慢性B型肝炎患者接受口服抗病毒藥物治療為目前治療之主流，根據國際指引共識指出治療目標以達到表面抗原(hepatitis B surface antigen)消失為終點，只是大多數患者即使接受長期治療也不易達到。另一方面，在適當的時機停藥也被證實可以增加表面抗原消失的比率，只是有一定比率產生肝炎復發的風險。因此，患者需要多久的治療或是該不該停藥目前並未有共識，主因在治療期間的表面抗原動態改變並沒有很好的分析模式，同時表面抗原的動態改變在預測停藥復發的角色上也還沒有定論。
目的：本研究運用時間序列分析的統計方法，針對慢性B型肝炎患者接受長期口服抗病毒藥物治療並嘗試停藥的患者，找出患者停藥前序列表面抗原的動態趨勢，並分析其與停藥後B型肝炎復發的關係。
方法：收集自2013年1月至2019年12月接受口服抗病毒藥物治療並嘗試停藥的慢性B型肝炎患者臨床資料進行回溯分析。以時間序列分析的自迴歸移動平均模型(Autoregressive moving average model，簡稱ARMA 模型)為工具，建立每位患者停藥前序列表面抗原動態的ARMA模型預測趨勢，分析其趨勢斜率值並加以分類，進一步分析不同類型的趨勢和患者停藥後肝炎復發的相關性。本研究定義B型肝炎復發為停藥後兩年內病毒復發(病毒量上升大於2000 IU/mL)合併肝功能(以麩丙酮酸轉氨脢為代表)上升大於80 U/L。
結果：共收集35位患者進行分析，男性27位(77.1%)，平均年齡48.6 ± 12.4歲，E抗原陰性患者30位(85.7%)，治療前肝功能大於200 U/L與病毒量高於5 log IU/mL分別為10位(28.6%)和26位(74.3%)。
治療用藥分別為Tenofovir 21位(60.0%) Telbivudine合併Adefovir 9位(25.7%)以及Telbivudine單一治療5位(14.3%)。其中10位有產生Telbivudine抗藥性。治療期間大於36個月者有24位(68.6%)，治療後在12個月內達病毒消失者有27位(77.1%)
。依ARMA模型分析之趨勢斜率不同將表面抗原值動態趨勢分類為Type A(穩定或上升型，n=7)， Type B(慢速下降型，n=5)， Type C(快速下降型，n=14)以及Type 0(無趨勢，n=9)四種。
停藥後B型肝炎復發有19位(54.3%)，累計3、6、12以及24個月復發率為34.3%、45.7%、51.7%以及56.6%。依ARMA模型而言，Type C型患者比非Type C型患者有顯著較低之復發率(28.6% v.s.71.4%，p=0.018)，特別是針對停藥時表面抗原值仍大於100 IU/mL之族群，Type C型患者比非Type C型患者有更顯著較低之復發率(30.8% v.s. 86.7%，p=0.006)。以Cox迴歸模式進行多變項分析顯示，治療後在12個月內達病毒量消失(hazard ratio: 3.402，95% C.I.: 1.283-9.019，p=0.014)以及ARMA模型趨勢Type C型(hazard ratio: 3.223，95% C.I.: 1.057-9.827，p=0.040)為停藥後B型肝炎復發率較低之相關因子。整體而言，同時合併兩項有利因子之患者(n=10)，比其餘患者(n=25)有顯著較低之復發率(10.0% v.s.72.0%，p=0.002)。
結論：依ARMA模型分析之表面抗原動態趨勢可以分類為4型，其中Type C型患者有顯著較低之停藥後B型肝炎復發率。另外，患者接受治療後在12個月內達病毒量消失也是停藥復發率較低之相關因子。合併兩項因子，可以找出適合停藥的族群，惟仍需更多個案之分析研究才能下定論。

Oral antiviral therapy is the main treatment for the chronic hepatitis B patients in recent years. According to the international guidelines, the treatment goal is to achieve hepatitis B surface antigen (HBsAg) loss. However, it is hard to achieve in the majority of patients even with long-term therapy. On the other hand, stopping antiviral therapy in proper time point before HBsAg loss are reported to have high rates of HBsAg loss from literatures. Nevertheless, risk of severe hepatitis flare and hepatic decompensation is the concern in this situation. Hence, the finite duration for antiviral therapy and the time point for stopping therapy are not concluded at the present. To our knowledge, the major problem is the lack of good analysis or predictive model for the trends of HBsAg kinetics during antiviral therapy. In addition, the relationship between the trends of HBsAg kinetics and the reactivation of chronic hepatitis B after stopping antiviral therapy are still unknown.
Aims: We would like to build up the trends of HBsAg kinetics by time series analysis model for the chronic hepatitis B patients who receiving long-term antiviral therapy and stopping treatment before HBsAg loss. Further analysis for the impact of the trends of HBsAg kinetics on the reactivation of hepatitis B (HBr) will be clarified.
Method: We collected this kind of patients retrospectively from Jan. 2013to Dec. 2019. By using the autoregressive moving average (ARMA) model in time series analysis, we built the trends of HBsAg kinetics according to the serial HBsAg data before stopping antiviral therapy for all of the enrolled patients. According to the drift of trends from ARMA model, we further clarified the types of the trends for all patients. The relationship between the types of the trends of HBsAg kinetics and HBr after stopping antiviral therapy would also be further analyzed. The definition of HBr is viral reactivation (viral load increase for more than 2000 IU/mL) combined with liver biochemistry (alanine aminotransferase, ALT) increase for more than 80 U/L.
Results: A total of 35 patients were enrolled. There were 27 (77.1%) male patients. The ages were 48.6 ± 12.4 years. Baseline HBeAg-negative, ALT > 200 U/L and viral load > 5 log IU/mL were in 30 (85.7%), 10 (28.6%) and 26 (74.3%) patients, respectively. The agents of antiviral therapy by tenofovir, telbivudine plus adefovir and telbivudine monotherapy were in 21 (60.0%), 9 (25.7%) and 5 (14.3%) patients, respectively. Ten patients experienced drugs resistance to telbivudine. The duration of antiviral therapy for more than 36 months were in 24 (68.6%) patients. Twenty-seven (77.1%) patients achieved viral load undetectable within 12 months of the initial treatment. According to the drifts of ARMA model, the trends of HBsAg kinetics were classified to 4 Types as follows: Type A (stable or increase, n=7); Type B (slow decline, n=5); Type C (rapid decline, n=14) and Type 0 (no pattern, n=9).
There were 19 (54.3%) patients experienced HBr after stopping antiviral therapy. The cumulative rates of HBr were 34.3%、45.7%、51.7% and 56.6% in 3, 6, 12 and 24 months, respectively. According to the ARMA model, the patients with Type C had lower rates of HBr than the patients with Type non-C (28.6% v.s.71.4%, p=0.018). For patients who had the HBsAg levels for more than 100 IU/mL at the time of stopping therapy, the difference between patients with Type C and Type non-C were more significant (30.8% v.s. 86.7%, p=0.006). Multivariate analysis by Cox-regression model revealed viral load undetectable within 12 months of initial treatment (hazard ratio: 3.402, 95% C.I.: 1.283-9.019, p=0.014) and ARMA Type C (hazard ratio: 3.223, 95% C.I.: 1.057-9.827, p=0.040) were both significant factors related to lower rate of HBr. In addition, for patients with both factors (n=10), the rates of HBr were significantly lower than the other patients without (n=25) (10.0% v.s.72.0%，p=0.002).
Conclusion: Four Types of the trends of HBsAg kinetics according to the ARMA models were classified. The patients with ARMA Type C had lower rates of HBr after stopping antiviral therapy than the patients with Type non-C. In addition, viral load undetectable within 12 months of initial treatment was the other significant factor linked to lower rates of HBr. Combined both factors, we could select the suitable patients for stopping antiviral therapy safely. However, further study with more cases was still needed to make a conclusion.

論文審定書 i
誌謝 ii
中文摘要 iii
ABSTRACT v
目錄 viii
圖次 x
表次 xi
第一章 緒論 1
第一節 研究背景與動機 1
第二節 問題陳述與研究目的 3
第二章 文獻探討 5
第一節 表面抗原的分析運用在B型肝炎自然病史之現況 5
第二節 停藥患者肝炎復發的研究與演化歷史 7
第三節 表面抗原的分析運用在停藥患者肝炎復發的研究 9
第三章 研究方法 11
第一節 研究設計與研究架構 11
第二節 研究對象與資料來源 12
第三節 研究假設 14
第四節 研究變項與操作型定義 15
第五節 時間序列分析模式 17
第六節 資料處理與分析方式 20
第四章 資料分析結果 22
第一節 研究對象基本特性 22
第二節 表面抗原分析資料與ARMA模式分析之結果與分類 24
第五章 討論、結論與建議 33
第一節 研究假設驗證 33
第二節 研究討論 35
第三節 研究限制 39
第四節 研究結論與實務意涵 40
第五節 後續研究建議 41
參考文獻 42

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