Tramadol with or without paracetamol (acetaminophen) for cancer pain (2024)

  • Journal List
  • Cochrane Database Syst Rev
  • PMC6465039

As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsem*nt of, or agreement with, the contents by NLM or the National Institutes of Health.
Learn more: PMC Disclaimer | PMC Copyright Notice

Tramadol with or without paracetamol (acetaminophen) for cancer pain (1)

The Cochrane Database of Systematic Reviews

Cochrane Database Syst Rev. 2017 Jan; 2017(1): CD012508.

Published online 2017 Jan 11. doi:10.1002/14651858.CD012508

PMCID: PMC6465039

Philip J Wiffen,Tramadol with or without paracetamol (acetaminophen) for cancer pain (2) Sheena Derry, and R Andrew Moore

Author information Copyright and License information PMC Disclaimer

This article has been updated. "Tramadol with or without paracetamol (acetaminophen) for cancer pain" in volume 2017, CD012508.

Abstract

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

To assess the benefits of tramadol with or without paracetamol (acetaminophen) for cancer pain in adults, and the adverse events associated with its use in clinical trials.

Background

This protocol is for a review of a suite of reviews on the efficacy and safety of opioid medicines to treat cancer pain.

Description of the condition

Cancer is estimated to cause over eight million deaths per annum ‐ approximately 13% of deaths worldwide (IARC 2012). Globally, 32 million people are living with cancer. In the UK alone in 2014, there were around 350,000 new cases of cancer annually, with around 50% of people surviving for 10 years or more after diagnosis (Cancer Research UK 2016).

Cancer pain is perhaps one of the most feared symptoms associated with the disease. Pain may be the first symptom to cause someone to seek medical advice that leads to a diagnosis of cancer and 30% to 50% of all people with cancer will experience moderate to severe pain (Portenoy 1999). Pain can occur at any time as the disease progresses but the frequency and intensity of pain tends to increase as the cancer advances (Portenoy 1999; van den Beuken‐van Everdingen 2016). For people with advanced cancer, some 75% to 90% will experience pain having a major impact on daily living (Wiffen 2013). Pain had a significant negative correlation with quality of life in people with cancer in China, Japan, and Palestine, for example (Deng 2012; Dreidi 2016; Mikan 2016). A recent systematic review has shown that approximately 40% of patients suffered pain after curative treatment, 55% during cancer treatment and 66% in advanced disease. Pain related to cancer is frequently described as distressing or intolerable by more than one third of patients (Beivik 2009; van den Beuken‐van Everdingen 2016).

Cancer pain can be the result of the cancer itself, interventions to treat the cancer, and sometimes other underlying pains. Prevalence is also linked to cancer type, with head and neck cancer showing the highest prevalence. Age also has an impact with younger patients experiencing more pain. (Prommer 2015). For this review, we will not consider post surgical pain related to surgery or neuropathic pains due to chemotherapy or nerve pressure.

Description of the intervention

Tramadol hydrochloride is an opioid analgesic originally marketed in West Germany in 1977. In 2016, tramadol, alone or in combination with paracetamol (acetaminophen), was available in products for oral use and by injection from almost 90 companies. Oral formulations include those designed for immediate release, and for modified release over a longer time. Preparations for rectal and parenteral administration are also available. The total oral daily dosage is usually up to 400 mg, although some licenses state that 400 mg is the maximum dose (Martindale 2016). When combined with paracetamol, daily dosage is typically a maximum of eight tablets, each containing tramadol 37.5 mg and paracetamol 325 mg.

Tramadol is used to treat a range of different pain conditions. Tramadol differs from traditional opioids in not only acting as a µ‐opioid agonist, but also having a range of other properties that may contribute to its analgesic effect, including serotonin reuptake inhibition and noradrenaline reuptake inhibition. It is licensed for use in moderate to severe pain and is less potent than morphine or similar drugs. It is considered to fit into step 2 of the World Health Organization (WHO) analgesic ladder (WHO 2016). In some parts of the world, tramadol is classified as a controlled substance (similar to codeine in this respect), but the exact classification and controls on prescribing vary markedly.

Tramadol has reasonable efficacy in acute postoperative pain as a single agent, and in combination with paracetamol (Edwards 2002; Moore 1997). It probably also has efficacy in neuropathic pain conditions (Hollingshead 2006), but has small benefits in osteoarthritis (Cepeda 2006). One previous systematic review concluded that the evidence base for tramadol was inadequate to recommend it as an alternative to paracetamol plus codeine for routine use in people with mild to moderate cancer pain (Tassinari 2011).

Tramadol is associated with typical opioid adverse events of nausea, dizziness, and dry mouth, although vomiting and constipation are less of a problem than with traditional opioids. Use of tramadol with concurrent serotonergic therapy poses a risk of serotonin syndrome (Beakley 2015).

Like other opioids, tramadol is subject to abuse. One study in Germany (looking at data from 1990 to 2009), where tramadol is not scheduled in the German Narcotic Drugs Act, calculated the incidence of abuse as 0.21 cases per million defined daily dosages (DDDs) and the incidence of dependency as 0.12 cases per million DDDs, with lower incidences in recent years (Radbruch 2013). The conclusion was that tramadol had a low potential for misuse, abuse, and dependency.

How the intervention might work

Tramadol is a synthetic 4‐phenyl‐piperidine analogue of codeine with a central analgesic effect. Both tramadol and its O‐desmethyl metabolite are selective, weak OP3‐receptor (µ) agonists. The mode of action is poorly understood (Minami 2015, Reeves 2008).

Tramadol is metabolised by N‐ and O‐demethylation via the cytochrome P450 isoenzymes CYP3A4 and CYP2D6 and glucuronidation or sulphation in the liver. Around 40% of the analgesic action of tramadol is provided by O‐desmethyl tramadol (M1) created by rapid metabolism of tramadol in the liver via the cytochrome P450 enzyme CYP2D6 (Bozkurt 2005; Grond 2004; Lintz 1998). Tramadol is also metabolised by N‐demethylation via the cytochrome P450 isoenzyme CYP3A4, and glucuronidation or sulphation in the liver (Grond 2004).

Tramadol is available as a racaemic mixture of (+) and (‐) enantiomers. The (+) enantiomer has only a weak affinity to µ‐opioid receptors and inhibits serotonin reuptake, while the (‐) enantiomer inhibits noradrenaline reuptake in the spinal cord (Bozkurt 2005; Scott 2000). These different modes action might explain the longer analgesic efficacy and the lower incidence of opioid adverse effects, but a range of other modes of action have been proposed (Bozkurt 2005; Grond 2004).

Tramadol is rapidly absorbed after oral administration and has an absolute bioavailability of 65% to 70% (Lintz 1998; Scott 2000). Generally, there are no significant differences in the pharmaco*kinetics (elimination half‐life, distribution, serum clearance and concentration of metabolites) of tramadol between adults and children after oral dosing or intravenous injection. Genetic variances probably influence analgesic efficacy (Gan 2007). About 8% of the white population has cytochrome P450 enzyme (CYP2D6) deficiency that reduces the analgesic effects of tramadol, and this may well be greater in some other populations (Pedersen 2005). Other drugs metabolised by CYP2D6 enzymes (e.g. ondansetron) can potentially interfere with tramadol metabolism, changing how well it works in individuals, and also possible adverse events.

Why it is important to do this review

In many countries, strong opioids such as morphine are severely restricted, if available at all. This leaves many people with cancer at risk of severe life‐limiting pain. If tramadol, with or without paracetamol, is effective, it may provide an alternative for people with moderate to severe pain. This review will inform policy makers such as the WHO on the possible utility of tramadol to treat cancer‐related pain. It is hoped that the review will inform patients and carers on the value or otherwise of tramadol in this context.

One previous systematic review examined only oral tramadol, not the combination with paracetamol, included observational studies as well as randomised trials, and is now out of date (Tassinari 2011). A new systematic review concentrating on randomised trial evidence is therefore appropriate.

Objectives

To assess the benefits of tramadol with or without paracetamol (acetaminophen) for cancer pain in adults, and the adverse events associated with its use in clinical trials.

Methods

Criteria for considering studies for this review

Types of studies

To be included, studies must:

  • be randomised (described as 'randomised' anywhere in the manuscript);

  • ideally be double‐blind, but we will include open studies;

  • have placebo or active controls, or both;

  • include a minimum of 10 participants per treatment arm.

We will exclude non‐randomised studies, studies of experimental pain, case reports and clinical observations. Studies have to be fully published or available as extended abstracts (e.g. from clinical trial websites); we will exclude short (usually conference) abstracts as these are often unreliable (PaPaS 2012).

Types of participants

Studies will include adults or children of any age who experience cancer pain.

Types of interventions

Tramadol with or without paracetamol for cancer pain. Tramadol may be administered at any dose and by any route, and compared to placebo or any active comparator.

Types of outcome measures

Pain has to be measured using a validated assessment tool. For pain intensity, for example, this could be a 100‐mm visual analogue scale (VAS) or 11‐point numerical rating scale (no pain to worst pain imaginable), or a 4‐point categorical scale (none, mild, moderate, severe), and for pain relief a 100 mm VAS (no relief to complete relief), or 5‐point categorical scale (none, a little, some, a lot, complete or words to that effect). Measures of 30% or greater (moderate) and 50% or greater (substantial) reduction of pain over baseline are recommended outcomes for chronic pain studies from the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT) (Dworkin 2008). When considering Patient Global Impression of Change (PGIC), 30% or greater reduction of pain over baseline equates to much improved and very much improved, and 50% or greater reduction of pain over baseline equates to very much improved. We will also use results equivalent to no pain or mild pain, because these are also outcomes acceptable to people with various types of pain (Moore 2013).

Primary outcomes
  • Number of participants with pain reduction of 30% or greater from baseline.

  • Number of participants with pain reduction of 50% or greater from baseline.

  • Number of participants with pain no worse than mild (Moore 2013).

  • Number of participants with PGIC of much improved or very much improved (or equivalent wording).

Secondary outcomes
  • Quality of life.

  • Use of rescue medication.

  • Participant satisfaction or preference.

  • Serious adverse events, defined as leading to withdrawal from treatment, including death.

  • Other adverse events, particularly reports of effects of treatment on somnolence, appetite, or thirst (Wiffen 2014).

  • Attrition: withdrawals due to lack of efficacy.

Search methods for identification of studies

Electronic searches

We will search the following databases without language or date restrictions.

  • The Cochrane Central Register of Controlled Trials (CENTRAL) (via CRSO).

  • MEDLINE (via Ovid).

  • Embase (via Ovid).

We will use a combination of MeSH or equivalent and text word terms and tailor search strategies to individual databases. Searches will be tailored to individual databases. The search strategy for MEDLINE is in Appendix 1.

Searching other resources

We will search the metaRegister of controlled trials (mRCT) (www.controlled‐trials.com/mrct), ClinicalTrials.gov (www.clinicaltrials.gov) and the WHO International Clinical Trials Registry Platform (ICTRP) (http://apps.who.int/trialsearch/) for ongoing trials. In addition, we will check reference lists of reviews and retrieved articles for additional studies and perform citation searches on key articles. We will contact experts in the field for unpublished and ongoing trials and authors where necessary for additional information.

Data collection and analysis

Selection of studies

Two review authors (PW, SD) will independently read the abstract of each study identified by the search, eliminate studies that clearly do not satisfy inclusion criteria, and obtain full copies of the remaining studies. Two review authors (PW, SD) will read these studies independently to select relevant studies for inclusion. In the event of disagreement, a third review author (RAM) will adjudicate. We will not anonymise the studies before assessment. We will include a PRISMA flow chart in the review which will show the status of identified studies as recommended in Section 11.2.1 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We will include studies in the review irrespective of whether measured outcome data are reported in a 'usable' way.

Data extraction and management

Two review authors (PW, SD) will independently extract data using a standard form and check for agreement before entry into Review Manager 5 (RevMan 2014). We will include information about the number of participants treated and demographic details, type of cancer, drug and dosing regimen, study design (placebo or active control) and methods, study duration and follow‐up, analgesic outcome measures and results, withdrawals and adverse events. We will collate multiple reports of the same study, so that each study rather than each report is the unit of interest in the review. We will collect characteristics of the included studies in sufficient detail to complete a 'Characteristics of included studies' table.

Assessment of risk of bias in included studies

Two review authors (PW, SD) will independently assess risk of bias for each study, using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (Chapter 8, Higgins 2011) and adapted from those used by the Cochrane Pregnancy and Childbirth Group, with any disagreements resolved by discussion. We will complete a 'Risk of bias' table for each included study using the Risk of bias tool in Review Manager 5 (RevMan 2014).

We will assess the following for each study.

  • Random sequence generation (checking for possible selection bias). We will assess the method used to generate the allocation sequence as: low risk of bias (any truly random process, e.g. random number table; computer random number generator); unclear risk of bias (method used to generate sequence not clearly stated). We will exclude studies using 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 will assess the methods as: low risk of bias (e.g. telephone or central randomisation; consecutively numbered sealed opaque envelopes); unclear risk of bias (method not clearly stated). We will exclude studies that do not conceal allocation (e.g. open list).

  • Blinding of participants and personnel (checking for possible performance bias). We will assess the methods used to blind study participants and personnel from knowledge of which intervention a participant received. We will assess methods as: low risk of bias (study states that it was blinded and describes the method used to achieve blinding, such as identical tablets matched in appearance or smell, or a double‐dummy technique); unclear risk of bias (study states that it was blinded but does not provide an adequate description of how it was achieved).

  • Blinding of outcome assessment (checking for possible detection bias). We will assess the methods used to blind study participants and outcome assessors from knowledge of which intervention a participant received. We will assess the methods as: low risk of bias (study has a clear statement that outcome assessors were unaware of treatment allocation, and ideally describes how this was achieved); unclear risk of bias (study states that outcome assessors were blind to treatment allocation but lacks a clear statement on how it was achieved).

  • Incomplete outcome data (checking for possible attrition bias due to the amount, nature and handling of incomplete outcome data). We will assess the methods used to deal with incomplete data as: low risk (less than 10% of participants did not complete the study or used ‘baseline observation carried forward’ analysis, or both); unclear risk of bias (used 'last observation carried forward' analysis); high risk of bias (used 'completer' analysis).

  • Size of study (checking for possible biases confounded by small size (Dechartres 2013; Nüesch 2010)). We will assess 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); high risk of bias (fewer than 50 participants per treatment arm).

Measures of treatment effect

We will use dichotomous data to calculate risk ratios (RR) with 95% confidence intervals (CI) using a fixed‐effect model, and calculate numbers needed to treat for one additional beneficial outcome (NNT) as the reciprocal of the absolute risk reduction (McQuay 1998). In the event of significant statistical heterogeneity, we will consider using a random‐effects model. For unwanted effects, the number needed to treat (NNT) becomes the number needed to treat for one additional harmful outcome (NNTH), and is calculated in the same manner.

We will use the following terms to describe adverse outcomes in terms of harm or prevention of harm.

  • When significantly fewer adverse outcomes occur with tramadol with or without paracetamol than with control (placebo or active control), we will use the term number needed to treat to prevent one event (NNTp).

  • When significantly more adverse outcomes occur with tramadol with or without paracetamol compared with control (placebo or active control) we will use the term number needed to treat for an additional harmful outcome or cause one event (NNH).

We do not plan to use continuous data for the primary outcome because it is inappropriate where there is an underlying skewed distribution, as is usually the case with analgesic response.

Unit of analysis issues

The unit of randomisation will be the individual participant.

Dealing with missing data

We plan to use intention‐to‐treat (ITT) analyses: participants who were randomised, took the study medication, and gave a minimum of one post‐baseline assessment. We will report per‐protocol data in the absence of anything else.

Assessment of heterogeneity

We will assess statistical heterogeneity using L'Abbé plots, a visual method for assessing differences in results of individual studies (L'Abbé 1987), and by use of the I2 statistic. We anticipate that there may be an effect of differences between participants, environment (inpatient versus outpatient), and outcome measures. We plan to explore these with subgroup and sensitivity analyses where there are sufficient data.

Assessment of reporting biases

We aim to use dichotomous data of known utility (Moore 2010). The review will not depend on what authors of the original studies chose to report or not.

We will undertake an assessment of publication bias if there are sufficient data for meta‐analysis, 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 NNT of 10 or higher) (Moore 2008).

Data synthesis

If data are sufficient, we will undertake a quantitative synthesis and present data in forest plots. We will analyse studies of tramadol alone separately from the tramadol plus paracetamol combination. In the event of substantial heterogeneity, we will switch off the totals in the forest plots.

  • We will undertake a meta‐analysis only if participants, interventions, comparisons, and outcomes are judged to be sufficiently similar to ensure an answer that is clinically meaningful.

  • We will use Review Manager 5 for meta‐analysis (RevMan 2014) and Excel for NNTs and NNHs.

Quality of the evidence

We will use the GRADE system to assess the quality of the evidence related to the key outcomes listed in Types of outcome measures, as appropriate (Appendix 2). Two review authors (PW, SD) will independently rate the quality of each outcome.

We will pay particular attention to inconsistency, where point estimates vary widely across studies or confidence intervals (CIs) of studies show minimal or no overlap (Guyatt 2011), and potential for publication bias, based on the amount of unpublished data required to make the result clinically irrelevant (Moore 2008).

In addition, there may be circ*mstances 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 studies use last observation carried forward (LOCF) imputation in circ*mstances 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 circ*mstances where there are no data reported for an outcome, we will report the level of evidence as very low quality (Guyatt 2013b).

'Summary of findings' table

We will include a 'Summary of findings' table as set out in the Pain, Palliative and Supportive Care Review Group author guide (PaPaS 2012) and recommended in Chapter 4.6.6 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011). We plan to include 'Summary of findings' table(s) to present the main findings in a transparent and simple tabular format. We will include key information concerning the quality of evidence, the magnitude of effect of the interventions examined, the sum of available data on the outcomes of at least 30% and at least 50% pain relief, and adverse events.

Subgroup analysis and investigation of heterogeneity

We will analyse separately the data for tramadol alone and tramadol plus paracetamol. If data allow, we will carry out sensitivity analyses for age of participants (younger than 18 years versus 18 years or older).

Acknowledgements

Cochrane Review Group funding acknowledgement: the National Institute for Health Research (NIHR) is the largest single funder of the Cochrane Pain, Palliative and Supportive Care (PaPaS) Review Group. Disclaimer: the views and opinions expressed therein are those of the authors and do not necessarily reflect those of the NIHR, National Health Service (NHS) or the Department of Health.

Institutional support was provided by the Oxford Pain Relief Trust.

Appendices

Appendix 1. MEDLINE search strategy

  1. exp Pain/

  2. pain.tw.

  3. 1 or 2

  4. exp Neoplasms/

  5. (cancer* or neoplas* or tumo* or carcinoma* or hodgkin* or nonhodgkin* or adenocarcinoma* or leuk?emia* or metasta* or malignan* or lymphoma* or sarcoma* or melanoma* or myeloma* or oncolog*).tw.

  6. 4 or 5

  7. Tramadol/

  8. (tramadol or tramacet or k‐315).mp.

  9. 7 or 8

  10. Acetaminophen/

  11. (acetaminophen or paracetamol or Panadol or Tylenol).mp.

  12. 10 or 11

  13. 9 and 12

  14. 9 or 13

  15. 3 and 6 and 14

  16. randomized controlled trial.pt.

  17. controlled clinical trial.pt.

  18. randomized.ab.

  19. placebo.ab.

  20. drug therapy.fs.

  21. randomly.ab.

  22. trial.ab.

  23. groups.ab.

  24. 16 or 17 or 18 or 19 or 20 or 21 or 22 or 23

  25. 15 and 24

Appendix 2. GRADE: criteria for assigning grade of evidence

The GRADE system uses the following criteria for assigning a quality level to a body of evidence (Chapter 12, Higgins 2011).

  • High: randomised trials; or double‐upgraded observational studies.

  • Moderate: downgraded randomised trials; or upgraded observational studies.

  • Low: double‐downgraded randomised trials; or observational studies.

  • Very low: triple‐downgraded randomised trials; or downgraded observational studies; or case series/case reports.

Factors that may decrease the quality level of a body of evidence are:

  1. limitations in the design and implementation of available studies suggesting high likelihood of bias;

  2. indirectness of evidence (indirect population, intervention, control, outcomes);

  3. unexplained heterogeneity or inconsistency of results (including problems with subgroup analyses);

  4. imprecision of results (wide confidence intervals);

  5. high probability of publication bias.

Factors that may increase the quality level of a body of evidence are:

  1. large magnitude of effect;

  2. all plausible confounding would reduce a demonstrated effect or suggest a spurious effect when results show no effect;

  3. dose‐response gradient.

Notes

New

Contributions of authors

Drafted the protocolPW
Will develop and run the search strategyPW, SD
PaPaS Information Specialist will provide support
Will obtain copies of studiesPW
Will select which studies to includePW, SD
Will extract data from studiesPW, SD
Will enter data into Review Manager 5PW, SD
Will carry out the analysisPW, SD
Will interpret the analysisAll
Will draft the final reviewPW, RAM
Will update the reviewPW

Sources of support

Internal sources

  • Oxford Pain Relief Trust, UK.

    General institutional support

External sources

  • The National Institute for Health Research (NIHR), UK.

    NIHR Cochrane Programme Grant: 13/89/29 ‐ Addressing the unmet need of chronic pain: providing the evidence for treatments of pain

Declarations of interest

PW: none known.

SD: none known.

RAM has received grant support from Grünenthal relating to individual patient level analyses of trial data regarding tapentadol in osteoarthritis and back pain (2015). He has received honoraria for attending boards with Menarini concerning methods of analgesic trial design (2014), with Novartis (2014) about the design of network meta‐analyses, and RB on understanding pharmaco*kinetics of drug uptake (2015). He has received honoraria from Omega Pharma (2016) and Futura Pharma (2016) for providing advice on trial and data analysis methods.

References

Additional references

  • Beakley BD, Kaye AM, Kaye AD. Tramadol, Pharmacology, Side Effects, and Serotonin Syndrome: A Review. Pain Physician 2015;18(4):395‐400. [PUBMED: 26218943] [PubMed] [Google Scholar]
  • Breivik H, Cherny N, Collett B, Conno F, Filbet M, Foubert AJ, et al. Cancer‐related pain: a pan‐European survey of prevalence, treatment, and patient attitudes. Ann Oncol 2009;20:1420‐33. [DOI: 10.1093/annonc/mdp001] [PubMed] [CrossRef] [Google Scholar]
  • Bozkurt P. Use of tramadol in children. Paediatric Anaesthesia 2005;15(12):1041‐7. [DOI: 10.1111/j.1460-9592.2005.01738.x] [PubMed] [CrossRef] [Google Scholar]
  • Cancer statistics for the UK. www.cancerresearchuk.org/health‐professional/cancer‐statistics (accessed 21 November 2016).
  • Cepeda MS, Camargo F, Zea C, Velencia L. Tramadol for osteoarthritis. Cochrane Database of Systematic Reviews 2006, Issue 3. [DOI: 10.1002/14651858.CD005522.pub2] [PubMed] [CrossRef] [Google Scholar]
  • Dechartres A, Trinquart L, Boutron I, Ravaud P. Influence of trial sample size on treatment effect estimates: meta‐epidemiological study. BMJ 2013;346:f2304. [DOI: 10.1136/bmj.f2304] [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  • Deng D, Fu L, Zhao YX, Wu X, Zhang G, Liang C, et al. The relationship between cancer pain and quality of life in patients newly admitted to Wuhan Hospice Center of China. American Journal of Hospital Palliative Care 2012;29(1):53‐9. [DOI: 10.1177/1049909111418636] [PubMed] [CrossRef] [Google Scholar]
  • Dreidi MM, Hamdan‐Mansour AM. Pain, Sleep Disturbance, and Quality of Life Among Palestinian Patients Diagnosed with Cancer. Journal of Cancer Education 2016;31(4):796‐803. [DOI: 10.1007/s13187-015-0946-5] [PubMed] [CrossRef] [Google Scholar]
  • Dworkin RH, Turk DC, Wyrwich KW, Beaton D, Cleeland CS, Farrar JT, et al. Interpreting the clinical importance of treatment outcomes in chronic pain clinical trials: IMMPACT recommendations. Journal of Pain 2008;9(2):105‐21. [DOI: 10.1016/j.jpain.2007.09.005] [PubMed] [CrossRef] [Google Scholar]
  • Edwards JE, McQuay HJ, Moore RA. Combination analgesic efficacy: individual patient data meta‐analysis of single‐dose oral tramadol plus acetaminophen in acute postoperative pain. Journal of Pain and Symptom Management 2002;23(2):121‐30. [DOI: 10.1016/S0885-3924(01)00404-3] [PubMed] [CrossRef] [Google Scholar]
  • Gan SH, Ismail R, Wan Adnan WA, Zulmi W. Impact of CYP2D6 genetic polymorphism on tramadol pharmaco*kinetics and pharmacodynamics. Molecular Diagnosis and Therapy 2007;11(3):171‐81. [DOI: 10.1007/BF03256239] [PubMed] [CrossRef] [Google Scholar]
  • Grond S, Sablotzki A. Clinical pharmacology of tramadol. Clinical Pharmaco*kinetics 2004;43(13):879‐932. [DOI: 10.2165/00003088-200443130-00004] [PubMed] [CrossRef] [Google Scholar]
  • Guyatt GH, Oxman AD, Kunz R, Woodco*ck J, Brozek J, Helfand M, et al. GRADE guidelines: 7. Rating the quality of evidence‐‐inconsistency. Journal of Cinical Epidemiology 2011;64(12):1294‐302. [DOI: 10.1016/j.jclinepi.2011.03.017] [PubMed] [CrossRef] [Google Scholar]
  • Guyatt G, Oxman AD, Sultan S, Brozek J, Glasziou P, Alonso‐Coello P, et al. GRADE guidelines: 11. Making an overall rating of confidence in effect estimates for a single outcome and for all outcomes. Journal of Clinical Epidemiology 2013;66:151‐7. [DOI: 10.1016/j.jclinepi.2012.01.006] [PubMed] [CrossRef] [Google Scholar]
  • Guyatt GH, Oxman AD, Santesso N, Helfand M, Vist G, Kunz R, et al. GRADE guidelines: 12. Preparing summary of findings tables‐binary outcomes. Journal of Clinical Epidemiology 2013;66:158‐72. [DOI: 10.1016/j.jclinepi.2012.01.012] [PubMed] [CrossRef] [Google Scholar]
  • Higgins JPT, Green S (editors). Cochrane Handbook for Systematic Reviews of Interventions. Available from www.cochrane‐handbook.org. Vol. 5.1.0, The Cochrane Collaboration, 2009 [Updated 2011]. [Google Scholar]
  • Hollingshead J, Dühmke RM, Cornblath DR. Tramadol for neuropathic pain. Cochrane Database of Systematic Reviews 2006, Issue 3. [DOI: 10.1002/14651858.CD003726.pub3] [PubMed] [CrossRef] [Google Scholar]
  • International Agency for Research on Cancer. GLOBOCAN 2012: Estimated cancer incidence, mortality and prevalence worldwide in 2012. globocan.iarc.fr/Pages/fact_sheets_cancer.aspx (accessed 21 November 2016).
  • L'Abbé KA, Detsky AS, O'Rourke K. Meta‐analysis in clinical research. Annals of Internal Medicine 1987;107:224‐33. [DOI: 10.7326/0003-4819-107-2-224] [PubMed] [CrossRef] [Google Scholar]
  • Lintz W, Barth H, Osterloh G, Schmidt‐Bothelt E. Pharmaco*kinetics of tramadol and bioavailability of enteral tramadol formulations. 3rd Communication: suppositories. Arzneimittel‐Forschung 1998;48(9):889‐99. [PUBMED: 9793614] [PubMed] [Google Scholar]
  • Tramadol hydrochloride. In: Brayfield A editor(s). Martindale ‐ the complete drug reference (available at medicinescomplete.com) (accessed 21 November 2016). 8th Edition. Pharmaceutical Press, 2016. [Google Scholar]
  • McQuay H, Moore A. An Evidence‐based Resource for Pain Relief. Oxford: Oxford University Press, 1998. [ISBN: 0‐19‐263048‐2] [Google Scholar]
  • Mikan F, Wada M, Yamada M, Takahashi A, Onishi H, Ishida M, et al. The Association Between Pain and Quality of Life for Patients With Cancer in an Outpatient Clinic, an Inpatient Oncology Ward, and Inpatient Palliative Care Units. American Journal of Hospital Palliative Care 2016;33(8):782‐90. [DOI: 10.1177/1049909116630266] [PubMed] [CrossRef] [Google Scholar]
  • Minami K, Ogata J, Uezono Y. What is the main mechanism of tramadol?. Naunyn Schmiedebergs Archives of Pharmacology 2015;388:999‐1007. [DOI: 10.1007/s00210-015-1167-5] [PubMed] [CrossRef] [Google Scholar]
  • Moore RA, McQuay HJ. Single‐patient data meta‐analysis of 3453 postoperative patients: oral tramadol versus placebo, codeine and combination analgesics. Pain 1997;69(3):287‐94. [DOI: 10.1016/S0304-3959(96)03291-5] [PubMed] [CrossRef] [Google Scholar]
  • Moore RA, Barden J, Derry S, McQuay HJ. Managing potential publication bias. In: McQuay HJ, Kalso E, Moore RA editor(s). Systematic Reviews in Pain Research: Methodology Refined. Seattle: IASP Press, 2008:15‐24. [ISBN: 978–0–931092–69–5] [Google Scholar]
  • Moore RA, Moore OA, Derry S, Peloso PM, Gammaitoni AR, Wang H. Responder analysis for pain relief and numbers needed to treat in a meta‐analysis of etoricoxib osteoarthritis trials: bridging a gap between clinical trials and clinical practice. Annals of the Rheumatic Diseases 2010;69(2):374‐9. [DOI: 10.1136/ard.2009.107805] [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  • Moore RA, Straube S, Aldington D. Pain measures and cut‐offs ‐ 'no worse than mild pain' as a simple, universal outcome. Anaesthesia 2013;68(4):400‐12. [DOI: 10.1111/anae.12148] [PubMed] [CrossRef] [Google Scholar]
  • Nüesch E, Trelle S, Reichenbach S, Rutjes AW, Tschannen B, Altman DG, et al. Small study effects in meta‐analyses of osteoarthritis trials: meta‐epidemiological study. BMJ 2010;341:c3515. [DOI: 10.1136/bmj.c3515] [PMC free article] [PubMed] [CrossRef] [Google Scholar]
  • Cochrane Pain, Palliative and Supportive Care Group. PaPaS Author and Referee Guidance. papas.cochrane.org/papas‐documents (accessed 21 November 2016).
  • Pedersen RS, Damkier P, Brosen K. Tramadol as a new probe for cytochrome P450 2D6 phenotyping: a population study. Clinical Pharmacology and Therapeutics 2005;77:458‐67. [DOI: 10.1016/j.clpt.2005.01.014] [PubMed] [CrossRef] [Google Scholar]
  • Portenoy RK, Lesage P. Management of cancer pain. Lancet 1999;353:1695‐700. [DOI: 10.1016/S0140-6736(99)01310-0] [PubMed] [CrossRef] [Google Scholar]
  • Prommer EE. Pharmacological Management of Cancer‐Related Pain. Cancer Control 2015;22(4):412‐25. [PUBMED: 26678968] [PubMed] [Google Scholar]
  • Radbruch L, Glaeske G, Grond S, Münchberg F, Scherbaum N, Storz E, et al. Topical review on the abuse and misuse potential of tramadol and tilidine in Germany. Substance Abuse 2013;34(3):313‐20. [DOI: 10.1080/08897077.2012.735216] [PubMed] [CrossRef] [Google Scholar]
  • Reeves RR, Burke RS. Tramadol: basic pharmacology and emerging concepts. Drugs Today (Barc) 2008;44(11):827‐36. [DOI: 10.1358/dot.2008.44.11.1289441] [PubMed] [CrossRef] [Google Scholar]
  • The Nordic Cochrane Centre, The Cochrane Collaboration. Review Manager. Version 5.3. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014.
  • Scott LJ, Perry CM. Tramadol: a review of its use in perioperative pain. Drugs 2000;60(1):139‐76. [DOI: 10.2165/00003495-200060010-00008] [PubMed] [CrossRef] [Google Scholar]
  • Tassinari D, Drudi F, Rosati M, Tombesi P, Sartori S, Maltoni M. The second step of the analgesic ladder and oral tramadol in the treatment of mild to moderate cancer pain: a systematic review. Palliative Medicine 2011;25(5):410‐23. [DOI: 10.1177/0269216311405090] [PubMed] [CrossRef] [Google Scholar]
  • Beuken‐van Everdingen MH, Hochstenbach LM, Joosten EA, Tjan‐HeijnenVC, Janssen DJ. Update on Prevalence of Pain in Patients With Cancer: Systematic Review and Meta‐Analysis. J Pain Symptom Manage 2016;51:1070‐90. [DOI: 10.1016/j.jpainsymman.2015.12.340] [PubMed] [CrossRef] [Google Scholar]
  • Anon. WHO analgesic ladder. http://www.who.int/cancer/palliative/painladder/en/ (accessed 21 November 2016).
  • Wiffen PJ, Wee B, Moore RA. Oral morphine for cancer pain. Cochrane Database of Systematic Reviews 2013, Issue 7. [DOI: 10.1002/14651858.CD003868.pub3] [PubMed] [CrossRef] [Google Scholar]
  • Wiffen PJ, Derry S, Moore RA. Impact of morphine, fentanyl, oxycodone or codeine on patient consciousness, appetite and thirst when used to treat cancer pain. Cochrane Database of Systematic Reviews 2014, Issue 5. [DOI: 10.1002/14651858.CD011056.pub2] [PMC free article] [PubMed] [CrossRef] [Google Scholar]

Articles from The Cochrane Database of Systematic Reviews are provided here courtesy of Wiley

Tramadol with or without paracetamol (acetaminophen) for cancer pain (2024)

References

Top Articles
Latest Posts
Article information

Author: Domingo Moore

Last Updated:

Views: 5654

Rating: 4.2 / 5 (53 voted)

Reviews: 92% of readers found this page helpful

Author information

Name: Domingo Moore

Birthday: 1997-05-20

Address: 6485 Kohler Route, Antonioton, VT 77375-0299

Phone: +3213869077934

Job: Sales Analyst

Hobby: Kayaking, Roller skating, Cabaret, Rugby, Homebrewing, Creative writing, amateur radio

Introduction: My name is Domingo Moore, I am a attractive, gorgeous, funny, jolly, spotless, nice, fantastic person who loves writing and wants to share my knowledge and understanding with you.