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研究生:李欣蓉
研究生(外文):Shin-Jung Lee
論文名稱:人類免疫不全病毒感染者之潛伏性結核感染
論文名稱(外文):Latent Tuberculosis Infection in HIV-infected persons
指導教授:方啟泰方啟泰引用關係
指導教授(外文):Chi-Tai Fang
口試委員:劉永慶王振源張鑾英林先和
口試委員(外文):Yung-Ching LiuJann-Yuan Wang
口試日期:2015-07-30
學位類別:博士
校院名稱:國立臺灣大學
系所名稱:流行病學與預防醫學研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2015
畢業學年度:103
語文別:英文
論文頁數:34
中文關鍵詞:潛伏性結核人類免疫缺乏病毒丙型干擾素釋放檢驗流程
外文關鍵詞:latent tuberculosisHIVIGRAalgorithm
相關次數:
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背景: 目前缺乏單一檢驗方法來預測人類免疫病毒(HIV)感染者得到結核病的風險。我們發展和驗證了一個臨床風險評估流程,利用CD4細胞數目,HIV病毒載量 (pVL),和丙型干擾素釋放檢驗(IGRA),來辨識在低到中負荷結核病情境,且已接受高效能抗反轉錄酶病毒治療(HAART)之HIV感染者,那些人具有得到結核病的高風險。
方法: 此為前瞻性五年之世代研究,在兩家醫院之HIV感染者進行,研究時間為2006年至2012年。HAART的啟用依據當時的治療準則(CD4細胞數目<=350/μL)。我們使用Cox迴歸分析來找出活動性結核的預測因子並建立評估流程。採用之驗證世代包括1455位已在國際雜誌發表之研究中的HIV感染者。我們計算模式以操作特徵曲線下面積(area under the receiver operating characteristic (ROC)curve)進行。
結論: 總共772位進入研究追蹤中位數為5.21年,其中17位發生活動性結核。CD4細胞數 < 350/μL 或 pVL ≥ 100,000/mL可以預測活動性結核發生(adjusted HR 4.87, 95% CI 1.49-15.90, P=0.009)。在CD4 ≥ 350/μL或 pVL < 100,000/mL (adjusted HR 6.09, 95% CI 1.52-24.40, P=0.01)的條件下,IGRA的結果陽性能正確預測潛伏性結核感染。比起僅使用IGRA的策略,此評估流程可使敏感度增加從37.5%到76.5%,
陰性預測值可增加從98.5%至99.2%。比起全面預防性治療策略,此評估流程可以避免468 (60.6%)人接受到不需要的結核病預防性治療。本研究世代的操作特徵曲線下面積為0.692 (95% CI: 0.587-0.798) ,在兩個驗證世代中分別為0.792 (95% CI: 0.776-0.808) 和0.766。
結論: 一個經過驗證的評估流程,包含CD4細胞數目,HIV病毒載量,和IGRA結果可以應用在低到中度結核病負荷情境,針對有例行接受HAART的HIV感染者,來引導結核病預防性治療的給予。此評估流程的實施將可避免低風險病患曝觸到不需要的藥物毒性,同時減少全面治療造成的醫療系統的負荷。


Background: Predicting the risk of tuberculosis (TB) in people living with HIV (PLHIV) using a single test is currently not possible. We aimed to develop and validate a clinical algorithm, using baseline CD4 cell counts, HIV viral load (pVL), and interferon-gamma release assay (IGRA), to identify PLHIV who are at high risk for incident active TB in low-to-moderate TB burden settings where highly active antiretroviral therapy (HAART) is routinely provided.
Methods: A prospective, 5-year, cohort study of adult PLHIV was conducted from 2006 to 2012 in two hospitals in Taiwan. HAART was initiated based on contemporary guidelines (CD4 count <= 350/μL). Cox regression was used to identify the predictors of active TB and to construct the algorithm. The validation cohorts included 1455 HIV-infected individuals from previous published studies. Area under the receiver operating characteristic (ROC) curve was calculated.
Results: Seventeen of 772 participants developed active TB during a median follow-up period of 5.21 years. Baseline CD4 < 350/μL or pVL ≥ 100,000/mL was a predictor of active TB (adjusted HR 4.87, 95% CI 1.49-15.90, P=0.009). A positive baseline IGRA predicted TB in patients with baseline CD4 ≥ 350/μL and pVL < 100,000/mL (adjusted HR 6.09, 95% CI 1.52-24.40, P=0.01). Compared with an IGRA-alone strategy, the algorithm improved the sensitivity from 37.5% to 76.5%, the negative predictive value from 98.5% to 99.2%. Compared with an untargeted strategy, the algorithm spared 468 (60.6%) from unnecessary TB preventive treatment. Area under the ROC curve was 0.692 (95% CI: 0.587-0.798) for the study cohort and 0.792 (95% CI: 0.776-0.808) and 0.766 in the 2 validation cohorts.
Conclusions: A validated algorithm incorporating the baseline CD4 cell count, HIV viral load, and IGRA status can be used to guide targeted TB preventive treatment in PLHIV in low-to-moderate TB burden settings where HAART is routinely provided to all PLHIV. The implementation of this algorithm will avoid unnecessary exposure of low-risk patients to drug toxicity and simultaneously, reduce the burden of universal treatment on the healthcare system.


目錄 ii-iii
List of Tables iv
List of Figures v
致謝 I
中文摘要 II
Abstract IV
1. Introduction 1
2.Materials and Methods 3
2.1 Study Design 3
2.2 Ethical Statement 3
2.3 Setting 3
2.4 Baseline Evaluation 3
2.5 Interferon-γ Release Assay (IGRA) 4
2.6 Follow up and ascertainment of outcome 4
2.7 Validation Cohort 5
2.8 Statistical Analysis 5
3. Results 7
3.1 Participants 7
3.2 Baseline CD4 cell counts and HIVviral load 7
3.3 Baseline IGRA results 8
3.4 Incidence and Risk Factors for Developing Active TB Disease 8
3.5 Sensitivity and Predictive Values of IGRA 9
3.6 Algorithm to predict risk of active TB in HIV-infected persons 9
3.7 Comparison of study algorithm with alternative approaches 10
3.8 Validation of the study algorithm 10
4. Discussion 12
5.References 18

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