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Cochrane Database of Systematic Reviews Protocol - Intervention

Opioid therapy for treating rheumatoid arthritis pain

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Abstract

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

To assess the efficacy and safety of opioid analgesics for treating pain in patients with RA.

Background

Rheumatoid arthritis (RA) is a chronic inflammatory, destructive joint disease that affects about 1% of the population (Alarcon 1995). RA can cause progressive joint destruction and deformity despite treatment.

The mainstays of therapy in RA, disease‐modifying anti‐rheumatic drugs (DMARDs) and non‐steroidal anti‐inflammatory drugs (NSAIDs), primarily target inflammatory pathways, however there are multiple contributors to the genesis of pain in RA in addition to inflammation, including joint damage or destruction and peripheral and central sensitization (McDougall 2006), which may not be responsive to treatment with these medications. Indeed, despite recent improvements in the management of RA, including earlier intervention, more aggressive use of DMARDs, and the advent of the biologic disease‐modifying therapies (bDMARDs), many patients with RA continue to experience musculoskeletal pain, even when inflammation is well‐controlled (Kvien 2004). Patients with RA perceive pain to be their predominant impairment (Minnock 2003) and report pain management as their highest priority (Heiberg 2002). Although opioids have been used successfully to control pain in RA, the potential for adverse effects, addiction and drug interactions have limited their use (Fitzcharles 2009).

Opioids produce their pharmacological effects via interaction with receptors located on neuronal cell walls. The synthetic analgesic tramadol combines an opioid receptor effect with monoamine reuptake inhibition (Raffa 1992). The presence of opioid receptors on peripheral afferent nerves within joints suggests that opioids may have peripheral analgesic effects in RA in addition to their effect on the CNS (Lang 2010). Because the analgesic properties of opioids do not depend on the presence of active inflammation, they are theoretically attractive as an analgesic option in RA patients with persistent pain despite the use of DMARDs. In addition, intra‐articular nociceptive nerve fibres may contribute to joint inflammation via the release of neuropeptides such as substance P and calcitonin gene‐related peptide, which raises the possibility that opioids may also act to inhibit joint inflammation (Fields 1980; McDougall 2006).

The role of opioids in the management of acute pain, post‐surgical pain and chronic cancer pain is clearly established (Hudcova 2006; Wiffen 2007). However, the role of opioid therapy for chronic non‐cancer pain remains an area of debate. There is weak evidence that opioids may provide clinically significant pain relief in a proportion of individuals with chronic non‐cancer pain, however many patients discontinue treatment due to adverse effects (Noble 2010). Although opioids are frequently used for the management of pain in RA sufferers (Grijalva 2008), current RA treatment guidelines offer scant guidance regarding the use of opioids in an evidence‐based management algorithm (ACR 2002; Luqmani 2009). There is a large unmet need for safe and effective analgesic medications in this population; this systematic review seeks to examine the evidence regarding the role of opioids in the management of RA pain.

Objectives

To assess the efficacy and safety of opioid analgesics for treating pain in patients with RA.

Methods

Criteria for considering studies for this review

Types of studies

All published randomized or quasi‐randomized controlled trials (RCTs or CCTs) which compare opioid therapy to another therapy (active or placebo, including non‐pharmacological therapies) for RA will be considered for inclusion.

Studies of opioid therapy in RA in the immediate post‐operative setting (e.g. after arthroplasty or arthroscopy) and studies that do not contain pain as an outcome measure will be excluded.

Only trials that are published as full articles or are available as a full trial report will be included.

Types of participants

Adult patients (aged 18 years or older) with a diagnosis of RA. Populations that include a mix of people with RA and other musculoskeletal pain will be excluded unless results for the RA population can be separated out from the analysis.

Types of interventions

All trials that evaluate opioid medications will be included. This includes tramadol and compound medications where an opioid is combined with a simple analgesic (such as codeine combined with paracetamol). Opioid administration may be via any route (including, but not limited to, oral, transdermal and intra‐articular), any formulation (e.g. immediate‐acting or sustained‐release), and any interval (e.g. on‐demand or regular intervals).

Comparators may be:

1. Placebo

2. A different opioid analgesic

3. Non‐opioid analgesic medications (including NSAIDs, paracetamol, neuromodulators and antidepressants, but excluding DMARDs and bDMARDs)

4. Non‐pharmacological analgesic modalities (e.g. acupuncture, massage, transcutaneous electrical nerve stimulation [TENS])

5. The same opioid medication via a different route, formulation or dosing interval

Types of outcome measures

There is considerable variation in the outcome measures reported in clinical trials of interventions for pain. For the purpose of this systematic review, we aim to include outcome measures that are considered to be of the greatest importance to patients with persistent pain and the clinicians who care for them. The Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) has published consensus recommendations for determining clinically important changes in outcome measures in clinical trials of interventions for chronic pain. Reductions in pain intensity of ≥30% and ≥50% reflect moderate and substantial clinically important differences, respectively, and it is recommended that the proportion of patients that respond with these degrees of pain relief be reported (Dworkin 2008).

Continuous outcome measures in pain trials (such as mean change on a 100mm visual analogue scale) may not follow a Gaussian distribution. Often, a bimodal distribution is seen instead, where patients tend to report either very good or very poor pain relief (Moore 2010). This creates difficulty in interpreting the meaning of average changes in continuous pain measures. For this reason, a dichotomous outcome measure (the proportion of subjects reporting ≥30% pain relief) is likely to be more clinically relevant and will be the primary efficacy measure in this review. It is recognised, however, that it has been the practice in most trials of interventions for chronic pain to report continuous measures and therefore the mean change in pain score will also be included as a secondary efficacy measure.

The pain state at the end of a clinical trial of an analgesic intervention, in contrast to measures of pain improvement, has also been recommended as a clinically relevant dichotomous outcome measure and will be included as a secondary efficacy measure in this review (Moore 2010). A global rating of treatment satisfaction, such as the Patient Global Impression of Change scale (PGIC), which provides an outcome measure that integrates pain relief, changes in function and side‐effects, into a single, interpretable measure, is also recommended by IMMPACT, and will be included as a secondary outcome measure (Dworkin 2008).

Primary outcomes

1. Efficacy: Patient reported pain relief of 30% or greater

2. Safety: Number of withdrawals due to adverse events

Secondary outcomes

3. Pain:

a. Patient reported pain relief of 50% or greater;

b. Patient reported global impression of clinical change (PGIC) much or very much improved

c. Proportion of patients achieving pain score below 30/100mm on visual analogue scale

d. Mean change in pain score on visual analogue scale or numerical rating scale

4. Number and type of adverse events (AEs) and serious adverse events (SAEs, defined as AEs that are fatal, life‐threatening, or require hospitalization)

5. Function ‐ as measured by the HAQ or modified HAQ (Fries 1980, Pincus 1983)

6. Quality of life ‐ as measured by either generic instruments (such as the Short‐Form‐36 [SF‐36]) or disease‐specific tools (such as the Rheumatoid Arthritis Quality of Life instrument [RAQoL])

7. Participant withdrawals due to inadequate analgesia

The duration of trials of interventions for pain varies considerably. The efficacy of interventions, and the relative balance of benefits and harms, may vary according to the duration of the trial and therefore the combination of results from trials of different durations may represent a source of bias in systematic reviews (Moore 2010). For the purpose of this review, trials will be grouped into those of duration <1 week, 1‐6 weeks,  and >6 weeks.

The short and long term outcomes for proportion reporting pain relief of 30% or greater, total number of withdrawals due to adverse effects, number of serious adverse events, function and quality of life will be presented in the summary of findings table.

Search methods for identification of studies

Electronic searches

We will search the following databases for RCTs or CCTs using the search strategies detailed in the appendices:

1. OVID Medline 1950‐present (Appendix 1);

2. EMBASE 1980‐present (Appendix 2);

3. The Cochrane Central Register of Controlled Trials (CENTRAL) (Appendix 3).

No language restrictions will be applied.

Searching other resources

Abstracts from the two major international rheumatology scientific meetings ‐ the American College of Rheumatology (ACR) and the European League Against Rheumatism (EULAR) ‐ will be searched for the years 2007‐2009.

The reference lists of included articles will be inspected for additional trials.

Data collection and analysis

Selection of studies

All identified studies will be assessed independently by two reviewers (SW, BR) to identify the trials that fulfil inclusion criteria. All possibly relevant articles will be retrieved in full text and any disagreement in study selection will be resolved by consensus or by discussion with a third reviewer (RB or EH) if needed. Studies will be translated into English where necessary.

Data extraction and management

Two independent reviewers (SW and BR) will extract relevant information from the included trials including study design, characteristics of study population, treatment regimen and duration, outcomes and timing of outcome assessment, using predetermined forms. The raw data (means and standard deviations for continuous outcomes and number of events or participants for dichotomous outcomes) will be extracted for outcomes of interest.

Differences in data extraction will be resolved by referring back to the original articles and establishing consensus. A third reviewer (RB or EH) will be consulted to help resolve differences if necessary.

Assessment of risk of bias in included studies

The potential for bias in included studies will be assessed using a risk of bias table. Two authors (SW, BR) will independently assess risk of bias for all included studies for the following items: random sequence generation, allocation concealment, blinding of participants, care provider, and outcome assessor for each outcome measure (see primary and secondary outcome measures), incomplete outcome data and other biases, conforming to the methods recommended by the Cochrane Collaboration (Higgins 2009a). To determine the risk of bias of a study, for each criterion the presence of sufficient information and the likelihood of potential bias will be evaluated. Each criterion is rated as Yes (low risk of bias), No (high risk of bias) or Unclear (either lack of information or uncertainty over the potential for bias). In a consensus meeting disagreements among the reviewers will be discussed and resolved. If consensus cannot be reached, a third reviewer (RB or EH) will make the final decision.

Measures of treatment effect

The data will be summarised in a meta‐analysis only if there is sufficient clinical and statistical homogeneity. For continuous data, results will be analyzed as mean differences between the intervention and comparator group (MD), with corresponding 95% confidence intervals. The mean difference between treated group and control group is weighted by the inverse of the variance in the pooled treatment estimate. However, when different scales are used to measure the same conceptual outcome (e.g. functional status or pain), standardized mean differences (SMD) will be calculated instead, with corresponding 95% confidence intervals. SMDs are calculated by dividing the MD by the standard deviation, resulting in a unitless measure of treatment effect. For dichotomous data, a relative risk (RR) with corresponding 95% confidence intervals will be calculated.

For studies containing more than two intervention groups, making multiple pair‐wise comparisons between all possible pairs of intervention groups possible, we will include the same group of participants only once in the meta‐analysis.

Unit of analysis issues

Unit of analysis problems are not expected in this review. In the event that cross‐over trials are identified in which the reporting of continuous outcome data precludes paired analysis, these data will not be included in a meta‐analysis, in order to avoid unit‐of‐analysis error.  Where carry‐over effects are thought to exist, and where sufficient data exist, data from the first period only will be included in the analysis (Higgins 2009b).

Dealing with missing data

Where data are missing or incomplete, further information will be sought from the study authors.

In cases where individuals are missing from the reported results, we will assume the missing values to have a poor outcome. For dichotomous outcomes that measure adverse events (e.g. number of withdrawals due to adverse events), the withdrawal rate is calculated using the number of patients that received treatment as the denominator (worst case analysis). For dichotomous outcomes that measure benefits (e.g. proportion of subjects achieving an ACR20 response), the worst case analysis will be calculated using the number of randomised subjects as the denominator. For continuous outcomes (e.g. pain), we will calculate the MD or SMD based on the number of patients analysed at the time point. If the number of patients analysed are not presented for each time point, the number of randomised patients in each group at baseline will be used.

Where possible, missing standard deviations will be computed from other statistics such as standard errors, confidence intervals or p‐values, according to the methods recommended in the Cochrane Handbook (Higgins 2009c). If standard deviations cannot be calculated, they will be imputed (e.g. from other studies in the meta‐analysis; Higgins 2009b).

Assessment of heterogeneity

Prior to meta‐analysis, we will assess studies for clinical homogeneity with respect to type of therapy, control group, and the outcomes. For any studies judged as clinically homogeneous, statistical heterogeneity will be assessed using the I2 statistic (Deeks 2009), using the following as a rough guide for interpretation: 0‐40% might not be important, 30‐60% may represent moderate heterogeneity, 50‐90% may represent substantial heterogeneity, and 75‐100% considerable heterogeneity. In cases of considerable heterogeneity (defined as I2 ≥75%), we will explore the data further, including subgroup analyses, in an attempt to explain the heterogeneity.

Assessment of reporting biases

In order to determine whether reporting bias is present, we will determine whether the protocol of the RCT was published before recruitment of patients of the study was started. For studies published after July 1st 2005, we will screen the Clinical Trial Register at the International Clinical Trials Registry Platform of the World Health Organisation (http://apps.who.int/trialssearch; DeAngelis 2004). We will evaluate whether selective reporting of outcomes is present (outcome reporting bias).

We will compare the fixed‐effect estimate against the random‐effects model to assess the possible presence of small sample bias in the published literature (i.e. in which the intervention effect is more beneficial in smaller studies). In the presence of small sample bias, the random‐effects estimate of the intervention is more beneficial than the fixed‐effect estimate (Sterne 2008).

The potential for reporting bias will be further explored by funnel plots if ≥10 studies are available.

Data synthesis

Where studies are sufficiently homogeneous that it remains clinically meaningful for them to be pooled, meta‐analysis will be performed using a random‐effects model, regardless of the I2 results. Analysis will be performed using Review Manager 5 and forest plots will be produced for all analyses.

Subgroup analysis and investigation of heterogeneity

Where sufficient data are available, the following sub‐group analyses are planned:

1. Patients' age (<65 years versus ≥65 years)

2. Opioid formulation (short‐acting versus sustained‐release)

3. Route of opioid administration (oral versus parenteral)

4. Duration of RA (≤2 years versus >2 years)

Sensitivity analysis

Where sufficient studies exist, sensitivity analyses are planned to assess the impact of any bias attributable to inadequate or unclear treatment allocation (including studies with quasi‐randomised designs).

Presentation of key results

A summary of findings table will be produced using GRADEpro software. This table provides key information concerning the quality of evidence, the magnitude of effect of the interventions examined, and the sum of available data on the outcomes (short and long term outcomes for pain, total number of withdrawals due to adverse effects, function and quality of life), as recommended by the Cochrane Collaboration (Schünemann 2008a). It includes an overall grading of the evidence related to each of the main outcomes using the GRADE approach (Schünemann 2008b).

In addition to the absolute and relative magnitude of effect provided in the summary of findings table, for dichotomous outcomes, number needed to treat to benefit (NNTB) or the number needed to treat to harm (NNTH) will be calculated from the control group event rate (unless the population event rate is known) and the relative risk using the Visual Rx NNT calculator (Cates 2004). For continuous outcomes, the NNT will be calculated using the Wells calculator software available at the CMSG editorial office. The minimal clinically important difference (MCID) for each outcome will be determined for input into the calculator.