Auditory Processing Disorder (APD)

Number: 0668

Table Of Contents

Policy
Applicable CPT / HCPCS / ICD-10 Codes
Background
References

Policy

Aetna considers any diagnostic tests or treatments for the management of auditory processing disorder (APD) (previously known as central auditory processing disorder (CAPD)) experimental and investigational because there is insufficient scientific evidence to support the validity of any diagnostic tests and the effectiveness of any treatment for APD.

Related Policies

Table:

CPT Codes / HCPCS Codes / ICD-10 Codes
Code Code Description

Information in the [brackets] below has been added for clarification purposes.   Codes requiring a 7th character are represented by "+":

CPT codes not covered for indications listed in the CPB:
92507 Treatment of speech, language, voice, communication, and/or auditory processing disorder; individual
92508     group, two or more individuals
92521 Evaluation of speech fluency (eg, stuttering, cluttering)
92522 Evaluation of speech sound production (eg, articulation, phonological process, apraxia, dysarthria)
92523 Evaluation of speech sound production (eg, articulation, phonological process, apraxia, dysarthria); with evaluation of language comprehension and expression (eg, receptive and expressive language)
92524 Behavioral and qualitative analysis of voice and resonance
92551 - 92588 Audiological function tests with medical diagnostic evaluation
92620 Evaluation of central auditory function, with report; initial 60 minutes
92621     each additional 15 minutes

Other HCPCS codes related to the CPB:
S9128 Speech therapy, in the home, per diem
V5008 Hearing screening

ICD-10 codes not covered for indications listed in the CPB:
H93.25 Central auditory processing disorder
H93.291 - H93.299 Other abnormal auditory perceptions

Background

Auditory processing disorder (APD), also known as central auditory processing disorder, supposedly interferes with both the input and integration of verbal information, and results in a potentially permanent cognitive dysfunction during the developmental period of acquisition of language.  The prevalence of APD in the general population has not been firmly established.  Chermak and Musiek (1997) estimated that APD occurs in 2 to 3 % of children, with a 2-to-1 ratio between boys and girls, while Cooper and Gates (1991) estimated the prevalence of adult APD to be 10 to 20 %.  Neurological disorders, diseases, and insults, including neurodegenerative diseases, probably account for most acquired APD in adults; however, such disorders probably account for 5 % or fewer of diagnosed cases of APD in children, especially as APD relates to learning disability (Musiek et al, 1985, 1992).

Although the exact cause(s) of APD remains unclear, it does not appear to be caused by peripheral hearing impairment.  The diagnosis of APD remains controversial, largely because of the purported co-morbidity with associated conditions such as attention-deficit/hyperactivity disorder, learning disabilities, and speech-language impairment, as well as the diversity of signs and symptoms associated with this disorder.  Some of the more common diagnostic tests for APD include Staggered Spondaic Word (SSW) Test, the SCAN Screening Test for auditory processing disorders, and the Multiple Auditory Processing Assessment (MAPA).  Moreover, there is no clear acceptance of a "gold standard" test battery for evaluating this disorder.

According to a review (Cacace and McFarland, 1998), the rationale to evaluate for APD in school-aged children is based on the premise that an impairment in auditory perception can be the underlying cause of many learning problems, including specific reading and language disabilities.  However, there is insufficient scientific evidence to validate this proposition.  There is no ICD-9 code for APD (ICD-9-CM, 6th Edition, 2003).  Furthermore, this disorder is not recognized as a unique entity affecting school-aged children (DSM-IV-TR, American Psychiatric Association, 2000).

In a review on APD, Bamiou and associates (2001) concluded that APD may be a feature of both neurological and developmental disorders.  However, whereas APD appears to be well documented in relation to certain syndromes, in other disorders the quality of existing evidence is inconclusive and the relation of APD to the coexisting disorder is poorly understood.  Further research into the interface between APD and neurological and developmental disorders is needed.  Clear insight into the nature of the auditory processing deficit may have implications for appropriate management, in agreement with the trend to provide multimodal intervention for these disorders.  Moreover, a detailed understanding of the structural and functional substrate of auditory processing disorders will enable phenotypic evaluation specifically for the purposes of genetic research.

A review on auditory processing and the development of language and literacy (Bailey and Snowling, 2002) found that evidence for basic auditory processing impairments associated with dyslexia and specific language impairment is inconsistent.  It appears that not all children with language difficulties have non-verbal auditory processing impairments, and for those who do, the impact on language development is poorly understood.  The authors stated that advances in the understanding of the role of auditory processing in the genesis of language difficulties have been hampered theoretically by a lack of agreement regarding the relationship between basic auditory skills, speech perception and phonological processing abilities, and also methodologically by frequent uncontrolled group differences in experimental studies.  Well-designed studies are needed to ascertain the extent to which there are auditory-specific learning disabilities.

There are no established therapies for the treatment of patients with APD.  Current approaches include signal enhancement, linguistic and cognitive strategies, auditory training (including auditory integration therapy), as well as medication.  Signal enhancement strategies aim to improve the signal to noise ratio.  This can be achieved by minimizing background noise or by using frequency-modulated systems in the classroom.  Linguistic and cognitive strategies aim to increase use of compensatory strategies.

Auditory training utilizes the brain's plasticity and can be formal or informal.  Formal auditory training may include computerized commercial programs such as Fast ForWord (Scientific Learning, Oakland, CA) and Earobics (Cognitive Concepts, Inc., Evanston, IL), which alter speech acoustics and adaptively speed up neural processing; or training in the audiology clinic with modified central auditory tasks.  Informal strategies can be applied at home or at school and include tasks such as vowel/consonant training, simple games such as "Simon", etc.  However, there is little scientific evidence on the effectiveness of various formal and informal auditory training programs.  More research is needed to ascertain whether these interventions actually enhance temporal processing abilities and the magnitude of language gains.

Methylphenidate (Ritalin), a drug traditionally prescribed for the management of patients with attention deficit/hyperactivity disorder (ADHD), has been used to treat children with APD.  However, it is unclear whether methylphenidate can improve auditory processing, thus, methylphenidate and related stimulant medications should not be prescribed routinely for treatment of APD in the absence of ADHD.

Given the problems associated with diagnosing APD, any therapies should be viewed cautiously.  The National Institute on Deafness and Other Communication Disorders (2001) stated that it is important to know that much research is still needed to understand auditory processing problems, related disorders, and the best interventions for each child or adult.  This is in accordance with the review on this subject by Chermak (2002) who concluded that "additional controlled case studies and single-subject and group research designs are needed to ascertain systematically the relative efficacy of various treatment and management approaches".

The National Institute on Deafness and Other Communication Disorders (2004) also stated that auditory integration training may be promoted by practitioners as a way to retrain the auditory system and decrease hearing distortion.  However, current research has not proven the benefits of this treatment.

In a review of the etiology of speech and language disorders in children, Carter and Musher (2006) state that "[e]valuation for a central auditory processing disorder (CAPD) in school-aged children is based upon the assumption that an auditory-specific perceptual deficit is the foundation of learning problems such as reading and language disabilities.  However, the diagnosis, management, and even the existence of a modality-specific dysfunction are controversial."

Aetna’s policy on APD is based upon the limited evidence for APD as a distinct pathophysiologic entity, upon a lack of evidence of established criteria and well validated instruments to diagnose APD and reliably distinguish it from other conditions affecting listening and/or spoken language comprehension, and upon the lack of evidence from well designed clinical studies proving the effectiveness of interventions for treating APD.  The reported frequent co-occurrence of APD with other disorders affecting listening and/or spoken language comprehension suggests that APD is not, in fact, a distinct clinical entity.

von Suchodoletz (2009) noted that the clinical relevance of CAPD is highly controversial.  The author stated that available literature reveals that numerous authors have described subnormal auditory abilities in groups of children with developmental language disorders, dyslexia or ADHD.  However, little or no relationship between the severity of clinical impairment and auditory deficits has been found.  Thus, auditory deficits do not appear to be causally related to learning disorders or conduct disorders.  With respect to the diagnostic process, this review made clear that the validity of the diagnosis CAPD is low.  There is no agreement about diagnostic criteria, and the reliability of most auditory tests is insufficient.  Moreover, while an auditory training can only improve the directly trained auditory functions, there is no transfer effect to learning ability of behavior.  Altogether, there is little evidence for a significant relevance of CAPD in child and adolescent psychiatry.

In a review on APD in relation to developmental disorders of language, communication and attention, Dawes and Bishop (2009) stated that "APD, as currently diagnosed, is not a coherent category, but that rather than abandoning the construct, we need to develop improved methods for assessment and diagnosis, with a focus on interdisciplinary evaluation".

Lemos and colleagues (2009) verified the existence of scientific evidence confirming the effectiveness of personal frequency modulation (FM) systems in the treatment of APD.  For this purpose, a systematic review of the literature was made, using data found in electronic databases (Medline, Lilacs and Cochrane library) as well as on the internet.  The articles retrieved were analyzed according to the CONSORT statement and then classified by their evidence level.  The search resulted in 1,589 references out of which only 19 met the inclusion criteria.  All of the analyzed articles were classified as having low level of evidence (expert opinion or case studies).  Strong scientific evidence supporting the use of personal FM systems for APD intervention was not found.  Since such device is frequently recommended for the treatment of APD, it becomes essential to carry out studies with high scientific evidence that could safely guide clinical decision making on this subject.

Rosen et al (2010) evaluated auditory and cognitive abilities in a group of children referred for an auditory evaluation on the grounds of a suspected APD (susAPD), and in age-matched children who were typically developing, in order to determine the extent to which any deficits in cognitive abilities could be related to auditory deficits.  A battery of auditory and cognitive tests was applied to 20 susAPD school-age children, all reported as having listening/hearing problems but performing within normal limits for standard audiometric assessments.  Also tested was a group of 28 age-matched controls.  The auditory tasks consisted of 2 simple same/different discrimination tasks, one using speech, and one non-speech.  The cognitive evaluation comprised a vocabulary test, a test of grammar and 4 non-verbal IQ measures.  Symptoms of ADHD were assessed in the susAPD group through a standardized questionnaire.  A significant proportion of susAPD children appeared to display genuine auditory deficits evidenced by poor performance on at least 1 of the auditory tasks, although about 1/3 had no detectable deficit.  Children in the susAPD group scored consistently lower than the controls on cognitive measures that were both verbal (vocabulary and grammar) and non-verbal.  Strikingly, susAPD children with relatively good auditory performance did not differ in cognitive ability from susAPD children with poor auditory performance.  Similarly, within-group correlations between auditory and cognitive measures were weak or non-existent.  Measures of ADHD did not correlate with any aspect of auditory or cognitive performance.  The authors concluded that although children suspected of having APD do show, on average, poorer performance on a number of auditory tasks, the presence or absence of an auditory deficit appears to have little impact on the development of the verbal and non-verbal skills tested here.  Furthermore, Lagace and colleagues (2010) stated that APD is a complex and heterogeneous disorder for which the underlying deficit is currently unclear.

Moore and associates (2010) tested the specific hypothesis that the presentation of APD is related to a sensory processing deficit.  Randomly chosen, 6- to 11-year-old children with normal hearing (n = 1,469) were tested in schools in 4 regional centers across the United Kingdom.  Caregivers completed questionnaires regarding their participating children's listening and communication skills.  Children completed a battery of audiometric, auditory processing (AP), speech-in-noise, cognitive (IQ, memory, language, and literacy), and attention (auditory and visual) tests.  AP measures separated the sensory and non-sensory contributions to spectral and temporal perception.  AP improved with age.  Poor-for-age AP was significantly related to poor cognitive, communication, and speech-in-noise performance (p < 0.001).  However, sensory elements of perception were only weakly related to those performance measures (r < 0.1), and correlations between auditory perception and cognitive scores were generally low (r = 0.1 to 0.3).  Multi-variate regression analysis showed that response variability in the AP tests, reflecting attention, and cognitive scores were the best predictors of listening, communication, and speech-in-noise skills.  The authors concluded that presenting symptoms of APD were largely unrelated to auditory sensory processing.  Response variability and cognitive performance were the best predictors of poor communication and listening.  These investigators suggested that APD is primarily an attention problem and that clinical diagnosis and management, as well as further research, should be based on that premise.

Fey and colleagues (2011) evaluated the peer-reviewed literature on the efficacy of interventions for school-age children with APD.  Searches of 28 electronic databases yielded 25 studies for analysis.  These studies were categorized by research phase (e.g., exploratory, efficacy) and ranked on a standard set of quality features related to methodology and reporting.  Some support exists for the claim that auditory and language interventions can improve auditory functioning in children with APD and those with primary spoken language disorder.  There is little indication, however, that observed improvements are due to the auditory features of these programs.  Similarly, evidence supporting the effects of these programs on spoken and written language functioning is limited.  The authors concluded that the evidence base is too small and weak to provide clear guidance to speech-language pathologists faced with treating children with diagnosed APD, but some cautious skepticism is warranted until the record of evidence is more complete.  Clinicians who decide to use auditory interventions should be aware of the limitations in the evidence and take special care to monitor the spoken and written language status of their young clients.

Miller (2011) provided information that may aid in understanding and interpreting research literature on the role of auditory processing in communication disorders.  A narrative review was used to summarize and synthesize the literature on auditory processing deficits in children with APD, specific language impairment (SLI), and dyslexia.  The history of auditory processing theories of these 3 disorders was described, points of convergence and controversy within and among the different branches of research literature were considered, and the influence of research on practice was discussed.  The theoretical and clinical contributions of neurophysiological methods were also reviewed, and suggested approaches for critical reading of the research literature were provided.  The author concluded that research on the role of auditory processing in communication disorders springs from a variety of theoretical perspectives and assumptions, and this variety, combined with controversies over the interpretation of research results, makes it difficult to draw clinical implications from the literature.  Neurophysiological research methods are a promising route to better understanding of auditory processing.  Progress in theory development and its clinical application is most likely to be made when researchers from different disciplines and theoretical perspectives communicate clearly and combine the strengths of their approaches.

In a review of the evidence for auditory processing disorder, Kamhi (2011) stated that there are compelling theoretical and clinical reasons to question whether APD is in fact a distinct clinical entity. The author noted that, not only is there little evidence that auditory perceptual impairments are a significant risk factor for language and academic performance, there is also no evidence that auditory interventions provide any unique benefit to auditory, language, or academic outcomes. The author concluded that, because there is no evidence that auditory interventions provide any unique therapeutic benefit, clinicians should treat children who have been diagnosed with APD the same way they treat children who have been diagnosed with language and learning disabilities. The author stated that the theoretical and clinical problems associated with APD should encourage clinicians to consider viewing auditory deficits as a processing deficit that may occur with common developmental language and reading disabilities rather than as a distinct clinical entity.

Ahmmed and colleagues (2014) identified the factors that may underlie the deficits in children with listening difficulties, despite normal pure-tone audiograms.  These children may have APD, but there is no universally agreed consensus as to what constitutes APD.  These investigators therefore referred to these children as children with suspected APD (susAPD) and aimed to clarify the role of attention, cognition, memory, sensorimotor processing speed, speech, and non-speech auditory processing in susAPD.  It was expected that a factor analysis would show how non-auditory and supra-modal factors relate to auditory behavioral measures in such children with susAPD.  This would facilitate greater understanding of the nature of listening difficulties, thus further helping with characterizing APD and designing multi-modal test batteries to diagnose APD.  They performed a factor analysis of outcomes from 110 children (68 males, 42 females; aged 6 to 11 years) with susAPD on a widely used clinical test battery (SCAN-C) and a research test battery (MRC Institute of Hearing Research Multi-center Auditory Processing "IMAP"), that have age-based normative data.  The IMAP included backward masking, simultaneous masking, frequency discrimination, non-verbal intelligence, working memory, reading, alerting attention and motor reaction times to auditory and visual stimuli.  SCAN-C included monaural low-redundancy speech (auditory closure and speech in noise) and dichotic listening tests (competing words and competing sentences) that assess divided auditory attention and hence executive attention.  Three factors were extracted
  1. "general auditory processing",
  2. "working memory and executive attention", and
  3. "processing speed and alerting attention". 
Frequency discrimination, backward masking, simultaneous masking, and monaural low-redundancy speech tests represented the "general auditory processing" factor.  Dichotic listening and the IMAP cognitive tests (apart from non-verbal intelligence) were represented in the "working memory and executive attention" factor.  Motor response times to cued and non-cued auditory and visual stimuli were grouped in the "processing speed and alerting attention" factor.  Individuals varied in their outcomes in different tests.  Poor performance was noted in different combinations of tests from the 3 factors.  Impairments solely related to the "general auditory processing" factor were not common.  The authors concluded that the findings of this study identified a general auditory processing factor in addition to 2 other cognitive factors, "working memory and executive attention" and "processing speed and alerting attention", to underlie the deficits in children with susAPD.  Impaired attention, memory, and processing speed are known to be associated with poor literacy and numeracy skills as well as a number of neurodevelopmental disorders.  Individuals with impairments in the "general auditory processing" tests along with tests from the other 2 cognitive factors may explain the co-occurrence of APD and other disorders.  The variation in performance by individuals in the different tests noted was probably due to a number of reasons including heterogeneity in susAPD and less-than ideal test-retest reliabilities of the tests used to assess APD.  They stated that further research is needed to explore additional factors, and consensus is needed to improve the reliability of tests or find alternative approaches to diagnose APD, based on the underlying factors.

Furthermore, an UpToDate review on "Etiology of speech and language disorders in children" (Carter and Musher, 2014) states that "Evaluation for a central auditory processing disorder (CAPD) in school-age children is based upon the assumption that an auditory-specific perceptual deficit is the foundation of learning problems such as reading and language disabilities.  However, the diagnosis, management, and even the existence of a modality-specific dysfunction are controversial".

Methods used to diagnose auditory processing disorder lack a clear evidence base, and the diagnosis of any particular child with APD is determined more by the referral route than by the symptoms (Moore et al, 2013). A study identified nine different sets of diagnostic criteria for auditory processing disorder and the resulting rates of diagnosis of auditory processing disorder ranged from 7.3% to 96% (Wilson,et al, 2012).

Substantial evidence regarding test performance (e.g. reliability, validity, sensitivity, and specificity) is lacking for most commonly used behavioral tests of auditory processing (Keith, 2009).

There are no widely accepted criteria as to when electrophysiologic tests should be included in the clinical evaluation of auditory processing disorder (Schochat et al, 2010). There are currently no available electrophysiological measures of sufficient utility and reliability to be useful in the clinical assessment of auditory processing disorder (McFarland and Cacace, 2012; Hornickel et al, 2012). A plethora of measures and stimuli is used inconsistently from study to study, with no clear evidence of replicability across studies (Moore et al, 2013).

Without established diagnostic criteria, the best methods for identifying and managing auditory processing disorder remain elusive. Auditory processing disorder will often co-exist with attention, language and learning impairments as well as autism spectrum disorder (Bellis, 2008; Dawes and Bishop, 2010; Witton, 2010). The status of auditory processing disorder as a distinct disorder has been questioned due to the overlap between clinical profiles of children diagnosed with auditory processing and those with other forms of learning disability (Jerger et al, 2009). Studies comparing children with a diagnosis of dyslexia and those with a diagnosis of auditory processing disorder found the two groups could not be distinguished (Ferguson et al, 2011; Dawes and Bishop, 2010; Miller and Wagstaff, 2011) and obtained similar findings in studies comparing children diagnosed with specific language impairment or auditory processing disorder (Corriveau et al, 2007; Dlouha et al, 2007).

Data specifically addressing the efficacy of interventions for auditory processing disorder are lacking and many of the recommendations commonly made are based on theory or inferred from approaches validated in other populations, e.g. specific language impairment and dyslexia.  The scientific evidence for the use of personal FM systems in auditory processing disorder is of low quality (Lemos et al, 2009).  Computer-based auditory training programs that originally developed and marketed for children with language, learning and reading difficulties have also recently been recommended for children with a specific diagnosis of auditory processing disorder, despite limited research evidence to support this (Loo, et al., 2010; Thibodeau, 2007). Phonological awareness training is widely used as an intervention for children with reading disability and there are programs that have been developed and evaluated using randomized controlled studies (Otaiba, et al., 2009). However, auditory processing disorder status has not been considered in these studies. There is a lack of studies evaluating formal auditory training programs for persons with the diagnosis of auditory processing disorder. Other programs and methods have no or a very low level of evidence or conflicting expert opinion. In a systematic evidence review, Fey, et al. (2011) found only weak evidence on the efficacy of auditory processing disorder training and auditory/language interventions for children. 

Mishra (2014) stated that APD affects about 2 to 5 % of children. However, the nature of this disorder is poorly understood. Children with APD typically have difficulties in complex listening situations. One mechanism thought to aid in listening-in-noise is the medial olivo-cochlear (MOC) inhibition. The author analyzed the published data on MOC inhibition in children with APD to examine if the MOC efferents are involved in these individuals. The oto-acoustic emission (OAE) methods used to assay MOC reflex were examined in the context of the current understanding of OAE generation mechanisms. Relevant literature suggested critical differences in the study population and OAE methods. Variables currently known to influence MOC reflex measurements (e.g., middle-ear muscle reflexes or OAE signal-to-noise ratio) were not controlled in most studies. The use of potentially weaker OAE methods and the remarkable heterogeneity across studies does not allow for a definite conclusion whether or not the MOC reflex is altered in children with APD. The authors concluded that further carefully designed studies are needed to confirm the involvement of MOC efferents in APD; knowledge of efferent functioning in children with APD would be mechanistically and clinically beneficial.

Micallef (2015) noted that APD is a disorder that affects the perception of sound, both verbal and non-verbal. Patients who are generally diagnosed with APD present with abnormal hearing but have normal audiograms.  There is no gold standard investigation for APD and no standardized criteria for diagnosis.  Because of its disabling effect and the overlap that exists with other neurodevelopmental disorders, there is an urgent need to develop tools and criteria for appropriate diagnosis.  There is a current significant focus in research on imaging techniques that can possibly be used in the future for the appropriate diagnosis of APD.  Over the years, several imaging techniques have contributed significantly to defining this disorder.  To-date, no studies have reported the routine use of imaging for the diagnosis of APD.

Beck and co-workers (2016) stated that the diagnosis of APD should indicate a problem processing auditory information within the traditionally recognized central auditory nervous system (CANS).  However, if the primary problem lies beyond the auditory cortex, such as cognitive processing, the diagnosis of APD is trumped by a more prominent differential diagnosis -- including ADHD, autism spectrum disorder (ASD), dyslexia, intellectual impairment, and specific language impairment.  Furthermore, the lack of standard metrics used to measure APD is problematic.  Specifically, there are no universally accepted diagnostic criteria, test batteries, or intervention strategies for APDs.  Similarly, there are no universally agreed-upon descriptions of how one fails an APD battery.  The American Academy of Audiology (AAA, 2010) guideline stated one is considered to have failed an APD screening if the resultant scores on (any) 2 AP tests fall 2 or more standard deviations (SDs) below the mean for at least one ear.  The British Society of Audiology (BSA, 2011) suggested that at least 1 test should use non-speech stimuli.  The American Speech Language Hearing Association (ASHA, 2005) stated performance deficits of at least 2 SDs below the mean on 2 or more tests or at least 3 SDs below for 1 test with a report of "significant functional difficulty" should indicate an APD failure.  The authors came to the following conclusions:

  • Present clinical practice in APD evolved from the perspective of audiologists who understand hearing problems derived from a malfunction of the ear or of the CANS.  However, the audiologist typically has less knowledge regarding listening problems having other origins.
  • Developmental APD should be viewed as a part of childhood learning problems which may closely overlap results when measured with speech-based tests.  Other more commonly used designations (e.g., ADHD, ASD, language impairment, and dyslexia) should take precedence where appropriate.
  • Rather than labelling a person with APD, it makes more sense to thoroughly and succinctly describe the presenting hearing and/or listening problem, and to outline an evidence-based approach to address the specific needs of the particular patient.
  • Audiologists (and teachers and parents) often attribute listening problems to impaired processing in the CANS when audiograms are normal.  However, contemporary evidence suggests most such problems are due primarily to language and other cognitive processing outside the traditional auditory system.
  • Most currently used tests of APD are tests of language and attention that lack sensitivity and specificity.
  • To test AP specifically, measures of auditory temporal, spectral, and spatial processing is recommended.
  • A smaller battery of tests that are well-validated, normalized, and relevant to the problems reported by clients should be developed.  DeBonis suggested 4 such measures (2 speech-in-noise tests and 2 questionnaires).
  • A top priority for further research, discussion, and clinical practice should be intervention.  New technologies, such as remote microphone devices, are very promising, but require further investigation.  Traditional techniques, such as "dyadic" reading between care-givers and children, as well as music lessons, should become more familiar to, and recommended by, audiologists.
  • It is unacceptable that children with listening problems are neither identified nor treated before age 7.  Pediatric audiology outcomes (hearing aid and cochlear implant fittings) clearly demonstrate that early identification and treatment provide maximal results.
In an interventional study, Lofti and colleagues (2016) examined the effects of an auditory lateralization training on speech perception in presence of noise/competing signals in children with suspected CAPD.  A total of 60 children were selected based on multiple auditory processing assessment sub-tests.  They were randomly divided into 2 groups
  1. control group (mean age of 9.07 years), and
  2.  training group (mean age of 9.00 years).  
Training program consisted of detection and pointing to sound sources delivered with inter-aural time differences under head-phones for 12 formal sessions (6 weeks).  Spatial word recognition score (WRS) and monaural selective auditory attention test (mSAAT) were used to follow the auditory lateralization training effects.  This study showed that in the training group, mSAAT score and spatial WRS in noise (p value ≤ 0.001) improved significantly after the auditory lateralization training.  The authors concluded that auditory lateralization training for 6 weeks improved speech understanding in noise significantly.  Moreover, they stated that generalization of these findings needs further investigation.  They stated that further studies with higher sample size, auditory lateralization training for more extended time period and long-term follow-up are needed.

Koohi and colleagues (2017) stated that stroke survivors may suffer from a range of hearing impairments that may restrict their participation in post-acute rehabilitation programs.  Hearing impairment may have a significant impact on listening, linguistic skills, and overall communication of the affected stroke patient.  However, no studies sought to systematically characterize auditory function of stroke patients in detail, to establish the different types of hearing impairments in this cohort of patients.  Such information would be clinically useful in understanding and addressing the hearing needs of stroke survivors.  In a case-control study, these researchers characterized and classified the hearing impairments, using a detailed audiological assessment test battery, in order to determine the level of clinical need and inform appropriate rehabilitation for this patient population.  A total of 42 stroke patients who were discharged from a stroke unit and 40 control participants matched for age were recruited for this study.  All participants underwent pure-tone audiometry and immittance measurements including acoustic reflex threshold, transient-evoked oto-acoustic emissions, auditory-evoked brainstem response, and a central auditory processing assessment battery, performed in a single session.  Hearing impairments were classified as peripheral hearing loss (cochlear and neural type), CAPD, and as a combination of CAPD and peripheral hearing loss.  Overall mean hearing thresholds were not significantly different between the control and stroke groups.  The most common type of hearing impairment in stroke patients was the combination type, "peripheral and CAPD", in the 61- to 80-year old subgroup (i55 %), and auditory processing deficits in 18- to 60-year old group (40%), which were both significantly higher than in controls.  The authors concluded that this was the first study to examine hearing function in detail in stroke patients.  They noted that given the importance of hearing for the efficiency of communication, it is essential to identify hearing impairments and differentiate peripheral and central deficits to define an appropriate intervention plan.  These preliminary findings need to be validated by well-designed studies.

Koravand and associates (2017) identified markers of neural deficits in children with CAPD by measuring latency and amplitude of the auditory cortical responses and mis-match negativity (MMN) responses.  Passive oddball paradigms were used with non-verbal and verbal stimuli to record cortical auditory-evoked potentials and MMN.  A total of 23 children aged 9 to 12 years participated in the study: 10 with normal hearing acuity as well as CAPD and 13 with normal hearing without CAPD.  No significant group differences were observed for P1 latency and amplitude.  Children with CAPD were observed to have significant N2 latency prolongation and amplitude reduction with non-verbal and verbal stimuli compared to children without CAPD.  No significant group differences were observed for the MMN conditions.  Moreover, electrode position affected the results in the same manner for both groups of children.  The authors concluded that the findings of the present study suggested that the N2 response could be a marker of neural deficits in children with CAPD; N2 results suggested that maturational factors or a different mechanism could be involved in processing auditory information at the central level for these children.

Iliadou and Kiese-Himmel (2018) noted that pediatric hearing evaluation based on pure tone audiometry does not always reflect how a child hears in everyday life.  This practice is inappropriate when evaluating the difficulties children experiencing APD in school or on the playground.  Despite the marked increase in research on pediatric APD, there remains limited access to proper evaluation worldwide.  These investigators presented 5 common misconceptions of APD that contribute to inappropriate or limited management in children experiencing these deficits.  The misconceptions discussed are: First, the disorder cannot be diagnosed due to the lack of a gold standard diagnostic test.  Second, making generalizations based on profiles of children suspected of APD and not diagnosed with the disorder.  Third, it is best to discard an APD diagnosis when another disorder is present.  Fourth, arguing that the known link between auditory perception and higher cognition function precludes the validity of APD as a clinical entity; and finally, APD is not a clinical entity.

The authors recognized that differential diagnosis is made difficult due to the overlapping symptoms across neurodevelopmental disorders and APD as well as many clinicians’ limited education in interpreting results of auditory processing tests and disentangling them from results obtained by the multi-disciplinary professional team, which sometimes are not even available for review.  In the future, these diagnostic challenges should be addressed through continuing in-depth education of audiologists and other health care professionals who are responsible for evaluating and managing children with APD.  Audiologists would benefit from additional research with children diagnosed with APD focused on comparing new evaluation techniques with clinically validated approaches to ensure that new approaches meet the essential psychometric requirements and documented sensitivity to CANS lesions.  The potential interactions between these new tools and cognitive, attention, and language indexes must also be examined.  Standardizing these novel techniques across typically developing children and children with known brain pathology will provide further validation needed for clinical adoption to more effectively diagnose and treat APD.

Quantitative Electroencephalogram (QEEG)

In a pilot study, Milner and colleagues (2018) showed an abnormal resting-state quantitative electroencephalogram (QEEG) pattern in children with CAPD.  A total of 27 children (16 male, 11 female; mean age of 10.7 years) with CAPD and no symptoms of other developmental disorders, as well as 23 age- and sex-matched, typically developing children (TDC, 11 male, 13 female; mean age of 11.8 years) underwent examination of CAPs and QEEG evaluation consisting of 2 randomly presented blocks of "Eyes Open" (EO) or "Eyes Closed" (EC) recordings.  Significant correlations between individual frequency band powers and CAP tests performance were found.  The QEEG studies revealed that in CAPD relative to TDC there was no effect of decreased delta absolute power (1.5 to 4 Hz) in EO compared to the EC condition.  Furthermore, children with CAPD showed increased theta power (4 to 8 Hz) in the frontal area, a tendency toward elevated theta power in EO block, and reduced low-frequency beta power (12 to 15 Hz) in the bilateral occipital and the left temporo-occipital regions for both EO and EC conditions.  Decreased middle-frequency beta power (15 to 18 Hz) in children with CAPD was observed only in the EC block.  The authors concluded that this study presented the preliminary electrophysiological results in children with a CAPD subtype characterized by deficits in auditory processing of competing acoustic signals and auditory pattern recognition (or temporal patterning).  Changes in the absolute delta, theta, low-, and middle-frequency beta power, may distinguish CAPD from normally developing children.  Thus, QEEG appeared to be a useful tool for improving CAPD evaluation.  A potential application of this method to discriminate between different CAPD subtypes and other neurodevelopmental disorders with overlapping symptoms may be an important topic of future research.

The authors stated that 1 major drawback of these present findings was the small sample size (n = 27 for CAPS patients), which prohibited making any strong conclusions based on the obtained results.  However, to the authors’ knowledge, this was the first study showing preliminary data on the resting-state bioelectrical activity and co-existing attentional deficits in children with listening difficulties.  Furthermore, these researchers aimed to present the results from larger cohort in the future.  They also intended to distinguish CAPD subtypes based on their behavioral and EEG data, which may allow us to design a neurofeedback therapy especially dedicated to particular groups of children with listening problems.  A larger sample size will also allow for more advanced analyses of EEG data (e.g., EEG signal coherence), with the examination of any correlation between behavioral and electrophysiological results.  These investigators also intended to compare the results in children with both listening difficulties and ADD/ADHD, since these disorders are heterogeneous and characterized by overlapping symptoms, particularly attention deficits, which could affect performance on CAP tests.  They believed this would be useful in clinical practice since ADD/ADHD appears to be a potential confounding factor in CAPD evaluation.

Auditory Temporal Ordering and Resolution Tests

Chowsilpa and colleagues (2021) stated that auditory temporal processing tests are key clinical measures that supposedly could diagnose CAPD.  Although these tests have been used for decades, there is no up-to-date evidence to determine the effectiveness of detecting the abnormalities in central auditory processing in adults while the available national CAPD guidelines predominantly address CAPD in the pediatric population.  In a systematic review and meta-analysis, these investigators examined the efficacy of the auditory temporal ordering tests (duration pattern test [DPT] and frequency pattern test [FPT]), and a temporal resolution test (gaps-in-noise [GIN] test) for detecting the central auditory processing abnormalities in adults with documented brain pathology.  A total of 4 databases, including PubMed, Web of Science, Embase, and Scopus, were systematically searched.  The publications in the English language that recruited adults (above 16 years of age) with pathologic brain conditions and described the diagnostic tests for auditory temporal processing were selected for review.  All data were systematically evaluated, extracted, categorized, and summarized in tables.  The meta-analysis was carried out to examine the effectiveness of the DPT, FPT, and GIN tests.  The results showed significantly poorer performance of DPT and FPT, compared between participants with confirmed brain disease and normal controls, at the mean differences of percent correct -21.93 (95 % confidence interval [CI]: -26.58 to -17.29) and -31.37 (95 % CI: -40.55 to -22.19), respectively.  Subjects with brain pathology also performed poorer in GIN test at the mean difference of 3.19 milliseconds (95 % CI: 2.51 to 3.87).  The authors concluded that the findings from this meta-analysis provided evidence that DPT, FPT, and GIN are sensitive detectors of auditory processing deficits in individuals with brain pathology.  Different types of brain pathology and different sites of lesion may differentially affect these test results, as this review also provided strong evidence that FPT is sensitive to the function of the auditory cortex, rather than other cerebral regions.  By extrapolation, these 3 sensitive clinical measures may have the potential to detect temporal resolution and temporal ordering deficits, indicating central auditory nervous system abnormalities, in adult individuals without obvious brain lesions documented on imaging, and this should be further investigated.  These researchers stated that clinicians should be cautioned to interpret these test results in the context of other patient characteristics (e.g., cognition) and be aware that not all brain pathologies will lead to deficits in auditory processing function, depending on its location characteristics and natural history of the neurological disorder.

The authors stated that the drawbacks of this study included a great variety of testing strategies and differences in units of the reported results.  For example, DPT has been reported in mean percent correct or the percentage of participants with abnormal results.  Furthermore, the normative value references and cut-off points also differed among studies, providing difficulties in data extraction.  A standard pattern of result reporting is needed to facilitate a meta-analysis.  The test strategies should be standardized with international protocols to reduce factors that may interfere with the test results.  Although there were variations in units of reporting results, each meta-analysis included only studies with similar reporting units.  These researchers stated that overall, there is a need for additional high-quality evidence (i.e., from randomized controlled trials (RCTs) using standardized outcome measures for CAPD) to demonstrate the effectiveness of CAPD diagnostic tests.  Such evidence is pivotal to guide service delivery models for evidence-based clinical practice.

Electrophysiological Screening for Children with Suspected Auditory Processing Disorder

Liu et al (2021) provided evidence for the early identification and intervention of children at risk for APD.  Electrophysiological studies on children with suspected APDs were systematically reviewed to understand the different electrophysiological characteristics of children with suspected APDs.  Computerized databases such as PubMed, Cochrane, Medline, Web of Science, and Embase were searched for retrieval of articles since the establishment of the database through May 18, 2020.  Cohort, case-control, and cross-sectional studies that examined the literature for the electrophysiological assessment of children with suspected APD were independently reviewed by 2 researchers for literature screening, literature quality assessment, and data extraction.  The Newcastle-Ottawa Scale and 11 entries recommended by the Agency for Healthcare Research and Quality (AHRQ) were used to evaluate the quality of the literature.  In accordance with the inclusion criteria, a total of 14 articles were included.  These articles entailed 7 electrophysiological testing techniques: click-evoked auditory brainstem responses (ABRs), frequency-following responses (FFRs), the binaural interaction component of the ABRs, the middle-latency response, cortical auditory evoked potential, mismatch negativity, and P300.  The literature quality was considered moderate.  The authors concluded that this systematic review of the 7 electrophysiological characteristics of children with suspected APD suggested that auditory electrophysiological tests are valuable in identifying children with abnormal auditory processing.  FFR has been widely studied, and its clinical application value has been confirmed. The clinical application value of middle- and late-latency physiological potentials in screening children with abnormal auditory processing needs more research for verification.  Owing to the complexity of the central auditory system and the heterogeneity and co-morbidity of auditory processing defects, the identification of auditory processing defects requires behavioral tests combined with electrophysiological tests of different latency responses.  It also requires multi-disciplinary collaboration in the differential diagnosis and intervention of auditory processing defects.  Furthermore, the auditory electrophysiological characteristics of children with different mental developmental disorders need to be further examined.  Standardized electrophysiological testing data of children in different regions and ages have to be established to provide a basis for the evaluation of the test results.

These investigators stated that no consensus has been reached regarding the diagnostic criteria for APD.  Different auditory behavioral tests and measures were used to identify children with suspected APD in the 14 included studies, although children with poorer performance in the behavioral tests generally showed poorer electrophysiological test results.  However, the electrophysiological characteristics of children with suspected APD identified in accordance with different diagnostic criteria may be more variable.  Thus, the electrophysiological test results of children with suspected APD may not be applicable to all children with APD.  Moreover, the study excluded the study of children with APD who were co-morbid with other mental development disorders, possibly some equally important reports were excluded, and analyses of the discriminative value of different electrophysiological tests for auditory processing characteristics in children with different mental developmental disorders are needed in the future.  Because the patterns presented by the results of the included studies were different, and not all studies could get a quantitative result, meta-analysis of the included studies could not be carried out, so more high-quality clinical trial studies are needed to provide strong evidence.  For electrophysiological testing, the stability of the testing tool, testing environment, and child status may affect the test results, and electrophysiological testing has the disadvantage of higher examination cost, which may limit the clinical applications of electrophysiological testing; therefore, new auditory processing evaluation tools with enhanced sensitivity and specificity, which are suitable for clinical application and promotion, still need to be developed.

References

The above policy is based on the following references:

  1. Ahmmed AU, Ahmmed AA, Bath JR, et al. Assessment of children with suspected auditory processing disorder: A factor analysis study. Ear Hear. 2014;35(3):295-305.
  2. Bailey PJ, Snowling MJ. Auditory processing and the development of language and literacy. Br Med Bull. 2002;63:135-146.
  3. Bamiou DE, Musiek FE, Luxon LM. Aetiology and clinical presentations of auditory processing disorders--a review. Arch Dis Child. 2001;85(5):361-365.
  4. Beck DL, Clarke JL, Moore DR. Contemporary issues in auditory processing disorders: 2016. Hearing Review. 2016;23(4):22.
  5. Bellis TJ, Chermak GD, Weihing J, Musiek FE. Efficacy of auditory interventions for central auditory processing disorder: A response to Fey et al. (2011). Lang Speech Hear Serv Sch. 2012;43(3):381-386.
  6. Bellis TJ. Treatment of (Central) Auditory Processing Disorders. In: Audiology Treatment. M Valente, H Hosford-Dunn, RJ Roeser, eds. New York, NY: Thieme; 2008 (cited in BSA, 2011).
  7. Cacace AT, McFarland DJ. Central auditory processing disorder in school-aged children: A critical review. J Speech Lang Hear Res. 1998;41(2):355-373.
  8. Cacace AT, McFarland DJ. The importance of modality specificity in diagnosing central auditory processing disorder. Am J Audiol. 2005;14(2):112-123.
  9. Cacace AT, McFarland  DT. Opening Pandora's Box: The reliability of CAPD tests. Am J Audiol. 1995;4 (2): 61–62.
  10. Carter J, Musher K. Etiology of speech and language disorders in children. UpToDate [online serial]. Waltham, MA: UpToDate; revised June 2013; May 2014..
  11. Chermak GD, Musiek FE. Central auditory processing disorders: New perspectives. San Diego, CA: Singular Publishing Group; 1997.
  12. Chermak GD. Deciphering auditory processing disorders in children. Otolaryngol Clin North Am. 2002;35(4):733-749.
  13. Chowsilpa S, Bamiou D-E , Koohi N. Effectiveness of the auditory temporal ordering and resolution tests to detect central auditory processing disorder in adults with evidence of brain pathology: A systematic review and meta-analysis. Front Neurol. 2021;12:656117.
  14. Cooper JC Jr., Gates GA. Hearing in the elderly -- the Framingham cohort, 1983-1985: Part II.  Prevalence of central auditory processing disorders. Ear Hear. 1991;12(5):304-311.
  15. Corriveau K, Pasquini E, Goswami U. Basic auditory processing skills and specific language impairment: A new look at an old hypothesis. J Speech Lang Hear Res. 2007;50 (3):647–666.
  16. Dawes P, Bishop D. Auditory processing disorder in relation to developmental disorders of language, communication and attention: A review and critique. Int J Lang Commun Disord. 2009;44(4):440-465.
  17. Dawes P, Bishop DV. Psychometric profile of children with auditory processing disorder and children with dyslexia.  Arch Dis Child. 2010;95(6):432-436.
  18. Dawes P, Sirimanna T, Burton M, et al. Temporal auditory and visual motion processing of children diagnosed with auditory processing disorder and dyslexia. Ear Hear. 2009;30(6):675-686.
  19. Delphi M, Zamiri Abdollahi F. Dichotic training in children with auditory processing disorder. Int J Pediatr Otorhinolaryngol. 2018;110:114-117.
  20. Dillon H, Cameron S, Glyde H, et al. An opinion on the assessment of people who may have an auditory processing disorder. J Am Acad Audiol. 2012;23(2):97-105.
  21. Dlouha O, Novak A, Vokral J. Central auditory processing disorder (CAPD) in children with specific language impairment (SLI). Central auditory tests. Int J Pediatr Otorhinolaryngol. 2007;71(6):903–907.
  22. Ferguson MA, Hall RL, Riley A, Moore DR. Communication, listening, cognitive and speech perception skills in children with auditory processing disorder (APD) or specific language impairment (SLI). J Speech Lang Hear Res. 2011;54(1):211-227.
  23. Fey ME, Kamhi AG, Richard GJ. Auditory training for children with auditory processing disorder and language impairment: A response to Bellis, Chermak, Weihing, and Musiek. Lang Speech Hear Serv Sch. 2012;43(3):387-392.
  24. Fey ME, Richard GJ, Geffner D, et al. Auditory processing disorder and auditory/language interventions: An evidence-based systematic review. Lang Speech Hear Serv Sch. 2011;42(3):246-264.
  25. Friel-Patti S. Clinical decision-making in central auditory processing disorders. Lang Speech Hear Serv Sch. 1999;30:345-352.
  26. Heimrath K, Fiene M, Rufener KS, Zaehle T. Modulating human auditory processing by transcranial electrical stimulation. Front Cell Neurosci. 2016;10:53.
  27. Hornickel J , Knowles E, Kraus N. Reliability of the auditory brainstem responses to speech over one year in school-age children: A reply to Drs . McFarland and Cacace. Hear Res. 2012;287:3-5.
  28. Idiazábal-Aletxa MA, Saperas-Rodríguez M. Auditory processing in specific language disorder. Rev Neurol. 2008;46 Suppl 1:S91-S95.
  29. Iliadou V, Kiese-Himmel C. Common misconceptions regarding pediatric auditory processing disorder. Front Neurol. 2018;8:732.
  30. Ingenix. ICD-9-CM Professional for Physicians, Volumes 1 & 2. 6th ed. Salt Lake City, UT: Ingenix; 2003.
  31. Jerger J, Musiek F. Report of the Consensus Conference on the Diagnosis of Auditory Processing Disorders in School-Aged Children. J Am Acad Audiol. 2000;11(9): 467-474.
  32. Jerger J. On the diagnosis of auditory processing disorder (APD). J Am Acad Audiol. 2009;20(3):1p preceding 161.
  33. Jerger J. The concept of auditory processing disorder: A brief history .In: Controversies in auditory processing disorder. AT Cacase, DJ McFarland, eds. San Diego, CA: Plural Publishing, Inc; 2009 (cited in BSA, 2011).
  34. Kamhi AG. What speech-language pathologists need to know about auditory processing disorder. Lang Speech Hear Serv Sch. 2011;42(3):265-272.
  35. Keith RW. Controversies in the standardization of auditory processing tests. In: Controversies in auditory processing disorder. AT Cacase, DJ McFarland, eds. San Diego, CA: Plural Publishing, Inc; 2009 (cited in BSA, 2011).
  36. Koohi N, Vickers DA, Lakshmanan R, et al. Hearing characteristics of stroke patients: Prevalence and characteristics of hearing impairment and auditory processing disorders in stroke patients. J Am Acad Audiol. 2017;28(6):491-505.
  37. Koravand A, Jutras B, Lassonde M. Abnormalities in cortical auditory responses in children with central auditory processing disorder. Neuroscience. 2017;346:135-148.
  38. Lagace J, Jutras B, Gagne JP. Auditory processing disorder and speech perception problems in noise: Finding the underlying origin. Am J Audiol. 2010;19(1):17-25.
  39. Lemos IC, Jacob RT, Gejao MG, et al. Frequency modulation (FM) system in auditory processing disorder: An evidence-based practice? Pro Fono. 2009;21(3):243-248.
  40. Liu P, Zhu H, Chen M, et al. Electrophysiological screening for children with suspected auditory processing disorder: A systematic review. Front Neurol. 2021 Aug 23;12:692840.
  41. Loo JH, Bamiou DE, Campbell N, Luxon LM. Computer-based auditory training (CBAT): Benefits for children with language- and reading-related learning difficulties. Dev Med Child Neurol. 2010;52(8):708-717.
  42. Lotfi Y, Moosavi A, Abdollahi FZ, et al. Effects of an auditory lateralization training in children suspected to central auditory processing disorder. J Audiol Otol. 2016;20(2):102-108.
  43. Lovett BJ, Auditory processing disorder: School psychologist beware?. Psychol. Schs. 2011;48: 855–867.
  44. McAnally KI, Hansen PC, Cornelissen PL, et al. Effect of time and frequency manipulation on syllable perception in developmental dyslexics. J Speech Lang Hear Res. 1997;40(4):912-924.
  45. McFarland DJ, Cacace AT. Potential problems in the differential diagnosis of (central) auditory processing disorder (CAPD or APD) and attention-deficit hyperactivity disorder (ADHD). J Am Acad Audiol. 2003;14(5):278-280.
  46. McFarland DJ, Cacace AT. Questionable reliability of the speech-evoked auditory brainstem response (sABR) in typically-developing children. Hear Res. 2012;287:1-2.
  47. McFarland DJ, Cacace AT. Modality specificity of auditory and visual pattern recognition: Implications for the assessment of central auditory processing disorders. Audiology. 1997;36(5):249-260.
  48. Micallef LA. Auditory processing disorder (APD): Progress in diagnostics so far. A mini-review on imaging techniques. J Int Adv Otol. 2015;11(3):257-261.
  49. Miller CA, Wagstaff DA. Behavioral profiles associated with auditory processing disorder and specific language impairment. J Comm Disord. 2011;44(6):745-763.
  50. Miller CA. Auditory processing theories of language disorders: Past, present, and future. Lang Speech Hear Serv Sch. 2011;42(3):309-319.
  51. Milner R, Lewandowska M, Ganc M, et al. Abnormal resting-state quantitative electroencephalogram in children with central auditory processing disorder: A pilot study. Front Neurosci. 2018;12:292.
  52. Mishra SK. Medial efferent mechanisms in children with auditory processing disorders. Front Hum Neurosci. 2014;8:860.
  53. Mody M, Studdert-Kennedy M, Brady S. Speech perception deficits in poor readers: Auditory processing or phonological coding? J Exp Child Psychol. 1997;64(2):199-231.
  54. Moore DR, Ferguson MA, Edmondson-Jones AM, et al. Nature of auditory processing disorder in children. Pediatrics. 2010;126(2):e382-e390.
  55. Moore DR, Ferguson MA, Halliday LF, Riley A. Frequency discrimination in children: Perception, learning, and attention. Hear Res. 2008;238 :147 -154.
  56. Moore DR, Rosen S, Bamiou DE, et al. Evolving concepts of developmental auditory processing disorder (APD): A British Society of Audiology APD special interest group 'white paper'. Int J Audiol. 2013;52(1):3-13.
  57. Moore DR, Sieswerda SL, Grainger MM, et al. Referral and diagnosis of developmental auditory processing disorder in a large, United States hospital-based audiology service. J Am Acad Audiol. 2018;29(5):364-377.
  58. Moore DR. Auditory processing disorder (APD)-potential contribution of mouse research. Brain Res. 2006;1091(1):200-206.
  59. Moore DR. Listening difficulties in children: bottom-up and top-down contributions. J Commun Disord. 2012;45(6):411-418.
  60. Moore DR. The diagnosis and management of auditory processing disorder. Lang Speech Hear Serv Sch. 2011;42(3):303-308.
  61. Musiek FE, Baran JA, Pinheiro ML. P300 results in patients with lesions of the auditory areas of the cerebrum. J Am Acad Audiol. 1992;3(1):5-15.
  62. Musiek FE, Baran JA, Schochat E. Selected management approaches to central auditory processing disorders. Scand Audiol Suppl. 1999;51:63-76.
  63. Musiek FE, Gollegly K, Ross M. Profiles of types of central auditory processing disorder in children with learning disabilities. J Child Comm Dis. 1985;9:43-63.
  64. National Institutes of Health (NIH), National Institute on Deafness and Other Communication Disorders (NIDCD). Auditory Processing Disorder in Children. NIH Pub. No. 01-4949. Bethesda, MD: NIH; updated February 2004. 
  65. Otaiba SA, Puranik C, Zilkowski R, Curran T. Effectiveness of early phonological awareness interventions for students with speech or language impairments. J Spec Edu. 2009;43(2):107-128.
  66. Rosen S, Cohen M, Vanniasegaram I. Auditory and cognitive abilities of children suspected of auditory processing disorder (APD). Int J Pediatr Otorhinolaryngol. 2010;74(6):594-600.
  67. Rosen S. A riddle wrapped in a mystery inside an enigma’: Defining central auditory processing disorder. Am J Audiol. 2005;14(2):139-150.
  68. Schochat E, Musiek FE, Alonso R, Ogata J. Effect of auditory training on the middle latency response in children with (central) auditory processing disorder.  Braz J Med Biol Res. 2010;43(8):777-785.
  69. Schow RL, Seikel JA, Chermak GD, et al. Central auditory processes and test measures: ASHA 1996 revisited. Am J Audiol. 2000;9(2):63-68.
  70. Sinha Y, Silove N, Hayen A, Williams K. Auditory integration training andother sound therapies for autism spectrum disorders (ASD). Cochrane Database Syst Rev. 2011;(12):CD003681.
  71. Thibodeau LM. Computer-based training (CBAT) for (Central) auditory processing disorders. In: Handbook of (Central) Auditory Processing Disorder. Comprehensive Intervention. Volume II. GD Chermak, FE Musiek, eds. San Diego, CA: Plural Publishing: 2007 (cited in BSA, 2011).
  72. Tierney CD, Kurtz M, Souders H. Clear as mud: Another look at autism, childhood apraxia of speech and auditory processing. Curr Opin Pediatr. 2012;24(3):394-349.
  73. von Suchodoletz W. Significance of auditory perceptual disorders for pediatric and adolescent psychiatric disorders. Z Kinder Jugendpsychiatr Psychother. 2009;37(3):163-172.
  74. Wilson WJ, Arnott W. Using different criteria to diagnose (central) auditory processing disorder: How big a difference does it make? J Speech Lang Hear Res. 2013;56(1):63-70.
  75. Witton C. Childhood auditory processing disorder as a developmental disorder: The case for a multi-professional approach to diagnosis and management. Int J Audiol. 2010;49(2):83-87.