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Physical exercise for people with cirrhosis

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Versión publicada: 05 junio 2017 Historial de versiones

https://doi.org/10.1002/14651858.CD012678

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Abstract

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

To assess the beneficial and harmful effects of physical exercise versus no intervention for people with cirrhosis.

Background

Description of the condition

Physical fitness predicts morbidity and mortality in several populations (Puhan 2013; Bernal 2014; Hellberg 2014). Cirrhosis is responsible for 2% of all deaths globally and is associated with decreased quality of life (Zatoński 2010; Mokdad 2014; Scaglione 2015). Malnutrition is a common complication of cirrhosis and reflects a complex pathophysiology (Periyalwar 2012). Important contributing factors include poor dietary intake, hypermetabolism, increased protein loss to the intestines, and decreased protein synthesis in the liver which leads to loss of skeletal muscle mass and muscle strength, known as sarcopenia. Sarcopenia is present in approximately 40% of people with cirrhosis referred for liver transplantation. It is linked with decreased physical fitness and associated with increased mortality and morbidity (Saunders 1981; D'Amico 1986; de Jongh 1992; D'Amico 2006; Dasarathy 2012; Montano‐Loza 2012; Meza‐Junco 2013; Thiele 2013; Cordoba 2014; Montano‐Loza 2014). Even after liver transplantation, pretransplant exercise capacity predicts post‐transplant survival (Jones 2012; Lemyze 2013). The risk of complications to cirrhosis is associated with decreased exercise capacity and sarcopenia, irrespective of the aetiology of the underlying liver disease (Biagini 2006; Hollingsworth 2008; Cerri 2010; Hollingsworth 2010; Galant 2012). Reduced exercise capacity is associated with the severity of the underlying liver disease (Galant 2013). Likewise, skeletal muscle loss in cirrhosis worsens with advancing severity of liver disease. However, even people with compensated cirrhosis are affected compared to healthy controls (Hayashi 2012).

Diagnosis of muscle wasting is difficult in the clinical setting where symptoms such as weakness and fatigue are diffuse and are likely present in people with chronic disease. The available methods have limitations primarily due to a lack of objectivity, reproducibility, and prognosis discrimination (Periyalwar 2012; Montano‐Loza 2014). Studies that include people without cirrhosis include the body mass index to indicate muscle mass and malnutrition (Woo 2015). However, in people with cirrhosis, ascites and peripheral oedema can affect the assessment of body mass index. Other physical measurements, which include assessment of the grip‐strength, appendicular lean mass, knee flexion, and knee extension, are objective but have low reproducibility (McLean 2014). Clinical studies have used computed tomography scan, magnetic resonance imaging, and dual energy X‐ray absorptiometry to quantify muscle mass (Dasarathy 2012; Montano‐Loza 2013; Roman 2013), and the peak and maximum oxygen consumption to quantify exercise capacity (Bandi 1998; Degoricija 2003; Faustini‐Pereira 2015).

Description of the intervention

The management of people with cirrhosis is aimed at controlling and alleviating the cirrhosis‐related complications. Liver transplantation may be curative, but this option is both costly and often unavailable due to donor‐shortage. Insertion of a transjugular intrahepatic portosystemic shunt (TIPS), which reduces the portal pressure, may reverse sarcopenia (Tsien 2013). However, the complications include hepatic encephalopathy and heart failure. Previous studies show that sarcopenia is the primary nutritional consequence of malnutrition but factors such as metabolic changes, chronic inflammation, and haemodynamic changes are also important (Periyalwar 2012; Montano‐Loza 2013). Improved nutrition may not in itself be enough to reverse sarcopenia. Physical exercise enhances or maintains physical muscle strength, but it also beneficially affects muscle mass, weight, and the cardiovascular system (Román 2014; Zenith 2014). The three main types of physical exercise include aerobic exercise, anaerobic exercise, and flexibility (NIH 2006). Aerobic exercise increases the uptake of oxygen in the larger muscle groups and has a beneficial effect on cardiovascular homeostasis. Anaerobic (resistance) exercise mainly affects muscle strength whereas flexibility exercise improve factors such as the range of motion.

How the intervention might work

The expected effects of physical exercise in people with cirrhosis are complex. Exercise improves glucose metabolism in both men and women, and aerobic as well as anaerobic exercise improve insulin sensitivity (Mikines 1989). This effect is important considering the known association between cirrhosis and insulin resistance (Goswami 2014). Cirrhosis is also associated with a disrupted balance in the protein synthesis and degradation (Morrison 1990). Repeated performance of resistance exercise for longer periods causes hypertrophy of skeletal muscle, as it increases muscle protein turnover. Following resistance exercise, protein synthesis is elevated for up to 48 hours and the synthesis of protein is regulated primarily at the level of translation and involves changes in signal transduction (Bolster 2004). The mammalian target of rapamycin (mTOR) is the important regulator of muscle protein synthesis.

In liver failure, ammonia removal in the liver is reduced and muscle can act as an alternative route of ammonia detoxification (Dam 2011). Physical exercise causes skeletal muscle hypertrophy, and hence may enhance the removal of ammonia. This decreases the risk of developing hepatic encephalopathy (Córdoba 2008). Increased ammonia concentration leads to autophagy (Polletta 2015), which also contributes to sarcopenia. Physical exercise may lower ammonia concentration and ameliorate the sarcopenia. Finally, cirrhosis is a proinflammatory state that contributes to the development of sarcopenia and decreased protein synthesis. Studies show that exercise limits the systemic inflammation (Lavie 2011); thus, it might decrease protein degradation and support muscle protein synthesis.

Why it is important to do this review

There is a large body of evidence that shows the negative clinical consequences of the reduced exercise capacity in cirrhosis. People with cirrhosis are less physically active than healthy controls and supervised exercise is generally recommended (Hayashi 2012). However, the best exercise programme is not known (Toshikuni 2014). Several randomised clinical trials (RCTs) have evaluated the effect of exercise versus no intervention for people with cirrhosis (Bandi 1998; Degoricija 2003; Román 2014; Zenith 2014; Roman 2016). One RCT including 19 participants with cirrhosis evaluated eight weeks of aerobic exercise versus no intervention (Zenith 2013; Zenith 2014). The trial found that exercise increased peak oxygen consumption and muscle mass and reduced fatigue. A similar RCT with 17 participants evaluated exercise capacity, muscle mass, and health‐related quality of life after a 12‐week supervised exercise programme versus no intervention (Román 2014). The trial found a beneficial effect of exercise on all three outcomes. The increased muscle mass may theoretically also reduce blood ammonia levels, and, therefore, be associated with a beneficial effect on hepatic encephalopathy (Córdoba 2008). One RCT found a beneficial effect of exercise versus no intervention on ammonia (Román 2014), but others found that blood ammonia increased (Sinniah 1970). The difference may reflect the severity of the underlying liver disease, as well as the intensity level of the exercise. At present, there are no studies that show a beneficial effect on manifestations of hepatic encephalopathy. However, the current evidence remains inconclusive, and a potential damaging effect remains possible in certain groups. Furthermore, physical exercise may have a detrimental effect on the portal pressure and blood flow to the muscles and brain in people with cirrhosis (Bandi 1998; Premaratna 2002; Bay 2005). One study that included 39 participants found that cirrhosis was associated with reduced oxygen consumption, myocardial thickening, and ventricular stiffness leading to decreased diastolic function as well as inotropic and chronotropic incompetence under conditions of stress (Wong 2001). Accordingly, cirrhotic cardiomyopathy may worsen the reduced exercise capacity but also negatively affect the beneficial effect of aerobic exercise. Likewise, a RCT that included 25 participants with cirrhosis found that moderate physical exercise combined with propranolol versus placebo increased the hepatic venous pressure gradient and decreased the hepatic blood flow (Bandi 1998). Exercise also has a potential detrimental effect on pulmonary gas exchange (Lemyze 2011; Lemyze 2013) and on electrocardiogram changes such as ST segment depression in the absence of coronary artery stenosis (Mori 2007). Theoretically, redistribution of blood from splanchnic organs to the central circulation could limit blood supply to skeletal muscles and the brain during exercise. However, a study that evaluated incremental cycling to exhaustion found that blood redistribution from splanchnic organs did not limit blood flow to the muscles or the brain (Bay 2005). Therefore, the body of evidence is complex. We have not identified any systematic reviews that evaluate the effects of physical exercise on people with cirrhosis.

Objectives

To assess the beneficial and harmful effects of physical exercise versus no intervention for people with cirrhosis.

Methods

Criteria for considering studies for this review

Types of studies

We will include RCTs regardless of the publication status or language. If, during the selection of trials, we identify observational studies (i.e. quasi‐randomised studies, cohort studies, or patient reports) that report adverse events caused by or associated with the interventions in our review, we will include these studies for a review of the adverse events. We will not specifically search for such observational studies for inclusion in this review, which is a known limitation of our systematic review.

Types of participants

We will include participants with histological or clinical cirrhosis of any age or sex, irrespective of the severity or the aetiology.

Types of interventions

Any form of exercise, irrespective of the type, intensity, or duration of the intervention versus no intervention. Co‐interventions such as diets, non‐absorbable disaccharides, or antibiotics are allowed if administered equally to the intervention and control group.

Types of outcome measures

We will evaluate all outcomes at the maximum follow‐up.

Primary outcomes

  1. All‐cause mortality.

  2. Serious adverse events. We will define serious adverse events as any untoward medical occurrence that resulted in death, was life‐threatening, required hospitalisation or prolongation of existing hospitalisation, or resulted in persistent or significant disability or incapacity or required intervention to prevent it (ICH‐GCP 1997).

  3. Quality of life.

Secondary outcomes

  1. Non‐serious adverse events.

  2. Lean and fat‐free body mass (measured using dual energy x‐ray absorptiometry scans, magnetic resonance imaging, computerised tomography, ultrasound).

  3. Muscle strength (including mid arm circumference, thigh circumference, hand grip strength, one‐repetition maximum kicking strength).

  4. Exercise oxygen uptake (maximum and peak).

  5. Functional tests (including six‐minute walking test, two‐minute step test).

  6. Body weight (body mass index and total weight).

Exploratory outcomes

  1. Insulin resistance (homeostasis model assessment of insulin resistance, glucose infusion rate during insulin clamp).

  2. Glycaemic control (glycated haemoglobin A1c; fasting blood glucose, response to oral glucose tolerance test).

  3. Serum albumin concentration.

  4. Serum creatinine concentration.

  5. Nitrogen balance.

  6. Portal venous pressure.

Search methods for identification of studies

Electronic searches

We will perform electronic searches of the Cochrane Hepato‐Biliary Group Controlled Trials Register (Gluud 2017), the Cochrane Central Register of Controlled Trials (CENTRAL) in the Cochrane Library, MEDLINE OvidSP, Embase OvidSP, LILACS, and Science Citation Index Expanded (Web of Science) (Royle 2003). Appendix 1 provides preliminary search strategies with the expected time spans of the searches.

With assistance from the Information Specialist of The Cochrane Hepato‐Bilary Group, we will attempt to search Russian, Chinese, and Japanese databases for publications of interest. If successful, we will add the search span and strategies to Appendix 1.

Searching other resources

We will scan the reference lists of relevant articles and proceedings from meetings of the British Society for Gastroenterology (BSG), the British Association for the Study of the Liver (BASL), the European Association for the Study of the Liver (EASL), the United European Gastroenterology Week (UEGW), the American Gastroenterological Association (AGA), the American Association for the Study of Liver Diseases (AASLD), and the International Society for Hepatic Encephalopathy and Nitrogen Metabolism (ISHEN) as well as write to the principal investigators of trials for additional information about completed trials and for information about any ongoing trials.

We will search Google Scholar using key words.

Finally, we will search online trial registries such as ClinicalTrial.gov (clinicaltrials.gov/), the European Medicines Agency (EMA) (www.ema.europa.eu/ema/), the World Health Organization International Clinical Trial Registry Platform (www.who.int/ictrp), the Food and Drug Administration (FDA) (www.fda.gov), and pharmaceutical company sources for ongoing or unpublished trials.

Data collection and analysis

We will follow the guidelines provided in the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011) and the Cochrane Hepato‐Biliary Group Module (Gluud 2017).

Three review authors (GD, AR, and LA) will extract data from the published trial reports in an independent manner. We will write to authors of included studies for additional information. For studies that are described in more than one record, we will use the record with the most complete information (the largest number of participants and the longest duration of follow‐up).

Selection of studies

We (LA, AR, and GD) will review trials identified through the electronic and manual searches, select trials using the criteria described above. We will list studies excluded after full‐text assessment, along with the reasons for exclusion, in a 'Characteristics of excluded studies' table. We will illustrate the study selection process in a PRISMA diagram. Any disagreements will be discussed among the authors until settlement.

Data extraction and management

We will extract the following data.

  1. Participants: inclusion criteria, mean age, proportion of men, and type of underlying liver disease.

  2. Interventions: type, intensity, frequency, and duration of interventions and co‐interventions.

  3. Trial: design (parallel or cross‐over) setting (hospital or outpatient), number of clinical sites, country of origin, and inclusion period.

We will also gather information about bias control and all outcomes.

Assessment of risk of bias in included studies

We will assess the risk of bias in the included studies using the domains described in the Cochrane Hepato‐Biliary Group Module (Gluud 2017). It is not possible to blind participants or personnel due to the nature of the intervention. However, according to the Cochrane Hepato‐Biliary Group recommendations, we have kept the bias domain 'Blinding of participants and personnel'.

Allocation sequence generation

  1. Low risk of bias: the study authors performed sequence generation using computer random number generation or a random number table. Drawing lots, tossing a coin, shuffling cards, and throwing dice were adequate if an independent person not otherwise involved in the study performed them.

  2. Unclear risk of bias: not specified.

  3. High risk of bias: the sequence generation was not random. We plan to include such studies for assessment of harms.

Allocation concealment

  1. Low risk of bias: the participant allocations could not have been foreseen in advance of, or during, enrolment. Allocation was controlled by a central and independent randomisation unit. The allocation sequence was unknown to the investigators (e.g. if the allocation sequence was hidden in sequentially numbered, opaque, and sealed envelopes).

  2. Unclear risk of bias: the method used to conceal the allocation was not described so that intervention allocations may have been foreseen in advance of, or during, enrolment.

  3. High risk of bias: the allocation sequence was likely to be known to the investigators who assigned the participants.

Blinding of participants and personnel

  1. Low risk of bias: i) the outcome was mortality, which according to previous empirical evidence, is unlikely to be influenced by lack of blinding (Hróbjartsson 2001; Savović 2012); or ii) blinding of participants and key study personnel ensured, and it is unlikely that the blinding could have been broken.

  2. Unclear risk of bias: insufficient information to permit judgement of ‘low risk’ or ‘high risk’.

  3. High risk of bias: no blinding or incomplete blinding, and the outcome is likely to be influenced by lack of blinding (non‐mortality outcomes).

Blinding of outcome assessors

  1. Low risk of bias: i) the outcome was mortality, which according to previous empirical evidence, is unlikely to be influenced by lack of blinding (Hróbjartsson 2001; Savović 2012); or ii) blinding of outcome assessment ensured, and unlikely that the blinding could have been broken.

  2. Unclear risk of bias: insufficient information to permit judgement of ‘low risk’ or ‘high risk’.

  3. High risk of bias: no blinding or inadequate blinding (e.g. intravenous versus orally administered drugs) and the outcome is likely to be influenced by lack of blinding (non‐mortality outcomes).

Incomplete outcome data

  1. Low risk of bias: missing data were unlikely to make treatment effects depart from plausible values. The investigators used sufficient methods such as intention‐to‐treat analyses with multiple imputations or carry‐forward analyses to handle missing data.

  2. Unclear risk of bias: there was insufficient information to assess whether missing data in combination with the method used to handle missing data induced bias on the results

  3. High risk of bias: the results were likely to be biased due to missing data.

Selective outcome reporting

  1. Low risk of bias: the trial reported the following outcomes: mortality and serious adverse events. If we have access to the original trial protocol, the outcomes should be those called for in that protocol. If we obtained information from a trial registry (such as www.clinicaltrials.gov), we will only use the information if the investigators registered the trial before inclusion of the first participant.

  2. Unclear risk of bias: predefined outcomes were not reported fully.

  3. High risk of bias: one or more predefined outcomes were not reported.

For‐profit bias

  1. Low risk of bias: the trial appears to be free of industry sponsorship or other type of for‐profit support.

  2. Unclear risk of bias: no information on clinical trial support or sponsorship was available.

  3. High risk of bias: the trial was sponsored by industry, received support in the form of terlipressin or placebo, or received any other type of support.

Other bias

  1. Low risk of bias: the trial appeared to be free of other biases including (as defined below).

  2. Unclear risk of bias: the trial may or may not have been free of other domains that could put it at risk of bias.

  3. High risk of bias: there were other factors in the trial that could put it at risk of bias such as follow‐up (e.g. the trial included different follow‐up schedules for participants in the allocation groups), or premature discontinuation of the trial.

Overall bias assessment

  1. Low risk of bias: all domains were low risk of bias using the definitions described above.

  2. High risk of bias: one or more of the bias domains were at unclear or high risk of bias.

Measures of treatment effect

We will use the risk ratio for dichotomous outcomes and mean differences for continuous outcomes, both with 95% confidence intervals.

Unit of analysis issues

For cross‐over trials, we will use data from the first treatment period in our primary analysis.

Dealing with missing data

We will gather data to allow intention‐to‐treat analyses and use simple imputation to evaluate the potential influence of missing data (Higgins 2008). The analyses will include missing values counted as failures or successes with the following strategies.

  • Worst‐case scenario analysis: All participants with missing data counted as failures.

  • Extreme worst‐case/best‐case scenario analysis: Participants with missing outcome data in the exercise arm counted as failures and in the control arm as successes and vice versa.

Assessment of heterogeneity

We will express heterogeneity as I² statistic values using the thresholds: 0% to 40% (unimportant), 40% to 60% (moderate), 60% to 80% (substantial), and greater than 80% (considerable).

Assessment of reporting biases

For meta‐analyses with at least 10 included RCTs, we will assess reporting and other dissemination biases based using the Harbord modified test for dichotomous outcomes and Egger tests for continuous outcomes (Egger 1997; Harbord 2006). The Harbord test regresses Z/sqrt(V) against sqrt(V), where Z is the efficient score and V is Fisher's information (the variance of Z under the null hypothesis). The Egger test performs a linear regression of the intervention effect estimates on their standard errors, weighting by 1/(variance of the intervention effect estimate).

Data synthesis

We will perform our analyses in Review Manager 5 (RevMan 2014), STATA (Stata 14), and Trial Sequential Analysis (TSA 2011).

Meta‐analysis

We will conduct fixed‐effect and random‐effects model meta‐analyses. If the estimates of the fixed‐effect and random‐effects model meta‐analyses are similar, then we will assume that any small‐study effects have little effect on the intervention effect estimate. If the random‐effects estimate is more beneficial, we will re‐evaluate whether it is reasonable to conclude that the intervention was more effective in the smaller studies. If the larger studies tend to be those conducted with more methodological rigour, or conducted in circumstances more typical of the use of the intervention in practice, then we will report the results of meta‐analyses restricted to the larger, more rigorous studies. Based on the expected clinical heterogeneity, we expect that a number of analyses will display statistical between trial heterogeneity (I² statistic > 0%). For random‐effect models, precision will decrease with increasing heterogeneity and confidence intervals will widen correspondingly. We plan to report the results of our analyses based on statistical model that generates the most conservative result.

Trial Sequential Analysis

We will perform Trial Sequential Analysis for the primary outcomes all‐cause mortality and serious adverse events with alpha set to 2.5% due to the three primary outcomes, beta 10% (power to 90%), and model‐based heterogeneity (TSA 2011). We will define the required information size as the number of participants needed to detect or reject an intervention effect estimated based on the assumed control risk (ACR), the relative risk reduction (RRR), and the model‐based diversity. We will construct the trial sequential monitoring boundaries based on the required information size (also known as the heterogeneity‐adjusted required information size) and define firm evidence as being established if the Z‐curve (the result of the cumulative meta‐analysis) crosses the monitoring boundary for benefit or harm before reaching the required information size. For the analysis of mortality and serious adverse events, we will conduct the analysis with the RRR set to 10%, use the ACR observed in the meta‐analyses, and use model‐based heterogeneity.

Subgroup analysis and investigation of heterogeneity

We will perform the following subgroup analyses (reported using the P value based on test for subgroup differences).

  1. Sex (men compared with women).

  2. Severity of liver disease (compensated compared with decompensated and Child's Class A or B/C).

  3. Type of exercise (aerobic or non‐aerobic),

  4. Type of control group (no intervention compared with diets).

  5. Co‐interventions.

  6. Bias control (RCTs assessed as 'low' or 'high risk' of bias).

For meta‐analyses with at least 10 RCTs, we will conduct univariable regression analysis for continuous trial characteristics (the intensity, duration, and frequency of therapy).

Sensitivity analysis

To evaluate the robustness of the overall results, we will repeat our analyses by performing the following.

  1. Worse‐best and best‐worse‐case scenario analyses.

  2. Comparison of RCTs published in abstract form, full‐text articles, and unpublished trials.

Summary of findings' tables

We will create 'Summary of findings' tables on all primary and secondary (lean and fat‐free body mass and muscle strength) review outcomes using GRADEpro software (GRADEpro 2014). 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 within‐study risk of bias, indirectness of the evidence (population, intervention, control, outcomes), unexplained heterogeneity or inconsistency of results (including problems with subgroup analyses), imprecision of effect estimates (wide confidence intervals as evaluated with Trial Sequential Analysis), and risk of publication bias(GRADEpro 2014; Guyatt 2008), We will report the conclusions using the EPICOT (evidence, population, intervention, comparison, outcome, time stamp) format (Brown 2006).