Corticosteroids for adult patients with advanced cancer who have nausea and vomiting (not related to chemo‐ or radiotherapy, or surgery)
Abstract
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:
To assess the effects of corticosteroids on nausea and vomiting not related to chemotherapy, radiotherapy, or surgery in adult cancer patients.
Background
Description of the condition
Nausea is a subjective phenomenon of an unpleasant, wavelike sensation experienced in the back of the throat and/or the epigastrium that may culminate in vomiting (emesis) (National Cancer Institute 2010). Vomiting is defined as the forceful expulsion of the contents of the stomach, duodenum, or jejunum through the oral cavity. Nausea may be accompanied by autonomic symptoms such as pallor, cold sweat, salivation, tachycardia, and diarrhoea (Davis 2000).
Nausea is a common symptom in advanced cancer with a prevalence of up to 70% (Davis 2000; Walsh 2000; Teunissen 2007). Nausea can be related to cancer treatments, e.g. chemotherapy and/or radiotherapy (Feyer 1998; Foubert 2005), or surgery (Gan 2007), but a significant number of people with advanced cancer also suffer from nausea unrelated to such therapies (Fainsinger 1991). Common causes include medications (especially opioids), gastro‐intestinal complications or progression of the underlying disease, metabolic abnormalities, brain metastases, or a combination of several of these factors. In addition to the physical effects, nausea and vomiting may cause psychological distress and have a negative impact on the quality of life of cancer patients (Porternoy 1994; Walsh 2000; Ballatori 2007; Harris 2010; Pirri 2013). As with pain, nausea is often under‐treated (Reuben 1986). Uncontrolled nausea and vomiting can result in significant loss of appetite and weight (Pirri 2013).
Description of the intervention
Corticosteroids are effective antiemetics in the prevention of acute and delayed nausea and vomiting induced by moderate to highly emetogenic chemotherapy (Ioannidis 2000). They are effective alone when compared to placebo, but provide even greater benefit when combined with other antiemetics (metoclopramide, serotonin (5HT‐3) and neurokinin 1 (NK1) receptor antagonists) (Joss 1994; Latreille 1998; Gralla 1999; Roila 2006). Since 1998, corticosteroids have been included in the antiemetic guidelines for chemotherapy‐induced nausea and vomiting of both the American Society of Clinical Oncology (ASCO) (Gralla 1999) and the Multinational Association of Supportive Care in Cancer (MASCC) (Perugia Consensus Conference 1998). The guidelines are based on the identification of the receptors supposedly involved in the generation of nausea and/or vomiting induced by chemotherapy and radiotherapy (Smith 2005; Harris 2010). Corticosteroids are also indicated in the prevention of post‐operative nausea and vomiting (PONV) (De Oliveira 2013). As an extrapolation of these findings, corticosteroids are also widely used for nausea and vomiting not related to chemo/radiotherapy or surgery in patients with cancer. The choice of an antiemetic in palliative care is based on either an empirical or a mechanistic approach (Hardy 2015). Most of the guidelines developed around the mechanistic approach recommend identifying the most likely cause of nausea and the underlying pathways and receptors involved. Drugs known to inhibit those particular receptors are then chosen (Glare 2004; Hardy 2015). In the empirical approach, a broad spectrum antiemetic is used irrespective of the presumed cause of nausea.
How the intervention might work
Nausea is promoted through the activation of neurotransmitters in three different centres. While these centres are anatomically clearly distinct in animal models, the pathways in humans are more diffuse and interactive (Hardy 2015). The vomiting centre within the blood‐brain barrier receives inputs from the vestibular system, somatic sensation, emotion and memory. The chemoreceptor trigger zone (CTZ), in the area postrema, lies outside the blood‐brain barrier on the floor of the fourth ventricle and is vulnerable to metabolic and chemical triggers. The solitary tractus nucleus (STN), in the medulla within the dorsal vagal complex, collects emetogenic inputs from the sympathetic and parasympathetic nervous systems. In the gastric tract there are dopamine receptors that affect gastric motility (Grunberg 2007). Stretch mechanoreceptors also signal distention and organomegaly through the vagal nerve (Harris 2010; Chu 2014).
The exact mechanism of action of corticosteroids on nausea is unclear, but is probably related to their potent anti‐inflammatory action, especially in nausea/vomiting related to raised intracranial pressure (Gralla 1999) and bowel obstruction (Laval 2000; Mercadante 2004). Furthermore, glucocorticoids inhibit the expression of serotonin, a potent emetogenic neurotransmitter (Mantovani 1997). Glucocorticoids maintain physiological functions of several organs and systems, particularly under stress which may also cause nausea and vomiting (Chu 2014). A glucocorticoid deficiency has been shown to induce nausea and vomiting (Hursti 1993).
Why it is important to do this review
Corticosteroids are used to manage a number of cancer‐specific complications e.g. spinal cord compression, raised intracranial pressure and lymphangitis carcinomatosis. They are also commonly used in palliative care for a wide variety of non‐specific indications, such as pain, nausea, anorexia, fatigue and low mood (Farr 1990; Hardy 2001; Riechelmann 2007). There is, however, little objective evidence of their efficacy in symptom control (Haywood 2015). Moreover, corticosteroids are associated with significant side effects (Hanks 2009), especially in a long‐term setting. Some authors have suggested greater vigilance in prescribing corticosteroids in the presence of limited clinical benefit (Gannon 2002). In view of their widespread use, it is important to seek evidence of their effects on nausea and vomiting not related to cancer treatment.
Objectives
To assess the effects of corticosteroids on nausea and vomiting not related to chemotherapy, radiotherapy, or surgery in adult cancer patients.
Methods
Criteria for considering studies for this review
Types of studies
Double blind randomised controlled trials (RCTs). If no randomised controlled trials are found, prospective controlled studies will be included.
Types of participants
Participants with cancer suffering from nausea and/or vomiting not related to chemotherapy, radiotherapy, or surgery, aged 18 years and above.
Types of interventions
Any corticosteroid at any dose. All routes of drug administration will be considered.
Comparison:
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placebo;
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no intervention;
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other antiemetics;
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usual treatment or supportive care; or
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alternative treatments for nausea/vomiting.
Types of outcome measures
We will include a 'Summary of findings' table as set out in the Cochrane PaPaS Group author guide (AUREF 2012) and recommended in the Cochrane Handbook, chapter 4.6.6 (Higgins 2011). The 'Summary of findings' table will include outcomes of nausea intensity and relief and the number of vomiting episodes in a predefined time interval.
Primary outcomes
Patient‐reported nausea intensity and relief using validated scales (visual analogue scales (VAS), numerical rating scales (NRS) and/or verbal rating scales (VRS)) and the number of vomiting episodes in a predefined time interval.
Secondary outcomes
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Adverse events (e.g. psychiatric events, hyperglycemias or diabetic decompensation, fluid retention and other).
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Quality of life.
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Patient satisfaction.
Search methods for identification of studies
Electronic searches
We will identify relevant trials by searching the following databases:
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The Cochrane Central Register of Controlled Trials ‐ CENTRAL (The Cochrane LIbrary);
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MEDLINE (Ovid);
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MEDLINE in process (Ovid);
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EMBASE.com;
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CINAHL (EbscoHOST);
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Science Citation Index (ISI Web of Science);
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Conference Proceedings Citation Index – Science (ISI Web of Science);
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LILACS (Latin America and Carribean Health Sciences).
Medical subject headings (MeSH) or equivalent and text word terms will be used. Searches will be tailored to individual databases. We will not apply any language, date or publication status restrictions to the search. An example of the search strategy for MEDLINE (Ovid) is shown in Appendix 1.
Searching other resources
We will check the bibliographic references and cited sources of relevant identified studies in order to find additional trials not identified by the electronic searches. We will also search www.clinicaltrials.gov, the metaRegister of Controlled Trials (mRCT) (www.controlledtrials.com/mrct/), the WHO International Clinical Trials Registry Platform (ICTRP) (http://apps.who.int/trialsearch/) and the Clinical Trials database of the National Cancer Institute at the National Institute of Health (http://www.cancer.gov/clinicaltrials) for ongoing trials. We will search the Internet using the Google scholar search engine (www.googlescholar.com) with selected terms from the above strategy, for any further literature.
Data collection and analysis
Selection of studies
One of the authors will run the searches in collaboration with the Cochrane PaPaS Group. Three of the review authors will independently assess the titles and abstracts of all the studies identified by the search for potential inclusion. We will independently consider the full records of all potentially‐relevant studies for inclusion by applying the selection criteria outlined in the 'Criteria for considering studies for this review' section. Potential disagreement will be resolved by discussion. If we cannot reach agreement, we will seek the opinion of a fourth review author for final judgement. We will include a Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) flow chart (Liberati 2009; Moher 2009) in the full review to document the screening process as recommended in Part 2, Section 11.2.1 of the Cochrane Handbook (Higgins 2011).
Data extraction and management
Three review authors will independently extract data from the studies using a standard form, and check for agreement before entry into Review Manager (RevMan 2014). Data extracted will include information about the year of study, study design, number of participants treated, participant demographic details, type of cancer, drug and dosing regimen, study design (placebo or active control) and methods, study duration and follow‐up, outcome measures (measurement of nausea and other relevant outcomes), withdrawals and adverse events. Data will be collected in sufficient detail to populate a table of 'Characteristics of included studies' in the full review. We will resolve potential disagreements by discussion. If there are studies for which only a subgroup of the participants meets the inclusion criteria for the current review, we will only extract data on this subgroup provided randomisation will not be broken. If there are any missing data, we will attempt to contact the authors of the original studies for clarification.
Assessment of risk of bias in included studies
Three authors will independently assess the risk of bias for each study, using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions (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 RevMan (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). Studies using a non‐random process (e.g. odd or even date of birth; hospital or clinic record number) will be excluded.
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). Studies that do not conceal allocation (e.g. open list) will be excluded.
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 states that it was blinded and describes the method used to achieve blinding, e.g. identical tablets; matched in appearance and smell); unclear risk of bias (study states that it was blinded but does not provide an adequate description of how it was achieved). Studies that were not double‐blind will be excluded.
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 (< 10% of participants did not complete the study and/or used ‘baseline observation carried forward’ analysis); 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)
We will assess studies as being at low risk of bias (≥ 200 participants per treatment arm); unclear risk of bias (50 to 199 participants per treatment arm); high risk of bias (< 50 participants per treatment arm).
Measures of treatment effect
For dichotomous outcomes between groups, we will estimate and compare the risk ratio (RR) using 95% confidence intervals (CIs). For continuous outcomes between groups, we will measure arithmetic mean and standard deviation (SD) and report the mean difference (MD) with 95% CI. When an outcome was derived with different instruments measuring the same construct, we will use standardised mean difference (SMD) with 95% CIs.
Unit of analysis issues
We will only include studies in which randomisation is by the individual patient; this may include cross‐over or n = 1 studies. For trials containing multiple arms, we will only include pair‐wise comparisons of each intervention arm to the control arm.
Dealing with missing data
In cases where an intended outcome has not been reported, we will endeavour to contact the authors to request additional data. In cases where summary data are missing, we will endeavour to contact the authors to obtain the relevant summary statistics. If intention‐to‐treat (ITT) analyses were not performed, we will perform the ITT analyses provided that the necessary data are available for this purpose. The method of assessing data processed from withdrawals will be ascertained where possible. Where there are substantial numbers (> 10%) of participants missing from analyses, we will perform sensitivity analyses of best‐ and worst‐case scenarios (Higgins 2011).
Assessment of heterogeneity
There may be an effect of differences between patients, environment (inpatient versus outpatient) and outcome measures. We will assess heterogeneity by using the I2 statistic in any meta‐analyses we conduct. We will consider I2 values above 50% to represent substantial heterogeneity in line with Higgins 2011 and we will assess potential sources of heterogeneity through subgroup analyses.
Assessment of reporting biases
We will use funnel plot symmetry to interpret the results of the statistical analysis. If there is evidence of small study effects, we will consider publication bias as only one of a number of possible explanations (Higgins 2011).
Data synthesis
Review Manager (RevMan 2014) will be used for data extraction and synthesis. Where possible, meta‐analysis of outcomes will be performed, taking into consideration heterogeneity and comparability of outcome measures. We will synthesise dichotomous outcomes by calculating the RR, and continuous variables by calculating the MD as an estimate of effect size, using a fixed‐effect or random‐effects model (depending on heterogeneity) with 95% CIs. If continuous variables are measured on different scales, we will calculate the SMD.
The overall quality of the evidence for each outcome will be assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach (Guyatt 2008) and presented in a 'Summary of findings' table (Langendam 2013). In particular, we will include key information concerning the quality of evidence, the magnitude of effect of the interventions examined, and the sum of available data on the main outcomes.
Subgroup analysis and investigation of heterogeneity
Different aspects of the trials are likely to contribute heterogeneity to the proposed main analyses. If there are sufficient data, we will perform subgroup analyses based on: type of corticosteroid, dose, route of administration, type of cancer, and length of the trial.
Sensitivity analysis
Sensitivity analyses will be performed by including and excluding studies with a high risk of bias to determine the impact that inclusion of studies of poorer methodological quality has on the outcomes. Where significant heterogeneity is identified, we will also conduct sensitivity analyses using a random‐effects model versus a fixed‐effect model. If any individual peculiarities of the studies under investigation are identified during the review process that are suitable for sensitivity analyses, we will perform additional sensitivity analyses.
