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采用一两种血清学检测方法诊断HBV和HCV感染的策略?预测模型的应用。

One or two serological assay testing strategy for diagnosis of HBV and HCV infection? The use of predictive modelling.

作者信息

Parry John V, Easterbrook Philippa, Sands Anita R

机构信息

Virus Reference Department, Public Health England, 61 Colindale Avenue, London, NW9 5HT, UK.

Centre for Research on Drugs & Health Behaviour, London School of Hygiene & Tropical Medicine, London, UK.

出版信息

BMC Infect Dis. 2017 Nov 1;17(Suppl 1):705. doi: 10.1186/s12879-017-2774-1.

Abstract

BACKGROUND

Initial serological testing for chronic hepatitis B virus (HBV) and hepatitis C virus (HCV) infection is conducted using either rapid diagnostic tests (RDT) or laboratory-based enzyme immunoassays (EIA)s for detection of hepatitis B surface antigen (HBsAg) or antibodies to HCV (anti-HCV), typically on serum or plasma specimens and, for certain RDTs, capillary whole blood. WHO recommends the use of standardized testing strategies - defined as a sequence of one or more assays to maximize testing accuracy while simplifying the testing process and ideally minimizing cost. Our objective was to examine the diagnostic outcomes of a one- versus two-assay serological testing strategy. These data were used to inform recommendations in the 2017 WHO Guidelines on hepatitis B and C testing.

METHODS

Few published studies have compared diagnostic outcomes for one-assay versus two-assay serological testing strategies for HBsAg and anti-HCV. Therefore, the principles of Bayesian statistics were used to conduct a modelling exercise to examine the outcomes of a one-assay versus two-assay testing strategy when applied to a hypothetical population of 10,000 individuals. The resulting model examined the diagnostic outcomes (true and false positive diagnoses; true and false negative diagnoses; positive and negative predictive values as a function of prevalence; and total tests required) for both one-assay and two-assay testing strategies. The performance characteristics assumed for assays used within the testing strategies were informed by WHO prequalification assessment findings and systematic reviews for diagnostic accuracy studies. Each of the presumptive testing strategies (one-assay or two-assay) was modelled at varying prevalences of HBsAg (10%, 2% and 0.4%) and of anti-HCV (40%, 10%, 2% and 0.4%), aimed at representing the range of testing populations typically encountered in WHO Member States. When the two-assay testing strategy was considered, the model assumed the independence of the two assays.

RESULTS

Modeling demonstrated that applying a single assay (HBsAg or anti-HCV), even with high specificity (99%), may result in considerable numbers of false positive diagnoses and low positive predictive values (PPV), particularly in lower prevalence settings. Even at very low prevalences shifting to a two-assay testing strategy would result in a PPV approaching 1.0. When test sensitivity is high (>99%) false negative reactions are rare at all but the highest prevalences; but a two-test strategy might yield more false negative diagnoses. The order in which the tests are used has no impact on the overall accuracy of a two-assay strategy though it may impact the total number of tests needed to complete the diagnostic strategy, incurring added cost and complexity. HBsAg assays may have a low sensitivity (<90%), and result in large numbers of false negative diagnoses, particularly in high prevalence settings, which would be exacerbated in the two-assay testing strategy. In contrast, most anti-HCV assays have high sensitivity and lead to fewer false negative results, both in the one-assay and two-assay testing strategies. At prevalences ≤2% the number of tests needed using a second assay was nearly always small, at <300 per 10,000 individuals tested, making sustainability of a second assay uncertain in such a setting.

CONCLUSIONS

A key public health objective of an effective testing strategy is to identify all individuals who would benefit from treatment. Therefore, a strategy that prioritizes a high NPV (minimal false negatives) may be acceptable even if the PPV is suboptimal (some false positives) as the implementation of such a public health programme must also take account of other factors such as costs, feasibility, impact on testing uptake and linkage to care, and consequences of a false-positive test. This rationale informed the development of the WHO Viral Hepatitis Testing Guidelines, with a conditional recommendation for a one-assay serological testing strategy in most testing settings and populations (≥0.4% prevalence in population tested). A one-test strategy results in few failures to diagnose infection and, although it is associated under most assumptions with a sub-optimal PPV, benefits include greater simplicity, easier implementation, lower costs and better feasibility, uptake and linkage to care. Furthermore, prior to antiviral therapy all those diagnosed either HBsAg or anti-HCV positive will require confirmation of viræmia, preventing unnecessary treatment of those who may be false positive on serology. For HBsAg, in low-prevalence settings (≤0.4%), a second recommendation was made to consider a two-assay testing strategy, using a confirmatory neutralization step or a second different HBsAg assay.

摘要

背景

慢性乙型肝炎病毒(HBV)和丙型肝炎病毒(HCV)感染的初始血清学检测采用快速诊断检测(RDT)或基于实验室的酶免疫测定(EIA),以检测乙型肝炎表面抗原(HBsAg)或抗HCV抗体,通常检测血清或血浆标本,某些RDT检测毛细血管全血。世卫组织建议采用标准化检测策略,即定义为一种或多种检测方法的序列,以在简化检测过程并理想地降低成本的同时最大化检测准确性。我们的目标是检验单检测与双检测血清学检测策略的诊断结果。这些数据用于为2017年世卫组织乙型和丙型肝炎检测指南提供建议。

方法

很少有已发表的研究比较HBsAg和抗HCV单检测与双检测血清学检测策略的诊断结果。因此,采用贝叶斯统计原理进行建模,以检验应用于10000人的假设人群时单检测与双检测策略的结果。所得模型检验了单检测和双检测策略的诊断结果(真阳性和假阳性诊断;真阴性和假阴性诊断;作为患病率函数的阳性和阴性预测值;以及所需的总检测次数)。检测策略中使用的检测方法的性能特征参考了世卫组织预认证评估结果和诊断准确性研究的系统评价。每种推定检测策略(单检测或双检测)在不同的HBsAg患病率(10%、2%和0.4%)和抗HCV患病率(40%、10%、2%和0.4%)下进行建模,旨在代表世卫组织成员国通常遇到的检测人群范围。考虑双检测策略时,模型假设两种检测方法相互独立。

结果

建模表明,即使采用高特异性(99%)的单一检测方法(HBsAg或抗HCV),也可能导致大量假阳性诊断和低阳性预测值(PPV),尤其是在患病率较低的情况下。即使在极低的患病率下,转向双检测策略也会使PPV接近1.0。当检测灵敏度较高(>99%)时,除了患病率最高的情况外,假阴性反应很少见;但双检测策略可能会产生更多假阴性诊断。检测方法的使用顺序对双检测策略的总体准确性没有影响,尽管它可能会影响完成诊断策略所需的总检测次数,从而增加成本和复杂性。HBsAg检测可能灵敏度较低(<90%),并导致大量假阴性诊断,尤其是在高患病率情况下,在双检测策略中这种情况会更加严重。相比之下

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9a0c/5688456/844160908fe8/12879_2017_2774_Fig1_HTML.jpg

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