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Pediatric Attention Disorders Diagnostic System:Clinical Utility and Psychometric acceptability

 Presented at the American Academy of Pediatrics

21st Century Symposium: Incorporating Mental Health Screening in Primary Care Settings

OCTOBER 7th, 2005

Thomas K. Pedigo Ed. D. Pediatric & Adolescent Psychology P.C.

Savannah, GA 31406

Vann B. Scott, Jr., Ph.D. Armstrong Atlantic State University

Department of Psychology, Savannah, GA 31419

Diane R. Savage-Pedigo, M.D. Pediatric Associates of Savannah P.C.

Savannah, GA 31401

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PEDIATRIC ATTENTION DISORDERS DIAGNOSTIC SYSTEM(PADDS OVERVIEW)

• PADDS is a system with proven clinical reliability and validity in the screening and of attention disorders. This process merges three short and enjoyable computer administered tasks (Target Subtests) with a computer administered/scored diagnostic interview.

• The padds ‘ cognitive tests are designed to closely re-create the basic demands of the typical classroom setting by tapping greater aspects of executive functioning, i.e. planning, organization, working memory, attention to detail and changes in stimuli.

• The Computer Assisted Diagnostic Interview or CADI covers the major areas of comorbidity needed to reliably screen ADHD. Both processes can be completed in total in under 45 minutes. All information from both components are maintained in a data base for collection and comparison over time. Since this system can be effectively administered by any clinician or assistant, the physician’s time can be more appropriately spent evaluating the results, collected along multiple lines of evidence, as well as face to face with the patient and family. This standardized evidence-based approach is directly in line with the current emphasis of “best practices” call for by prominent healthcare agencies.

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Relevance/Application to the Primary Care Setting  According to the American Academy of Pediatrics, ADHD is the most

commonly diagnosed childhood psychiatric disorder affecting school age children. Epidemiological studies have shown a prevalence rate ranging from 3 percent to 6 percent of school age children. Concern has been expressed for the these large numbers coupled with reportedly wide variations in clinical practice and research approaches; all point to the need to develop pragmatic assessment tools and approaches for use in the major systems of service entry. Specifically of importance are assessment approaches that can be used within primary care settings, schools and clinics as well as within the private sector.

 Reference: Chan, E., Hopkins, M., Perrin, J. M., Herrerias, C., & Homer,

C. J., (2002) VARIATIONS IN DIAGNOSTIC PRACTICES FOR ATTENTION DEFICIT HYPERACTIVITY DISORDER: A NATIONAL SURVEY OF PRIMARY CARE PHYSICIANS Homer Division of General Pediatrics, Children's Hospital, Boston, MA; American Academy of Pediatrics, Elk Grove Village, IL; Center for Child and Adolescent Health Policy, MassGeneral Hospital for Children, Boston, MA; National Initiative for Children's Healthcare Quality, Institute for Healthcare Improvement, Boston, MA. (2002) Pediatric Academic Societies Abstract.

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During the 1998 NIH Consensus Development Conference it was determined that Development and Validation of Diagnostic Tools Grounded in the Basic Sciences was warranted.  

Key areas of interest to the NIH The Development and Validation of Diagnostic Tools Grounded in the Basic Sciences 

Consequently, there is a continued need to develop more objective assessment tools, rating scales and/or diagnostic interviews that map onto basic underlying processes as well as a need to supplement behavioral assessment tools with improved cognitive and/or neuropsychological measures.

The Development of Strategies for Assessing, Monitoring and Administering Treatment in Primary Care Settings 

Many of the currently utilized assessment measures and treatments for ADHD are incompatible with the primary care setting. There is also a dearth of practical decision-making tools for medication monitoring, differential diagnosis, and the distinction of referral service needs based upon impairment severity. Consequently, there is a great need for the development of practical, reliable and valid procedures to be used in primary care settings to identify and manage ADHD symptoms, as well as to distinguish appropriate referral needs.

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Diagnostic Challenges/Comorbidity with ADHD  Other Comorbid conditions often occur with ADHD.

These conditions may include but are not limited to Mood Disorders, Anxiety Disorders, Disruptive Behavior Disorders and Learning Disorders.

Bipolar Disorder is becoming increasingly recognized by some professionals within adolescent populations.

The importance of considering other conditions that may mimic or exacerbate the presence of ADHD is essential to successful intervention.

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The following listing of ranges for ADHD and Comorbid conditions was adapted from the following source: Pliszka, S. R., Carlson, C. L., & Swanson, J. M., (1999). ADHD with Comorbid Disorders: Clinical assessment and Management. New York, N.Y. The Guilford Press.

  Primary Diagnosis / Secondary Diagnosis Range of Prevalence Page Number

ADHD/ODD-CD 15% to 61% 90

ODD-CD/ADHD 35% to 87% 90  ADHD/Depression 0% to 38% 127

Depression/ADHD 0% to 57% 127  ADHD/Anxiety 23% to 30% 151

Anxiety/ADHD 9% to 35% 151

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Primary Diagnosis / Secondary Diagnosis Range of Prevalence Page Number

ADHD/LD 7% to 60% 192(Across- Reading, Spelling, & Math) ADHD/OCD 6% to 33% 214 

Other related conditions needing assessment/ consideration include: Neurological Impairment  Developmental disabilities  PDD/Autistic spectrum disorders

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Executive Functions and Diagnosis of ADHD

Recent developments within the field of ADHD have increasingly pointed to the need to evaluate the various executive operations and working memory of children suspected of Attention Disorders. (Brown, T.E., 2002, 2000,1999; Barkley, R.A. 1997,1998; Denckla M, 1996.) Generally, executive functions are defined as controls that allow one to perform complex behaviors that require among other things: planning, attending, organizing input, storing and retrieving information, modulating emotions and sustaining effort.

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While the identification of significantly hyperactive children can be simple, the evaluation of children who only display difficulty in learning or in completing more complex activities is where the greatest need for improvement lies. Difficulties in these Executive Processes (planning, attending, organizing input, storing and retrieving information,

modulating emotions and sustaining effort) exemplify the complaints of teachers and parents.

Situations that require an orchestration of these abilities are often most problematic for AD/HD students. Parents will often report confusion at their child's ability to play video games, watch television or engage in favorite activities. However, on closer inspection, these activities often do not produce the same demands as found within the classroom. These favorite activities are often overlearned, fast pace, and allow the child to move freely in and out of the activity. Changing the structure of these activities (implementing learning demands) can quickly produce frustration in AD/HD children.

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Basic Demands of the Classroom: Attending to instructionAssimilating informationAccommodating informationOrganizing, sequencing, manipulating informationMonitoring emotional activityFormulating a plan of actionImplementing the plan Other Factors: Working under time pressureAvoiding distractionBeing adequately prepared

THE PADDS TARGET SUBTESTS WERE DESIGNED TO PRODUCE WORK DEMANDS SIMILAR TO THOSE OUTLINED ABOVE:

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The Clinician’s ADHD Toolbox• Computer based system to collect and compare multiple

lines of evidence for ADHD diagnosis

• Patient Information Database and Reporting

• Comprehensive Parent and Teacher Interviews in a self-running or Clinician input format

• A battery of newly developed cognitive tests presented in a challenging, enjoyable format

• Automatic Report Generator with domain specific alerts and recommendations, and follow up comparisons of Treatments and Progress

PADDS

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COMPUTER ASSISTED DIAGNOSTIC INTERVIEW (CADI)

The Computer Assisted Diagnostic Interview or CADI covers the major areas of comorbidity needed to reliably screen ADHD. A review of systems includes:

Medical History/Systems Review Developmental History Social/Emotional Functioning Depression/Anxiety Attention/Hyperactivity

Behavioral/School History The CADI provides two options for administration. The first is a protocol completed by the parent and then quickly input by the examiner or assistant. The second is presented in the auditory domain via the computer for those that cannot read or have not completed the protocol. All information from both components are maintained in a data base for collection and comparison over time. A concise report is generated highlighting parental concerns and/or comorbid issues that can be cross validated in a straight forward interview. Parents have expressed appreciation at the range of information asked of them as opposed to only focusing on behavioral scales and ADHD symptoms. This report will consolidate this important information by domain so the examiner can efficiently review and validate concerns or needs for further referral.

BELOW ARE ABBREVIATED EXAMPLES OF THE CADI INPUT AND REPORT

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Medical History/Systems Review

Developmental History

Social/Emotional Functioning

Depression/Anxiety

Attention/Hyperactivity

Behavioral/School History

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Cognitive TestsExecutive Functioning

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Target Recognition presents five large colored squares with smaller squares inside them. Through 153 presentations some number of the large squares will have smaller squares of the same color and some number will be different colors. The child is taught a strategy to read from left to right and count only the number of squares with matching colors. This task requires suppression of information, attention to detail, formulation of a response to changes in stimuli, modulation of emotions and persistence.

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Target Sequencing presents five large colored circles. In each of 39 trials a small colored square appears and then disappears in each circle, in varied sequences. The child is taught to only attend to circles with a matching colored square. At the end of the trial the child is required to click on each matching circle in the order observed ( first match first, second match second and last match last). Target Sequencing requires the ability to avoid distraction, attention to detail, organization and sequencing during input of information, planning and organization of a response, modulation of emotion and sustained effort.

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Target Tracking presents four colored shapes at the top and bottom of the computer screen. The computer moves two or three shapes from the top to the bottom shapes. The child is required to remember the order of these moves and to recreate them once all shapes have returned to the top of the screen. Target Tracking requires the ability to organize two and three step instructions, and to recreate these instructions in the order presented while modulating emotions and sustaining effort across 20 trials.

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Generate ReportsCognitive Tests Report

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SUBJECT SELECTION, METHODOLOGY AND RESULTS

Clinical pilot testing of the PADDS consisted of 200 children age 6 to 12 years. 95 children diagnosed with either ADD or ADHD and 105 typical peers age 6 to 12 years. Data collection consisted of two phases with subject sizes of 113 and 87 respectfully.

Phase 1 reviewed the performance of Target Recognition and Target Sequencing. Phase Two also included review of the original two subtests along with a third new subtest Target Tracking. All subjects lived in small to moderate size cities within

Georgia. Consent forms were sent home to parents explaining the study and all children with parents consenting were tested and received a 10.00 dollar gift certificate to TOYS R US for participating. Children not meeting the screening criteria outlined below were excluded from the study. As ADD/ADHD crosses age, gender, race and socioeconomic

status, no data regarding these demographics were considered appropriate to report. The ADD/ADHD children were drawn from a clinical pediatric psychology practice where comprehensive evaluation and testing had previously been completed and a diagnosis rendered. The ADD/ADHD children included in the study had recently or

previously received a diagnostic assessment and testing process including: background, developmental, medical and family histories, reports from home and school, any

available school testing and grades, intelligence testing, cognitive testing for short term auditory and visual memory, performance on a continuous performance tests and a review of criteria met for diagnosis based on the DSM-IV Criteria for ADD/ADHD.

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SUBJECT SELECTION, METHODOLOGY AND RESULTS CONTINUEDPrior to clinical testing with the PADDS Target Subtests each of the ADD/ADHD children had been prescribed medication with reports of improvement from home and school. While taking the PADDS all ADD/ADHD children were taken off their psychostimulant medication. Within the ADD/ADHD sample, all psychological testing data was reviewed to ensure there were no significant emotional disorders or clinically significant signs of depression or anxiety evident in any of the subjects. Only ADD/ADHD children receiving psychostimulants were included in the clinical study. All ADD/ADHD children were tested with the PADDS during the morning hours to limit diurnal effects on test performance.

The typical or non ADD/ADHD children were drawn from different elementary schools or Boy Scout Troops within a moderate size city from the same state and region. The typical children were also tested in the morning and were screened with the Conners Rating Scale-Teacher Version prior to inclusion in the study. All subjects with a significant Conners Rating Scale were compared to their PADDS performance to assess agreement between measures; however, their results were not included in the clinical study of the PADDS. Initial review of the 3 Target Subtests showed they demonstrated acceptable ability to discriminate between ADD/ADHD children and their typical peers. Tables 1-3 show the results of the Receiver Operating Characteristics analysis performed to determine the best cut points for Sensitivity and Specificity for true diagnosis.

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PADDS SUPPORTIVE RESEARCH

(TABLE1)

FOR TARGET RECOGNITION

PHASE 1 N=113 PHASE 2 N=87 TOTAL N=200

ADHD

N = 95

Correctly ID = 87

Missed = 8

Total Discrimination = 91.7%

TYPICAL

N = 1O5

Correctly ID = 88

Missed = 17

Total Discrimination = 84%

TOTAL DISCRIMINATION = 87.85% of 200 test subjects

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PADDS SUPPORTIVE RESEARCH

(TABLE 2)

FOR TARGET SEQUENCING

PHASE 1 N=113 PHASE 2 N=87 TOTAL N=200

ADHD

N = 95

Correctly ID = 80

Missed = 15

Total Discrimination = 84.%

TYPICAL

N = 1O5

Correctly ID = 89

Missed = 16

Total Discrimination = 85%

TOTAL DISCRIMINATION = 84.5% of 200 test subjects

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PADDS SUPPORTIVE RESEARCH

(TABLE 3)

FOR TARGET TRACKING

PHASE 2 N=87

ADHD

N = 41

Correctly ID = 40

Missed = 1

Total Discrimination = 97.6%

TYPICAL

N = 46

Correctly ID = 40

Missed = 6

Total Discrimination = 87%

TOTAL DISCRIMINATION = 92.% of 87 test subjects

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PADDS SUPPORTIVE RESEARCH

(TABLE 4)

TEST / RETEST: 43 SUBJECTS DRAWN FROM BOTH STUDIES

43 ADHD SUBJECTS

N = 43

Trial 1 = 40

Miss = 3

R = .93

INTERCORRELATIONS OF TARGET SUBTESTS P<.05

Variable Phase 1

Target R

Target S

Target R 1.00 .56*

Target S .56* 1.00

Variable Phase 2

Target R Target S Target T

Target R 1.00 .61* .42*

Target S .61* 1.00 .40*

Target T .42* .40* 1.00

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PADDS PHASE 1-2 DIVERGENT VALIDITYMARKED CORRELATIONS ARE SIGNIFICANT AT P<.0500

N=54 &N=41TABLE 5

PHASE 1 AGE DX

AGE

1.00 .47*

DX .47* 1.00

CMSATT -.25 -.06

CMSVER -.22 -.10

FSIQ -.26 .05

TRECOG .41* .39*

TSEQ .51* .51*

PHASE 2 AGE DX

AGE

1.00 .27

DX .27 1.00

CMSATT -.25 .00

CMSVER -.22 -.10

FSIQ -.26 .05

TRECOG .41* .39*

TSEQ .51* .51*

TTRACK .44* .39*

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PHASE 1 & 2 DESCRIPTIVE STATISTICSTABLE 6

PHASE 1 N MEAN MEDIAN SD

AGE ADHD 54 7.67 7.50 1.54AGE TYP 59 8.78 9.00 1.78TR-ADHD 54 71.57 75.50 35.43TR-TYP 59 122.56 132.00 25.73TS-ADHD 54 17.69 19.50 9.69TS-TYP 59 32.31 35.00 7..97

PHASE 2

AGE ADHD 41 7.95 8.00 1.44AGE TYP 46 8.85 9.00 1.94TR-ADHD 41 60.88 46.00 31.86TR-TYP 46 125.43 132.00 27.37TS-ADHD 41 17.83 16.00 9.17TS-TYP 46 32.15 33.50 6.91TT-ADHD 41 4.15 4.00 1.85TT-TYP 46 12.78 13.50 4.18

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RELIABILITY & VALIDITY OVERVIEW:

The PADDS is a new measure and as such will continue to under go psychometric evaluation. As pointed out by Rosenthal & Rosnow (1991), When using a test to measure differences among individuals, Reliability refers to the ability of the test to consistently discriminate individuals at some point in time and over time. As such the principal criteria for determining the reliability of a psychological test is that tests ability to consistently discriminate between individuals at one point in time referred to as internal consistency and the ability to do so over time which is referred to as test retest reliability. The criteria used to establish acceptability of the PADDS's reliability was in keeping with the guidelines suggested by Rosenthal & Rosnow (1991) of .85 or better for use of clinical measures.

Validity refers to the ability of a test to measure the traits or dimensions intended. As stated in the Standards for Educational and Psychological testing (1999), It is the interpretations derived from a tests use that are validated, not the test itself. In the development of the PADDS special attention was given to provide evidence of Discriminate , Convergent and Divergent validity. Data collected during the clinical trials of the PADDS were analyzed using the SPSS software program for windows by SPSS 1999.

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RESULTS OF TARGET SUBTESTS PSYCHOMETRIC PERFORMANCE

ROC (receiver operating characteristic curve) analysis was used to determine the best cut-point to use for TargetR and TargetS to predict ADHD and Typical status. Sensitivity is the probability of a positive test result, given that the patient truly is positive. Specificity is the probability of a negative test result, given that the patient truly is negative. The ROC curve analysis calculates the sensitivity and specificity of the Target Tests for every possible cut point in the sample. An area of 1 would indicate perfect discrimination for each group. Review of the ROC shows that the TargetR, TargetS and TargetT subtests are very similar with respect to overall accuracy. Review of Phase 1 data shows that the area under the curve (95% confidence interval) was 0.896 (0.836, 0.956) versus 0.887 (0.825, 0.949) for TargetR and TargetS respectively. Review of Phase 2 data shows the area under the curve (95% confidence interval) was 0.932 (0.876-0.988) for Target Recognition, 0.905 (0.839-0.972), for Target Sequencing and 0.971 (0.937-1) for The new Target Tracking Subtest. Thus, TargetR , TargetS and TargetT appear to be clinically significantly with respect to overall accuracy of prediction.

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RESULTS PSYCHOMETRIC PROPERTIES CONTINUED

Tables 1- 3 shows the sensitivity and specificity for the final cut-points selected for analysis. The cut-off value is included for positive classification. For example, Target R was set at a cut-point of >=114 (normal) versus <114 (ADHD). When this cut point is used is used, the sensitivity of the test is estimated to be 0.91% and the specificity is estimated to be 0.84%. This results in an overall accuracy of prediction of 87.85% with 200 subjects. The Target S cut-point was set at >=28 (normal) versus <28 (ADHD). When this cut point is used, the sensitivity of the test is estimated to be 0.84% and the specificity is estimated to be 0.85%. This results in an overall accuracy of prediction of 84.5% with 200 subjects. The Target T cut-point was set at >=8 (normal) versus <8 (ADHD). When this cut point is used, the sensitivity of the test is estimated to be 0.97% and the specificity is estimated to be 0.87%. This results in an overall accuracy of prediction of 92.% with 87 subjects. Table 4 shows the results of 43 randomly selected test subjects that were retested with all three target subtests to determine Test-Retest reliability. The result was correct reclassification of 40 out of 43 subjects with a corresponding reliability coefficient of . 93. Review of the Target Tests subtests indicates clinically acceptable reliability to support further clinical development.

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PADDS PHASE 1&2 CONVERGENT AND DIVERGENT VALIDITY

As can be seen in tables 1-3 the individual subtests all met clinical levels of acceptability for the discrimination of known clinical and non-clinical groups. Further Table 4 shows the highly significant inter-correlations of these subtests between themselves. This would indicate convergent validity for subtests designed to correctly screen for the clinical condition of ADHD. Evaluation of diagnostic sensitivity of the PADDS subtests was compared with each other and to that of several measures unrelated to the clinical diagnosis of ADHD. Table 5 presents the results of correlations from dummy coding classification of ADHD status as 1 and Typical or non-clinical status as 0. Thus low scores on the Target subtests would be more indicative of ADHD status. The expectation would be highly significant but negative correlations with Target subtests as ADHD subjects perform significantly worse on these measures than do their typical or non-clinical peers. As indicated by the highly significant negative correlations of the ADHD subjects with performance on the PADDS subtests these subtests are indeed performing the discriminations at clinically acceptable levels and well beyond other measures that have no ADHD diagnostic purpose. In general this would be indicative of acceptable divergent validity in the clinical evaluation of the PADDS subtests as related to ADHD Screening.

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DISCUSSION OF THE PADDS PHASES 1-2 PSYCHOMETRIC PERFORMANCE

The initial performance of the PADDS clinical subtests included 200 children aged 6 to 12 years split evenly between known diagnostic classification of ADHD and NON-ADHD. Across phases 1nad 3 all Target subtests met the threshold for reliable and valid screening of ADHD to warrant further external cross validation of the acceptability of the Target subtests use in ADHD screening. Future evaluation will require external evaluation that includes children from more diverse geographic regions, convergent comparison with other tests that have received sound clinical acceptance with the screening of ADHD and comparison of sensitivity and specificity across the individual age groupings of the research samples.

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INDEPENDENT/EXTERNAL CROSS VALIDATION OF THE PEDIATRIC ATTENTION DISORDERS DIAGNOSTIC SCREENER

Vann Scott, Ph.D. Department of Psychology at Armstrong Atlantic State University

• An independent validation of the Pediatric Attention Disorder Diagnostic Screener (PADDS), conducted by Vann Scott, Ph.D. of the Department of Psychology at Armstrong Atlantic State University, was completed in the 2004-2005 academic year. A synopsis of this work follows. The Pediatric Attention Disorder Diagnostic Screener (PADDS) is a recently developed diagnostic tool designed for use with other diagnostic criteria to reduce the over identification of children with ADHD. It is well recognized in the literature on ADHD that multiple sources of information should be consulted in the diagnosis of the disorder. While multiple instruments exist for diagnosis of ADHD (such as rating scales and other psychological assessments) the costs of these tools can be prohibitive and in many cases unnecessary. The ultimate goal of the development of this tool is to provide physicians, clinical psychologists, and other mental health care workers with a quick and enjoyable computer-based assessment that, when properly combined with other assessment criteria, will provide the clinician with appropriate information to more accurately determine appropriate referrals and/or treatment. It is important to note that PADDS is not designed to be a stand-alone diagnostic tool.

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PADDS INDEPENDENT CROSS VALIDATION PROCEDURES

• A sample of 88 typical children (aged M = 9.44, SD = 1.16) from a school district in Idaho were assessed for ADHD utilizing the PADDS and the Connor’s Behavior Rating Scale (Teacher’s version). This group of typical children was compared to a sample of 99 children from Savannah, Georgia (aged M = 8.24, SD =1.71) who were diagnosed with ADHD and were temporarily removed from medication for the purposes of testing. The clinical sample was administered the PADDS and other diagnostic tools (e.g., the Brown rating scale, Conners continuous performance test 2nd ed,,). Particular consideration was given to the degree of specificity and sensitivity of the PADDS based on previously established cut-points.Also evaluated were convergent validity with other established ADHD clinical measures and divergent validity in comparison of the diagnostic sensitivity of the PADDS Target subtests with other non ADHD measures (IQ and memory measures). Recommendations for development are offered.

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PADDS INDEPENDENT CROSS VALIDATION RESULTS:

• In the overall sample, Table 7 shows the sensitivity (probability of a positive result when the child has ADHD) was 91.9% whereas specificity (probability of a negative result when the child does not have ADHD) was 86.4%. This resulted in an overall hit rate 89.3% (167 of 187). These results are highly consistent with the initial phase 1 and 2 results reported earlier in this presentation and suggest acceptable reliability was again established. Convergent validity was assessed with a sample of 40 ADHD subjects looking at the rate of diagnostic sensitivity for the PADDS, the Brown ADHD Rating Scales and the Conner’s CCPT-II. Table 8 shows the rates of sensitivity within and between each measure for ADHD classification. As can be seen the PADDS discriminated 95% of this sample correctly followed by 77.5% for the Brown and Conner’s measures respectfully. Also, significant correlation was established between the PADDS and each of these measures with respect to diagnostic sensitivity. Collectively this points to strong convergent validity for ADHD diagnosis with two clinically accepted measures of ADHD screening. As found in the phase 1 and 2 samples, the PADDS subtests were significantly correlated with poorer performance among the ADHD group while unrelated measures like IQ and Memory testing were not correlated to diagnosis. This further suggests divergent validity for the Target subtests with regard to ADHD screening. Finally, Age was correlated with performance on each of the subscales of the PADDS – target recognition (r = .46, p < .001), target sequencing (r = .52, p = .001), and target tracking (r = .56, p < .001). As a result, further research should be conducted to determine the levels of specificity and sensitivity of the PADDS within various age groups.

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PADDS INDEPENDENT CROSS VALIDATION

SENSITIVITY AND SPECIFICITY

(TABLE 7)

TOTAL N=187

ADHD

N = 99

Correctly ID = 91

Missed = 8

Total Discrimination = 91.9%

TYPICAL

N = 88

Correctly ID = 76

Missed = 12

Total Discrimination = 86.4%

TOTAL DISCRIMINATION = 89.3% of 187 test subjects

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SENSITIVITY FOR ADHD CLASSIFICATION FOR: PADDS, BROWN RATING SCALES & CONNER’S CCPT-11

TABLE 8N=40

RATE OF DIAGNOSTIC SENSITIVITY/AGREEMENT

PADDS BROWN SCALES CONNER’S CCPT-II

38/40 31/40 31/40

95% 77.5% 77.5%

PADDS/BROWN PADDS/CONNER’S

29/40 24/40

.72 .62

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Clinical diagnosisADHD

Pearson Correlation 1

N 78

FSIQPearson Correlation -.165

N 78

CMSVISPearson Correlation -.031

N 76

CMSVPearson Correlation -.066

N 76

CMSATTPearson Correlation -.183

N 76

TRPearson Correlation -.423(**)

N 78

TSPearson Correlation -.446(**)

N 77

TTPearson Correlation -.496(**)

N 77

AGEPearson Correlation -.394(**)

N 78

External Cross Validation AASU Convergent &Divergent Validity TABLE 9 P <.0001

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RECOMMENDATIONS FOR FURTHER DEVELOPMENT

• Phases 1-2 pilot testing and subsequent independent cross validation of the TARGET subtests of the PADDS ADHD screening system have demonstrated clinically acceptable reliability and validity to discriminate between and properly classify ADHD children and their typical age peers with samples of 200 and 187 respectfully.

• Collectively these results are taken to support the continued research and development of these measures for clinical use. Future study should focus at minimum on the following areas:

• 1. Subsequent independent cross validation utilizing samples from a wider geographical representation for both ADHD and Typical children.

• 2. Evaluation of sensitivity and specificity across each age grouping within the PADDS samples (6-12).

• 3. Further review of validity with other clinically accepted measures of executive functions.

• 4. Given the visual nature of the Target Subtests, Evaluation of children with primary Reading disabilities would be prudent to determine the degree if any that these measures significantly identify that group.

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References:Barkley, R. A.(1998). Attention-Deficit Hyperactivity Disorder: A Clinical Workbook, Second Edition. New York: Guliford Press. Barkley, R. A. (1997). ADHD and the Nature of Self-Control. New York: Guliford Press.

Barkley, R. A; Grodzinsky, G; & DuPaul G. J. (1992). Frontal lobe functions in attention deficit disorder with and without hyperactivity: a review and research report. Journal of Abnormal child Psychology 20 (2), 163-184.

Brown, T.E., (Ed). (1999). DSM-IV: ADHD and Executive Function Impairments. Advanced Studies in Medicine, 2 (25), 910-914.Brown, T.E., (Ed). (1999). Attention Deficit Disorders and Comorbidities in Children, Adolescents and Adults. Washington D.C.: American Psychiatric Press.

Chan, E., Hopkins, M., Perrin, J. M., Herrerias, C., & Homer, C. J., (2002) VARIATIONS IN DIAGNOSTIC PRACTICES FOR ATTENTION DEFICIT HYPERACTIVITY DISORDER: A NATIONAL SURVEY OF PRIMARY CARE PHYSICIANS Homer Division of General Pediatrics, Children's Hospital, Boston, MA; American Academy of Pediatrics, Elk Grove Village, IL; Center for Child and Adolescent Health Policy,

MassGeneral Hospital for Children, Boston, MA; National Initiative for Children's Healthcare Quality, Institute for Healthcare Improvement, Boston, MA. (2002) Pediatric Academic Societies Abstract.

Cohen, M.J., Riccio, C.A., & Gonzalez, J. J. (1994). Methodological differences in the diagnosis of Attention Deficit Hyperactivity Disorder: Impact on prevalence. Journal of Emotional and Behavioral Disorders. 02 (01), 31-38.

Denckla M. Theory and model of executive function: a neuropsychological perspective. In: Lyon, Krasnegor, eds. Attention, Memory and Executive Function. Baltimore, Md: Brookes; 1996:263-278.

Pliszka, S.R., Carlson, C.L., & Swanson, J. M., (1999). ADHD with Comorbid Disorders: Clinical Assessment and Management. New York, N.Y. The Guilford Press.