Differential Diagnosis of Children With Suspected Childhood Apraxia of Speech

JSLHR

Research

Differential DiagnosiSuspected Childhood

a R

then manifests itself as disordered articulation, difficultysequencing sounds and syllables, inconsistent production ofrepeated sounds and syllables, and disruption at the supra-

Giacheti, & Richieri-Costa, 1995) or deleterious geneticmutation (e.g., Brunetti-Pierri et al., 2011; Carr et al., 2010;Palka et al., 2012). The majority of reported cases, however,

all casest havech fea-ing errorssody (e.g.,

aThe Universi

Corresponden

Editor: Jody K

DownloaTerms oRevision received May 10, 2013Accepted October 13, 2014DOI: 10.1044/2014_JSLHR-S-12-0358

Disclosure: The authors have declared that no competing interests existed at the timeof publication.Munson, Bjorum, & Windsor, 2003; Thoonen, Maassen,Wit, Gabrels, & Schreuder, 1996), speech production

Associate Editor: Ben A. M. Maassen

Received November 9, 2012Journal o

ded From: httf Use: http://puneurological or behavioral diagnostic marker forof CAS (ASHA, 2007b). Behavioral measures thabeen considered have included inconsistent speetures (e.g., Iuzzini, 2012), coarticulation and tim(e.g., Sussman, Marquardt, & Doyle, 2000), pro

ty of Sydney, Australia

ce to Elizabeth Murray: [email protected]

reimansegmental level (i.e., dysprosody; ASHA, 2007b). Despitethe recent advances in our theoretical understanding of CAS,

are idiopathic.Despite much research, there is currently no singleChildhood apraxia of speech (CAS) is considered animpairment of speech motor control or praxis. Mostresearchers agree that the core deficit for childrenwith CAS is a reduced or degraded ability to convert abstractphonological codes to motor speech commands, referred toas motor planning and/or programming (American Speech-Language-Hearing Association [ASHA], 2007b; Nijland,Maassen, & Van der Meulen, 2003; Shriberg, Lohmeier,Strand, & Jakielski, 2012). This consensus has been supportedby behavioral studies (see ASHA, 2007b, and McCauley,Tambyraja, & Daher-Twersky, 2012, for reviews), classifi-cation paradigms (Shriberg et al., 2010), and computationalmodeling studies (Terband & Maassen, 2010; Terband,Maassen, Guenther, & Brumberg, 2009). The impairmentf Speech, Language, and Hearing Research Vol. 58 4360 February

p://jslhr.pubs.asha.org/ by a Proquest User on 02/20/2015bs.asha.org/ss/Rights_and_Permissions.aspxit remains difficult to differentially diagnose CAS fromother disorders. In the absence of a clinically available vali-dated assessment procedure, the current gold standard fordiagnosis is expert opinion (Maas, Butalla, & Farinella,2012). The purpose of this study was to determine, after ex-pert diagnosis, if a quantitative measure or set of measuresdifferentiated CAS from non-CAS in a sample of childrenreferred from the community with suspected CAS.

Diagnosis of Childhood Apraxia of SpeechCAS may occur as a result of known neurodevelop-

mental disorders (Kummer, Lee, Stutz, Maroney, & Brandt,2007; Shriberg, Potter, & Strand, 2011; Spinelli, Rocha,Elizabeth Murray,a Patricia McCabe,

Purpose: The gold standard for diagnosing childhoodapraxia of speech (CAS) is expert judgment of perceptualfeatures. The aim of this study was to identify a set ofobjective measures that differentiate CAS from other speechdisorders.Method: Seventy-two children (412 years of age)diagnosed with suspected CAS by community speech-language pathologists were screened. Forty-sevenparticipants underwent diagnostic assessment includingpresence or absence of perceptual CAS features. Twenty-eight children met two sets of diagnostic criteria for CAS(American Speech-Language-Hearing Association, 2007b;Shriberg, Potter, & Strand, 2009); another 4 met the CAScriteria with comorbidity. Fifteen were categorized asnon-CAS with phonological impairment, submucous cleft,Article

s of Children withApraxia of Speech

obert Heard,a and Kirrie J. Ballarda

or dysarthria. Following this, 24 different measures fromthe diagnostic assessment were rated by blinded raters.Multivariate discriminant function analysis was used toidentify the combination of measures that best predictedexpert diagnoses.Results: The discriminant function analysis model, includingsyllable segregation, lexical stress matches, percentagephonemes correct from a polysyllabic picture-naming task,and articulatory accuracy on repetition of /ptk/, reached91% diagnostic accuracy against expert diagnosis.Conclusions: Polysyllabic production accuracy and an oralmotor examination that includes diadochokinesis may besufficient to reliably identify CAS and rule out structuralabnormality or dysarthria. Testing with a larger unselectedsample is required.2015 Copyright 2015 American Speech-Language-Hearing Association 43

(e.g., Thoonen, Maassen, Gabrels, & Schreuder, 1999),speech perception (e.g., Nijland, 2009), linguistic skills (e.g.,Lewis, Freebairn, Hansen, Iyengar, & Taylor, 2004), andnonspeech oral motor skills (e.g., Murdoch, Attard, Ozanne,

DownloaTerms o& Stokes, 1995). Diagnostic marker research has includedmany contributions by Shriberg and colleagues utilizing theirMadison Speech Assessment Protocol and Speech DisordersClassification System (SDCS) to attempt to differentiateCAS and acquired apraxia of speech from normal develop-ment and other communication disorders (e.g., Shriberg,Aram, & Kwiatkowski, 1997; Shriberg et al., 2010;Shriberg, Green, Campbell, McSweeny, & Scheer, 2003;Shriberg, Lohmeier, et al., 2009).

In the absence of a single pathognomonic feature, acore set of validated differential diagnostic markers for CASis needed (ASHA, 2007b; Davis, Jakielski, & Marquardt,1998). In 2007, ASHA released a position statement andtechnical report on CAS (ASHA, 2007a, 2007b). The tech-nical report provided a consensus position on a small setof perceptual speech features associated with the primaryplanning and programming deficits in CAS. Following athorough literature review of group-comparison diagnosticstudies and community consultation, three features withsome consensus were identified across investigators in apraxia:(a) inconsistent errors on consonants and vowels in re-peated productions of syllables or words, (b) lengthened anddisrupted coarticulatory transitions between sounds andsyllables, and (c) inappropriate prosody, especially in therealization of lexical or phrasal stress (ASHA, 2007b,pp. 4, 54, and 59).1 These features have been used for diag-nosis in recent CAS treatment studies in which participantshad to demonstrate all three features to warrant diagnosis(e.g., Ballard, Robin, McCabe, & McDonald, 2010; Maaset al., 2012). Another recent checklist that is used for CASdiagnosis in research and clinical settings is Strands 10-pointchecklist (Shriberg et al., 2012; Shriberg, Potter, & Strand,2009). This checklist provides 10 segmental and supra-segmental features that can be present in CAS, althoughany combination of at least four out of the 10 features acrossthree tasks warrants the diagnosis of CAS.

As Strand and colleagues remarked, feature lists do notdirectly lead to an assessment procedure (Strand, McCauley,Weigand, Stoeckel, & Baas, 2013). Commercial norm-referenced assessment tools offer clear assessment tasksbut often lack essential psychometric properties (McCauley &Strand, 2008) and may also rely on feature lists or scales fordiagnosis of CAS (Blakeley, 2001; Hickman, 1997; Kaufman,1995). The recently published Dynamic Evaluation of MotorSkills is reliable but risks failing to identify some personswith CAS (Strand et al., 2013). To date, there is no ac-cepted, operationally defined, diagnostic testing protocol orclinically available and validated set of behavioral features,

1The Royal College of Speech and Language Therapists also released aPolicy Statement with a wider list of consensus features for CAS (RoyalCollege of Speech and Language Therapists, 2011). This statement waspublished after data collection for the current study and was therefore

not considered.

44 Journal of Speech, Language, and Hearing Research Vol. 58 43

ded From: http://jslhr.pubs.asha.org/ by a Proquest User on 02/20/2015f Use: http://pubs.asha.org/ss/Rights_and_Permissions.aspxwith >90% sensitivity (identifying true cases of CAS) andspecificity (identifying true cases of non-CAS), discriminat-ing CAS from a range of other expressive communicationdisorders (Shriberg et al., 2012). The closest we have isthe contribution of 89% sensitivity and 100% specificity byThoonen and colleagues (1996, 1997, 1999) in discriminatingspastic dysarthria and CAS using maximum performancetasks. However, the procedure may not discriminate acrosssubtypes of dysarthria and other speech sound disorders (SSD)because the validation sample used was small (n = 11).

Developing a reliable testing protocol that capturesthe small set of agreed behavioral features of CAS is chal-lenging when one considers a number of methodologicalconstraints of previous research. These include lack of spec-ificity of features within checklists used for diagnosis ofCAS, various participant selection criteria, insufficientoperationalization of methods for replication, and differentcomparison groups. Checklists of features, such as Strands10-point checklist (Shriberg, Potter, et al., 2009) and others,guide the clinician in making presence or absence decisionsfor a range of speech, oromotor, and linguistic behaviors;however, for the most part, these checklists do not includeexplicit definitions of the features or methods for determininghow much or how often any single behavior must be observed(e.g., Crary, 1995; Davis & Velleman, 2000; cf. Thoonenet al., 1999; see Rvachew, Hodge, & Ohberg, 2005, for a tu-torial), which affects application or replication of the results.The wide range of behaviors that have been considered forthese checklists reflects (a) the belief that symptoms of CASchange with age and/or maturation and (b) the differenttheoretical perspectives as to whether CAS has core or co-occurring motor, oral motor, phonological or linguisticmemory, and literacy impairments (Alcock, Passingham,Watkins, & Vargha-Khadem, 2000; Marion, Sussman, &Marquardt, 1993; Shriberg et al., 2012). Furthermore, manysurface behavioral features are shared by a range of commu-nication disorders, making discrimination of phonological-linguistic and phonetic-motoric errors difficult (see Ballard,Granier, & Robin, 2000; McCabe, Rosenthal, & McLeod,1998; McNeil, Robin, & Schmidt, 2009).

The methods of participant selection have also dif-fered across studies, contributing to confusion. Some studieshave included participants with CAS and concomitanthearing impairment, intellectual disability, and languageimpairment (Shriberg et al., 2012), whereas others haveused participants with idiopathic CAS and no knowncomorbid disorders (e.g., Thoonen et al., 1997). Whencomparison groups have been used, specifically selected diag-noses have been compared to CAS. Groups of participantshave included those with speech delay or speech sounddisorder with or without language delay or impairment(e.g., Lewis et al., 2011; Shriberg, Green, et al., 2003; Shriberget al., 2012), dysarthria (e.g., Morley, Court, & Miller,1954; Rosenbek & Wertz, 1972; Thoonen et al., 1999),phonological impairment (e.g., Williams, Ingham, &Rosenthal, 1981; Yoss & Darley, 1974), specific languageimpairment (e.g., Lewis et al., 2004), or stuttering (Byrd &

Cooper, 1989). Ultimately a set of quantifiable measures is

60 February 2015

it is likely that expert and novice clinicians would demon-

DownloaTerms ostrate low interrater reliability, as already shown in studiesof CAS (Shriberg, Campbell, et al., 2003) and dysarthria(Zeplin & Kent, 1996; Zyski & Weisiger, 1987) using per-ceptual, criterion-based diagnostic methods. This uncertaintyresults in clinicians in practice making diagnoses of sus-pected CAS, with risk of under- or overidentification(Davis et al., 1998; Forrest, 2003; Shriberg, Campbell,et al., 2003). The result can be selection of inappropriatetreatment approaches and the frequent complaint that chil-dren with CAS progress slowly in therapy (Forrest, 2003).This study furthers the CAS diagnostic literature by attempt-ing to determine if clinically available measures can discrimi-nate CAS from other expressive communication disordersin a sample of verbal children.

AimThe primary aim of this study was to use objective

statistical methods to identify one or more quantitativemeasures of speech that reproduce the expert classificationof children as having CAS or not. This approach has poten-tial to provide a clinically feasible assessment protocol thatreliably identifies features of CAS and reduces reliance onexpert opinion.

MethodThis study formed part of a larger clinical trial:

Unique Trial Number: U1111-1132-5952, Trial RegistrationNumber: ACTRN12612000744853. The research protocolwas approved by the Human Research Ethics Committee ofthe University of Sydney (12924) and is published (Murray,McCabe, & Ballard, 2012).

This diagnostic study was conducted in three phasesto address the research aim. First, children were recruitedand assessed according to the protocol below. Second, thefirst two authors (E. M. and P. M.) judged presence or ab-sence of CAS by listening to speech samples from the as-sessment protocol and completing two published checklistsof CAS characteristics. Third, the first author (E. M.) andindependent raters blinded to the diagnosis of CAS gener-needed that will reliably discriminate CAS from a widerange of communication disorders that could present withoverlapping symptoms, whether within individuals or be-tween groups of individuals.

The predominant approach in current treatment re-search studies of CAS is, first, to use a range of standard-ized assessments to rule out frank receptive and expressivelanguage impairment or orofacial structural abnormalityand, second, to use a checklist to note CAS-type behaviorsobserved and so justify a diagnosis of CAS (e.g., Ballardet al., 2010; Martikainen & Korpilahti, 2011). However, asstated above, the feature lists have not been operationalized,validated, or standardized, and no standard approach orbattery for assessment of these specific features has yet beendeveloped (ASHA, 2007b; Shriberg et al., 2012). As such,ated 24 quantitative measures from assessment data. These

ded From: http://jslhr.pubs.asha.org/ by a Proquest User on 02/20/2015f Use: http://pubs.asha.org/ss/Rights_and_Permissions.aspxmeasures were analyzed using bivariate and hierarchicalmultivariate discriminant function analysis (DFA) to deter-mine if any combination of the 24 measures reliably pre-dicted the assigned CAS diagnosis.

ParticipantsParticipants were recruited via a website advertisement

and flyers as well as e-mails and listserv posts to speech-language pathologists (SLPs), inviting them to volunteer fora research treatment study. The treatment component of thisstudy is not discussed in this article. The inclusion criteria forparticipants were (a) a clinical diagnosis of suspected CASby a community-based SLP, (b) age between 4 and 12 years,(c) no previously identified language comprehension diffi-culty, (d) normal or adjusted-to-normal hearing and vision,(e) native English speaker, and (f ) no other developmentaldiagnoses not associated with CAS (e.g., intellectual disability,autism, cerebral palsy). All participants had received speech-language pathology intervention prior to this study, andall had normal hearing. Participants needed to attend theon-campus clinic at the University of Sydney, Australia, toparticipate. Parents or caregivers provided informed consenton behalf of the children prior to participation.

Of the 72 participants who inquired regarding thestudy, 47 passed the initial screening. All 47 were Australian-English speaking, 33 (70%) were boys, 14 were girls (30%),and the average age was 70.5 months (SD = 25.7 months).No information on race, ethnicity, or socioeconomic statuswas collected.

Clinical AssessmentThe first author responded to all inquiries to partic-

ipate in the study and followed a two-tiered assessmentprotocol (see Figure 1). The first tier involved screening ofparents or caregivers and SLPs by phone and previousspeech pathology reports to determine the childs suitabilityfor entry into the study. The criteria were applied conser-vatively, in that children with another developmental disor-der that could be associated with symptoms or consequencesof CAS (e.g., selective mutism) were included. For thosewho passed the initial screening, the second tier involved ad-ministration of a 2-hour speech and language assessmentbattery. Responses to these tests were used for all analysesof CAS features and to determine the presence of comorbidconditions such as dysarthria or language impairment. Theassessment battery was video- and audio-recorded usinga Sony IC Recorder ICD-UX71F and either an Echo Layla24/96 multitrack recording system, Marantz PMD660 solid-state recorder, or Roland Quad-Capture UA-55 at the sam-pling rate 48000 Hz with 16-bit resolution with an AKGC520 headset microphone at 5 cm mouth-to-microphonedistance.

Speech and Language Assessment BatteryFirst, a parent or caregiver case history questionnaireand hearing screening was completed (ASHA, 1978). Next,

Murray et al.: Differential Diagnosis of CAS 45

hildho

DownloaTerms oFigure 1. Results of differential diagnostic process. Participants with cand non-CAS or excluded in blue.the diagnostic assessment utilized five published tests com-monly available in clinical settings and culturally appropriatefor Australian children (see Table 1). The five tests were thefollowing:

1. the Diagnostic Evaluation of Articulation andPhonology (DEAP) Inconsistency subtest (Dodd, Hua,Crosbie, Holm, & Ozanne, 2002), used to assess theword-level token-to-token inconsistency in naming25 pictured words over three test administrations, eachadministration separated by other assessment tasks;

2. the Single-Word Test of Polysyllables (Gozzard, Baker,& McCabe, 2004, 2008), a 50-item picture-namingtask used to assess articulation, sound and syllablesequencing, and lexical stress accuracy (i.e., prosody);

3. a connected speech sample of at least 50 utterancesrecorded over at least 10 minutes (McLeod, 1997), fordetection of perceptual features of CAS in connectedspeech and calculation of articulation rate;

4. the Oral and Speech Motor Control Protocol, apublished oral motor assessment (OMA) includingdiadochokinesis (DDK; Robbins & Klee, 1987), usedto rule out any structural or functional abnormalitiesin the oral mechanism; and

5. the Clinical Evaluation of Language Fundamentals(CELF; 4th edition or Preschool2nd edition,Australian versions), used to assess receptive and

aNo articulation or prosodic impairment noted by parents, no CAS suspect


ded From: http://jslhr.pubs.asha.org/ by a Proquest User on 02/20/2015f Use: http://pubs.asha.org/ss/Rights_and_Permissions.aspxod apraxia of speech (CAS) shown in white, comorbid CAS in yellow,expressive language skills (Semel, Wiig, & Secord,2006; Wiig, Secord, & Semel, 2006).All tests were administered and scored by the first

author according to the relevant manual. For the DEAPInconsistency subtest, broad transcription was used and dis-tortions and developmental errors were scored as correct,according to Dodd et al. (2002, p. 25).

Initially, responses to the assessments were used forexpert qualitative judgments of presence or absence of CASand, following this, for generating 24 quantitative measuresfor statistical analyses in order to address the study aim.There was overlap in the speech samples used to make theexpert judgments and to generate the quantitative measures;however, expert judgments were made prior to extracting thewider range of measures from the assessment, and measurecalculations did not influence the initial classifications made.In addition, three raters scored 100% of the data: the firstauthor and two independent raters blinded to expert diagnosis.A detailed description of reliability calculations is providedin the Reliability section.

Expert Diagnosis of CASThe first and second authors, each with more than

10 years experience in differential diagnosis of pediatricspeech sound disorders, independently diagnosed the pres-ence and severity of CAS for all children based on their per-ceptual ratings of each childs speech samples, using the

ed by SLP.

60 February 2015

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DownloaTerms oTable 1. Assessment tasks and diagnostic features used for assigning

ASHA consensus-basedfeature lista Strands 10-point che

1. Inconsistent errors onconsonants and vowels inrepeated productions ofsyllables or words

Not in list

2. Lengthened and disruptedcoarticulatory transitionsbetween sounds and syllables

1. Difficulty achieving initial articconfigurations and transitions

2. Syllable segregation3. Inappropriate prosody,

especially in the realization oflexical or phrasal stress

3. Lexical stress errors or equal

4. Vowel or consonant distortiondistorted substitutions

5. Groping (nonspeech)

6. Intrusive schwa

7. Voicing errors

8. Slow rate

9. Slow DDK rate

10. Increased difficulty with longphonetically complex words

Note. DEAP = Diagnostic Evaluation of Articulation and Phonology;aASHA, 2007b, p. 4. ASHA criteria = 3/3 needed for CAS diagnosis. bShdiagnosis over three tasks. cThe primary task or tasks used for detectionfeatures over three tasks shown in brackets. dDodd et al. (2002). eGozfollowing procedure. To be diagnosed with CAS in this study,participants had to demonstrate (a) the three consensus-basedfeatures listed in the ASHA Technical Report (2007b) and(b) any four of the 10 features in Strands 10-point checklist(Shriberg, Potter, et al., 2009) over at least three assessmenttasks as shown in Table 1. For the 10-point checklist, a verbalbehavior was marked as present when perceptually detectedin speech samples from three tasks: the DEAP Inconsistencyassessment, the Single-Word Test of Polysyllables, and theconnected speech sample. Neither published list providesoperational definitions or cutoffs for the listed features.

The first and second authors listened to the speechsamples and independently made a perceptual judgment asto whether each feature was present or absent, except in thecase of inconsistency of speech errors. For this one featureonly, decisions were based on the score from the DEAPInconsistency subtest, the only published test that definesinconsistency in the same way as the ASHA TechnicalReport (ASHA, 2007b). Syllable segregation was definedby the authors as noticeable gaps between syllables. Twocriteria showed potential overlap, and, because no previousdefinitions were available, the authors defined difficultyachieving initial articulatory configurations and transitionsinto vowels to include within-speech groping, false starts,restarts, and hesitations and defined groping as nonspeechoral groping. Nonspeech groping was determined overthree oral motor tasks within the Robbins and Klee protocol(1987). For expert perceptual diagnosis of CAS, interrateragreement between the first two authors was 100%.

ded From: http://jslhr.pubs.asha.org/ by a Proquest User on 02/20/2015f Use: http://pubs.asha.org/ss/Rights_and_Permissions.aspxspeech sample )

ryvowels

Polysyllable Test,e connected speech samplef

(DEAP Inconsistency subtestd)

s Polysyllable Teste (DEAP Inconsistency subtest,d

Connected speech samplef)luding Polysyllable Teste (DEAP Inconsistency subtest,d

Connected speech samplef)Oral and Speech Motor Control Protocol (any three

nonspeech tasks)g

Polysyllable Teste (DEAP Inconsistency subtest,d

Connected speech samplec)Polysyllable Teste (DEAP Inconsistency subtest,d

Connected speech samplef)Connected speech samplef Polysyllable Teste

(DEAP Inconsistency subtestd)Polysyllable Teste (DEAP Inconsistency subtest,d

Connected speech samplef)more Polysyllable Teste (DEAP Inconsistency subtest,d

Connected speech samplef)

= diadochokinesis.

g, Potter, et al., 2009, p. 8. Strand criteria = 4/10 needed for CASh feature are written first, with the secondary tasks needed to determineet al. (2004, 2008). fMcLeod (1997). gRobbins and Klee (1987).xpert diagnosis of childhood apraxia of speech.

tb Assessment tasks considered in diagnosisc

DEAP Inconsistency subtest (inconsistency definedd eAppendix A shows the CAS features judged to bepresent and the expert decision on presence or absence ofCAS for each of the 47 participants. Of these 47, 28 (60%)were classified as having CAS (ASHA, 2007b; Shriberg,Potter, et al., 2009). Another four children (8%) met thecriteria for CAS but presented with comorbid dysarthria andreceptive and/or expressive language disorder (CAS+),similar to the Shriberg et al. (2010) classification of motorspeech disorderapraxia of speech plus (MSD-AOS+).Children diagnosed with CAS or CAS+ showed a medianof 6 out of 10 features on the Strand 10-point checklist(Shriberg, Potter, et al., 2009). Fifteen (32%) of the 47 chil-dren did not demonstrate all three features in the ASHAfeature list and were classified as non-CAS in this study.Non-CAS diagnoses included ataxic dysarthria (n = 1), flacciddysarthria (n = 1), submucous cleft (n = 3), or primarilyphonological disorder (n = 10; see Figure 1). Phonologicalimpairment was identified in those who had clear phono-logical processes (Rvachew & Brosseau-Lapr, 2012) andan absence of oromotor or speech-motor deficits (e.g., DDKdeficits) excluding them from motor speech diagnosis.

Quantitative MeasuresQuantitative measures were extracted from the five

tests administered in the clinical assessment. Table 2 liststhe 24 measures generated and the associated assessmenttask and CAS checklist feature. For measures that werenot standard scores from norm-referenced tools, nine raters


Table 2. Extracted measures from assessment tasks completed with means and standard deviations for the CAS, CAS+, and non-CAS groups.

Assessment task Relation to diagnostic criteria Measure

CAS (n = 28) CAS+ (n = 4) Non-CAS (n = 15)

M SD M SD M SD

Case history None Age in months 66.5 24.8 72.5 26.0 77.3 27.6DEAP Inconsistency

subtestaInconsistent errors on consonants and

vowels in repeated productions ofsyllables or wordsf

Percentage inconsistency (across threerepetitions of 25 words)a

63.8 12.8 75.0 15.1 39.9 24.5

Single-Word Test ofPolysyllablesb

Inappropriate prosody, especially in therealization of lexical or phrasal stressf

and equal or lexical stress errorsg

Percentage of lexical stress matches(relative to gloss)h

9.8 9.1 16.3 17.5 67.3 22.4

Vowel or consonant distortions includingdistorted substitutionsg

Distortion occurrences (out of a possible328 phones)h

39.4 5.8 45.2 8.8 46.24 22.9

Syllable segregationg Syllable segregation occurrences (out of apossible 114 opportunities)i

30.2 8.8 8.5 8.4 1.2 2.1

Intrusive schwag Intrusive schwa occurrences (out of apossible 15 clusters)i

1.1 1.3 0.5 1.0 0.0 0.0

Voicing errorsg Voicing error occurrences (out of a possible90 opportunities)h

3.4 4.0 6.0 5.0 1.7 1.5

None PPCh 52.2 15.0 24.3 20.6 73.1 26.3None PCC-Rh,j 54.0 20.0 22.8 23.8 70.9 26.4None PVC on polysyllable testh 50.5 11.4 24.8 19.0 75.1 27.3

DEAP Inconsistencya vs.Polysyllablesb

Increased difficulty with longer or morephonetically complex wordsg

Magnitude of change score: NPC on 12monosyllables/NPC on 12 polysyllables(>1 = difficulty with polysyllables)

1.23 0.2 2.52 1.9 2.07 2.91

Connected speechsamplec

Slow rateg Articulation rate (syllables per minute)k 1.7 1.0 0.9 0.6 2.6 1.0Lengthened and disrupted coarticulatory

transitions between sounds andsyllablesf and difficulty achieving initialarticulatory configurations andtransitions into vowelsg

Presence of false articulatory starts andrestarts and/or inaudible within-speechgroping and/or audible within-speechgroping and/or hesitations (min. = 0,max. = 1)l,m

0.9 0.4 1.0 0.0 0.1 0.3

Oral and Speech MotorControl Protocold

Groping (nonspeech)g Presence of nonspeech groping in lip andtongue oral function tasks (min. = 0,max. = 1)

0.5 0.5 0.5 0.6 0.0 0.0

Slow DDK rateg /p/ rate over 3 s on two trialsd 3.6 1.0 3.0 1.7 4.5 2.0Slow DDK rateg /t/ rate over 3 s on two trialsd 3.4 1.0 2.3 1.3 4.4 1.7Slow DDK rateg /k/ rate over 3 s on two trialsd 2.9 1.2 1.8 1.0 4.3 1.9Slow DDK rateg /ptk/ rate over 3 s on two trialsd 0.9 0.6 0.5 0.1 1.4 0.8Slow DDK rateg /ptikek/ rate over 3 s on two trialsd 1.0 0.4 0.7 0.4 1.3 0.7None Accuracy on /ptk/ DDK task on

two trialsd48.5 22.6 23.8 9.0 75.9 23.2

None Oral structure scored 23.4 .91 21.0 2.6 21.8 2.4None Oral function scored 76.6 13.8 60.0 12.4 88.5 18.7None Maximum Phonation Timed 9.6 5.5 6.8 4.1 12.9 6.7

CELFe None Receptive Language Scoree 100.3 12.2 65.5 18.4 91.4 20.5None Expressive Language Scoree 85.5 20.1 55.5 7.3 79.7 19.2

Note. CAS = childhood apraxia of speech;M = mean; SD = standard deviation; PPC = percentage phonemes correct; PCC-R = percentage consonants correctrevised; PVC = percentagevowels correct; NPC = number of phonemes correct; CELF = Clinical Evaluation of Language Fundamentals (Semel, Wiig, & Secord, 2006; Wiig, Secord, & Semel, 2006).aDodd et al. (2002). bGozzard et al. (2004, 2008). cMcLeod (1997). dRobbins and Klee (1987) includes some norms. eSemel et al. (2006) with norms. fASHA (2007b). gShriberg, Potter, et al.(2009). hComputerized Profiling (Long et al., 2006). iCounted by hand from transcription. jShriberg, Austin, Lewis, McSweeny, and Wilson (1997). kLogan et al. (2011) includes norms.lMcNeil et al. (2009). mDuffy (2012).

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DownloaTerms owere employed to make measurements blinded to the chil-drens expert diagnosis of CAS presence or absence for reli-ability purposes. The raters were qualified SLPs with anaverage of 3 years of clinical experience. The first authortrained each rater on the methods of measurement until aminimum of 85% interrater reliability was achieved on atraining sample not included in the study. Raters were ran-domly assigned samples from 12 to 13 children (M = 12.70,SD = 4.83). Each participants results were rated by at leasttwo raters. In the event of any discrepancies, a third inde-pendent rater measured the sample(s), and the first authorsscores were retained based on above 85% agreement for allmeasures (see the Reliability section).

The DEAP Inconsistency subtest was transcribedusing broad transcription following the manual (Dodd et al.,2002). Responses to the Single-Word Test of Polysyllableswere transcribed from an audio recording into the PROPHmodule in Computerized Profiling (Long, Fey, & Channell,2006). Transcription included broad transcription supple-mented with International Phonetic Alphabet diacritics tomark distortions where clearly present. Measures extractedfrom PROPH were percentage consonants correctrevised(PCC-R; Shriberg, Austin, Lewis, McSweeny, & Wilson,1997), percentage vowels correct (PVC), percentage phonemescorrect (PPC), occurrences of intrusive schwa, distortedsubstitutions and voicing errors, and percentage of syllablestress matches compared to the gloss for each word. Syllablesegregation (transcribed as a plus sign + between sylla-bles) and intrusive schwas between cluster elements wererecorded by simple counts of occurrence.

PCC-R is a revised metric of PCC in which typical andatypical distortions are removed from the index (Shriberg,Austin, et al., 1997). PCC-R was used as a general index ofspeech sound disorder severity, regardless of specific diag-nosis, for all three groups. This metric was used in this studybecause it was more sensitive for the participant sample witha diverse range of age and speech status (Shriberg, Austin,et al., 1997). Distortions were counted as their own measurebecause it may facilitate differential diagnosis of speechdisorders. Thus, using PCC-R allowed us to identify anypotential effects related to general severity rather than a spe-cific diagnosis of CAS. Accordingly, distortions were notpart of the PCC-R measure, yet were measured separatelyunder the Occurrence of Consonant and Vowel DistortionsIncluding Distorted Substitutions measurement accordingto Shriberg, Potter, and Strand (2009).

The Oral and Motor Speech Protocol and connectedspeech sample were transcribed or scored from a video re-cording. Articulation rate was calculated by counting sylla-bles per second produced in 1 min of monologue connectedspeech. This was undertaken using Adobe Audition software(Version 1.5), in which the sample was isolated and vowelsidentified and counted to determine syllable rate followingLogan, Byrd, Mazzocchi, and Gillam (2011).

Strands criterion of increased difficulty with polysyl-labic words (Shriberg, Potter, et al., 2009) was determinedusing a magnitude-of-change score. This score was gen-

erated for each participant, comparing the mean number of

ded From: http://jslhr.pubs.asha.org/ by a Proquest User on 02/20/2015f Use: http://pubs.asha.org/ss/Rights_and_Permissions.aspxphonemes correct from the 12 monosyllable items in theDEAP Inconsistency subtest to the first 12 items from theSingle-Word Test of Polysyllables. A magnitude of changescore greater than 1 indicated that production of polysyllabicwords was more difficult than monosyllabic words. The onlybinary measures used were for presence of (a) false articula-tory starts, restarts, or within-speech groping and (b) non-speech groping movements.

ReliabilityIntrarater and interrater transcription reliability was

calculated on a point-to-point basis for all unstandardizeddependent measures for each participant. Intrarater reliabil-ity was calculated on 20% of each measure for every partici-pants data with each rater calculating the measure onceand then a second time at least 2 weeks after the initial cal-culation. Intrarater reliability for the first author was 94.6%(SD = 3.6, range = 8898) and across all nine independentraters was 95.2% (SD = 3.1, range = 88.399.1). Interraterreliability was calculated first between the first authorand the nine raters and then between the nine independentraters. Mean interrater reliability with the first author acrossall measures was 94.2% (SD = 1.7, range = 91.396.2) andbetween the nine raters was 93.3% (SD = 2.9, range = 86.498.1). For a breakdown of each raters and measures reli-ability, see online Supplemental Appendices 1 and 2.

Statistical AnalysesSimple bivariate followed by hierarchical multi-

variate, discriminant function analysis (DFA; McKean &Hettmansperger, 1976) was used to determine whether oneor more of the 24 quantitative measures could reliably predictthe expert assignment of children to CAS or non-CASgroups. Therefore, the results from the expert judgment ofCAS presence or absence and the quantitative measures werecompared in this phase to address the primary research aim.

The sample size was sufficient to minimize Type Iand Type II errors (Serlin & Harwell, 2004). Before comple-tion of the analysis, data were screened for normality, line-arity, and homoscedasticity. No violations of assumptionswere noted. Mahalanobis Distance was calculated to checkfor outliers (Allen & Bennett, 2008) and did not exceed thecritical c2 of 26.13 (df = 8; a = .001) for any cases. Finally,screening for multicollinearity above a conservative 0.80(cf. 0.90; Allen & Bennett, 2008; Poulsen & French, 2004;Tabachnick & Fidell, 2007) identified the following highlycorrelated measures: PVC, PCC-R, and PPC; PVC and per-centage stress matches; PPC and oral function score; PCC-Rand oral function score, percentage stress matches andpresence of articulatory false starts, restarts and/or within-speech groping; and finally, /p/, /t/, and /k/ rates. Whentwo measures showed multicollinearity, only one measureat a time was entered into the DFA as a predictor. There isno consensus on the minimum ratio of predictor variablesto cases in DFA. Given this, we followed guidelines by

Poulsen and French (2004) and Tabachnick and Fidell


DownloaTerms o(2007) and used up to eight predictors at a time. BivariateDFA (for each measure predicting the given diagnosis) wasinitially completed to determine the top eight theoreticalmeasures in discriminating CAS in this sample, taking intoaccount multicollinearity, for entry into the multivariateDFA. Results for all 24 measures are presented in Table 3for all participants and Table 4 when CAS+ and sub-mucous clefts were excluded. When a potential predictorcorrelated highly with another potential predictor, the DFAwas run twice, each time with only one of the highly corre-lated predictors. For example, the measures that were ini-tially entered as predictors into the first multivariate DFAwere (a) presence of articulatory false starts, restarts, and/orwithin-speech groping; (b) syllable segregation; (c) DEAP(Inconsistency subtest); (d) PPC; (e) DDK accuracy; (f) non-speech groping; (g) intrusive schwa; and (h) /k/ rate. Thepredictor percentage lexical stress matches correlated highly(i.e., was multicollinear with) the variable presence of artic-ulatory false starts. Similarly, PVC was multicollinear withthe variable PPC and /ptk/ rate was multicollinear withthe variable /k/ rate. Analyses were rerun, substituting per-centage stress matches, PVC or PCC-R, and /ptk/ formulticollinear variables. As measures were identified as notmaking a significant contribution to the hierarchical DFA,they were replaced by other measures of potential clinicalimportance (r < .5) to see whether they increased the predic-tive accuracy of the model. For ease of reading, DFA anal-yses are presented in the more familiar layout of multipleregression, rather than as traditional discriminant functions.

ResultsTable 4 reports the descriptive statistics for each of

the 24 variables measured across the three groups: CAS( expressive language disorder), CAS+ (CAS with comorbiddysarthria and language impairment), and non-CAS (sub-mucous cleft, phonological disorder, and dysarthria). First,the effects of severity and age were determined. In termsof PCC-R, an index of general severity of speech sound dis-order, the groups were significantly different, F(2, 45) =7.865, p = .001. A Scheff post-hoc analysis adjusted formultiple comparisons revealed that the CAS+ group hadsignificantly lower PCC-R scores than the CAS, p = .042,and non-CAS, p = .02, groups, but importantly, the lattertwo were not differentiated. This is consistent with theCAS+ group having more complex speech disorders. Therewas no effect of age between groups, F(2, 45) = 0.853,p = .433, and age was not correlated above .5 with any ofthe 24 measures.

Two models are provided that represent the set ofvariables with highest predictive accuracy for (a) all partici-pants with CAS (n = 32) against all non-CAS (n = 15) and(b) participants with CAS (CAS+ cases removed, n = 28)against non-CAS (cases with submucous cleft removed,n = 12). Model 2 removed cases with CAS+ (where partici-pants shared features across both groups and confoundedthe analysis) and participants in the non-CAS group with

submucous cleft palates, which could be discriminated in


ded From: http://jslhr.pubs.asha.org/ by a Proquest User on 02/20/2015f Use: http://pubs.asha.org/ss/Rights_and_Permissions.aspxa relatively straightforward manner with visual inspectionof the palate before differential diagnosis of CAS. The un-standardized (B) and standardized (b) regression coefficientsare reported for the two hierarchical DFAs in Table 5. Thestandardized b coefficient indicates the contribution of eachmeasure in predicting the initial diagnosis. A high score ona positive coefficient and a low score on a negative coeffi-cient predict CAS.

For Model 1 (full data set), the hierarchical DFAshowed that together the percentage of stress matches andoccurrence of syllable segregation, both from the Single-Word Test of Polysyllables, accounted for a significant 82%of the variance in CAS diagnosis in this sample, R2 = .82,adjusted R2 = .81, F(2, 45) = 96.26, p .001. Additionalmeasures increased the overall predictive accuracy by only1%2%, at most.

For Model 2 (limited data set), results indicated thatfour measures in conjunction accounted for 91% of the CASdiagnosis, R2 = .91, adjusted R2 = .90, F(4, 38) = 87.45,p .001. These measures were syllable segregation, percent-age of stress matches, PPC on the Single-Word Test ofPolysyllables, and accuracy on DDK tasks (/ptk/) fromthe Oral and Speech Motor Control Protocol. A formulacan be derived from Model 2 for predicting an individualsgroup membership. This involves multiplying each individ-uals raw scores on the four measures in the DFA with theirstandardized b coefficient (i.e., a weighting), then summingthe products and the model constant, and rounding theresulting single value to zero decimal places. A result of 1indicates likely CAS, a score of 0 indicates likely non-CAS:

DiagnosisConstant% lexical stress matches its weightsyllable segregation score its weightPPC its weight=ptk=DDK accuracyits weightRound0All participants used to create the model were correctly

diagnosed using the formula (100% sensitivity, or true positiverate, and 100% specificity, or true negative rate). The modelhad high positive and zero negative likelihood ratios and adiagnostic odds ratio of 1,425; see Appendix B). The strengthof Model 2 was further tested with data from the four CAS+children and three children with submucous cleft who hadbeen excluded from the DFA. Sensitivity remained highat 97%, with one CAS+ participant being misdiagnosed asnon-CAS; specificity remained at 100%, with all submucouscleft participants accurately classified as non-CAS. Again,the positive likelihood ratio was high (16), and there was azero negative ratio, with a diagnostic odds ratio of 651 (seeAppendix B). Table 6 demonstrates use of this formula forfour participants in this sample, two with CAS and two with-out CAS. The formula can be used in Microsoft Excel tocalculate automatically from evaluation test score input.

DiscussionThe purpose of the current study was to determinewhether expert diagnosis of CAS in a sample of 47 children

60 February 2015

Table 4. Model 2: Results from bivariate DFA for all measures in order from highest to lowest accuracy in discriminating the CAS group(excluding CAS+) from the non-CAS group (excluding submucous cleft) participants.

Measure Bivariate DFA pAccuracy in diagnosing

CAS vs. non-CAS

Percentage of stress matches (lexical stress) on polysyllable test 0.893

could be predicted from a combination of quantitative mea-sures often collected as part of standard clinical practice.The current standard for diagnosing CAS utilizes expertsmaking judgments of presence or absence of a small setof speech behaviors, with no operational definitions or stan-dardized testing protocol for eliciting the behaviors. Inthe current sample, 28 children were judged by this method

cleft, phonological impairment, or dysarthria (non-CAS).Following the diagnosis, 24 quantitative measures were ex-tracted from clinically based assessments by raters blinded toexpert diagnosis. A series of multivariate DFAs revealed thata combination of the two quantitative measures of percent-age of stress matches and occurrence of syllable segregationshowed 82% diagnostic accuracy with expert diagnosis of

Table 5. Unstandardized (B) and standardized () regression coefficients for each predictive measure in a regression model predicting CASdiagnosis.

Participants included Variable/measure B a pAccuracy in diagnosingCAS in this sample

Model 1 (Constant) 0.707

DownloaTerms oThe procedures used here for expert judgment onpresence of CAS resulted in 32% of this sample being diag-nosed as non-CAS, from a group of children referred withsuspected CAS according to Australian community-basedSLPs. This is consistent with American studies (Davis et al.,1998; Forrest, 2003; Shriberg et al., 2011) in which childrenwith CAS seemed to be over- rather than underdiagnosed.However, we did not determine if CAS was underdiagnosedin a general SSD population here. This finding is not sur-prising, given the lack of validated and/or published assess-ment batteries currently available for this population (ASHA,2007b).

Of concern, there was a subset of children who pre-sented with frank structural and neurological deficits thatwere not identified prior to the community clinician assign-ing the diagnosis of suspected CAS. An OMA is a practical,inexpensive screen to check for any overt structural deficitsor functional impairments related to muscle strength andtone. If such deficits are found, referral to a neurologist orhead and neck team should follow. This may seem obvious,yet there were participants as old as 12 years in this samplewhose structural and/or dysarthric symptoms had not beenpreviously detected. This is relevant for those with a CASdiagnosis as well because such diagnoses can co-occur. Fur-thermore, only 52% (22 out of 42) of the studies reviewedby McCauley et al. (2012) included OMAs. OMAs shouldbe part of standard pediatric practice, reflected in policyguidelines and clinical pathways. In this sample, those withdysarthria exhibited low oral structure scores on our oralstructure and function test (Robbins & Klee, 1987) due tolack of tongue symmetry, the presence of tongue fascicula-tions, and tongue atrophy. In the submucous cleft cases inthe non-CAS group, all three had low oral structure scoresdue to poor palatal juncture and one had a bifid uvula.

Given that the primary aim of this study was to iden-tify a set of measures for identifying children with CAS ina group with no other known diagnoses (e.g., hearing im-pairment, structural abnormality, or other diagnosed de-velopmental diagnoses), the final analysis excluded thosewith comorbid CAS and submucous clefts. This exclusionallowed us to determine how performance on a specific setof measures, taken from existing clinical tests, interactedwith each other to differentiate children with CAS fromchildren with other speech sound disorders. This differentia-tion is likely to be the difficulty SLPs face in identifyingtrue cases of CAS in order to provide effective manage-ment. Importantly, age and severity of speech sound dis-order (PCC-R) of these 40 children did not explain thevariance in the CAS and non-CAS groups. Discriminantfunction analysis revealed that four measures in combina-tion had 91% predictive accuracy: (a) syllable segregation,(b) percentage of lexical stress matches, (c) PPC, and (d) ac-curacy on repetition of /ptk/ in the DDK task. Thefour measures are considered together in a hierarchical for-mat to make a diagnosis; individual cutoff scores for eachmeasure, although clinically appealing, do not capturethe relationships between the variables and therefore the

diagnosis. These four measures were derived from just

ded From: http://jslhr.pubs.asha.org/ by a Proquest User on 02/20/2015f Use: http://pubs.asha.org/ss/Rights_and_Permissions.aspxtwo assessment tasks: a polysyllabic word picture-namingtask (Gozzard et al., 2004) and an oral mechanism exam-ination (Robbins & Klee, 1987), taking less than 30 min tocomplete.

Two of the four informative measuressyllable seg-regation and percentage of lexical stress matchesmeasureprosodic features now considered core symptoms of CAS.These perceptual features are strongly related to acousticmeasures identified as potential markers for CAS, includingthe lexical stress ratio (Shriberg, Campbell, et al., 2003), thecoefficient of variation ratio (Shriberg, Green, et al., 2003),and the pairwise variability index (Ballard et al., 2010). Ourresults support dysprosody as a potential core feature ofCAS in children with some ability to produce polysyllablecombinations. Prosody was a discriminant measure in bothDFAs regardless of whether participants with neurological,linguistic, or structural impairments were included.

The Strand 10-point checklist (Shriberg, Potter, et al.,2009) includes both syllable segregation and lexicalstress errors as features. PPC was not used to measure anycriterion from this feature list. Instead, six individual seg-mental error features were noted (out of the 10 features; seeTable 1), but they were not individually discriminative. Amore global measure such as PPC could have teased apartdifferences between groups, particularly because the non-CAS group was diverse. Accuracy on the /ptk/ DDKtask was not used to measure any criterion from this check-list. However, Thoonen et al. (1999) adds support for thisfinding. They found trisyllabic repetition of /pataka/ wasdiscriminative for CAS against controls. However, theyfound it did not discriminate CAS from dysarthria, whichwas achieved using fricative maximum phonation. Thismeasure was not calculated here but could be added to anOMA as an assessment measure in future studies. Impor-tantly, Strands checklist (Shriberg, Potter et al., 2009) doesnot require children to exhibit these features to be diag-nosed with CAS: Children need to show evidence of anyfour of the 10 features on the list, with no differentialweighting of any feature. To our knowledge, the sensitivityand specificity of Strands checklist has not been tested, andit may not be sufficiently specific, with potential risk of di-agnosing negative cases with CAS. The present results showthat two participants with clear dysarthria and one with asubmucous cleft were diagnosed with CAS using the Strandchecklist.

The inclusion of lexical stress errors in the ASHA listof strong discriminative behaviors of CAS was supportedby our analyses, but the other two features (inconsistencyand lengthened and disrupted coarticulatory transitions)were not supported. The second feature, lengthened anddisrupted coarticulatory transitions between sounds andsyllables, was measured as a binary score if false starts, re-starts, hesitations, or within-speech groping were demon-strated by a participant in connected speech and on thepolysyllable test. In the bivariate DFA, this measure had54% accuracy in diagnosis when used independently but didnot add any additional information to the Model 2 (limited

data set) analysis. This diagnostic information may have


DownloaTerms obeen captured in the DFA by syllable segregation, whichis associated with disrupted coarticulatory transitions aswell as dysprosody. Additionally, the motorically complexDDK task of repetitions of /ptk/ also examined disruptedtransitions and within-speech groping as part of its score.Descriptions of such coarticulation, sequencing, and grop-ing errors are common in CAS (e.g., ASHA, 2007b; Grigos& Kolenda, 2010; Thoonen et al., 1996, 1999). These resultssuggest that some behaviors on perceptually based featurechecklists are not independent.

The third ASHA criterion, inconsistent errors onconsonants and vowels in repeated productions of wordsand syllables (ASHA, 2007b, p. 4), was most notably ab-sent from the models of discriminative measures. Forrest(2003) found that inconsistency was the feature most usedby clinicians to identify CAS. The present study measuredinconsistency with the DEAP Inconsistency subtest, as itmost closely matched the definition of inconsistency usedin the ASHA Technical Report (2007b)that is, token-to-token inconsistency across separate productions of the samewords. This has also been referred to as target stability(Marquardt, Jacks, & Davis, 2004). When used as a solepredictor in a bivariate DFA, inconsistency discriminatedthe groups with 30% accuracy and did not contribute signif-icantly to predicting diagnosis in the multivariate analyses.Other available measures that capture a different type ofinconsistency might yield a different result. In Model 2(limited data set), inconsistency may have been captured bythe repeated productions of the /ptk/ DDK task, whichwould be more similar to token accuracy whereby greateraccuracy demonstrates less inconsistency (Marquardt et al.,2004). Alternatively, the Inconsistency Severity Percentage(Iuzzini & Forrest, 2010), for example, is a measure of pho-nemic and phonetic inconsistency, similar to total tokenvariability (Marquardt et al., 2004), from multiple elicita-tions of all consonants in English across varied syllableor word contexts. Iuzzini (2012) reported that the Inconsis-tency Severity Percentage differentiated CAS from phono-logical disorder better than the DEAP Inconsistency measure.Further investigation of the construct of inconsistency andfactors influencing its nature and degree would help to de-termine the most informative measure and whether thatmight have greater predictive accuracy when combinedwith measures of other features associated with CAS. Giventhe debate over inconsistency in the acquired AOS litera-ture (see Staiger, Finger-Berg, Aichert, & Ziegler, 2012, fora review) and the absence of this feature in Strands 10-pointchecklist, further research is required on this feature, onethat is strongly represented in clinicians impressions of bothCAS and AOS.

Nonspeech groping (i.e., groping during nonspeechtasks) was another noteworthy measure. Such nonspeechoromotor tasks have been used in CAS checklists over time,presumably as a feature of CAS (e.g., Ozanne, 2005; RoyalCollege of Speech and Language Therapists, 2011), and non-speech groping was the second most prevalent feature indiagnosis in a survey of clinicians (Forrest, 2003). Despite

this, the current study results suggest that nonspeech groping


ded From: http://jslhr.pubs.asha.org/ by a Proquest User on 02/20/2015f Use: http://pubs.asha.org/ss/Rights_and_Permissions.aspxhad low diagnostic accuracy independently and did not addto the diagnostic models. The current data support the find-ings of Strand and McCauley (2008), as well as studies ofacquired AOS and oral apraxia (see Ballard et al., 2000, fora review), in that nonspeech features are indicative of oralapraxia rather than CAS. Therefore nonspeech difficultiesare best considered as concomitant rather than core featuresof CAS (ASHA, 2007b; Strand et al., 2013).

The findings from this study indicate that four mea-sures, in combination, diagnosed CAS with high accuracyagainst the current gold standard of expert opinion. Thefinal model (Model 2) did not include children with CAS+or with the frank oral structural impairment of submucouscleft, and data from these seven children were used to fur-ther test the robustness of the model (see Appendix B). Thisshows that although Model 2 was not being developed withthose participants with CAS+dysarthria, it can be used todetermine presence of CAS contributions to their speechimpairment. Only one participant with CAS+ (ParticipantNo. 1328) was misdiagnosed by the models formula asnon-CAS. This diagnosis was most likely due to his comor-bid flaccid dysarthria and a lack of syllable segregation,considering he was unable to produce any polysyllabic words.Three important points are demonstrated here. First, repli-cation of this study is required with an unselected samplethat includes more children with dysarthria. Second, an OMAwould be required before using these criteria in the futureto determine frank dysarthria and/or submucous clefts inthose who may also have CAS. Third, children with mini-mal verbal output may not be diagnosed accurately using themeasures described here because they are based on attemptsat polysyllabic words. In future, syllable segregation couldalso be examined in utterances between words rather thanjust within polysyllable words using, for example, Shribergsinappropriate pauses measure (Shriberg, 2013).

Data from four participants are included as case stud-ies in Table 6 to illustrate the use of the criteria and furtherexplain the weightings and use of the DFA results. Thefirst two cases are 4-year-old girls, each with severe artic-ulation difficulties, one of whom was diagnosed with CASand the other with ataxic dysarthria by the first and secondauthors. These cases illustrate the importance all four cri-teria and weightings of the measures: Each girl had poorlexical stress matches and a high occurrence of syllable seg-regation, both of which are features of CAS and ataxic dys-arthria (ASHA, 2007b; Duffy, 2012). However, althoughthe child with CAS had poor (46%) accuracy for the PPCmeasure, this child was relatively better than the participantwith ataxic dysarthria (with imprecise articulation), whohad 25% accuracy. This may have helped discriminate thesecases, because the child with ataxic dysarthria exhibitedmany more distortions and articulation errors, despite thefirst two measures showing a similar pattern. The secondpair of cases illustrates the contrast between two 12-year-oldboys with mild articulation difficulties, one with CAS andthe other with residual phonological impairment. The boywith CAS had fewer lexical stress matches (26%, compared

to 88% for the child with phonological impairment), more

60 February 2015

DownloaTerms oinstances of syllable segregation, and poorer /ptk/ accu-racy than the boy with phonological impairment. Thesethree features are hypothesized to reflect an underlying im-pairment of motor planning and programming or praxis(e.g., ASHA, 2007b; Shriberg, Green, et al., 2003; Thoonenet al., 1999). The criteria provide confidence in discrimi-nating those with some underlying praxis deficits, demon-strating CAS is at least part of the childs presentation.Therefore, the combination of the four discriminant measuresand their weightings appear crucial in separating verbalparticipants with different types of expressive communica-tion disorder and wide ranges of age and severity.

It is important to consider that the assessment proce-dures used here assessed children at a single time point.Any developmental disorder can vary in its surface presen-tation across time. Regular review is therefore indicated toensure early detection of other clinical behaviors that mightemerge with therapy and maturation (e.g., literacy concerns)and ultimately to ensure that management strategies matchpresenting and prognostic features (Stackhouse & Snowling,1992; Zaretsky, Velleman, & Curro, 2010).

Our findings advance the field in this area by identify-ing two tests that appear central to differentiating CASfrom other disorders, at least in this sample: namely, a com-plete OMA including a DDK task and a sufficiently largesample of polysyllabic single-word production (e.g., Gozzardet al., 2004). Both the real-word polysyllabic test and thenonword /ptk/ DDK task from the OMA were motori-cally challenging and appear to successfully elicit behaviorsthat reflected the underlying motor planning and program-ming deficits in CAS at both the segmental and supra-segmental level (Shriberg, Lohmeier, et al., 2009; Thoonenet al., 1996, 1999). These two tests alone may be sufficientfor reliable diagnosis of CAS in verbal children. However,SLPs using these tasks still need to consider normal acquisi-tion and development in terms of both segmental and prosodyaccuracy (Ballard, Djaja, Arciuli, James, & van Doorn,2012; James, 2006; Shriberg, Lohmeier, et al., 2009).

LimitationsThe major limitation of this study is that it utilized

a selected clinical sample of children with suspected CASand selection criteria designed to find idiopathic CASrather than comorbid CAS. This approach is not uncom-mon (e.g., McCauley et al., 2012; Rosenbek & Wertz, 1972;Yoss & Darley, 1974) and was warranted in exploring andidentifying quantitative measures for further investigation.As in any multivariate DFA, the results are heavily relianton the characteristics of the sample used and the measuresselected for analysis, and there are inherent risks that thefindings might not generalize to a similar but larger sampleor to a different group of children (e.g., children with co-morbid intellectual disability and receptive language im-pairment). For example, the formula used PPC to separateCAS from ataxic dysarthria. However, there is no reasonthat a child with CAS may not have severe articulation

errors. Also, the participant sample here all demonstrated

ded From: http://jslhr.pubs.asha.org/ by a Proquest User on 02/20/2015f Use: http://pubs.asha.org/ss/Rights_and_Permissions.aspxprosodic errors being features on both the ASHA consensus-based (2007b) and Strand 10-point feature lists (Shriberg,Potter, & Strand, 2009). However, other samples that dem-onstrate dysprosody may only account for a subset of chil-dren with CAS (e.g., Shriberg, Aram, & Kwiatkowski, 1997)or younger children may demonstrate other errors of greaterprevalence, such as inconsistency (Iuzzini & Forrest, 2010).The Model 2 DFA formula based on the limited data set isreported here to facilitate replication, but caution must beused in applying it to clinical populations or cases that differfrom the studied sample.

In this study, we relied on perceptual measures ofspeech behaviors. This was a deliberate decision consideringthat perceptual measures are most commonly used in clini-cal practice (Duffy, 2012) and have been shown to correlatehighly with acoustic measures of lexical stress (e.g., pair-wise variability indices; Ballard et al., 2010). However, therelatively subjective nature of these measures could resultin discrepancies and therefore errors in the formulas result,particularly in untrained SLPs. In the current study, train-ing of raters was required to maintain interrater reliabilityabove 85%. To assist replication in future studies, efforts tomake the measures more objective would be warranted byproviding defined features, measures, and scoring criteria ina manual or by finding more objective kinematic or acous-tic measures. There are promising acoustic measures (e.g.,Ballard et al., 2012; McKechnie et al., 2008; Shriberg, Green,et al., 2003) and potential for automation (Hosom, Shriberg,& Green, 2004; Rosen et al., 2010). However, manualacoustic measures can be time-intensive and also require a pe-riod of training. It may be some time before such acousticmeasures are available in a format that allows rapid auto-mated analysis in clinical settings.

Studies aiming to identify quantitative measures thatdifferentiate disorders suffer an inherent circularity (ASHA,2007b; Guyette & Diedrich, 1981). However, here the aimwas not to validate the original checklists (ASHA, 2007b;Shriberg et al., 2009) used for initial diagnosis. Instead, theaim was to test the assessment samples to determine whethera more replicable and efficient method (or set of measuresin combination) could reproduce the initial expert diagnosiswith high accuracy. Our findings suggest that a set of justfour measures strongly predicts presence of CAS in verbalchildren. This is a more parsimonious solution than achecklist of multiple features, of which only a subset is re-quired for diagnosis (e.g., Hickman, 1997; Kaufman, 1995;Shriberg, Potter, & Strand, 2009).

Further DirectionsThis preliminary study used a sample of children ages

412 years with idiopathic and comorbid CAS and com-pared them to others who had been suspected to have CASbut were instead diagnosed with phonological or languageimpairment, submucous cleft, or dysarthria. Further re-search is necessary to test the robustness of the sets of cri-teria and the resultant formula identified when diagnosing

an unselected community sample of children with suspected


tional neuroimaging studies might shed light on the natureof CAS and its response to different types of intervention.

and other assessment tools for CAS features would alsobe beneficial.

DownloaTerms oAcknowledgmentsThis research was supported by the Douglas and Lola Douglas

Scholarship on Child and Adolescent Health, the Nadia VerrallMemorial Research Grant (2010), a Postgraduate Research Award(2011) from Speech Pathology Australia, and the James KentleyMemorial Scholarship and Postgraduate Research Support Schemesto Elizabeth Murray; a University of Sydney International Pro-gram Development Fund Grant to Patricia McCabe and KirrieBallard; and an Australian Research Council Future Fellowship(FT120100255) to Kirrie Ballard. Parts of this article were presentedat the Motor Speech Conference in Santa Rosa, CA, 2012, and theSpeech Pathology Australia Conference in Hobart, Tasmania, 2012.We thank research assistants Morin Beausoleil, Virginia Caravez,Claire Formby, Jennifer Fortin Zornow, Sally Hanna, Claire Layfield,Aimee-Kate Parkes, Gemma Patterson, Alyssa Piper, and CaitlinWinkelman. Thank you also to Samantha Warhurst, Kate Anderson,and Claire Layfield for editorial suggestions.ConclusionsThis study commenced with expert diagnosis of chil-

dren with suspected CAS and then extracted quantitativemeasures that were analyzed to determine whether any com-bination predicted the expert diagnosis. CAS and non-CASin verbal 4- to 12-year-olds in this sample could be discrimi-nated with 91% accuracy based on four measures, follow-ing completion of a thorough OMA including accuracy on/ptk/ (Robbins & Klee, 1987) and a 50-word sampleof polysyllable words (Gozzard et al., 2004). The results metShribergs criteria of >90% sensitivity and specificity, butwithin a selected sample. A formula based on the results ofDFA is provided to assist in application and replication.Further research is required with a larger unselected sam-ple to ensure that the four measures and the resultant for-mula can differentiate CAS in a wider population fromtypical development and other speech, neurological, andlinguistic disorders. Investigation of kinematic, acoustic,expressive communication disorders. Other measures couldalso be assessed for their predictive accuracyfor example,the Inconsistency Severity Percentage inconsistency mea-sure (Iuzzini & Forrest, 2010).

Further research is also warranted for children withsuspected CAS and limited verbal output. The majority ofparticipants in this study used some polysyllabic words.Use of dynamic assessment procedures is likely to be a bet-ter interim option for diagnosing children with only mono-syllabic productions (e.g., observing the childs responsesto cues such as reducing speech rate and providing tactilecues; see Strand et al., 2013). Moreover, efforts should bedirected toward finding and testing more objective, possiblyacoustic or kinematic measures of behaviors that correlatehighly with our perception. Likewise, structural and func-56 Journal of Speech, Language, and Hearing Research Vol. 58 43

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Appendix A

Diagnostic results for all participants using the ASHA criteria (2007b) and the Strand 10-point checklist (Shriberg, Potter, &Strand, 2009).

Group Participant no.ASHA

criteria metaStrand

criteria metb Diagnosis

Non-CAS 1217 2/3 5/10 Ataxic dysarthria (+ receptive & expressive language disorder)1314 2/3 6/10 Flaccid dysarthria (CNXII) (+ receptive & expressive language

disorder)0236 0/3 1/10 Phonological disorder (+ expressive language disorder)1313 1/3 1/10 Phonological disorder0235 0/3 0/10 Phonological disorder (+ receptive & expressive language disorder);

hoarse voice0333 0/3 3/10 Phonological disorder (+ receptive & expressive language disorder);

suspected global developmental delay0334 0/3 1/10 Phonological disorder (+ receptive & expressive language disorder)0237 0/3 2/10 Phonological disorder (+ receptive & expressive language disorder)1311 0/3 2/10 Phonological disorder2315 0/3 1/10 Phonological disorder1312 0/3 2/10 Comorbid phonological disorder and stuttering0138 1/3 6/10 Submucous cleft palate (+ expressive language disorder)0139 1/3 2/10 Submucous cleft palate1310 1/3 2/10 Submucous cleft palate0335 0/3 1/10 Phonological disorder (+ expressive language disorder)

No. of non-CAS participantsthat met criteria

0/15 3/15

CAS+ 0332 3/3 7/10 CAS and ataxic dysarthria (+ receptive & expressive languagedisorder)

2105 3/3 8/10 CAS and flaccid dysarthria (CNXII) (+ expressive language disorder)1214 3/3 9/10 CAS and flaccid dysarthria (CNXII) (+ expressive language disorder)1328 3/3 8/10 CAS, flaccid dysarthria (CNXII) (+ receptive & expressive language

disorder)No. of CAS+ participants

that met criteria3/3 8/10

CAS 1103 3/3 8/10 CAS (+ expressive language disorder)1101 3/3 7/10 CAS1102 3/3 8/10 CAS (+ expressive language disorder)1104 3/3 6/10 CAS2106 3/3 9/10 CAS (+ expressive language disorder)2107 3/3 7/10 CAS (+ expressive language disorder)2108 3/3 8/10 CAS (+ expressive language disorder)1210 3/3 8/10 CAS1211 3/3 9/10 CAS (+ expressive language disorder)2212 3/3 8/10 CAS (+ expressive language disorder)1213 3/3 7/10 CAS (+ expressive language disorder)1215 3/3 6/10 CAS2216 3/3 7/10 CAS

Documents

Differential Diagnosis of Children With Suspected Childhood Apraxia of Speech