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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 葉彥伯 | zh_TW |
| dc.contributor.author | 劉軒秀 | zh_TW |
| dc.contributor.author | Hsuan-Hsiu Liu | en |
| dc.date.accessioned | 2021-05-19T17:45:31Z | - |
| dc.date.available | 2024-02-28 | - |
| dc.date.copyright | 2018-10-09 | - |
| dc.date.issued | 2018 | - |
| dc.date.submitted | 2002-01-01 | - |
| dc.identifier.citation | Alavian, Seyed Moayed ; Imanieh, Mohammad Hadi ; Imanieh, Mohammad Hossein. Predictive Factors in the Incidence of Cirrhosis in Chronic Hepatitis B Virus Infections. Hepatitis Monthly, 2016,16(5).
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El-Serag HB, Kanwal F, Davila JA, Kramer J, Richardson P. A new laboratory-based algorithm to predict development of hepatocellular carcinoma in patients with hepatitis C and cirrhosis. Gastroenterology 2014; 146: 1249-55. El-Serag HB, Kanwal F. Epidemiology of hepatocellular carcinoma in the United States: where are we? Where do we go? Hepatology 2014; 60: 1767-1775. Fattovich G, Stroffolini T, Zagni I, Donato F. Hepatocellular carcinoma in cirrhosis: incidence and risk factors. Gastroenterology 2004; 127: S35-S50. Flemming JA, Yang JD, Vittinghoff E, Kim WR, Terrault NA. Risk prediction of hepatocellular carcinoma in patients with cirrhosis: the ADRESS-HCC risk model. Cancer 2014; 120: 3485-3493. Iloeje UH, Yang HI, Su J, et al., Predicting cirrhosis risk based on the level of circulating hepatitis B viral load. Gastroenterology. 2006;130(3):678-86. Jan CF, Chen CJ, Chiu YH, et al. A population-based study investigating the association between metabolic syndrome and hepatitis B/C infection (Keelung Community-based Integrated Screening study No. 10). Int J Obes (Lond). 2006;30(5):794-9. John U, Hanke M. Liver cirrhosis mortality, alcohol consumption and tobacco consumption over a 62 year period in a high alcohol consumption country: a trend analysis. BMC Res Notes. 2015;8:822. Kabbany MN, Conjeevaram Selvakumar PK, Watt K, Lopez R, Akras Z, Zein N, Carey W, Alkhouri N. Prevalence of Nonalcoholic Steatohepatitis-Associated Cirrhosis in the United States: An Analysis of National Health and Nutrition Examination Survey Data. Am J Gastroenterol. 2017;112(4):581-587. Ko WH, Chiu SY, Yang KC, Chen HH. Diabetes, hepatitis virus infection and hepatocellular carcinoma: A case-control study in hepatitis endemic area. Hepatol Res. 2012;42(8):774-81. Kuo MJ, Yeh HZ, Chen GH, Poon SK, Yang SS, Lien HC, Chang CS. Improvement of tissue-adhesive obliteration of bleeding gastric varices using adjuvant hypertonic glucose injection: a prospective randomized trial. Endoscopy 2007; 39: 487-491 Kuo MJ, Chen HH, Chen CL et al. Cost-effectiveness analysis of population-based screening of hepatocellular carcinoma: Comparing ultrasonography with two-stage screening. World journal of gastroenterology, 2016, 22(12), 3460-70. Lachenmeier DW, Monakhova YB, Rehm J. Influence of unrecorded alcohol consumption on liver cirrhosis mortality. World J Gastroenterol. 2014;20(23):7217-22. Lai MS, Hsieh MS, Chiu YH, Chen TH. Type 2 diabetes and hepatocellular carcinoma: A cohort study in high prevalence area of hepatitis virus infection. Hepatology. 2006 Jun;43(6):1295-302. Lee MH, Yang HI, Liu J, et al. R.E.V.E.A.L.-HBV Study Group. Prediction models of long-term cirrhosis and hepatocellular carcinoma risk in chronic hepatitis B patients: risk scores integrating host and virus profiles. Hepatology. 2013;58(2):546-54. Lu SN, Wang JH, Liu SL, Hung CH, Chen CH, Tung HD, et al. Thrombocytopenia as a surrogate for cirrhosis and a marker for the identification of patients at high-risk for hepatocellular carcinoma. Cancer. 2006; 1107(9):2212-22. Mancebo A, González-Diéguez ML, Cadahía V, Varela M, Pérez R, Navascués CA, Sotorríos NG, Martínez M, Rodrigo L, RodríguezM. Annual incidence of hepatocellular carcinoma among patients with alcoholic cirrhosis and identification of risk groups. Clin Gastroenterol Hepatol 2013; 11: 95-101. Rehm J, Taylor B, Mohapatra S, Irving H, Baliunas D, Patra J, Roerecke M. Alcohol as a risk factor for liver cirrhosis: a systematic review and meta-analysis. Drug Alcohol Rev. 2010;29(4):437-45. Schiefelbein E, Zekri AR, Newton DW, et al., Hepatitis C virus and other risk factors in hepatocellular carcinoma. Acta Virol. 2012;56(3):235-40. Schuppan D, Afdhal NH. Liver cirrhosis. Lancet. 2008 ;371(9615):838-51. Singal AG, Mukherjee A, Elmunzer BJ, Higgins PD, Lok AS, Zhu J, Marrero JA, Waljee AK. Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma. Am J Gastroenterol 2013; 108:1723-1730. Suh B, Yun JM, Park S, et al. Prediction of future hepatocellular carcinoma incidence in moderate to heavy alcohol drinkers with the FIB-4 liver fibrosis index. Cancer. 2015;121(21):3818-25. Velázquez RF, Rodríguez M, Navascués CA, Linares A, Pérez R, Sotorríos NG, Martínez I, Rodrigo L. Prospective analysis of risk factors for hepatocellular carcinoma in patients with liver cirrhosis. Hepatology 2003; 37: 520-527. Wang WC, Chen CL, Chen HH. Personalized dynamic prediction model for hepatocellular carcinoma. Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University Master Thesis. Wong A, Le A, Lee MH, et al. Higher risk of hepatocellular carcinoma in Hispanic patients with hepatitis C cirrhosis and metabolic risk factors. Sci Rep. 2018 May 8;8(1):7164. doi: 10.1038/s41598-018-25533-2. Wu CL, Yang MC. Morbidity Costs and Associated Factors of Patients with Hepatocellular Carcinoma from a Medical Center. Chin J Pub Health 1998: 148-157 Yang HI, Yuen MF, Chan HL, et al.; REACH-B Working Group. Risk estimation for hepatocellular carcinoma in chronic hepatitis B (REACH-B): development and validation of a predictive score. Lancet Oncol. 2011;12(6):568-74. Yeh YP, Hu TH, Cho PY, Chen HH, Yen AM, Chen SL, Chiu SY, Fann JC, Su WW, Fang YJ, Chen ST, San HC, Chen HP, Liao CS; Changhua Community-Based Abdominal Ultrasonography Screening Group. Evaluation of abdominal ultrasonography mass screening for hepatocellular carcinoma in Taiwan. Hepatology. 2014;59(5):1840-9. Yi SW, Choi JS, Yi JJ, Lee YH, Han KJ. Risk factors for hepatocellular carcinoma by age, sex, and liver disorder status: A prospective cohort study in Korea. Cancer. 2018. doi: 10.1002/cncr.31406. Yu EW, Chie WC, Chen TH. Does screening or surveillance for primary hepatocellular carcinoma with ultrasonography improve the prognosis of patients? Cancer J 2004; 10: 317-325 Yu MW, Hsu FC, Sheen IS, Chu CM, Lin DY, Chen CJ, Liaw YF. Prospective study of hepatocellular carcinoma and liver cirrhosis in asymptomatic chronic hepatitis B virus carriers. Am J Epidemiol. 1997;145(11):1039-47. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7522 | - |
| dc.description.abstract | 研究背景 運用腹部超音波進行族群肝癌篩檢對於降低肝癌死亡風險之短期效益在過往的研究中已藉由非隨機分派研究之實驗設計得到證據。然而如何運用合宜對照組對於此腹部超音波族群篩檢策略以及近年來發展之抗病毒治療在長期追蹤之整體與邊際效益與成本效益評估扮演重要的角色。此一評估方法在過往的研究中曾以預測模式之建構結合電腦模擬之研究設計以到建立在未進行腹部超音波與抗病毒藥物之情境下之模擬對照組達到評估之目的。
目的 本研究論文之目的在於 (1) 運用肝病毒以及非病毒之因子發展肝癌預測模型; (2) 在(1)之風險預測模型下發展肝癌疾病自然進展模型以及肝癌存活之預後模型作為建構未進行腹部超音波篩檢之狀態下的模擬對照組之基礎; (3) 運用上述之模擬對照組評估腹部超音波篩檢策略對於降低肝癌死亡之長期效益; (4) 運用模擬對照組評估腹部超音波篩檢結合抗病毒治療對於降低肝癌死亡之長期追蹤效益; (5) 以模擬對照組評估腹部超音波篩檢結合抗病毒治療對於不同風險族群在 降低肝癌死亡之長期追蹤效益。 材料與方法 本研究所運用之資料乃源於以風險分數為導向之腹部超音波篩檢介入。此一介入計畫於2008年10月開始於彰化縣執行。該介入策略之目標族群為45-74歲,參與彰化社區整合式萬人健檢(Changhua community-based integrated screening, CHCIS)計畫之民眾。各鄉診中之目標族群民眾在運用肝病毒感染、麩丙酮酸轉胺脢(ALT)、麩草醋酸轉胺脢(AST)、第二型糖尿病、血小板計數構成之風險分數區辨之高風險民眾,將邀請接受由腸胃科專科醫師執行之腹部超音波篩檢,以達到偵測民眾中具有肝硬化以及疑似肝癌病患之目的。 本論文發展電腦模擬架構結合肝癌疾病自然史及實證資料發展出的預測模式及存活資料以建構對照組,進一步進行不同肝癌防治策略之成本效益分析。 結果 本論文主要發現歸納以下五點: (1)本研究所建構之肝癌危險預測模型,其預測力相當佳,作業接受曲線下的面積達0.89 (95% CI: 0.85-0.93)。 (2)以危險分層為邀請基礎之超音波肝癌篩檢,若再加上30%順從率之抗病毒藥物治療,經八年追蹤觀察與模擬控制組相較,可避免60%肝癌死亡率(RR=0.39, 95% CI: 0.32-0.46)。 (3)藉由與模擬控制組相較之模擬結果,僅考量抗病毒藥物使用之順從率在30%時,可貢獻約22%之肝癌死亡避免,若順從率提高至50%及70%時,避免肝癌死亡之貢獻可分別增加至30%至35%。 (4)藉由與模擬控制組相較之模擬結果,若將中風險族群超音波篩檢涵蓋率自25%增加至75%時,避免肝癌死亡之貢獻僅能多增加3%。 (5)本研究經濟決策模式結果顯示,與模擬控制組相較,運用超音波進行肝癌篩檢,每挽救一個人年命,其增加成本效果比為美金22,849元,除超音波篩外,若再增加30%順從率之抗病毒藥物治療,每挽救一個人年命,其增加成本效果比為美金101,849元,若抗病毒藥物治療順從率分別提高至50%及70%時,每挽救一個人年命,其增加成本效果比則分別為美金141,805及181,919元。 結論 本論文以彰化縣腹部超音波肝癌篩檢實證資料驗證使用電腦模擬建構對照組評估族群肝癌篩檢效益的效度及可行性,進一步將此設計結合肝癌疾病自然史及實證資料發展出的預測模式及存活資料,進行腹部超音波結合抗病毒藥物療法之效益評估與成本效益分析。 | zh_TW |
| dc.description.abstract | Background Mass screening for hepatocellular carcinoma (HCC) with abdominal ultrasound (AUS) has been demonstrated in short-term follow-up without using a randomized controlled design. How to have a suitable control group plays an important role in the evaluation of long-term effectiveness of the overall and marginal effectiveness and cost-effectiveness of AUS as well as the recently emerging anti-viral therapy. Such an evaluation has been addressed before by the development of predictive model and also the creation of a computer simulation design characterized by the pseudo-control group in the absence of intervention on AUS and anti-viral therapy.
Aims The objective of this thesis is to (1) develop a predictive model based on viral and non-viral factors for the risk of HCC; (2) develop a natural history model for the disease progression of HCC embedded with the risk prediction model developed in (1) and the prognosis model in relation to the survival of HCC in order to form a pseudo-control group in the absence of AUS screening; (3) estimate long-term effectiveness and cost-effectiveness of AUS in reducing HCC mortality based on the pseudo-control group; (4) estimate long-term effectiveness and cost-effectiveness of AUS and anti-viral therapy in reducing HCC mortality based on the pseudo-control group; (5) estimate long-term effectiveness and cost-effectiveness of AUS and anti-viral therapy in reducing HCC mortality by risk groups based on the pseudo-control group; Data and methods Data used here are based on a risk score-guided invitation for abdominal ultrasound which has been launched since October, 2008 in Changhua. Subjects aged 45-74 years attended the Changhua community-based integrated screening (CHCIS) program were targeted. Those with high score based on hepatitis virus infection, ALT, AST, type 2 diabetes, platelet count were invited to receive ultrasonography screening performed by board-certified gastroenterologists in town-based health center to identify liver cirrhosis, and suspected HCC cases. We developed a pseudo-control group by building up the disease natural history of progression of HCC embedded with the risk prediction model for HCC and the survival of HCC by detection modes. The pseudo-control model was further used to the development of health economic decision model for cost-effectiveness of various preventive strategies. Results The main results of this thesis include (1) The predictive validity of the risk prediction model for HCC is very good on the basis of ROC curve performance with AUC higher up to 0.89 (95% CI: 0.85-0.93); (2) The observed 8-year HCC mortality reduction with AUS by risk groups together with around 30% coverage rate of anti-viral therapy was around 60% (RR=0.39, 95% CI: 0.32-0.46); (3) The simulated results by using the pseudo-control group indicate additional contribution of 30% compliance rate of anti-viral therapy (empirical estimate) to HCC mortality reduction was around 22%. The corresponding figures are be raised to 30% and 35% when the compliance rate of anti-viral therapy is enhanced to 50% and 70%, respectively. (4) The simulated results by using the pseudo-control group show 3% HCC mortality reduction attributable to additional contribution of screening intermediate risk group with AUS when the coverage rate of this group is enhanced from 25% to 75%.; (5) The results of health economic decision model show the ICER (incremental cost-effectiveness ratio) values were $22,849 for the administration of AUS, $101,849 for the administration of AUS plus 30% compliance to anti-viral therapy. The corresponding figure for 50% and 70% compliance rate to anti-viral therapy were $141,805 and $181,919, respectively. Conclusions The population-based screening programme for HCC in Changhua confirmed the validity and feasibility for the developed risk score applied in our screening programme. We further used a computer study design with a pseudo-control group that was developed on the basis of disease natural history of HCC embedded with the predictive model and the survival part of HCC to estimate long-term effectiveness of the overall and marginal effectiveness and cost-effectiveness of AUS and anti-viral therapy. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-19T17:45:31Z (GMT). No. of bitstreams: 1 ntu-107-P05849003-1.pdf: 1545476 bytes, checksum: 122d13dafedf3784ec8d1523d5485a50 (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | Abstract ..........i
Chapter 1 Introduction .......................... 1 Chapter 2 Literature Review ........................... 3 2.1 Factors affecting progression to cirrhosis ........ 3 2.2 Predictive model for cirrhosis ........ 12 2.3 Predictive model for HCC among cirrhosis patients ........ 13 2.4 Predictive model for patients infected with HBV ........ 16 2.5 Dynamic prediction for HCC ..... 19 2.6 Cost-effectiveness analysis for HCC screening .......... 23 Chapter 3 Materials and Methods ........... 27 3.1 Population-based HCC Screening in Changhua ......... 27 3.2 Predictive model with logistic regression .......... 31 3.3 Multi-state predictive model ............... 33 3.4 Markov Decision Analysis .............. 35 3.5 Cost-effectiveness analysis ......... 37 Chapter 4 Results ......................... 43 4.1 Invitation and Attendance to the Changhua community-based AUS screening program .................... 43 4.2 Findings from the screening. ............ 44 4.3 The predictive model with logistic regression for HCC/liver cirrhosis ............... 46 4.4 The predictive model with logistic regression for HCC .......... 48 4.5 Survival of HCC ...................... 49 4.6 Evaluation for the effectiveness of AUS for HCC by comparing the empirical HCC mortality with the pseudo control ............... 50 4.7 Simulated results of effectiveness of AUS and anti-viral therapy ................ 52 4.8 Cost-effectiveness analysis for the Changhua AUS Screening ............... 53 Chapter 5 Discussion ............................ 73 5.1 Summary of main findings .................. 73 5.2 Predictive model for HCC ............... 74 5.3 The disease natural history of HCC ............ 76 5.4 The value of pseudo-control group .............. 77 5.5 Limitations ............ 78 Chapter 6 Conclusion ............................ 79 References ...... 80 | - |
| dc.language.iso | en | - |
| dc.subject | 腹部超音波 | zh_TW |
| dc.subject | 肝癌 | zh_TW |
| dc.subject | 效益 | zh_TW |
| dc.subject | 電腦模擬 | zh_TW |
| dc.subject | 抗病毒藥物 | zh_TW |
| dc.subject | Hepatocellular carcinoma | en |
| dc.subject | Effectiveness | en |
| dc.subject | Computer Simulation | en |
| dc.subject | Anti-viral therapy | en |
| dc.subject | Abdominal ultrasound screening | en |
| dc.title | 以電腦模擬建構對照組評估族群肝癌篩檢效益 | zh_TW |
| dc.title | Effectiveness of Abdominal Ultrasound Screening Policy for Hepatocellular Carcinoma Evaluated by Computer Simulation with Pseudo Control Design | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 106-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.coadvisor | 陳秀熙 | zh_TW |
| dc.contributor.coadvisor | ; | en |
| dc.contributor.oralexamcommittee | 陳祈玲 | zh_TW |
| dc.contributor.oralexamcommittee | ; | en |
| dc.subject.keyword | 腹部超音波,抗病毒藥物,電腦模擬,效益,肝癌, | zh_TW |
| dc.subject.keyword | Abdominal ultrasound screening,Anti-viral therapy,Computer Simulation,Effectiveness,Hepatocellular carcinoma, | en |
| dc.relation.page | 86 | - |
| dc.identifier.doi | 10.6342/NTU201802411 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2018-08-06 | - |
| dc.contributor.author-college | 公共衛生學院 | - |
| dc.contributor.author-dept | 流行病學與預防醫學研究所 | - |
| dc.date.embargo-lift | 2023-10-09 | - |
| 顯示於系所單位: | 流行病學與預防醫學研究所 | |
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