Acetyl‐L‐carnitine for patients with hepatic encephalopathy
Abstract
This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:
To assess the clinical benefits and harms of acetyl‐L‐carnitine for treating patients with hepatic encephalopathy.
Background
Description of the condition
Hepatic encephalopathy is a common and potentially devastating neuropsychiatric complication of acute liver failure or chronic liver disease (McPhail 2010; Felipo 2013; Rahimi 2013; Nusrat 2014). Hepatic encephalopathy can be categorised into three broad groups: type A which occurs in acute liver failure; type B which occurs in patients with portosystemic bypass and no intrinsic hepatocellular disease; and the most commonly recognised form, type C, which occurs in patients with chronic liver disease (Frederick 2011; Perazzo 2012; Felipo 2013; Romero‐Gomez 2014).
The spectrum of symptoms extends from a subclinical syndrome that may not be clinically apparent (early‐stage or 'minimal' hepatic encephalopathy) to full‐blown neuropsychiatric manifestations such as cognitive impairment, confusion, slow speech, loss of fine motor skills, asterixis, peripheral neuropathy, clonus, Babinski sign, decerebrate and decorticate posturing, seizures, extrapyramidal symptoms, and coma (Frederick 2011; Perazzo 2012; Felipo 2013). Mental state in patients with this liver complication can be assessed with The West Haven criteria (Shores 2008). See Appendix 1 for details of the criteria.
The severity of the underlying liver insufficiency and portal systemic shunt determine either prevalence or incidence of hepatic encephalopathy (Vilstrup 2014). See Appendix 2 for details of the epidemiological data.
Hepatic encephalopathy arises when the liver cannot detoxify the portal venous blood (Wakim‐Fleming 2011). The pathogenesis of hepatic encephalopathy is multifactorial (Frederick 2011; Wakim‐Fleming 2011; Felipo 2013; Sturgeon 2014). There are many hypothesis about how hepatic encephalopathy develops. Ammonia is the molecule key of multiple neurotoxins and inflammatory mediators, and it has been implicated in the pathogenesis of hepatic encephalopathy (Munoz 2008; Sundaram 2009; Frederick 2011; Perazzo 2012; Felipo 2013). Accumulation of ammonia from the gut and other sources due to impaired hepatic clearance or portosystemic shunting can lead to accumulation of glutamine in brain astrocytes, leading to swelling (Liou 2014). Several precipitating factors of hepatic encephalopathy have been described such as acidosis, alkalosis, constipation, diuretic use, dehydration, gastrointestinal bleeding, hyponatraemia, infection, protein excess, renal decompensation, sedative use, and trauma (Wakim‐Fleming 2011). Hepatic encephalopathy is associated with significantly increased mortality among liver patients, including patients awaiting liver transplantation (Wong 2014).
The treatment of hepatic encephalopathy is complex (Frontera 2014). It involves a pre‐emptive approach to address potential precipitating factors, medical therapy to reduce the production and absorption of ammonia from the gut, and surgical or interventional therapies (Frederick 2011; Wakim‐Fleming 2011). Several Cochrane reviews have been published, assessing the clinical benefits and harms of interventions for hepatic encephalopathy treatment: dopaminergic agonists (Als‐Nielsen 2004a), benzodiazepine receptor antagonists (Als‐Nielsen 2004b), nonabsorbable disaccharides (Als‐Nielsen 2004c), branched‐chain amino acids (Als‐Nielsen 2003), probiotics (McGee 2011), naloxone (Sun 2009), and antibiotics (Falavigna 2007). Acetyl‐L‐carnitine is another medical approach for reducing the ammonia toxicity in patients suffering from hepatic encephalopathy (Malaguarnera 2003; Malaguarnera 2005; Malaguarnera 2006; Malaguarnera 2008; Shores 2008; Malaguarnera 2011; Malaguarnera 2011a; Malaguarnera 2011b; Malaguarnera 2012; Malaguarnera 2013).
Description of the intervention
Acetyl‐L‐carnitine is an ester of L‐carnitine and acetate, and it is synthesised in the human brain, liver, and kidney by the enzyme acetyl‐L‐carnitine transferase (Malaguarnera 2013).
Carnitine is an essential dietary nutrient that acts as a carrier of fatty acids across the inner mitochondria membrane (Malaguarnera 2012). The liver is a central organ for carnitine metabolism and for the distribution of carnitine to the body. Therefore, it is not surprising that carnitine metabolism is impaired in patients with certain types of chronic liver disease (Krähenbühl 1996). The authors of Selimoglu 2001 have pointed out that children with cirrhosis have low plasma carnitine concentrations prominent in children with Wilson's disease. Based on this perspective, they have suggested mandatory carnitine supplementation for patients with cirrhosis in childhood, especially those with Wilson's disease. However, controversy exists on this issue. One study shows that patients with noncirrhotic liver disease had no change in the plasma carnitine pool, whereas patients with cirrhosis had a 29% increase in the long‐chain acyl‐carnitine concentration (Krähenbühl 1997).
How the intervention might work
Hepatic encephalopathy is a functional disturbance of cells involved in cerebral neurotransmission (Munoz 2008). The major factor for affecting cerebral transmission is hyperammonaemia which is directly neurotoxic (Munoz 2008). It causes swelling of astrocytes and brain oedema (Wakim‐Fleming 2011). Astrocytes swelling generates mitochondrial dysfunction and blood–brain barrier rupture which causes neuronal dysfunction (Wakim‐Fleming 2011).
Theoretically, acetyl‐L‐carnitine might be an effective intervention for treating patients affected by hepatic encephalopathy through two mechanisms; first, increasing an urea genesis to decrease blood and brain ammonia levels (Malaguarnera 2013); second, since acetyl‐L‐carnitine is transported across the blood–brain barrier and accumulated in cerebral spinal fluid and brain, it would facilitate the uptake of acetyl‐CoA into the mitochondria during fatty acid oxidation, enhance acetylcholine production, and stimulate protein and membrane synthesis of phospholipids. All these mechanisms could provide a substrate reservoir for cellular energy production, thereby preventing excessive neuronal cell death (Malaguarnera 2012; Malaguarnera 2013).
Why it is important to do this review
There is controversy regarding the true role of acetyl‐L‐carnitine in treating patients with hepatic encephalopathy (Krähenbühl 1996; Krähenbühl 1997; Selimoglu 2001). The costs for over 40,000 US patients hospitalised with hepatic encephalopathy in 2003 have been estimated to be $932 million (Poordad 2007). Between 2005 and 2009, there were 110,000 hospitalisations reported in the USA (Stepanova 2012). It has been pointed out that in the European Union, this burden is similar (Vilstrup 2014). Malaguarnera 2003; Malaguarnera 2005; Malaguarnera 2006; Malaguarnera 2008; Malaguarnera 2011; Malaguarnera 2011a; Malaguarnera 2011b; and Malaguarnera 2013 have conducted trials assessing the clinical benefits and harms of acetyl‐L‐carnitine in patients with hepatic encephalopathy. All of the trials come from a group in Italy. A non‐Cochrane review on L‐acyl‐carnitine for treatment of hepatic encephalopathy was published in 2008 (Shores 2008). Three non‐meta‐analysed randomised clinical trials are discussed in it, and their quality was assessed using the outdated Jadad score. The review recommends further trials of L‐acyl‐carnitine (Shores 2008). The Jiang 2013 meta‐analysis on acetyl‐L‐carnitine for people with hepatic encephalopathy showed that acetyl‐L‐carnitine significantly reduced serum ammonia levels and improved mental state, assessed with the trail making test (Conn 1977; Jiang 2013). However, the meta‐analysis employed no assessment of risk of bias or risks of random errors of the included randomised clinical trials. Thus, a systematic assessment of the beneficial and harmful effects of L‐acyl‐carnitine in the treatment of patients with hepatic encephalopathy is warranted.
Objectives
To assess the clinical benefits and harms of acetyl‐L‐carnitine for treating patients with hepatic encephalopathy.
Methods
Criteria for considering studies for this review
Types of studies
Randomised clinical trials irrespective of the publication status (unpublished or published as an article, abstract, or letter), language, and blinding.
We will exclude quasi‐randomised studies in our assessment of benefit, but we will include these and other observational studies for the report on harms if such studies are retrieved by the searches.
Types of participants
Patients with hepatic encephalopathy, irrespective of aetiology.
Types of interventions
Acetyl‐L‐carnitine administered at any dose, by any route of administration and duration of treatment, versus no intervention, placebo, or other interventions.
Since hepatic encephalopathy requires different medical and endoscopic treatments (that is, primary interventions), acetyl‐L‐carnitine is considered as a supplementary intervention. Thus, for the purpose of the review, eligible randomised clinical trials will be those that compare the same primary interventions with and without acetyl‐L‐carnitine drug supplementation.
Types of outcome measures
We will extract data on outcomes at the end of treatment and at maximal follow‐up.
Primary outcomes
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All‐cause mortality.
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Quality of life (any validated scale used by the trialists such as SF‐36).
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Serious adverse events. A serious adverse event, defined according to the International Conference on Harmonisation (ICH) Guidelines for Good Clinical Practice (ICH‐GCP 1997), is any untoward medical occurrence that at any dose results in death, is life‐threatening, requires inpatient hospitalisation or prolongation of existing hospitalisation, results in persistent or significant disability or incapacity, or is a congenital anomaly or birth defect. All other adverse events will be considered nonserious.
Secondary outcomes
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Nonserious adverse events (ICH‐GCP 1997).
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Days of hospitalisation.
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Blood ammonium levels.
Search methods for identification of studies
We will search the Cochrane Hepato‐Biliary Group Controlled Trials Register (Gluud 2014), the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE (Ovid SP), EMBASE (Ovid SP), Science Citation Index Expanded, and LILACS. We present preliminary search strategies with the expected time spans of the searches in Appendix 3.
We will also search the Chinese BioMedical Database, Traditional Chinese Medical Literature Analysis and Retrieval System, China National Knowledge Infrastructure, Chinese VIP Information, Chinese Academic Conference Papers Database, and Chinese Dissertation Database.
We will look through the reference lists of the retrieved publications and review articles. We will also search the WHO International Clinical Trials Registry Platform Search Portal (apps.who.int/trialsearch/) and the metaRegister of Controlled Trials (www.controlled‐trials.com/mrct/search.html) for ongoing and unpublished trials.
Data collection and analysis
We will summarise data by standard Cochrane Collaboration methods (Higgins 2011).
Selection of studies
Two review authors will independently select studies for eligibility using Early Review Organizing Software (EROS) (Ciapponi 2011). We will review titles and abstracts of all identified studies to determine whether they have fulfilled the inclusion criteria. We will assess the full texts of selected studies to confirm their relevance for inclusion. We will resolve any disagreement by consulting with a third review author. We will not be blinded to the authors’ names and institutions, the journal of publication, or the study results at any stage of the review.
Data extraction and management
We will use a form to extract data from each relevant trial (Zavala 2006). Two authors will independently extract data from the trial publications and will contact the authors if data are missing. Arturo Martí‐Carvajal (AMC) will enter the data into Review Manager 5.3 (RevMan 2014), and Ingrid Arévalo‐Rodríguez (IAR) will independently check the data. We also plan to extract information on study design and patient characteristics (age, sex, and hepatic encephalopathy severity as measured by West Haven grade).
Assessment of risk of bias in included studies
We will assess the following 'Risk of bias' domains of the randomised clinical trials (Schulz 1995; Moher 1998; Kjaergard 2001; Wood 2008; Higgins 2011; Lundh 2012; Savovic 2012; Savovic 2012a).
Allocation sequence generation
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Low risk of bias: sequence generation is achieved using computer random number generation or a random number table. Drawing lots, tossing a coin, shuffling cards, and throwing dice are adequate if performed by an independent person not otherwise involved in the trial.
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Uncertain risk of bias: the sequence generation method is not specified.
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High risk of bias: the sequence generation method is not random.
Allocation concealment
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Low risk of bias: the participant allocations could not have been foreseen in advance of, or during, enrolment. Allocation is controlled by a central and independent randomisation unit. The allocation sequence was unknown to the investigators (for example, the allocation sequence was hidden in sequentially numbered, opaque, and sealed envelopes).
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Uncertain risk of bias: the method used to conceal the allocation is not described so that intervention allocations may have been foreseen in advance of, or during, enrolment.
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High risk of bias: the allocation sequence is likely to be known to the investigators who assigned the participants.
Blinding of participants, personnel, and outcome assessors
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Low risk of bias: blinding is performed adequately, or the assessment of outcomes is not likely to be influenced by lack of blinding.
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Uncertain risk of bias: there is insufficient information to assess whether blinding is likely to induce bias in the results.
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High risk of bias: there is no blinding or incomplete blinding, and the assessment of outcomes is likely to be influenced by lack of blinding.
Incomplete outcome data
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Low risk of bias: missing data are unlikely to make treatment effects depart from plausible values. Sufficient methods, such as multiple imputation, have been employed to handle missing data.
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Uncertain risk of bias: there is insufficient information to assess whether missing data in combination with the method used to handle missing data are likely to induce bias in the results.
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High risk of bias: the results are likely to be biased due to missing data.
Selective outcome reporting
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Low risk: all pre‐defined, or clinically relevant and reasonably expected, outcomes are reported on. If the original trial protocol is available, the outcomes should be those called for in that protocol. (Note: If the trial protocol is obtained from a trial registry (e.g. www.clinicaltrials.gov), the outcomes to be sought are those enumerated in the original protocol if the trial protocol was registered before or at the time that the trial was begun; if the trial protocol was registered after the trial was begun, those outcomes will not be considered to be reliable in representing the outcomes initially being sought). If the trial protocol is not available (or if the protocol was registered after the trial was begun), we will expect the following clinically relevant and reasonably expected outcomes to be reported by trial authors: all‐cause mortality, quality of life, serious adverse events, nonserious adverse events, and days of hospitalisation.
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Unclear risk: not all pre‐defined, or clinically relevant and reasonably expected, outcomes are reported fully, or it is unclear whether data on these outcomes were recorded or not.
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High risk: one or more predefined or clinically relevant and reasonably expected outcomes were not reported, despite the fact that data on these outcomes should have been likely to have been available and even recorded.
For‐profit bias
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Low risk of bias: the trial appears to be free of industry sponsorship or other kind of for‐profit support that may manipulate the trial design, conduct, or results of the trial.
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Uncertain risk of bias: the trial may or may not be free of for‐profit bias, as no information on clinical trial support or sponsorship is provided.
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High risk of bias: the trial is sponsored by the industry or has received other kind of for‐profit support.
Other bias
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Low risk of bias: the trial appears to be free of other components (for example, academic bias) that can put it at risk of bias.
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Uncertain risk of bias: the trial may or may not be free of other components that can put it at risk of bias.
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High risk of bias: there are other factors in the trial that can put it at risk of bias (for example, authors have conducted trials on the same topic, etc.).
We will judge trials as being at low risk of bias if assessed as having low risk of bias in all domains. In all other cases, we will judge the trials as being at high risk of bias.
Measures of treatment effect
For binary outcomes, such as all‐cause mortality and adverse (serious and nonserious) events, we plan to calculate the relative risk (RR) with 95% confidence interval (CI).
For continuous outcomes, such as quality of life (e.g., SF‐36), days of hospitalisation, and blood ammonium levels, we plan to calculate the mean difference (MD) with 95% CI. If different scales are used for measuring the same outcome, e.g., quality of life, we plan to use the standardised mean difference (SMD) with 95% CI. As recommended by the Cochrane Handbook for Systematic Reviews of Interventions, if necessary we will multiply the mean values from one set of studies by ‐1 to ensure that all the scales point in the same direction (Higgins 2011).
Dealing with missing data
If data are missing from the publications, we will attempt to contact the publication authors.
We will attempt to use intention‐to‐treat analysis.
Regarding the primary outcomes, we will include patients with incomplete or missing data in sensitivity analyses by imputing them according to the following scenarios (Hollis 1999).
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Extreme case analysis favouring the experimental intervention ('best‐worse' case scenario): none of the drop‐outs/participants lost from the experimental arm, but all of the drop‐outs/participants lost from the control arm experienced the outcome, including all randomised participants in the denominator.
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Extreme case analysis favouring the control ('worst‐best' case scenario): all drop‐outs/participants lost from the experimental arm, but none from the control arm experienced the outcome, including all randomised participants in the denominator.
Assessment of heterogeneity
We will quantify the impact of statistical heterogeneity using the I2 statistic which describes the percentage of total variation across studies that is due to heterogeneity rather than sampling error (Higgins 2003). If the identified trials are comparable enough, we will summarise their findings using a random‐effects model. In the case of substantial heterogeneity (I2 > 50%), we will do further research to identify possible causes of heterogeneity, by exploring the impact of participants' characteristics.
Assessment of reporting biases
We will attempt to assess publication bias by using a funnel plot which is usually used to illustrate variability between trials in a graphical way. We will need at least 10 trials in order to be able to make judgements about asymmetry, and, if asymmetry is present, we will attempt to explore the its causes (Sterne 2011).
Data synthesis
Meta‐analysis
We will perform meta‐analyses with 95% confidence intervals (CI), using both the fixed‐effect and random‐effects model. In case of statistically significant discrepancy between the results produced with both models, we will report both results. Otherwise, we will choose one of the models to report the results.
Trial sequential analysis
We will apply trial sequential analysis (TSA), as cumulative meta‐analyses are at risk of producing random errors due to sparse data and repetitive testing of the accumulating data (Brok 2008; Wetterslev 2008; Brok 2009; Thorlund 2009, Wetterslev 2009; Thorlund 2010).To minimise random errors, we will calculate the required information size (i.e., the number of participants needed in a meta‐analysis to detect or reject a certain intervention effect) (Wetterslev 2008). The required information size calculation should also account for the heterogeneity or diversity present in the meta‐analysis (Wetterslev 2008; Wetterslev 2009). In our meta‐analysis, the diversity‐adjusted required information size will be based on: the event proportion in the control group; assumption of a plausible RR reduction of 20% or the RR reduction observed in the included trials with low risk of bias; a risk of type I error of 5%; a risk of type II error of 20%; and the empirical diversity of the meta‐analysis (Wetterslev 2009). Regarding, continuous data effect measures, e,g., quality of life, we will conduct TSA if this outcome was measured with the same scale. We will add the trials according to the year of publication, and if more than one trial has been published in a year, trials will be added alphabetically according to the last name of the first author. On the basis of the required information size, trial sequential monitoring boundaries will be constructed (Lan 1983; Wetterslev 2008; Thorlund 2011). These boundaries will determine the statistical inference one may draw regarding the cumulative meta‐analysis that has not reached the required information size; if the trial sequential monitoring boundary for benefit or harm is crossed before the required information size is reached, firm evidence may perhaps be established and further trials may turn out to be superfluous. On the other hand, if the boundary is not surpassed, it is probably necessary to continue doing trials in order to detect or reject a certain intervention effect. That can be determined by assessing if the cumulative Z‐curve crosses the trial sequential boundaries for futility. If futility boundaries are crossed, then further trials may be unnecessary (CTU 2011).
We will conduct TSA using software from the Copenhagen Trial Unit (CTU 2011; Thorlund 2011).
Subgroup analysis and investigation of heterogeneity
We anticipate clinical heterogeneity in the effects of the intervention, and for each comparison, we plan to conduct the following subgroup analyses if data are available.
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Risk of bias. Trials at low risk of bias compared to trials at high risk of bias.
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According to the West Haven grade of hepatic encephalopathy at entry.
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Aetiology of hepatic encephalopathy.
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Acute liver disease compared to chronic liver disease.
We will perform subgroup analyses for primary outcomes only.
Sensitivity analysis
In addition to the described sensitivity analyses under Dealing with missing data, we will perform the following sensitivity analysis in order to explore the influence of these factors on the intervention effect size:
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Repeating the analysis taking attrition bias into consideration.
'Summary of findings' tables
We plan to use the principles of the GRADE system (Guyatt 2011) in order to assess the quality of the body of evidence associated with specific outcomes (all‐cause mortality, quality of life, and serious adverse events) in our review, and construct a 'Summary of findings' (SoF) table using the GRADEPro software.
The GRADE approach appraises the quality of a body of evidence based on the extent to which one can be confident that an estimate of effect or association reflects the item being assessed. The quality of a body of evidence considers five factors regarding limitations in the design and implementation of available studies suggesting high likelihood of bias: indirectness of evidence (indirect population, intervention, control, outcomes); unexplained heterogeneity or inconsistency of results (including problems with subgroup analyses); imprecision of results (wide confidence intervals); and high probability of publication bias (Balshem 2011; Guyatt 2011a; Guyatt 2011b; Guyatt 2011c; Guyatt 2011d; Guyatt 2011e; Guyatt 2011f; Guyatt 2011g; Guyatt 2013; Guyatt 2013a; Guyatt 2013b; Mustafa 2013).
