当前位置:科学网首页 > 小柯机器人 >详情
研究揭示评估结核病发病的新方法
作者:小柯机器人 发布时间:2020/10/20 16:13:07

伦敦大学Ibrahim Abubakar团队近日取得一项新成果,即在低传播条件下发现和验证个体化结核病(TB)风险预测因子。该研究成果于2020年10月19日发表在《自然-医学》上。

从来自20个国家包括80,468名接受结核分枝杆菌潜伏性感染(LTBI)测试个体的18个系统性鉴定研究汇总数据中,研究人员发现在未经LTBI治疗的人群中儿童接触者的5年累积性结核病发病率为15.6%(95%的置信区间(CI),8.0-29.2%)、成人接触者为4.8%(95%CI,3.0–7.7%)、移民接触者为5.0%(95%CI,1.6–14.5%)、免疫损伤接触者为4.8%(95%CI,1.5–14.3%)。

研究人员在风险组中确认了高度可变的估计值,因此有必要采用个性化方法进行风险分层。因此,研究人员开发了个体化结核病风险预测因子(PERISKOPE-TB),该指标结合了对T细胞致敏性和临床协变量的定量测量。对模型的内部-外部交叉验证显示,对于结核病的随机效应荟萃分析C统计量为0.88(95%CI,0.82-0.93)。

在决策曲线分析中,与治疗所有或没有LTBI的人相比,该模型证明了针对预防性治疗的临床效用。研究人员以基于实事和以患者为中心的方法挑战了当前对LTBI患者结核病发病风险的粗略估计方法,并有助于在全球范围内消除结核病。

据悉,在LTBI个体中结核病的发病概率是可变的,但缺乏对个体化发病风险的有效估计。

附:英文原文

Title: Discovery and validation of a personalized risk predictor for incident tuberculosis in low transmission settings

Author: Rishi K. Gupta, Claire J. Calderwood, Alexei Yavlinsky, Maria Krutikov, Matteo Quartagno, Maximilian C. Aichelburg, Neus Altet, Roland Diel, Claudia C. Dobler, Jose Dominguez, Joseph S. Doyle, Connie Erkens, Steffen Geis, Pranabashis Haldar, Anja M. Hauri, Thomas Hermansen, James C. Johnston, Christoph Lange, Berit Lange, Frank van Leth, Laura Muoz, Christine Roder, Kamila Romanowski, David Roth, Martina Sester, Rosa Sloot, Giovanni Sotgiu, Gerrit Woltmann, Takashi Yoshiyama, Jean-Pierre Zellweger, Dominik Zenner, Robert W. Aldridge, Andrew Copas, Molebogeng X. Rangaka, Marc Lipman, Mahdad Noursadeghi, Ibrahim Abubakar

Issue&Volume: 2020-10-19

Abstract: The risk of tuberculosis (TB) is variable among individuals with latent Mycobacterium tuberculosis infection (LTBI), but validated estimates of personalized risk are lacking. In pooled data from 18 systematically identified cohort studies from 20 countries, including 80,468 individuals tested for LTBI, 5-year cumulative incident TB risk among people with untreated LTBI was 15.6% (95%confidence interval (CI), 8.0–29.2%) among child contacts, 4.8% (95% CI, 3.0–7.7%) among adult contacts, 5.0% (95% CI, 1.6–14.5%) among migrants and 4.8% (95% CI, 1.5–14.3%) among immunocompromised groups. We confirmed highly variable estimates within risk groups, necessitating an individualized approach to risk stratification. Therefore, we developed a personalized risk predictor for incident TB (PERISKOPE-TB) that combines a quantitative measure of T cell sensitization and clinical covariates. Internal–external cross-validation of the model demonstrated a random effects meta-analysis C-statistic of 0.88 (95% CI, 0.82–0.93) for incident TB. In decision curve analysis, the model demonstrated clinical utility for targeting preventative treatment, compared to treating all, or no, people with LTBI. We challenge the current crude approach to TB risk estimation among people with LTBI in favor of our evidence-based and patient-centered method, in settings aiming for pre-elimination worldwide.

DOI: 10.1038/s41591-020-1076-0

Source: https://www.nature.com/articles/s41591-020-1076-0

期刊信息

Nature Medicine:《自然—医学》,创刊于1995年。隶属于施普林格·自然出版集团,最新IF:30.641
官方网址:https://www.nature.com/nm/
投稿链接:https://mts-nmed.nature.com/cgi-bin/main.plex