Intervention Protocol

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Hyperbaric oxygen therapy for autism spectrum disorder (ASD) in children and adults

  1. Tao Xiong,
  2. Hongju Chen,
  3. Rong Luo,
  4. Dezhi Mu*

Editorial Group: Cochrane Developmental, Psychosocial and Learning Problems Group

Published Online: 17 JAN 2014

DOI: 10.1002/14651858.CD010922


How to Cite

Xiong T, Chen H, Luo R, Mu D. Hyperbaric oxygen therapy for autism spectrum disorder (ASD) in children and adults (Protocol). Cochrane Database of Systematic Reviews 2014, Issue 1. Art. No.: CD010922. DOI: 10.1002/14651858.CD010922.

Author Information

  1. West China Second University Hospital, Sichuan University, Department of Pediatrics, Chengdu, Sichuan, China

*Dezhi Mu, Department of Pediatrics, West China Second University Hospital, Sichuan University, No. 17, Section Three, Ren Min Nan Lu Avenue, Chengdu, Sichuan, 610041, China. dezhimu@yahoo.com. dezhi.mu@ucsf.edu.

Publication History

  1. Publication Status: New
  2. Published Online: 17 JAN 2014

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Background

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Contributions of authors
  7. Declarations of interest
  8. Sources of support
 

Description of the condition

Autism spectrum disorders (ASD) comprise a group of neurodevelopmental disorders characterized by classic symptoms of impairment in social interaction and communication, repetitive behaviors (Ghanizadeh 2012), and selective (inward) attention (Hughes 2008). Three different types of ASD are known: autistic disorder, Asperger syndrome, and pervasive developmental disorder not otherwise specified (PDD-NOS) (APA 1994). People with ASD often present with challenging behaviors, including aggression, tantrums, irritability, hyperactivity, inattention, impulsivity, self-injury, and pica (Matson 2011).

The number of people diagnosed with ASD has escalated over the past decade, and prevalence rates continue to increase; 1% of individuals in the United States are reported to have ASD (Gal 2012). Although the causes are unclear, studies on grey and white matter implicate abnormalities of brain development in ASD pathology. These abnormalities predominantly affect association areas and undermine functional integration (O'Hearn 2008). Changes to the structure and function of synapses and dendrites have also been strongly implicated (Pardo 2007). It is possible that impairments of microglial function contribute to several major etiologic factors in ASD (Maezawa 2011). Recent studies suggest the existence of hundreds of ASD risk genes, which indicate a strong genetic basis for ASD susceptibility (Devlin 2012). Multiple genetic loci predispose a person to ASD (Yang 2007). A specific mutation or deletion of a gene may determine one's susceptibility to ASD, and interactions between multiple genes may cause "idiopathic" ASD. Evidence suggests that impaired methylation and genetic polymorphisms of cytochrome enzymes are linked to the condition (Currenti 2010). Other studies show that environmental factors interact with the underlying genetic profile and foster the clinical heterogeneity seen in ASD. For example, exposure to toxins may modulate the expression of many genes involved in processes such as proliferation, apoptosis, neuronal differentiation and migration, synaptogenesis, and synaptic activity (Dufour-Rainfray 2011; Muhle 2004).

The 2012 American ASD guidelines state that early intensive behavioral intervention (EIBI) and applied behavioral analysis (ABA) may be effective treatments for ASD (Maglione 2012). Two systematic reviews reported a large and moderate positive effect of EIBI for a composite outcome of full-scale intelligence and adaptive behavior in young children with autism (Eldevik 2009; Howlin 2009). A Cochrane systematic review also calculated a beneficial effect of EIBI treatment for adaptive behavior, intelligence, and communication and language skills (Reichow 2012). EIBI for young children with ASD may be helpful in the short term for language function, cognitive skills, and some challenging behaviors (Warren 2011). In addition, ABA, a form of behavioral therapy, could be used in the behavioral manifestations of ASD. Two studies observed significant gains in IQ and behavioral problems after two and four years (Lovaas 1987; Sallows 2005). There is evidence that the ABA intervention may benefit the family of the child with ASD too (Cebula 2012). Another developmental behavioral intervention, the Early Start Denver Model, is associated with normalized patterns of brain activity, which, in turn, are related to gains in IQ, language, and adaptive behavior of children with ASD (Dawson 2012). Young children often receive interventions for joint attention and/or joint engagement, for which moderate to large effects have been noted (Kasari 2012). It is especially beneficial for long-term spoken language outcomes in ASD children (Kasari 2012a). A preliminary study suggests that telehealth technology could help parents to understand and use early intervention practices to promote children's spontaneous language and imitation skills (Vismara 2012).

In the field of pharmacotherapy, risperidone and aripiprazole are useful for controlling challenging and repetitive behaviors in children with ASD (Hughes 2009; Matson 2011; McPheeters 2011). Evidence from Cochrane systematic reviews suggests that aripiprazole is effective in treating some behavioral abnormalities in children caused by the condition. After treatment with aripiprazole, children showed less irritability, hyperactivity, and fewer stereotypies (repetitive, purposeless actions) (Ching 2012). Risperidone may be beneficial for some features of autism such as irritability, repetition, and social withdrawal (Jesner 2007). No recommendation can be advanced regarding the use of combined vitamin B6-magnesium treatment (Nye 2005), intravenous secretin (Williams 2012), or elective serotonin reuptake inhibitors (Williams 2010). Risperidone and aripiprazole are the only US Food and Drug Administration (FDA)-approved drugs for the treatment of autism, specifically, for the symptomatic treatment of irritability.

 

Description of the intervention

The Undersea and Hyperbaric Medical Society defines hyperbaric oxygen therapy (HBOT) as treatment in which a person is intermittently placed in a chamber, where the atmospheric pressure is compressed to a pressure greater than sea level (a pressure greater than one atmosphere absolute (ATA), where 1 ATA = 760 mmHg = 0.1 MPa), and breathes 100% oxygen (Gill 2004). In this way, it is possible to deliver greatly increased partial pressure of oxygen to the tissues. As the Undersea and Hyperbaric Medical Society has reported, HBOT has proved effective in conditions such as air or gas embolism, carbon monoxide/cyanide poisoning, gas gangrene, crush injuries, intracranial abscess, etc. (Gill 2004). Systematic reviews and randomized clinical trials support the clinical use of hyperbaric oxygen for refractory diabetic wound healing and radiation injury; treatment of compromised flaps and grafts, and ischemia-reperfusion disorders is supported by animal studies and by a small number of clinical trials (Thom 2011). Several other HBOT trials on frost injury, hypoxic ischemic encephalopathy, osteoradionecrosis, and traumatic brain injury are in progress. HBOT is well tolerated, but middle ear barotrauma is a common adverse effect (Muller-Bolla 2006).

HBOT was reported to be useful in improving behavioral and physiological abnormalities in some children with ASD. In a study of seven Thai autistic children, HBOT (1.3 ATA/100% oxygen, 10 sessions) improved the major autistic symptoms, including social interaction, fine motor and eye-hand co-ordination, language and gross motor skills, as well as self-help scores (Chungpaibulpatana 2008).

 

How the intervention might work

Recently, studies in ASD have implicated many specific dysfunctions such as cerebral abnormal hypoperfusion (Ito 2005), inflammation (Vargas 2005), immune dysregulation (Li 2009a), neurotransmitter abnormalities (Connors 2006), and mitochondrial dysfunction (Rossignol 2012b; Rossignol 2007). The effects of HBOT could be explained by the results of high pressure (>1 ATA) and pure oxygen (100%). Theoretically, if hyperbaric therapy at oxygen concentrations less than 100% works, HBOT may play a protective role in these dysfunctions. The following studies include HBOT or hyperbaric therapy for ASD.

It has been demonstrated that hypoperfusion in ASD is positively correlated with the severity of autistic behaviors. Lower cerebral perfusion has been significantly associated with increasing age in children with ASD (Rossignol 2012a). The outcome of hypoperfusion is hypoxia in the brain, which could be improved by HBOT through better oxygen delivery. Application of HBOT is based on the theory that inhalation of oxygen at increased atmospheric pressure produces marked elevation of oxygen partial pressure in arterial blood and thus improves hypoxia in the brain (Calvert 2007). HBOT has been reported to improve hypoxic-ischemic brain injury in different cerebral regions, including the cortex (Wang 2009), white matter (Wang 2007b), and hippocampus (Bai 2008). In addition, HBOT could improve oxygen delivery capacity by inducing angiogenesis of the brain (Lin 2012a). Direct angiogenesis may be attributed to proliferation of neural stem cells under HBOT (Ichim 2007).

HBOT has shown anti-inflammatory effects in animal studies. In a middle cerebral artery occlusion rat model, HBOT inhibited neutrophil infiltration in the injured brain by reducing inflammation (Miljkovic-Lolic 2003). In an animal model of inflammatory pain, HBOT decreased inflammation and subsequent pain (Wilson 2006). In the immune system, HBOT could moderate immune-mediated delayed neurological dysfunction following carbon monoxide poisoning (Thom 2006).

Lack of the neurotransmitter serotonin may be linked to autism. Mice genetically depleted of brain serotonin display symptoms of autism (Kane 2012). HBOT could exert antidepressant effects similar to those produced by some selective serotonin reuptake inhibitors in a rat model of forced swimming (Sumen-Secgin 2005). On the other hand, impairments of neuroplasticity may contribute to the pathophysiology of ASD (Abdallah 2012). Increasing the level of oxygen dissolved by HBOT could activate neuroplasticity in patients with chronic neurologic deficiencies resulting from stroke (Efrati 2013). HBOT can intensify neuroplastic responses by promoting axonal sprouting and synapse remodelling, both of which contribute to the recovery of locomotor performance in rats (Brkic 2012).

Mitochondrial dysfunction has been observed in approximately 5% of children with ASD (Anitha 2012). Mitochondrial dysfunction is associated with apoptosis. Causes of autism may include mechanisms of apoptosis (El-Ansary 2012). Members of the caspase family, the most important pre-apoptosis molecules, are significantly increased in children with ASD (Siniscalco 2012). HBOT might ameliorate mitochondrial dysfunction and apoptosis in ASD. HBOT reduces apoptosis via the mitochondrial pathway after ischemia-reperfusion brain injury (Li 2009; Yin 2013). Opening of mitochondrial adenosine triphosphate (ATP)-sensitive potassium channels plays a role in this effect (Lou 2006). Thus, HBOT significantly increased neuronal survival (Malek 2013), resulting in increased cell proliferation and infarct size reduction in the hippocampus after stroke (Mu 2013).

Neural stem cells can differentiate into neurons, can induce angiogenesis, and can non-specifically modulate the immune response (Ichim 2007). HBOT could promote proliferation, migration, and differentiation of neural stem cells (Wang 2007a; Wang 2009), and this may contribute to neural recovery in children with ASD.

HBOT plays a role in gene regulation and in protein expression of neurons that might be neuroprotective in ASD. Genes and proteins regulated by HBO include factors associated with stress responses, transport, neurotransmission, signal transduction, and transcription factors (Chen 2009a). Thus, HBO therapy may activate ion channels (Mrsić-Pelcić 2004), upregulate superoxide dismutase (SOD; Freiberger 2006), decrease caspases (Chen 2009b), suppress p38 mitogen-activated protein kinase (Yamashita 2009), induce heat shock protein (HSP)-70 overexpression (Lin 2012b), and activate Wnt signalling (Wang 2007a).

Consequently, HBOT may improve behaviors in children with ASD and may alleviate symptoms such as eczema, chronic diarrhea, and abdominal distention. Such improvements include behavior, memory, social interaction, cognitive functioning, coloring skills, speech, and self-help skills. Children completing 80 sessions of HBOT showed improvement on the clinician-rated Clinical Global Impression—Improvement scale (Leckman 1989) and on several parent-completed measures of behavior (Bent 2012).

 

Why it is important to do this review

To date, although some treatments may ameliorate certain ASD symptoms, these treatments do not result in full remission of all ASD symptoms. HBOT as a potential treatment for ASD remains controversial. Although some evidence supports the benefits of HBOT (Chungpaibulpatana 2008; Rossignol 2009a; Rossignol 2012a), other findings do not (Ghanizadeh 2012; Granpeesheh 2010). Therefore, HBOT was assigned only a Grade B recommendation for ASD in a systematic review (Rossignol 2009b). Furthermore, existing studies have various limitations, such as the use of parent-rated scales and lack of control participants, small numbers of participants, or an open-label or retrospective design. These limitations weaken the evidence base for efficiency of HBOT in ASD. Recently, new RCTs on the effectiveness of HBOT have been completed. It is important to present the strengths and weaknesses of methodologies used that are relevant to HBOT for ASD in general. To assess the potential benefits and harms of this treatment, a systematic review of RCTs that examine the effects of HBOT in the treatment of ASD is needed.

 

Objectives

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Contributions of authors
  7. Declarations of interest
  8. Sources of support

To determine whether treatment with hyperbaric oxygen:

  • improves the core symptoms of ASD, including social communication problems, and stereotypical and repetitive behaviors;
  • improves noncore symptoms such as challenging behaviors;
  • improves comorbid states such as depression and anxiety; or
  • causes adverse effects.

 

Methods

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Contributions of authors
  7. Declarations of interest
  8. Sources of support
 

Criteria for considering studies for this review

 

Types of studies

Randomized and/or quasi-randomized (such as alternation, date of birth, or case record number) controlled trials and/or cluster trials of any dose, duration, and frequency of hyperbaric oxygen. We will exclude cross-over trials.

 

Types of participants

Participants of any age diagnosed with ASD, including autistic disorder, Asperger syndrome or PDD-NOS, as per Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) (APA 1994) or International Classification of Diseases, Tenth Edition (ICD-10; WHO 1993) criteria. We will accept diagnosis by assessment tools such as the Autism Diagnostic Observation Scale (ADOS) (Lord 1997) and the Autism Diagnostic Interview-Revised (ADI-R) (Lord 1994).

 

Types of interventions

All forms of HBO therapy regardless of duration, frequency, and pressure of treatment.

Control interventions could consist of no treatment and sham treatment. Sham treatment refers to treatment at 1 ATA and/or room air (21% oxygen). We will investigate the following comparisons.

  • HBO only versus no treatment.
  • HBO only versus sham treatment.
  • HBO plus baseline treatment versus the same baseline treatment alone.
  • HBO plus baseline treatment versus sham treatment plus the same baseline treatment.

We will exclude trials that compared only different forms of HBO.

 

Types of outcome measures

 

Primary outcomes

Core features of ASD, that is, social interaction and communication and behavioral problems, including stereotypy or restricted, repetitive patterns of behavior, interests, or activities, as measured by validated instruments and by behavioral observation tools such as the Aberrant Behavior Checklist (ABC) (Aman 1987), the Ritvo-Freeman Real Life Rating Scale (RFRLRS) (Freeman 1986), the Autism Treatment Evaluation Checklist (ATEC) (Rimland 1999), and the Autism Diagnostic Observation Scale (ADOS) (Lord 1997).

 

Secondary outcomes

  • Communication and linguistic abilities, as measured by standardized instruments such as the Reynell Language Developmental Scale (RLDS) (Edwards 1997) and the Symbolic Play Test (SPT) (Lowe 1976).
  • Cognitive functioning, as measured by standardized instruments such as the Griffiths Mental Developmental Scale (GMDS) (Griffiths 1996), the Leiter International Performance Scale-Revised (Leiter-R) (Leiter 1980), and the Stanford-Binet Intelligence Scale-Fourth Edition (Thorndike 1986).
  • Global functioning, as measured by standardized instruments such as the Pediatric Evaluation Disability Inventory (PEDI) (Haley 1992) and the Functional Independence Measure for Children (WeeFIM) (Msall 1994).
  • Safety of HBOT: incidence of adverse reactions such as barotrauma to the ear, round window blowout, "sinus squeeze," visual refractive changes, and numb fingers (Phillips 2005).

When possible, we will group outcomes as short term (less than three months), medium term (three to six months), and long term (longer than six months).

We will present the primary outcomes in Summary of findings tables.

 

Search methods for identification of studies

 

Electronic searches

We will search the following databases. No date or language limits will be applied. 

  1. The Cochrane Central Register of Controlled Trials (CENTRAL), part of The Cochrane Library
  2. Ovid MEDLINE
  3. EMBASE
  4. Science Citation Index (SCI)
  5. Social Sciences Citation Index (SSCI)
  6. Conference Proceedings Citation Index-Science (CPCI-S)
  7. Conference Proceedings Citation Index-Social Sciences & Humanities (CPCI-SSH)
  8. CINAHL (Comprehensive Index of Nursing and Allied Health Literature)
  9. ERIC
  10. HBO Evidence The Database of Randomised Controlled Trials in Diving and Hyperbaric Medicine hboevidence.unsw.wikispaces.net/
  11. WHO International Clinical Trials Registry Platform (ICTRP) who.int/trialsearch
  12. ClinicalTrials.gov clinicaltrials.gov
  13. metaRegister of Controlled Trials (mRCT) controlled-trials.com/mrct
  14. Research Autism researchautism.net
  15. Autism Data autism.org.uk
  16. Australian New Zealand Clinical Trials Registry (ANZCTR) anzctr.org.au/trialSearch.aspx
  17. CNKI (China National Knowledge Infrastructure)
  18. WEIPU periodical database
  19. Wan Fang Data
  20. CBM (Chinese Biologic Medical Database)

The following search strategy will be used for Ovid MEDLINE and will be adapted for other databases.

1 exp child development disorders, pervasive/
2 Developmental Disabilities/
3 pervasive development$ disorder$.tw.
4 (pervasive adj3 child$).tw.
5 (PDD or PDDs or PDD-NOS or ASD or ASDs).tw.
6 autis$.tw.
7 asperger$.tw.
8 kanner$.tw.
9 childhood schizophrenia.tw.
10 or/1-9
11 Hyperbaric Oxygenation/
12 (Hyperbaric adj3 oxygen$).tw.
13 oxygen therap$.tw.
14 (Hyperbaric adj3 therap$).tw.
15 HBO.tw.
16 HBOP.tw.
17 or/11-16
18 10 and 17

 

Searching other resources

We will contact known experts in the field to identify additional published or unpublished trials. We will handsearch reference lists from relevant studies. For non–English language articles, we will read the English title or abstract first. If it is relevant, we will translate the main text into English.

 

Data collection and analysis

 

Selection of studies

We will assess all published articles identified by the literature search as potentially relevant. Abstracts retrieved from the search will be read independently by TX and HC in an effort to identify all trials that meet the inclusion criteria. We will retrieve full-text articles, if needed. We will involve a third review author (RL) to help resolve differences in opinion. We will contact the trial authors for clarification if details of the primary trials are not clear.

 

Data extraction and management

We will extract the following data.

Study methods

  • Design.
  • Randomization method (including list generation).
  • Method of allocation concealment.
  • Blinding method.
  • Stratification factors.

Participants

  • Inclusion/exclusion criteria.
  • Number (total/per group).
  • Age and sex distribution.
  • Specific diagnosis/diagnostic subtypes.
  • Comorbidities.
  • Duration of disorder.
  • Previous treatments.

Intervention and control

  • Type of HBOT.
  • Details of treatment, including duration of treatment.
  • Types of controls.
  • Details of control treatment, including drug dosage.
  • Details of co-interventions.

Follow-up data

  • Duration of follow-up.
  • Dates of treatment withdrawal and reasons for treatment withdrawal.
  • Withdrawal rates.

Outcome data as described above

Analysis data

  • Methods of analysis (intention-to-treat/per-protocol analysis).
  • Comparability of groups at baseline (yes/no).
  • Statistical techniques.

We will design a form that can be used to extract the data. Two review authors (TX and HC) will use the data extraction form to independently extract, assess, and code all data for each available study. We will involve a third review author (RL) to negotiate differences in opinion. TX will enter the data into Review Manager (RevMan) (Review Manager 2013), and DM will check the accuracy of data input.

 

Assessment of risk of bias in included studies

For each included study, two review authors (TX and HC) will independently complete The Cochrane Collaboration's tool for assessing risk of bias (Higgins 2011). We will resolve any disagreements by consulting with the third review author (RL). For each included study, we will evaluate the risk of bias as low, high, or unclear across each of the following domains, and we will enter the results into the Risk of bias table (Higgins 2011).

 
Adequate sequence generation
 
Selection bias

For each included study, we will categorize the risk of selection bias as follows:

  • Low risk: adequate (as in any truly random process such as random number table, computer random number generator).
  • High risk: inadequate (as in any nonrandom process such as odd or even date of birth or hospital or clinic record number).
  • Unclear risk: no or unclear information provided.

 
Allocation concealment
 
Selection bias

For each included study, we will categorize the risk of bias regarding allocation concealment as follows:

  • Low risk: adequate (for example, telephone or central randomization, consecutively numbered sealed opaque envelopes).
  • High risk: inadequate (for example, open random allocation unsealed or nonopaque envelopes, alternation, date of birth).
  • Unclear risk: no or unclear information provided.

 
Blinding of participants and personnel
 
Performance bias

For each included study, we will categorize the methods used to blind study personnel from knowledge of which intervention a participant received.

  • Low risk: adequate for personnel (for example, a placebo that could not be distinguished from the active drug that was used in the control group when no blinding was provided, but the outcome and the outcome measurement are not likely to be influenced).
  • High risk: inadequate (that is personnel were aware of group assignment).
  • Unclear risk: no or unclear information provided.

Blinding of outcome assessors

 
Detection bias

For each included study, we will categorize the methods used to blind outcome assessors from knowledge of which intervention a participant received. Blinding will be assessed separately for different outcomes or classes of outcomes. We will categorize the methods used with regard to detection bias as follows:

  • Low risk: adequate (that is follow-up was performed with assessors blinded to the group).
  • High risk: inadequate (that is assessors at follow-up were aware of group assignment).
  • Unclear risk: no or unclear information provided.

 
Incomplete data addressed
 
Attrition bias

For each included study and for each outcome, we will describe the completeness of data, including attrition and exclusions from the analyses. We will note whether attrition and exclusions were reported, the numbers included in the analyses at each stage (compared with the total number of randomly assigned participants), whether reasons for attrition or exclusion were reported, and whether missing data were balanced across groups or were related to outcomes. When sufficient information is reported or is supplied by the trial authors, we will re-include missing data in the analyses. We will categorize the methods with respect to risk attrition bias as follows:

  • Low risk: adequate (for example, no missing outcome data, reasons for missing outcome data are unlikely to be related to the true outcome, data are missing for similar reasons across groups, missing proportions are balanced).
  • High risk: inadequate (for example, reasons for missing outcome data are likely to be related to true outcome, an inappropriate imputation is used for missing data).
  • Unclear risk: no or unclear information provided.

 
Selective outcome reporting
 
Reporting bias

For each included study, we will describe how we investigated the risk of selective outcome reporting bias and what we found. We will assess the methods as follows:

  • Low risk: adequate (that is when it is clear that all of the study's prespecified outcomes and all expected outcomes of interest to the review have been reported, and the methods and results sections of a study agree on what is reported).
  • High risk: inadequate (that is when not all of the study's prespecified outcomes have been reported, when one or more reported primary outcomes were not prespecified, when the outcomes of interest are reported incompletely and so cannot be used, when the study fails to include the results of a key outcome that would have been expected to have been reported, and when the methods and results sections of a study disagree on what is reported).
  • Unclear risk: no or unclear information provided (that is the study protocol is not available).

 
Other bias

For each included study, we will describe any important concerns that we have about other possible sources of bias (for example, whether a potential source of bias is related to the specific study design, whether the trial was stopped early because of some data-dependent process). We will assess whether each study was free of other problems that could put it at risk of bias such as the following:

  • Low risk: no concerns of other bias raised.
  • High risk: potential source of bias related to the specific study design used; fraudulent or some other problem; differences in numbers of participants enrolled in abstract and reported in final publications of the paper.
  • Unclear: concerns raised about potential sources of bias that could not be verified by contacting the study authors.

 

Measures of treatment effect

We will calculate risk ratio (RR), odds ratio (OR), and risk difference (RD) with their 95% confidence intervals (CIs) for dichotomous data. We will calculate the mean difference (MD) and its 95% CI for continuous data. If studies used different scales to measure the same outcomes, we will calculate the standardized mean difference (SMD) with 95% CI. If studies used different measures of effect, we will transform the data, when possible, using the relevant method, as described in Section 12.5.4 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011).

Because different scales may be used to measure the same outcomes in ASD trials, SMDs may be used widely in our review. Final values and changes from baseline data should not be combined together as SMDs. When final values and changes from baseline data are available in included trials, we shall analyse them separately.

 

Unit of analysis issues

For most outcomes, the unit of analysis will be the individual participant.

We will include cluster-randomized trials in the analyses, along with individually randomized trials. We will analyze them as detailed in Section 16.3 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2011), using an estimate of the intracluster correlation coefficient (ICC) derived from the trial (if possible) or from another source. If ICCs from other sources are used, we will report this fact and we will conduct sensitivity analyses to investigate the effects of variation in the ICC. If we identify both cluster-randomized trials and individually randomized trials, we will synthesize the relevant information. We will consider it reasonable to combine the results derived from both if little heterogeneity between the study designs is noted, and if interaction between the effect of intervention and the choice of randomization unit is considered unlikely. We will also acknowledge heterogeneity in the randomization unit, and we will perform a separate meta-analysis. 

For a study with multiple intervention groups, we will apply two methods. First, and if appropriate, we will combine the groups. Second, we will determine which comparisons are relevant to the review (see section on "Types of interventions"). If relevant, we will use subgroup analysis.

Indirect comparisons are not randomized comparisons. They are observational findings across trials and may suffer the biases of observational studies (Higgins 2011). Thus, we will exclude indirect comparisons.

 

Dealing with missing data

When possible, we will obtain data from the primary investigator when published data are incomplete. If this approach is unsuccessful, we will restrict analyses to available data. We will note differential dropout in the intervention group, and we will assess the reasons for withdrawal as these can potentially bias the study results. We will report reasons for missing data when reasons are provided in the published trials. We will explore the impact of missing data by examining the distribution and reasons; we will address in the discussion section the potential impact of missing data on the findings of the review. We will use sensitivity analyses to examine whether overall findings are robust to the potential influence of missing data. We will assess how sensitive results are to reasonable changes in assumptions made. Issues of intention-to-treat analysis (ITT) will be critically appraised and compared with specifications of primary outcome parameters and power calculations.

 

Assessment of heterogeneity

We will consider three types of heterogeneity: clinical, methodological, and statistical. We will assess clinical heterogeneity by comparing the distribution of important participant factors between trials such as age, gender, specific diagnosis and/or diagnostic subtypes, duration of the disorder, and associated neuropsychiatric diseases. We will assess methodological heterogeneity by comparing trial characteristics such as randomization concealment, blinding, and losses to follow-up. We will assess statistical heterogeneity by examining Chi2 and I2. We will use the Chi2 test (P ≤ 0.10 shows substantial or considerable heterogeneity) to determine whether statistically significant heterogeneity is present. The Chi2 test is not very reliable when few studies or small sample sizes form the dataset. This means that a nonsignificant result must not be taken as evidence of no heterogeneity. The degree of statistical heterogeneity will be assessed by examining I2. We will grade the degree of heterogeneity as follows: 0% to 30%: might not be important; 31% to 50%: moderate heterogeneity; 51% to 75%: substantial heterogeneity; and 76% to 100%: considerable heterogeneity. We will explore trials to investigate possible explanations for heterogeneity. If heterogeneity is identified among a group of studies, we will check the data and again will explore the reasons for heterogeneity. When heterogeneity is present that cannot be readily explained, we may divide the data into subgroups if an appropriate basis is identified. 

 

Assessment of reporting biases

We will try to obtain the study protocols of all included studies, so that we can compare outcomes reported in the protocol versus those reported in the findings. When we suspect reporting bias, we will attempt to contact study authors to ask them to provide missing outcome data. When this is not possible, and the missing data are thought to introduce serious bias, we will conduct a sensitivity analysis to evaluate the impact of including such studies in the overall assessment of results.

Publication bias will be tested using funnel plots or other corrective analytical methods, depending on the number of clinical trials included in the systematic review. The funnel plot should be seen as a generic means of displaying small-study effects. Asymmetry may arise as the result of publication bias or of a relationship between trial size and effect size. True heterogeneity in intervention effects is only one cause of funnel plot asymmetry (Egger 1997; Higgins 2011).

 

Data synthesis

If more than one eligible trial is identified and sufficient homogeneity is observed among the studies with respect to participants and reported outcomes, statistical analyses will be performed using the RevMan software for meta-analysis. We will use both the fixed-effect model and random-effects models in the meta-analysis. Both models will yield similar results, provided that no significant heterogeneity and no publication bias are noted among the trials. If no significant heterogeneity is present, we will report the results of the fixed-effect model only. However, the asymmetry of the funnel plot may be due to true heterogeneity. If significant heterogeneity or severe asymmetry of the funnel plot is observed, we will report the results of the random-effects model. Categorical data will be presented as RRs and RDs with their 95% CIs. MDs with 95% CIs will be used for outcomes measured on a continuous scale. 

 

Subgroup analysis and investigation of heterogeneity

We will perform subgroup analysis based on the following.

  • Participant age.
  • ASD severity.
  • Therapeutic time window of HBOT.
  • Peak pressure of HBOT.
  • Number of courses of HBOT.

 

Sensitivity analysis

We will perform sensitivity analyses for missing data and for study risk of bias.

We will employ sensitivity analysis using different approaches to impute missing data. Last observation carried forward (LOCF), ITT, and per-protocol analysis (PP) will be critically appraised and compared with primary outcome parameters and power calculations.

If appropriate, we will conduct sensitivity analyses by study risk of bias based on the presence or absence of a reliable random allocation method, concealment of allocation, and blinding of participants or outcome assessors. Robustness of the results will be tested by including or excluding studies of poor quality.

 

Acknowledgements

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Contributions of authors
  7. Declarations of interest
  8. Sources of support

We would like to thank the staff at the Cochrane Developmental, Psychosocial and Learning Problems Group.

 

Contributions of authors

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Contributions of authors
  7. Declarations of interest
  8. Sources of support

Tao Xiong is responsible for all aspects of the protocol: its design, development, and revision.

Rong Luo contributed to the methodology.

Hongju Chen is responsible for writing the background information.
Dezhi Mu is responsible for revising the protocol and is supervising the progress of the whole review.

 

Declarations of interest

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Contributions of authors
  7. Declarations of interest
  8. Sources of support

Tao Xiong - none known.
Rong Luo - none known.
Hongju Chen - none known.
Dezhi Mu - none known.

 

Sources of support

  1. Top of page
  2. Background
  3. Objectives
  4. Methods
  5. Acknowledgements
  6. Contributions of authors
  7. Declarations of interest
  8. Sources of support
 

Internal sources

  • Department of Pediatrics, West China, Second University Hospital, Sichuan University, Chengdu, China.

 

External sources

  • Chinese Cochrane Center, West China, Second University Hospital, Sichuan University, Chengdu, China.
  • the National Natural Science Foundation of China (No.81330016 to Dezhi Mu; No. 81300525 to Tao Xiong), China.
  • Cochrane Developmental, Psychosocial and Learning Problems Group,, Not specified.

References

Additional references

  1. Top of page
  2. Abstract
  3. Background
  4. Objectives
  5. Methods
  6. Acknowledgements
  7. Contributions of authors
  8. Declarations of interest
  9. Sources of support
  10. Additional references
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