Cannabis‐based medicines for chronic neuropathic pain in adults
This review is one of a series on drugs used to treat chronic neuropathic pain. Estimates of the population prevalence of chronic pain with neuropathic components range between 6% and 10%. Current pharmacological treatment options for neuropathic pain afford substantial benefit for only a few people, often with adverse effects that outweigh the benefits. There is a need to explore other treatment options, with different mechanisms of action for treatment of conditions with chronic neuropathic pain. Cannabis has been used for millennia to reduce pain. Herbal cannabis is currently strongly promoted by some patients and their advocates to treat any type of chronic pain.
To assess the efficacy, tolerability, and safety of cannabis‐based medicines (herbal, plant‐derived, synthetic) compared to placebo or conventional drugs for conditions with chronic neuropathic pain in adults.
In November 2017 we searched CENTRAL, MEDLINE, Embase, and two trials registries for published and ongoing trials, and examined the reference lists of reviewed articles.
We selected randomised, double‐blind controlled trials of medical cannabis, plant‐derived and synthetic cannabis‐based medicines against placebo or any other active treatment of conditions with chronic neuropathic pain in adults, with a treatment duration of at least two weeks and at least 10 participants per treatment arm.
Data collection and analysis
Three review authors independently extracted data of study characteristics and outcomes of efficacy, tolerability and safety, examined issues of study quality, and assessed risk of bias. We resolved discrepancies by discussion. For efficacy, we calculated the number needed to treat for an additional beneficial outcome (NNTB) for pain relief of 30% and 50% or greater, patient’s global impression to be much or very much improved, dropout rates due to lack of efficacy, and the standardised mean differences for pain intensity, sleep problems, health‐related quality of life (HRQoL), and psychological distress. For tolerability, we calculated number needed to treat for an additional harmful outcome (NNTH) for withdrawal due to adverse events and specific adverse events, nervous system disorders and psychiatric disorders. For safety, we calculated NNTH for serious adverse events. Meta‐analysis was undertaken using a random‐effects model. We assessed the quality of evidence using GRADE and created a ‘Summary of findings’ table.
We included 16 studies with 1750 participants. The studies were 2 to 26 weeks long and compared an oromucosal spray with a plant‐derived combination of tetrahydrocannabinol (THC) and cannabidiol (CBD) (10 studies), a synthetic cannabinoid mimicking THC (nabilone) (two studies), inhaled herbal cannabis (two studies) and plant‐derived THC (dronabinol) (two studies) against placebo (15 studies) and an analgesic (dihydrocodeine) (one study). We used the Cochrane ‘Risk of bias’ tool to assess study quality. We defined studies with zero to two unclear or high risks of bias judgements to be high‐quality studies, with three to five unclear or high risks of bias to be moderate‐quality studies, and with six to eight unclear or high risks of bias to be low‐quality studies. Study quality was low in two studies, moderate in 12 studies and high in two studies. Nine studies were at high risk of bias for study size. We rated the quality of the evidence according to GRADE as very low to moderate.
Cannabis‐based medicines may increase the number of people achieving 50% or greater pain relief compared with placebo (21% versus 17%; risk difference (RD) 0.05 (95% confidence interval (CI) 0.00 to 0.09); NNTB 20 (95% CI 11 to 100); 1001 participants, eight studies, low‐quality evidence). We rated the evidence for improvement in Patient Global Impression of Change (PGIC) with cannabis to be of very low quality (26% versus 21%;RD 0.09 (95% CI 0.01 to 0.17); NNTB 11 (95% CI 6 to 100); 1092 participants, six studies). More participants withdrew from the studies due to adverse events with cannabis‐based medicines (10% of participants) than with placebo (5% of participants) (RD 0.04 (95% CI 0.02 to 0.07); NNTH 25 (95% CI 16 to 50); 1848 participants, 13 studies, moderate‐quality evidence). We did not have enough evidence to determine if cannabis‐based medicines increase the frequency of serious adverse events compared with placebo (RD 0.01 (95% CI ‐0.01 to 0.03); 1876 participants, 13 studies, low‐quality evidence).
Cannabis‐based medicines probably increase the number of people achieving pain relief of 30% or greater compared with placebo (39% versus 33%; RD 0.09 (95% CI 0.03 to 0.15); NNTB 11 (95% CI 7 to 33); 1586 participants, 10 studies, moderate quality evidence). Cannabis‐based medicines may increase nervous system adverse events compared with placebo (61% versus 29%; RD 0.38 (95% CI 0.18 to 0.58); NNTH 3 (95% CI 2 to 6); 1304 participants, nine studies, low‐quality evidence). Psychiatric disorders occurred in 17% of participants using cannabis‐based medicines and in 5% using placebo (RD 0.10 (95% CI 0.06 to 0.15); NNTH 10 (95% CI 7 to 16); 1314 participants, nine studies, low‐quality evidence).
We found no information about long‐term risks in the studies analysed.
We are uncertain whether herbal cannabis reduces mean pain intensity (very low‐quality evidence). Herbal cannabis and placebo did not differ in tolerability (very low‐quality evidence).
The potential benefits of cannabis‐based medicine (herbal cannabis, plant‐derived or synthetic THC, THC/CBD oromucosal spray) in chronic neuropathic pain might be outweighed by their potential harms. The quality of evidence for pain relief outcomes reflects the exclusion of participants with a history of substance abuse and other significant comorbidities from the studies, together with their small sample sizes.
Plain language summary
Cannabis products for adults with chronic neuropathic pain
There is a lack of good evidence that any cannabis‐derived product works for any chronic neuropathic pain.
Neuropathic pain is pain coming from damaged nerves. It is different from pain messages that are carried along healthy nerves from damaged tissue (for example, a fall, or cut, or arthritic knee). Neuropathic pain is treated by different medicines to those used for pain from damaged tissue.
Several products based on the cannabis plant have been suggested as treatment for pain, including neuropathic pain. These products include inhaled herbal cannabis, and various sprays or tablets containing active cannabis ingredients obtained from the plant, or made synthetically.
Some people with neuropathic pain claim that cannabis‐based products are effective for them, and that is often highlighted in the media.
In November 2017 we searched for clinical trials that used cannabis products to treat conditions with chronic neuropathic pain in adults. We found 16 studies involving 1750 people. Studies lasted 2 to 26 weeks. Studies compared different cannabis‐based medicines. Ten studies compared an oromucosal (mouth) spray with a plant‐derived combination of tetrahydrocannabinol (THC), the principal psychoactive constituent of cannabis, and cannabidiol (CBD), an anti‐inflammatory ingredient of cannabis, against a fake medication (placebo). Two studies each compared inhaled herbal cannabis and cannabis plant‐derived THC with placebo, and one study compared a man‐made cannabinoid mimicking the effects of THC (nabilone) with placebo. One study compared nabilone with a pain killer (dihydrocodeine).
Key results and quality of the evidence
We rated the quality of the evidence from studies using four levels: very low, low, moderate, or high. Very low‐quality evidence means that we are very uncertain about the results. High‐quality evidence means that we are very confident in the results.
There was no high‐quality evidence.
All cannabis‐based medicines pooled together were better than placebo for the outcomes substantial and moderate pain relief and global improvement. All cannabis‐based medicines pooled together were better than placebo in reducing pain intensity, sleep problems and psychological distress (very low‐ to moderate‐quality evidence).
There was no difference between all cannabis‐based medicines pooled together and placebo in improving health‐related quality of life, stopping the medication because it was not effective, and in the frequency of serious side effects (low‐quality evidence).
More people reported sleepiness, dizziness and mental problems (e.g. confusion) with all cannabis‐based medicines pooled together than with placebo (low‐quality evidence). There was moderate‐quality evidence that more people dropped out due to side effects with cannabis‐based medicines than with placebo.
Herbal cannabis was not different from placebo in reducing pain and the number of people who dropped out due to side effects (very low‐quality evidence).
Summary of findings
The protocol for this review was based on a template for reviews of drugs used to relieve neuropathic pain. The aim is for all reviews to use the same methods, based on new criteria for what constitutes reliable evidence in chronic pain (Moore 2010a; Moore 2012; Appendix 1).
Description of the condition
The 2011 International Association for the Study of Pain definition of neuropathic pain is “pain caused by a lesion or disease of the somatosensory system” (Jensen 2011), and based on a definition agreed at an earlier consensus meeting (Treede 2008). Neuropathic pain is a consequence of a pathological maladaptive response of the nervous system to ‘damage’ from a wide variety of potential causes. It is characterised by pain in the absence of a noxious stimulus and may be spontaneous (continuous or paroxysmal) in its temporal characteristics or be evoked by sensory stimuli (dynamic mechanical allodynia where pain is evoked by light touch of the skin). Neuropathic pain is associated with a variety of sensory loss (numbness) and sensory gain (allodynia) clinical phenomena, the exact pattern of which vary between people and disease, perhaps reflecting different pain mechanisms operating in an individual person and, therefore, potentially predictive of response to treatment (Demant 2014; Helfert 2015; von Hehn 2012). Pre‐clinical research hypothesises a bewildering array of possible pain mechanisms that may operate in people with neuropathic pain, which largely reflect pathophysiological responses in both the central and peripheral nervous systems, including neuronal interactions with immune cells (Baron 2012; Calvo 2012; von Hehn 2012). Overall, the treatment gains in neuropathic pain, to even the most effective of available drugs, are modest (Finnerup 2015; Moore 2013a), and a robust classification of neuropathic pain is not yet available (Finnerup 2013).
Neuropathic pain is usually divided according to the cause of nerve injury. There may be many causes, but some common causes of neuropathic pain include diabetes (painful diabetic neuropathy (PDN)), shingles (postherpetic neuralgia), amputation (stump and phantom limb pain), neuropathic pain after surgery or trauma, stroke or spinal cord injury, trigeminal neuralgia, and HIV infection. Sometimes the cause is unknown.
Many people with neuropathic pain conditions are significantly disabled with moderate or severe pain for many years. Chronic pain conditions comprised five of the 11 top‐ranking conditions for years lived with disability in 2010 (Vos 2012), and are responsible for considerable loss of quality of life and employment, and increased healthcare costs (Moore 2014a). A study in the USA found that healthcare costs were three‐fold higher for people with neuropathic pain than matched control participants (Berger 2004). A UK study and a German study showed a two‐ to three‐fold higher level of use of healthcare services in people with neuropathic pain than those without (Berger 2009; Berger 2012). For postherpetic neuralgia, for example, studies demonstrate a large loss of quality of life and substantial costs (Scott 2006; Van Hoek 2009).
In systematic reviews, the overall prevalence of neuropathic pain in the general population is reported to be between 7% and 10% (Van Hecke 2014), and about 7% in a systematic review of studies published since 2000 (Moore 2014a). In individual countries, prevalence rates have been reported as 3.3% in Austria (Gustorff 2008), 6.9% in France (Bouhassira 2008), and up to 8% in the UK (Torrance 2006). Some forms of neuropathic pain, such as PDN and post‐surgical chronic pain (which is often neuropathic in origin), are increasing (Hall 2008).
Estimates of incidence vary between individual studies for particular origins of neuropathic pain, often because of small numbers of cases. In primary care in the UK, between 2002 and 2005, the incidences (per 100,000 person‐years’ observation) were 28 (95% confidence interval (CI), 27 to 30) for PHN, 27 (95% CI, 26 to 29) for trigeminal neuralgia, 0.8 (95% CI, 0.6 to 1.1) for phantom limb pain, and 21 (95% CI, 20 to 22) for PDN (Hall 2008). Other studies have estimated an incidence of 4 in 100,000 per year for trigeminal neuralgia (Katusic 1991; Rappaport 1994), and 12.6 per 100,000 person‐years for trigeminal neuralgia and 3.9 per 100,000 person‐years for PHN in a study of facial pain in the Netherlands (Koopman 2009). One systematic review of chronic pain demonstrated that some neuropathic pain conditions, such as PDN, can be more common than other neuropathic pain conditions, with prevalence rates up to 400 per 100,000 person‐years (McQuay 2007).
Neuropathic pain is difficult to treat effectively, with only a minority of people experiencing a clinically relevant benefit from any one intervention (Kalso 2013; Moore 2013b). A multidisciplinary approach is now advocated, combining pharmacological interventions with physical or cognitive (or both) interventions. The evidence for interventional management is very weak, or non‐existent (Dworkin 2013). Conventional analgesics such as paracetamol and nonsteroidal anti‐inflammatory drugs (NSAIDs) are not thought to be effective, but without evidence to support or refute that view (Moore 2015a). Some people may derive some benefit from a topical lidocaine patch or low‐concentration topical capsaicin, although evidence about benefits is uncertain (Derry 2012; Derry 2014). High‐concentration topical capsaicin may benefit some people with PHN (Derry 2017). Treatment is often by so‐called pain modulators such as antidepressants (duloxetine and amitriptyline; Lunn 2014; Moore 2017; Moore 2015b; Sultan 2008), or antiepileptics (gabapentin or pregabalin; Moore 2009; Moore 2014b; Wiffen 2013). Evidence for efficacy of opioids is unconvincing (Gaskell 2016; Sommer 2015; Stannard 2016).
The proportion of people who achieve worthwhile pain relief (typically at least 50% pain intensity reduction; Moore 2013a) is small, generally only 10% to 25% more than with placebo, with numbers needed to treat for an additional beneficial outcome (NNTB) usually between 4 and 10 (Kalso 2013; Moore 2013b). Neuropathic pain is not particularly different from other chronic pain conditions in that only a small proportion of trial participants have a good response to treatment (Moore 2013b).
The current National Institute for Health and Care Excellence (NICE) guidance for the pharmacological management of neuropathic pain suggests offering a choice of amitriptyline, duloxetine, gabapentin, or pregabalin as initial treatment for neuropathic pain (with the exception of trigeminal neuralgia), with switching if the first, second, or third drugs tried are not effective or not tolerated (NICE 2013). This concurs with other recent guidelines (Finnerup 2015).
There is a need to explore other treatment options, with different mechanisms of action and from different drug categories, for treatment of neuropathic pain syndromes. Medical cannabis has been promoted by some patient organisations and advocates for the treatment of chronic pain refractory to conventional treatment and is available for pain management in some countries of the world, e.g. Canada and Israel (Ablin 2016). However, the use of cannabis for medical reasons is highly contested because of the adverse health effects of long‐term cannabis use for recreational purposes (Volkow 2014).
Description of the intervention
The cannabinoid system is ubiquitous in the animal kingdom, with multiple functions that move the organism back to equilibrium. A large body of evidence currently supports the presence of cannabinoid (CB) receptors and ligands in the peripheral and central nervous system, but also in other tissues such as bone and in the immune system (Owens 2015).
The endocannabinoid system has three broad and overlapping functions in mammals. The first is a stress recovery role, operating in a feedback loop in which endocannabinoid signalling is activated by stress and functions to return endocrine, nervous, and behavioural systems to homeostatic balance. The second is to control energy balance through regulation of the intake, storage, and utilisation of food. The third involves immune regulation; endocannabinoid signalling is activated by tissue injury and modulates immune and inflammatory responses (Hillard 2012). Thus, the endocannabinoid neuromodulatory system appears to be involved in multiple physiological functions, such as anti‐nociception, cognition and memory, endocrine function, nausea and vomiting, inflammation, and immune recognition (De Vries 2014; Hillard 2012). Cannabis is a genus of the flowering plant in the family Cannabaceae. The number of species within the genus is disputed. Three species may be recognized, Cannabis sativa, Cannabis indica and Cannabis ruderalis. These plants, commonly known as marijuana, have been used for pain relief for millennia, and have additional effects on appetite, sleep, and mood (Kalant 2001). Data from clinical trials with synthetic and plant‐based cannabis‐based medicines suggest a promising approach for the management of chronic neuropathic pain of different origins (De Vries 2014; Jensen 2015).
How the intervention might work
Cannabis contains over 450 compounds, with at least 70 classified as phytocannabinoids. Two are of particular medical interest. Delta 9‐tetrahydrocannabinol (delta 9‐THC) is the main active constituent, with psychoactive (e.g. reduction of anxiety and stress) and pain‐relieving properties. The second molecule of interest is cannabidiol (CBD), which has lower affinity for the cannabinoid (CB) receptors and the potential to counteract the negative effects of THC on memory, mood, and cognition, but also has an effect on pain modulation by anti‐inflammatory properties. The specific roles of currently identified endocannabis‐based medicines that act as ligands at CB receptors within the nervous system (primarily but not exclusively CB 1 receptors) and in the periphery (primarily but not exclusively CB 2 receptors) are only partially elucidated, but there are abundant pre‐clinical data to support their influence on nociception (Owens 2015).
It is also hypothesised that cannabis reduces alterations in cognitive and autonomic processing in chronic pain states (Guindon 2009). The frontal‐limbic distribution of CB receptors in the brain suggests that cannabis may preferentially target the affective qualities of pain (Lee 2013). In addition, cannabis may attenuate low‐grade inflammation, another postulate for the pathogenesis of neuropathic pain (Zhang 2015).
The content of THC and CBD in medical cannabis is highly variable and ranges from 1% to 22% THC and 0.05% to 9% CBD. In contrast the THC/CBD concentration in THC/CBD (nabiximols) oromucosal spray and the THC content in plant‐derived and synthetic THC are standardised (Häuser 2017).
Taking into consideration the poorly understood pathogenesis of chronic neuropathic pain syndromes, the complexity of symptom expression, and the absence of an ideal treatment, the potential for manipulation of the cannabinoid system as a therapeutic modality is attractive.
Why it is important to do this review
While recent guidance tends to be generally in agreement about the role of antidepressants and anticonvulsants in the management of chronic neuropathic pain (Finnerup 2015; NICE 2013), the role of opioids (Sommer 2015) and of cannabis‐based medicines (Häuser 2017, Häuser 2018) is under debate. Recent systematic reviews on the use of cannabis‐based medicines to treat chronic pain came to different conclusions on their importance in chronic neuropathic pain (Boychuk 2015; Finnerup 2015; Petzke 2016; Whiting 2015). This was probably due to the inclusion of different trials, different standards to evaluate the quality of evidence, and different weighting of the outcomes of efficacy, tolerability, and safety. Due to the conflicting conclusions of recent systematic reviews on the importance of cannabis‐based medicines in treating chronic neuropathic pain, as well as the public debate on the medical use of herbal cannabis for chronic pain (Ablin 2016; Fitzcharles 2014), we saw the need for a Cochrane Review applying the standards of Cochrane Pain, Palliative and Supportive Care (PaPaS).
To assess the efficacy, tolerability, and safety of cannabis‐based medicines (herbal, plant‐based, synthetic) compared to placebo or conventional drugs for conditions with chronic neuropathic pain in adults.
Criteria for considering studies for this review
Types of studies
We included studies if they were randomised, double‐blind, controlled trials (RCTs) of at least two weeks’ duration (drug titration and maintenance or withdrawal). We included studies with a parallel, cross‐over, and enriched enrolment randomised withdrawal (EERW) design with at least 10 participants per treatment arm. We required full journal publication, with the exception of online clinical trial results summaries of otherwise unpublished clinical trials, and abstracts with sufficient data for analysis. We did not include short abstracts. We excluded studies that were not randomised, studies of experimental pain, case reports, and clinical observations. We included studies that reported at least one outcome of efficacy and one of safety as defined below.
Types of participants
Studies included adults aged 18 years and above with one or more chronic (three months and more) neuropathic pain condition including (but not limited to):
central neuropathic pain (e.g. multiple sclerosis);
complex regional pain syndrome (CRPS) Type II;
painful diabetic neuropathy;
peripheral polyneuropathy of other aetiologies, for example toxic (alcohol, drugs);
phantom limb pain;
postoperative or traumatic peripheral nerve lesions;
spinal cord injury;
nerve plexus injury;
Where included studies had participants with more than one type of neuropathic pain, we analysed results according to the primary condition. Studies had to state explicitly that they included people with neuropathic pain (by title). We excluded studies that assessed pain in people with neurological diseases without specifying that the pain assessed was of neuropathic nature. We excluded studies with fibromyalgia because the nature of fibromyalgia (neuropathic or not) is under debate (Clauw 2015); cannabis‐based medicines in fibromyalgia are the subject of another Cochrane Review (Häuser 2016). We excluded studies with ‘mixed pain’ (Baron 2004), because the concept is neither internationally accepted nor sufficiently validated and the focus of this review is only neuropathic pain.
Types of interventions
Cannabis‐based medicines, either herbal cannabis (hashish, marihuana), plant‐based cannabinoids (dronabinol: nabiximols), or pharmacological (synthetic) cannabinoids (e.g. levonantradol, nabilone), at any dose, by any route, administered for the relief of neuropathic pain and compared to placebo or any active comparator. We did not include studies with drugs under development that manipulate the endocannabinoid system by inhibiting enzymes that hydrolyse endocannabninoids and thereby boost the levels of the endogenous molecules (e.g. blockade of the catabolic enzyme fatty acid amide hydrolase (FAAH)) (Long 2009).
Types of outcome measures
The standards used to assess evidence in chronic pain trials have changed substantially in recent years, with particular attention being paid to trial duration, withdrawals, and statistical imputation following withdrawal, all of which can substantially alter estimates of efficacy. The most important change is the move from using mean pain scores, or mean change in pain scores, to the number of people who have a large decrease in pain (by at least 50%) and who continue in treatment, ideally in trials of eight to 12 weeks’ duration or longer. These standards are set out in the PaPaS Author and Referee Guidance for pain studies of Cochrane Pain, Palliative and Supportive Care (Cochrane PaPaS 2012). This Cochrane Review assessed evidence using methods that make both statistical and clinical sense, and will use criteria for what constitutes reliable evidence in chronic pain (Moore 2010a).
We anticipated that studies would use a variety of outcome measures, with most studies using standard subjective scales (numerical rating scale (NRS) or visual analogue scale (VAS) for pain intensity or pain relief, or both). We were particularly interested in Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) definitions for moderate and substantial benefit in chronic pain studies (Dworkin 2008).
Participant‐reported pain relief of 50% or greater. We preferred composite neuropathic pain scores over single‐scale generic pain scores if both measures were used by studies;
PGIC (Patient Global Impression of Change) much or very much improved;
Withdrawals due to adverse events (tolerability);
Serious adverse events (safety). Serious adverse events typically include any untoward medical occurrence or effect that at any dose results in death, is life‐threatening, requires hospitalisation or prolongation of existing hospitalisation, results in persistent or significant disability or incapacity, is a congenital anomaly or birth defect, is an ‘important medical event’ that may jeopardise the person, or may require an intervention to prevent one of the above characteristics/consequences.
Participant‐reported pain relief of 30% or greater. We preferred composite neuropathic pain scores over single‐scale generic pain scores if both measures were used by studies;
Mean pain intensity. We preferred composite neuropathic pain scores over single‐scale generic pain scores if both measures were used by studies;
Health‐related quality of life;
Withdrawals due to lack of efficacy;
Any adverse event;
Specific adverse events, particularly nervous system (e.g. dizziness, somnolence, headache) and psychiatric disorders (e.g. confusion state; paranoia, psychosis, substance dependence) according to the Medical Dictionary for Regulatory Activities (MedDRA) (International Council for Harmonisation 2016).
Search methods for identification of studies
We searched the following databases, without language restrictions:
The Cochrane Central Register of Controlled Trials (CENTRAL) via the Cochrane Register of Studies Online (CRSO) (searched 7 November 2017);
MEDLINE (via Ovid) (1946 to 7 November 2017);
Embase (via Ovid) (1974 to 7 November 2017).
Appendix 2 shows the search strategies.
Searching other resources
We reviewed the bibliographies of any RCTs identified and review articles, and searched the following clinical trials databases: US National Institutes of Health clinical trial register (www.ClinicalTrials.gov), European Union Clinical Trials Register (www.clinicaltrialsregister.eu), World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) (apps.who.int/trialsearch/), and International Association for Cannabinoid Medicines (IACM) databank (www.cannabis-med.org/studies/study.php) to identify additional published or unpublished data. We contacted trial investigators to request missing data.
Data collection and analysis
We performed separate analyses according to particular neuropathic pain conditions. We combined different neuropathic pain conditions in analyses for exploratory purposes only.
Selection of studies
Two review authors (WH, FP) determined eligibility by reading the abstract of each study identified by the search. We eliminated studies that clearly did not satisfy the inclusion criteria, and obtained full copies of the remaining studies. Two review authors (WH, FP) independently read these studies and reached agreement by discussion. We did not anonymise the studies before assessment. We created a PRISMA flow chart (Moher 2009).
Data extraction and management
Two review authors (WH, FP) extracted data independently using a standard form and checked for agreement before entering data into Review Manager 5 (RevMan 2014). Two review authors (WH, MM) extracted independently data calculated by imputation. We included information about the pain condition and number of participants treated, study setting, inclusion and exclusion criteria, demographic and clinical characteristics of the study samples (age, gender, race, pain baseline), prior recreational cannabis use, drug and dosing regimen, co‐therapies allowed, rescue medication, study design (placebo or active control), study duration and follow‐up, analgesic outcome measures and results, withdrawals, and adverse events (participants experiencing any adverse event or serious adverse event).
Assessment of risk of bias in included studies
Two review authors (WH, FP) independently assessed risk of bias for each study, using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011a), and adapted from those used by Cochrane Musculoskeletal for recent reviews on drug therapy in fibromyalgia, with any disagreements resolved by discussion. We assessed the following for each study.
Random sequence generation (checking for possible selection bias). We assessed the method used to generate the allocation sequence as: low risk of bias (i.e. any truly random process, e.g. random number table; computer random number generator); unclear risk of bias (when the method used to generate the sequence was not clearly stated). We excluded studies at a high risk of bias that used a non‐random process (e.g. odd or even date of birth; hospital or clinic record number).
Allocation concealment (checking for possible selection bias). The method used to conceal allocation to interventions prior to assignment determines whether intervention allocation could have been foreseen in advance of, or during, recruitment, or changed after assignment. We assessed the methods as: low risk of bias (e.g. telephone or central randomisation; consecutively numbered, sealed, opaque envelopes); unclear risk of bias (when method was not clearly stated). We excluded studies that did not conceal allocation and were therefore at a high risk of bias (e.g. open list).
Blinding of participants and personnel/treatment providers (systematic performance bias). We assessed the methods used to blind participants and personnel/treatment providers from knowledge of which intervention a participant received. We assessed the methods as: low risk of bias (study stated that it was blinded and described the method used to achieve blinding, e.g. identical tablets; matched in appearance and smell); unclear risk of bias (study stated that it was blinded but did not provide an adequate description of how it was achieved); high risk of bias (blinding of participants was not ensured, e.g. tablets different in form or taste).
Blinding of outcome assessment (checking for possible detection bias). We assessed the methods used to blind study outcome assessors from knowledge of which intervention a participant received. We assessed the methods as: low risk of bias (study stated that outcome assessors were blinded to the intervention or exposure status of participants); unclear risk of bias (study stated that the outcome assessors were blinded but did not provide an adequate description of how it was achieved); high risk of bias (outcome assessors knew the intervention or exposure status of participants).
Incomplete outcome data (checking for possible attrition bias due to the amount, nature, and handling of incomplete outcome data). We assessed the methods used to deal with incomplete data as: low risk of bias (i.e. less than 10% of participants did not complete the study or used ‘baseline observation carried forward’ (BOCF) analysis, or both); unclear risk of bias (used ‘last observation carried forward’ analysis); or high risk of bias (used ‘completer’ analysis).
Reporting bias due to selective outcome reporting (reporting bias). We checked if an a priori study protocol was available and if all outcomes of the study protocol were reported in the publications of the study. There is low risk of reporting bias if the study protocol is available and all of the study’s pre‐specified (primary and secondary) outcomes that are of interest in the review are reported in the pre‐specified way, or if the study protocol is not available but it is clear that the published reports include all expected outcomes, including those that are pre‐specified (convincing text of this nature may be uncommon). There is a high risk of reporting bias if not all of the study’s pre‐specified primary outcomes are reported; one or more primary outcomes is reported using measurements, analysis methods or subsets of the data (e.g. subscales) that are not pre‐specified; one or more reported primary outcomes are not pre‐specified (unless clear justification for their reporting is provided, such as an unexpected adverse effect); one or more outcomes of interest in the review are reported incompletely so that they cannot be entered in a meta‐analysis; the study report did not include results for a key outcome that would be expected to have been reported for such a study. There is unclear risk of bias if insufficient information is available to permit judgement of ‘Low risk’ or ‘High risk’.
Group similarity at baseline (selection bias). We assessed similarity of the study groups at baseline for the most important prognostic clinical and demographic indicators. There is low risk of bias if groups are similar at baseline for demographic factors, value of main outcome measure(s), and important prognostic factors. There is an unclear risk of bias if important prognostic clinical and demographic indicators are not reported. There is high risk of bias if groups are not similar at baseline for demographic factors, value of main outcome measure(s), and important prognostic factors.
Size of study (checking for possible biases confounded by small size). We assessed studies as being at low risk of bias (200 participants or more per treatment arm); unclear risk of bias (50 to 199 participants per treatment arm); or high risk of bias (fewer than 50 participants per treatment arm).
Two review authors (WH, FP) assessed the included studies using the Cochrane ‘Risk of bias’ tool. We defined studies with zero to two unclear or high risks of bias to be high‐quality studies, with three to five unclear or high risks of bias to be moderate‐quality studies, and with six to eight unclear or high risks of bias to be low‐quality studies (Schaefert 2015).
Measures of treatment effect
We calculated numbers needed to treat for an additional beneficial outcome (NNTB) as the reciprocal of the absolute risk reduction (ARR; McQuay 1998). For unwanted effects, the NNTB becomes the number needed to treat for an additional harmful outcome (NNTH) and is calculated in the same manner. We used dichotomous data to calculate risk differences (RD) with 95% CIs using a fixed‐effect model unless we found significant statistical or clinical heterogeneity (see below). We set the threshold for a clinically relevant benefit or a clinically relevant harm for categorical variables by an NNTB or NNTH less than 10 (Moore 2008).
We calculated standardised mean differences (SMD) with 95% CIs for continuous variables using a fixed‐effect model unless we found significant statistical or clinical heterogeneity. We used Cohen’s categories to evaluate the magnitude of the effect size, calculated by SMD, with Hedges’ g value of 0.2 = small, 0.5 = medium, and 0.8 = large (Cohen 1988). We labelled a g value less than 0.2 to be a ‘not substantial’ effect size. We assumed a minimally important difference if the Hedges’ g value was 0.2 or greater (Fayers 2014).
Unit of analysis issues
We split the control treatment arm between active treatment arms in a single study if the active treatment arms were not combined for analysis.
We included studies with a cross‐over design where separate data from the two periods were reported, data were presented that excluded a statistically significant carry‐over effect, or statistical adjustments were carried out in case of a significant carry‐over effect.
Dealing with missing data
We used intention‐to‐treat (ITT) analysis where the ITT population consisted of participants who were randomised, took at least one dose of the assigned study medication, and provided at least one post‐baseline assessment.
Where means or standard deviations (SDs) were missing, we attempted to obtain these data through contacting trial authors. Where SDs were not available from trial authors, we calculated them from t values, P values, CIs, or standard errors, where reported by the studies (Higgins 2011b). Where rates of pain relief of 30% and of 50% or greater were not reported or provided on request, we calculated them from means and SDs using a validated imputation method (Furukawa 2005).
Assessment of heterogeneity
We dealt with clinical heterogeneity by combining studies that examined similar conditions. We assessed statistical heterogeneity visually (L’Abbé 1987), and using the I 2 statistic (Higgins 2003). When the I 2 value was greater than 50%, we considered possible reasons for this.
Assessment of reporting biases
We assessed publication bias using a method designed to detect the amount of unpublished data with a null effect required to make any result clinically irrelevant (usually taken to mean an NNTB of 10 or higher; Moore 2008).
We intended to use a fixed‐effect model for meta‐analysis. We used a random‐effects model using the inverse variance method in Review Manager 5 for meta‐analysis (RevMan 2014) because there was significant clinical heterogeneity due to the different types of neuropathic pain conditions included.
Quality of the evidence
Two review authors (WH, FP) independently rated the quality of the outcomes. We used the GRADE system to rank the quality of the evidence using the GRADEpro Guideline Development Tool software (GRADEpro GDT 2015), and the guidelines provided in Chapter 12.2 of the CochraneHandbook for Systematic Reviews of Interventions (Schünemann 2011).
The GRADE approach uses five considerations (study limitations, consistency of effect, imprecision, indirectness and publication bias) to assess the quality of the body of evidence for each outcome. The GRADE system uses the following criteria for assigning grade of evidence:
high: we are very confident that the true effect lies close to that of the estimate of the effect;
moderate: we are moderately confident in the effect estimate; the true effect is likely to be close to the estimate of effect, but there is a possibility that it is substantially different;
low: our confidence in the effect estimate is limited; the true effect may be substantially different from the estimate of the effect;
very low: we have very little confidence in the effect estimate; the true effect is likely to be substantially different from the estimate of effect.
We decreased the grade rating by one (‐ 1) or two (‐ 2) if we identified:
serious (‐ 1) or very serious (‐ 2) limitation to study quality;
important inconsistency (‐ 1);
some (‐ 1) or major (‐ 2) uncertainty about directness;
imprecise or sparse data (‐ 1);
high probability of reporting bias (‐ 1).
In addition, there may be circumstances where the overall rating for a particular outcome needs to be adjusted as recommended by GRADE guidelines (Guyatt 2013a). For example, if there are so few data that the results are highly susceptible to the random play of chance, or if a study uses last observation carried forward (LOCF) imputation in circumstances where there are substantial differences in adverse event withdrawals, one would have no confidence in the result, and would need to downgrade the quality of the evidence by three levels, to very low quality. In circumstances where there were no data reported for an outcome, we planned to report the level of evidence as very low quality (Guyatt 2013b).
See also Appendix 3: GRADE: criteria for assigning grade of evidence.
‘Summary of findings’ table
We included one ‘Summary of findings’ table to present the main findings in a transparent and simple tabular format. In particular, we included key information concerning the quality of evidence, the magnitude of effect of the interventions examined, and the sum of available data on the outcomes. The ‘Summary of findings’ table includes the primary outcomes and the secondary outcomes of participant‐reported pain relief of 30% or greater, and nervous system disorders and psychiatric disorders as specific adverse events.
Subgroup analysis and investigation of heterogeneity
We performed subgroup analyses according to individual neuropathic pain syndromes because placebo response rates for the same outcome can vary between conditions, as can the drug‐specific effects (Moore 2013b). We performed subgroup analyses (different cannabis‐based medicines; very short‐term (less than four weeks), short‐term (four to 12 weeks), intermediate‐term (13 to 26 weeks), and long‐term (more than 26 weeks) study duration) where there were at least two studies available. We post‐hoc decided to perform subgroup analyses of studies with and without publication in peer‐reviewed journals. We performed subgroup analyses if at least two studies for a subgroup were available.
We planned no sensitivity analysis because the evidence base is known to be too small to allow reliable analysis.
Relieving Phantom Pain and Opioid Dependency with Medicinal Marijuana
In January 2006, when I was 29, I lost my left leg below the knee in a car accident. After months in a coma, I awoke to my brain fixating on the last signal from the now-missing limb: being crushed. Twelve years later, I still experience phantom pain – with very specific manifestations. I’ve wakened my husband more than once because my third metatarsal has shooting pain, or my heel is on fire, or my big toe is being crushed. But none of them are there. It’s disorienting and tormenting.
Phantom pain results from psychogenic and physiological (mental and physical) activity and post-amputation changes in the residual limb and the brain. The prevalence of phantom pain in the first two years post-amputation is 65-80 percent; however, severe, chronic phantom pain past the second or third year affects only 5-10 percent of amputees.
A Foot No Longer There, a Pain That Never Leaves
Pain disabled me for 18 months. The agony of feeling my left foot crushed, all day, every day, and the associated depression and despair left me unable to function. A neurologist managed my pain for years with high-dose, long-acting opiates supplemented by short-acting opiates for so-called “breakthrough pain,” which broke through far too often. I used to joke that I had enough fentanyl on my person to kill a city block.
The narcotics enabled me to return to full-time work but with high costs. I was chronically nauseated and constipated and had to take additional medications for side effects. I built a tolerance to opioids and had to constantly change dosages and formulations. The medicines became less effective. I worried about not having enough medication – and worried even more that I was taking too much medicine for little relief. I had yet another diagnosis: opioid dependency. (Note: This is not addiction but the body adjusting to needing the opiates to function normally. Don’t call me an addict. I’ve heard that enough from robotic but well-meaning ER docs when the phantom pain wouldn’t respond to even the most massive opiate intervention I could safely take.)
There are several treatment options for phantom pain, including gabapentin (what I call the “workhorse” of my pain regimen), Lyrica, mirror therapy, cognitive-behavioral therapy, NSAIDs, biofeedback, hypnotism, acupuncture, surgery, and even ketamine infusions, but they all failed, adding to my despair.
An Ancient Cure: A Modern Option?
Cannabis is now medically or recreationally legal in 29 states and the District of Columbia. Ninety-one percent of Americans support legalizing medical marijuana, and 58 percent support legalizing recreational cannabis nationwide, even though cannabis remains federally illegal.
In January 2016, days after medical marijuana became legal in the District of Columbia, my primary care physician advised me that he was enrolling me in the program, because he was tired of seeing me suffer. “The worst that’ll happen is you’ll get the munchies,” he laughed. “There’s anecdotal evidence that this can help. You’ve failed everything else, and cannabis can’t hurt; no one’s ever died from a cannabis overdose. It’s medicine, Meredith. Use it.”
So, Do I Get High All Day?
Medical cannabis use is not like recreational consumption. Yes, I medicate with cannabis all day, every day. But I am rarely “high.” I take small amounts every few hours for pain control. Two years in, I take 70 percent fewer opiates than in 2016. I may never fully get off narcotics, but such a dramatic decrease has reduced their side effects.
Now, in 2018, cannabis patients have easier access and more choices. I don’t have to light a joint every couple of hours, which is conspicuous and inconvenient. I can use a vape pen, add tincture to my tea, or have a medicated candy, all portable (but always within state lines!) and discreet options. If I don’t smoke flower (also called buds) but instead use concentrates (or “dabs”), I must use a “live” extract of the whole plant containing the critical terpenes (essential oils found in plants) needed for the combined, therapeutic “entourage effect.” Emerging data suggests that THC and CBD, the two main cannabinoids in marijuana, are most therapeutic when combined with other trace cannabinoids – psychoactive and non‑psychoactive compounds specific to the cannabis plant – and terpenes.
It’s all been an educated guess at trying various strains and modes of consumption, taking detailed notes about my sensations before and after trying them and finding my own regimen. It’s been worthwhile, because I’ve been able to reduce my opioid intake and have better pain control. In 2016, there were more than 44,000 deaths attributable to opioids in the U.S. No cannabis‑related deaths have ever been recorded.
I’m so grateful to cannabis and to the forward-thinking physician who saw potential in it for giving me greater freedom over my pain management and for reducing my opioid need. Because cannabis is federally illegal, there is virtually no research allowed in the U.S. So it’s up to individual patients to discover what strains and delivery methods work best for them and share their knowledge with others to build a community understanding of the medication and its effects. Medical marijuana should be considered a viable treatment option for phantom pain sufferers, and as regulations evolve nationwide, one that will surely be more widely available.
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Published in inMotion, Volume 28, Issue 2 | March/April 2018, Page 38