用成大醫院電子病歷的資料，評估現有風險預測模型的預測能力 (模型鑑別力與校準度)，並選出表現最佳之模型，進一步重新校準，使其適用於臺灣族群。

    研究結果
    針對微量白蛋白尿事件，RECODe 模型的鑑別力不甚理想 (C 統計量為 0.62)，模型校準斜率和校準截距分別為 1.6 和 0.16。針對巨量白蛋白尿事件，RECODe 模型的鑑別力較好 (C 統計量為 0.76)，模型校準斜率和校準截距分別為 4.38 和 0.0486。針對腎衰竭事件，CHIME 模型的鑑別力 (C 統計量為0.78) 優於 RECODe 模型 (C統計量為0.64) 和UKPDS-OM2模型 (C統計量為 0.60)。然而，三個模型都高估腎衰竭的發生 (校準斜率介於 0.15 和 0.33 之間，校準截距介於 0.004 和 0.03 之間)。基於以上結果，本研究選擇 RECODe 模型以及 CHIME 模型以分別預測白蛋白尿和腎衰竭的發生，並對這些模型進行進一步的更新。重新校準後，風險預測模型的校準度改善 (校準斜率接近於 1，校準截距接近於 0)，更適用於臺灣第二型糖尿病族群。

    研究結論
    針對臺灣第二型糖尿病患者，更新的 RECODe 和 CHIME 模型可分別用於預測蛋白尿及腎衰竭的發生。在臨床上本研究結果可被應用於篩選出糖尿病腎病變的高風險患者，提早給予有效治療。此外，亦能透過本研究所建立的模型評估具有腎臟保護效果之糖尿病藥品所帶來的長期療效、安全性及經濟效益。同時，本研究提供風險預測模型的驗證和更新流程，可應用於未來其他疾病領域的研究。

    Various types of simulation models have been developed to reflect the progression of diabetic kidney disease (DKD), including risk equation-based models that utilize individual factors to predict the probability of renal outcomes. However, Taiwan currently lacks a risk equation-based model to predict the overall progression of DKD among patients with diabetes. Existing risk equations are primarily based on Caucasian populations, and their performance among the Taiwanese population remains unknown. Hence, the main objective of this study is to validate and update the existing risk equations and subsequently construct a risk equation-based multi-state transition model for the overall progression of DKD among the Taiwanese population with type 2 diabetes. To achieve this objective, a multi-state transition model with five health states was constructed based on a literature review. These health states include normal albuminuria, microalbuminuria, macroalbuminuria, renal failure, and death. The transition probabilities for this model were calculated using the RECODe, UKPDS-OM2, and CHIME models. The predictive ability of these risk prediction models, in terms of discrimination and calibration, will be evaluated using electronic medical records data from National Cheng Kung University Hospital. Regarding albuminuria, the RECODe model demonstrated acceptable discrimination but underestimated the results. For renal failure, the CHIME model exhibited superior discriminative ability compared to the RECODe model and the UKPDS-OM2 model. However, all three models overestimated the occurrence of renal failure. After recalibration, the updated RECODe and CHIME models will be more feasible for predicting albuminuria and renal failure among the Taiwanese type 2 diabetes population.

    中文摘要 I EXTENDED ABSTRACT III 誌謝 VI 目錄 IX 表目錄 XIII 圖目錄 XV 第一篇、運用臺灣第二型糖尿病患者建立與驗證糖尿病腎病變之健康與經濟評估模型 1 第一章、研究背景 1 第二章、文獻回顧 2 第一節、第二型糖尿病簡介 2 2.1.1 第二型糖尿病之定義與診斷 2 2.1.2 第二型糖尿病之流行病學 3 2.1.3 第二型糖尿病之慢性併發症 5 2.1.4 第二型糖尿病之治療 6 第二節、糖尿病腎病變簡介 10 2.2.1 糖尿病腎病變之定義 10 2.2.2 糖尿病腎病變之流行病學 10 2.2.3 糖尿病腎病變之危險因子 13 2.2.4 第二型糖尿病腎病變之分期 16 2.2.5 第二型糖尿病腎病變之疾病進展 17 2.2.6 第二型糖尿病腎病變之治療 20 第三節、疾病風險預測模型 28 2.3.1 疾病風險預測模型之定義及應用 28 2.3.2 疾病風險預測模型之驗證 28 2.3.3 疾病風險預測模型之調整及更新 31 第四節、糖尿病腎病變之風險預測模型 33 第五節、糖尿病腎病變之多狀態轉換模型 43 第三章、研究目的及重要性 50 第四章、研究方法 51 第一節、研究架構 51 第二節、風險預測模型之選擇 51 4.2.1 馬可夫模型建置 51 4.2.2 風險預測模型之選擇 52 第三節、風險預測模型之驗證及更新 53 4.3.1 研究類型 53 4.3.2 研究材料 53 4.3.3 研究對象 53 4.3.4 觀察區間 55 4.3.5 研究對象之基本特質 55 4.3.6 遺失值的插補 57 4.3.7 研究事件 57 4.3.8 研究事件的觀察及預測機率之計算 57 4.3.9 資料分割 60 4.3.10 研究名詞及操作型定義 61 4.3.11 敏感性分析 64 第四節、模型重新校準 65 第五節、統計分析 68 4.5.1 統計工具 68 4.5.2 統計模式 68 4.5.3 資料分析方法 68 第五章、研究結果 70 第一節、研究對象之篩選流程 70 第二節、研究對象之基本特徵 71 第三節、資料分割之結果 74 第四節、研究事件之觀察與預測發生機率 76 第五節、風險預測模型的預測能力之結果 78 5.5.1 微量白蛋白尿 78 5.5.2 巨量白蛋白尿 79 5.5.3 腎衰竭 80 第六節、敏感性分析之結果 83 5.6.1 調整微量白蛋白尿的定義 83 5.6.2 調整巨量白蛋白尿的定義 84 5.6.3 調整腎衰竭的定義 85 第七節、風險預測模型更新之結果 87 5.7.1 調整整體基礎風險的模型重新校準 87 5.7.2 針對不同風險群調整基礎風險的模型重新校準 93 第六章、討論 96 第一節、糖尿病腎病變多狀態轉換模型之架構 96 第二節、糖尿病腎病變風險預測模型 98 6.2.1 臺灣糖尿病腎病變風險預測模型 98 6.2.2 其他國家糖尿病腎病變風險預測模型 99 6.2.3 本研究結果與過去研究之比較 102 6.2.4 敏感性分析之結果 114 6.2.5 風險預測模型之更新結果 115 第三節、研究優勢與限制 117 6.3.1 研究優勢 117 6.3.2 研究限制 117 第七章、研究結果的應用 119 第八章、結論 121 第九章、未來研究方向 122 第二篇、藥師早期介入評估嚴重藥物皮膚不良反應 123 第一章、前言 123 第一節、嚴重型藥物皮膚不良反應之簡介 123 第二節、臨床藥師在評估 SCARs 的角色 123 第三節、成大醫院評估嚴重藥物皮膚不良反應之流程 125 第二章、臨床服務目的與方法 126 第一節、服務目的 126 第二節、服務方法 126 2.2.1 服務區問 126 2.2.2 服務對象 126 2.2.3 收案方式 126 2.2.4 服務內容與流程 126 第三章、結果分析 129 第一節、服務期間收錄之 SCARs 案例 129 第二節、服務期間收錄案例之記載 130 第三節、整理成大醫院 2022 年 SCARs 案例 134 第四章、未來方向 143 參考文獻 144 附錄 156 附錄 1、RECODE、UKPDS-OM2、CHIME 模型所納入的變項之定義 156 附錄 2、UPCR 換算成 UACR 之公式 161 附錄 3、研究族群共病症與觀察結果之ICD-9-CM、ICD-10-CM 診斷碼 162 附錄 4、藥品之 ATC 代碼 163 附錄 5、有/沒有發生腎藏相關事件的患者基本特徵之比較 164 附錄 6、敏感性分析之結果 170

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