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Antiretroviral resistance testing in people living with HIV

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Abstract

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:

To evaluate the effectiveness of antiretroviral resistance testing (genotypic or phenotypic) in reducing mortality and morbidity in people living with HIV.

Background

Description of the condition

Almost 36.7 million people are living with human immunodeficiency virus (HIV) worldwide (UNAIDS 2016). The widespread use of antiretroviral therapy (ART) has reduced the mortality and morbidity associated with HIV infection. Although only 40% of those eligible for ART are currently receiving it, efforts are underway to improve access to ART (WHO 2017). Effective ART inhibits viral replication and reduces viral load. The World Health Organization (WHO) Model List of Essential Medicines contains a list of antiretroviral (ARV) drugs that are grouped into three classes: nucleoside/nucleotide reverse transcriptase inhibitors (NRTI), non‐nucleoside reverse transcriptase inhibitors (NNRTI), and protease inhibitors (PIs) (WHO 2015). Recommendations on how they should be used include considerations of cost, availability, ease of administration, efficacy, toxicity, and potency. The potency of these medications is severely compromised in the presence of drug resistance. In addition, the combination of drugs should be chosen with care, given that in some classes of ARV drugs (NNRTI) cross resistance is possible: any mutation that confers resistance to one drug will lead to resistance in all the other drugs in the class (Deeks 2001). The WHO recommends initiating ART with two NRTIs in addition to one NNRTI, or one integrase strand transfer inhibitor (INSTI). In the event of treatment failure the NNRTI should be switched to a PI (WHO 2016).

Many problems exist in HIV care, including limited access to ART in some parts of the world, suboptimal levels of adherence, drug resistance, and treatment failure (UNAIDS 2014; UNAIDS 2016). International commitment to a unified response to the HIV pandemic is improving access to ART, and numerous research efforts attempt to pinpoint the best adherence enhancement strategies (Mills 2006; Nachega 2012; Thompson 2012; Chaiyachati 2014; Ramjan 2014). However, viral resistance to ART is a growing problem in people living with HIV. Resistance can either result from the acquisition of mutant strains of the virus or from their selection within the individual (from inappropriate use, poor adherence in terms of dosing and timing, and treatment interruptions).

For people living with HIV and taking ART, the emergence of drug resistance poses a serious threat to a sustained virological response to treatment; it reduces effective therapeutic options; and would increase morbidity, mortality, and infectivity (Gupta 2012; Cambiano 2013). All of the above would lead to increased healthcare burden for individuals and society. Drug resistance may be transmitted (transmitted drug resistance: TDR) or acquired (drug resistance mutation: DRM; Rojas Sánchez 2014).

Close to 80% of treatment‐experienced patients who fail treatment have resistance to at least one drug (WHO 2012). Even though recent data suggest that these numbers are dropping (De Luca 2015), treatment‐experienced patients who are failing treatment would still benefit from earlier detection of resistance and an informed selection of a new regimen. They are more likely to die if switching is delayed (Petersen 2008).

There is increasing evidence of ARV resistance in treatment‐naive individuals (Duwe 2001; Torti 2004; Bakhouch 2009; Barrow 2013; Rojas Sánchez 2014). This drug resistance would most likely be TDR in newly‐infected individuals. Even though TDR may be stabilizing in high‐income countries (10% to 17% with resistance to at least one drug; WHO 2012), it is on the rise in low‐income countries (Pham 2014). As the number of people on ART is increasing, the frequency of DRMs is also increasing (Boender 2016; Rowley 2016; Villabona‐Arenas 2016). People with pretreatment drug resistance are more likely to fail treatment (Wittkop 2011; Hamers 2012), have higher mortality rates (Cambiano 2013; Pinoges 2015), and will need more treatment switches in their course of care (Boender 2015). Irrespective of how drug resistance occurs, it represents a serious threat to the potency of ART in both ART‐naive and ART‐experienced patients.

Description of the intervention

ARV resistance testing may be conducted in one of two ways: genotypic testing involves direct examination of the genetic material of the virus to determine which medications it is resistant to; and phenotypic testing measures the susceptibility of the virus to ARV medications in a controlled environment. Both methods require at least a week to obtain results. Genotypic testing is less expensive and more widely used, but phenotypic testing is easier for care providers to interpret. These tests provide information on resistance to the four main classes of ART (NRTIs, NNRTIs, PIs, and INSTIs; AIDSinfo 2016). Genotypic tests detect resistant mutations in viral genes by sequencing the genes known to confer drug resistance (reverse transcriptase, protease, and integrase). Genotypic testing can be completed within one to two weeks, but their interpretation may be challenging without knowledge of the specific gene mutations and the potential for cross‐resistance. Expert advice is helpful in choosing ART after genotypic testing (Tural 2002). Phenotypic tests take longer (two to three weeks), and they involve examining the ability of the virus to grow in various concentrations of ARV drugs. Viral replication in the presence of ARV drugs is then compared with replication of a reference HIV strain. Expert assistance may also be helpful in guiding interpretation. Genotypic testing is the recommended approach for treatment‐naive and treatment experienced‐patients because it costs less, results can be available faster, and it is more sensitive in detecting mixtures of resistant and wild‐type virus. Phenotypic testing can be added when complex mutation patterns are known or suspected (AIDSinfo 2016). Both tests are costly, unlikely to detect resistant viruses that constitute less than 10% to 20% of the circulating virus population, and do not have uniform standards for quality assurance (AIDSinfo 2016).

Additional considerations in the use of resistance testing include the timing of the test and the viral load. The best results are obtained before or within four weeks of treatment discontinuation, but are challenging to perform in patients with low viral loads (AIDSinfo 2016).

How the intervention might work

Currently, the WHO recommends population‐based surveys to measure levels of drug resistance (WHO 2014), yet it is unclear what levels of resistance call for action and exactly what action should be taken (Cambiano 2013). Data from such surveys in resource‐limited settings suggests that at 12 months, two‐thirds of those who do not achieve viral suppression on first‐line therapy may have drug resistance (Hosseinipour 2013). Viral resistance in treatment‐naive persons can potentially compromise virological response during ART initiation, which suggests the need for HIV resistance testing before ART initiation (Barennes 2014). Drug‐resistance testing is currently recommended to guide the choice of the therapeutic regimen in treatment‐experienced and treatment naive individuals (Durant 1999; Baxter 2000; Cingolani 2002; Meynard 2002; Tural 2002; Hirsch 2008).

If pre‐ART resistance testing of treatment‐naive people with HIV shows evidence of resistance, then it might be necessary to carry out routine resistance testing prior to ART initiation in order to guide therapeutic choices and potentially stall the advent of virological failure. The initiation of ART in treatment‐naive patients without resistance testing may lead to the use of non‐potent drugs and subsequently poor clinical outcomes.

In patients who are already exposed to ART, resistance testing is useful to inform the selection of new drugs (AIDSinfo 2016). In the case of treatment failure, genotypic testing is recommended, but both phenotypic and genotypic testing are recommended for more complex mutation patterns (AIDSinfo 2016). It is unclear what additional benefits may be reaped by detecting resistant mutations prior to clinical and virological signs that indicate failure. The availability of the results of a resistance test may influence how care is provide in several ways including: recycling of previous drugs for which there is no resistance, selection of more potent regimens and the use of this information in combination of medical history (previous drug reactions and adherence) to develop an optimal therapeutic regimen.

Why it is important to do this review

Both the European HIV Drug Resistance Guidelines Panel (Vandamme 2011), and the International Antiviral Society‐USA Panel (Günthard 2014), recommend resistance testing prior to initiation of ART. The WHO does not recommend this approach for resource‐limited countries in which there are limited treatment options, the costs of resistance testing are prohibitive, and may not be necessary if pretreatment drug prevalence is low. The WHO recommends a survey‐based method to detect population‐level development of resistance, in addition to supporting adherence and drug supply continuity (Bennett 2008). However, it is unclear at which threshold of population‐level resistance a drug should be discontinued. In addition, certain subpopulations (such as women who received ARV drugs for prevention of mother‐to‐child‐transmission) may have higher levels of TDR than the general population (Bissio 2017). In resource‐rich countries, baseline resistance testing is possible for patients initiating ART but this recommendation is based on observational data (AIDSinfo 2016). One economic evaluation, based on a hypothetical cohort reported that resistance testing is cost effective for treatment‐naive individuals (Sax 2005). The results on cost‐effectiveness are conflicting for treatment‐experienced patients depending on what kind of resistance testing is used (Corzillius 2004; Phillips 2014). Previous systematic reviews from over 10 years ago have focused solely on patients failing treatment, and on short‐term outcomes (12 months) in high‐income countries, and therefore do not fully address the relative merits of resistance testing as a management strategy in the cascade of care for people living with HIV (Torre 2002; Dunn 2004; Ena 2006). Torre 2002 found that viral suppression was more likely at three and six months after genotypic resistance testing but not after phenotypic testing. Viral suppression was also more likely when expert advice was provided. Dunn 2004 reported a higher proportion of patients achieving viral suppression at three to six months among those who had resistance testing. Ena 2006 reported similar results at three months, and additional benefits if genotypic resistance testing was coupled with expert interpretation. However, given that there now are more options for second‐ and third‐line treatment in resource‐limited settings (WHO 2016), the question of how to choose a new regimen after failing the first‐line regimen in such settings is still pertinent, and should be answered with more recent and applicable evidence addressing long‐term outcomes. In this systematic we will use data from randomized trials and cohort studies to highlight the benefits or harms of conducting resistance testing in individuals prior to initiation of ART and after failure of first‐line treatment.

Objectives

To evaluate the effectiveness of antiretroviral resistance testing (genotypic or phenotypic) in reducing mortality and morbidity in people living with HIV.

Methods

Criteria for considering studies for this review

Types of studies

Randomized controlled trials (RCTs) and cohort studies that evaluate the clinical and biological outcomes in treatment‐naive or treatment‐experienced people with HIV who undergo resistance testing compared to no resistance testing.

Types of participants

HIV‐positive individuals of any age (with documented HIV‐infection as reported by the authors).

Types of interventions

Intervention

Any type of resistance testing in HIV‐positive adults prior to initiation of therapy or after failing first‐line therapy. We will include genotypic and phenotypic tests. Regarding studies that compare different methods of resistance testing against each other, we will only include these if they have a control arm with no testing.

Control

No resistance testing.

Types of outcome measures

Primary outcomes

  • Mortality (proportion of deaths).

  • Virological success: the proportion of participants achieving undetectable viral load (using lower limits for detection and time frames defined by the study authors).

Secondary outcomes

  • Change in mean CD4‐T‐lymphocyte count (immunologic response) over time.

  • Clinical progression to AIDS: the proportion of participants that develop CDC‐defined AIDS (stages III and IV).

  • Development of a second or new opportunistic infection.

  • Quality of life (as reported by the study authors).

Adverse events

  • Any adverse events reported by the study authors.

Search methods for identification of studies

We will perform our literature search with the assistance of an Information Specialist. We will adopt a comprehensive and exhaustive search strategy to identify studies in all languages irrespective of publication status (published, unpublished, in press, or in progress). We will conduct the searches from 1989, the year in which the first case of antiretroviral (ARV) drug resistance was identified (Larder 1989).

In addition to key ARV terms used in the Cochrane Infectious Diseases Group's standard search strategies, we will include all appropriate terms relevant to ARV resistance testing, including Medical Subject Heading (MeSH) terms. We will also use Cochrane Collaboration's Highly Sensitive Strategy for identifying reports of RCTs, and additional terms for observational studies.

Electronic searches

We will search the following electronic databases for relevant randomized controlled trials and observational studies.

  • Cochrane Central Register of Controlled Trials (CENTRAL) (1 January 1989 to search date).

  • PubMed (1 January 1989 to search date).

  • Embase (1 January 1989 to search date).

  • CINAHL (1 January 1989 to search date).

  • Latin American and Caribbean Health Sciences Literature (LILACS) (1 January 1989 to search date).

  • Web of Science (1 January 1989 to search date).

Detailed search strategies are outlined in Appendix 1, Appendix 2, and Appendix 3.

Conference abstracts

We will search conference abstract archives on the websites of the Conference on Retroviruses and Opportunistic Infections (CROI); the International AIDS Conference (IAC); and the International AIDS Society Conference on HIV Pathogenesis, Treatment and Prevention (IAS), for all available abstracts presented at all conferences from 1989 to 2017.

Ongoing trials

  • World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP).

  • ClinicalTrials.gov.

Searching other resources

We will check the reference lists of pertinent studies for other relevant studies. In addition, we will contact experts in the field.

Data collection and analysis

We will present a summary of the identification, screening, and inclusion of studies in this review as a PRISMA diagram (Moher 2010).

Selection of studies

Two review authors (TA, RS, or JT) will independently inspect the titles and abstracts of each reference identified by the search for relevance. We will obtain the full‐text copies of all potentially relevant articles and will screen them using a pre‐tested eligibility form. We will only include those studies that fulfil our inclusion criteria. We will resolve any disagreements by consensus. When a consensus can't be reached, we will consult a third review author (LM) for adjudication. We will examine the included manuscripts to ensure that they contain unique patients whose data is not used in another included study. We will report the excluded studies and their reasons for exclusion in a 'Characteristics of excluded studies' table. We will illustrate the study selection process in a PRISMA diagram.

Data extraction and management

We will use pilot‐tested data extraction forms to record data from the included studies. Two review authors (TA, RS, or JT) will independently extract the data from included trials. In case of any disagreement between the two review authors, a third independent review author will adjudicate (LM). When necessary (missing information or unclear reports), we will contact the study authors for clarification. For reports not published in English, we will invite other scientists with Cochrane methods expertise to assist with screening and data extraction. We will collect bibliometric information, data on the participants, interventions, comparisons, outcomes and study duration.

Bibliometric information

  • Full reference.

  • Country of study.

Patient information

  • Inclusion criteria.

  • Exclusion criteria.

  • Age.

  • Comorbidities.

  • Antiretroviral therapy (ART) exposure (naive versus experienced).

  • Numbers in interventions and control arms.

Intervention

  • Type of testing used.

  • Use of expert interpretation.

  • Drug regimens.

Comparison

  • Details on nature of control group (no testing or delayed testing).

Outcomes

  • Number of participants who experienced an event for dichotomous outcomes.

  • Means and standard deviations for normally distributed continuous outcomes. We will standardize continuous data not reported on the same scale and report with standard errors.

Study duration

  • Duration of study.

  • Timing of outcome measurement (in weeks or months).

Assessment of risk of bias in included studies

We will assess the risk of bias for randomized trials using the Cochrane 'Risk of bias' assessment tool for the following items.

  • Sequence generation: how the allocation sequence was generated and whether it was adequate.

  • Allocation concealment: how the allocation sequence was concealed and whether it was adequate.

  • Blinding of participants, personnel, and outcome assessors.

  • Description of the completeness of outcome data for each main outcome.

  • Selective outcome reporting.

  • Other potential sources of bias (for example, funding).

We will grade the included studies as either at high, low, or unclear risk of bias, corresponding to assessments of yes, no, or unclear risk of bias. Two review authors will independently perform the 'Risk of bias' assessment and complete the 'Risk of bias' tables.

Regarding methodological quality of cohort studies, we will appraise this using the Newcastle‐Ottawa Scale (NOS; Wells 2009). The NOS includes assessments for the following items in three domains.

  • Selection: representativeness of exposed cohort, selection of the non‐exposed cohort, ascertainment of exposure, demonstration that outcome of interest was not present at start of study.

  • Comparability: comparability of cohorts on the basis of the design or analysis.

  • Outcome: assessment of outcome, duration of follow‐up, adequacy of follow‐up.

Measures of treatment effect

We will analyse the data using Review Manager 5 (RevMan 5) (RevMan 2014). We will calculate the risk ratio (RR) or the odds ratio (OR) for binary data, the weighted mean difference (WMD) for continuous data measured on the same scale, and the standard mean difference (SMD) for continuous data measured on different scales. We will present these results with 95% confidence interval (CI) values.

Certainty of the evidence

We will assess the certainty of the body of evidence using the GRADE approach (GRADEpro 2015), which defines the quality of evidence for each outcome as the extent to which one can be confident that an estimate of effect or association is close to the quantity of specific interest. The certainty rating across studies has four levels: high, moderate, low, or very low. RCTs are categorized as high certainty evidence but can be downgraded; similarly, other types of controlled trials and observational studies are categorized as low certainty but can be upgraded. Factors that decrease the certainty of the evidence include limitations in design, indirectness of evidence, unexplained heterogeneity or inconsistency of results, imprecision of results, or high probability of publication bias. Factors that can increase the certainty level of a body of evidence include having a large magnitude of effect, whether plausible confounding would reduce a demonstrated effect, and if there is a dose‐response gradient (Guyatt 2011). We will use the GRADEpro Guideline Development Tool (GDT) software to produce 'Summary of findings' tables (GRADEpro 2015).

Unit of analysis issues

The unit of analysis will be the individual. We do not anticipate finding any cluster trials or cross‐over trials.

Dealing with missing data

For missing or unclear data, we will contact the authors of the included studies during the eligibility assessment and data extraction stage. We may also seek the missing data from secondary publications of the same study. In the event that we are unable to obtain the missing data, we will conduct a complete‐case analysis.

Assessment of heterogeneity

We will first assess included studies for clinical heterogeneity. If studies are similar enough to combine (with regards to the participants, interventions, comparisons, and outcomes), we will perform a meta‐analysis and will assess statistical heterogeneity. We will assess statistical heterogeneity using the Chi² test for homogeneity with a level of significance, alpha = 0.10, and the I² statistic to quantify inconsistency.

Assessment of reporting biases

We will assess reporting bias (selective outcome reporting) using the Cochrane 'Risk of bias' assessment tool. Also we will assess publication bias using a funnel plot if 10 or more studies meet the inclusion criteria (Higgins 2011).

Data synthesis

We will perform a fixed‐effect meta‐analysis to synthesize sufficiently similar quantitative data. We will pool the results from the included studies to determine the OR and RR values for the two compared interventions of achieving lower mortality and virological success. We will not pool data from randomized and non‐randomized studies. We will conduct the analyses per exposure, and we will analyse data from treatment‐naive and treatment‐experienced people separately.

Subgroup analysis and investigation of heterogeneity

We will perform the Chi² test of homogeneity to ensure that the differences between the results of each study could not be expected by chance. If there is significant unexplained statistical heterogeneity we will use a random‐effects model.

Regarding subgroup analyses, we will conduct these separately for studies that include treatment‐naive and treatment‐experienced people. Possible sources of heterogeneity in this review and potential subgroups might include the following.

  • Potency of antiretroviral therapy (ART) used (NNRTI or PI‐based regimens).

  • Type of resistance testing used (genotype or phenotype).

  • Level of advancement of disease (Centers for Disease Control and Prevention (CDC) or WHO stage).

  • Expert interpretation (the use of expert advice to guide interpretation of resistance testing results).

  • Age (children versus adults).

We hypothesize that studies, with more potent ART, more sophisticated resistance testing techniques, with patients at early stages of the disease and whose choice of regimen is supported by expert advice will have better outcomes. Likewise, we also expect that the benefits in ART‐naive patients will be better because they have greater variety of potent drugs to choose from after resistance testing; patients in settings with a higher population‐level resistance rate would also experience more benefits from resistance testing prior to initiating therapy. We will restrict the number of subgroup analyses to those most relevant.

Sensitivity analysis

We will undertake a sensitivity analysis to evaluate the bias introduced by variability in study design (observational versus randomized) and risk of bias.