Results: Diagnosis of ADHD

Bradley S. Peterson; Joey Trampush; Margaret Maglione; Maria Bolshakova; Morah Brown; Mary Rozelle; Aneesa Motala; Sachi Yagyu; Jeremy Miles; Sheila Pakdaman; Mario Gastelum; Bich Thuy (Becky) Nguyen; Erin Tokutomi; Esther Lee; Jerusalem Z. Belay; Coleman Schaefer; Benjamin Coughlin; Karin Celosse; Sreya Molakalapalli; Brittany Shaw; Tanzina Sazmin; Anne N. Onyekwuluje; Danica Tolentino; Susanne Hempel

4Results: Diagnosis of ADHD

The Key Question (KQ) is divided into four subquestions:

KQ1a. What is the comparative diagnostic accuracy of approaches that can be used in the primary care practice setting or by specialists to diagnose attention deficit hyperactivity disorder (ADHD) among individuals younger than 7 years of age?
KQ1b. What is the comparative diagnostic accuracy of electroencephalogram (EEG), imaging, or approaches assessing executive function that can be used in the primary care practice setting or by specialists to diagnose ADHD among individuals aged 7 through 17?
KQ1c. For both populations, how does the comparative diagnostic accuracy of these approaches vary by clinical setting, including primary care or specialty clinic, or patient subgroup, including, age, sex, or other risk factors associated with ADHD?
KQ1d. What are the adverse effects associated with being labeled correctly or incorrectly as having ADHD?

The gold standard or reference standard against which diagnostic tools were compared was diagnosis by a mental health specialist, such as a psychologist, psychiatrist or other care provider. In many cases, clinicians used published scales or semi-structured diagnostic interviews to ensure a well-validated and reliable process of confirming the diagnosis of ADHD according to the Diagnostic and Statistical Manual of Mental Disorders (DSM), as outlined in more detail in the evidence table. Many identified studies included a broader age range rather than differentiating clearly between younger (KQ1a) or older (KQ1b) than seven years of age. Hence, we added a section describing the results for parental ratings, teacher ratings, clinician tools, and biomarkers before addressing the Key Questions. The section summarizes results by test and most studies evaluated a combined sample of children and adolescents. The KQ1a section describes all diagnostic approaches for children younger than seven years of age regardless of the applied test. The KQ1b section describes EEG, imaging, and executive function tests for children seven and up.

4.1. KQ1, ADHD Diagnosis Key Points

Key points pertaining to the diagnosis of ADHD are as follows.

Multiple approaches showed promising diagnostic performance (e.g., using parental rating scales), but estimates of performance varied considerably across studies, and the strength of evidence (SoE) was generally low.
Diagnostic test performance likely depends on whether youth with ADHD are being differentiated from typically developing children or from clinically referred children who had some kind of mental health or behavioral issue.
Rating scales for parent, teacher, or self-assessment as a diagnostic tool for ADHD have high internal consistency but poor to moderate reliability between raters, indicating that obtaining ratings from multiple informants (the youth, both parents, and teachers) may be valuable to inform clinical judgement.
Studies evaluating neuropsychological tests of executive functioning (e.g., Continuous Performance Test) used study-specific combinations of individual cognitive measures, making it difficult to compare performance across studies.
Diagnostic performance of biomarkers, EEG, and magnetic resonance imaging (MRI) scans show great variability across studies and their ability to aid clinical diagnosis for ADHD remains unclear. Studies have rarely assessed test-retest reliability, no findings have been replicated prospectively using the same measure in independent samples, and real-world effectiveness studies of diagnostic performance have not been conducted.
Very few studies have assessed performance of diagnostic tools for ADHD in children under the age of 7 years and more research is needed.
The identified diagnostic studies did not assess the adverse effects of being labeled correctly or incorrectly as having a diagnosis of ADHD.

4.2. KQ1, ADHD Diagnosis Summary of Findings

We identified 231 studies addressing the performance of tests aiming to diagnose ADHD.¹⁸^, ²¹^, ²⁴^, ²⁷^, ²⁸^, ¹¹¹^, ¹¹²^, ¹¹⁵^, ¹¹⁷^, ¹¹⁹^–¹²¹^, ¹²⁴^, ¹³⁴^, ¹³⁵^, ¹⁴⁰^–¹⁴³^, ¹⁵²^, ¹⁵³^, ¹⁵⁷^, ¹⁵⁹^, ¹⁶²^, ¹⁶⁷^–¹⁷⁰^, ¹⁷²^, ¹⁷⁷^, ¹⁷⁹^, ¹⁸¹^–¹⁹²^, ¹⁹⁷^, ¹⁹⁸^, ²¹⁰^, ²¹¹^, ²¹³^, ²¹⁴^, ²¹⁸^, ²²³^, ²³⁰^, ²³¹^, ²³³^, ²³⁴^, ²³⁷^, ²⁴¹^, ²⁴²^, ²⁴⁴^–²⁴⁶^, ²⁵¹^, ²⁵³^, ²⁶⁰^, ²⁶³^, ²⁶⁷^, ²⁷⁶^, ²⁷⁷^, ²⁸²^–²⁸⁵^, ²⁸⁷^, ²⁹³^, ²⁹⁷^–³⁰¹^, ³⁰³^, ³⁰⁷^, ³⁰⁹^, ³¹¹^, ³¹²^, ³¹⁴^–³¹⁶^, ³¹⁹^, ³²²^, ³²³^, ³²⁷^, ³³¹^, ³³⁶^, ³³⁸^–³⁴⁰^, ³⁴²^, ³⁴⁴^, ³⁴⁶^, ³⁴⁷^, ³⁵¹^, ³⁵²^, ³⁵⁵^, ³⁵⁶^, ³⁵⁹^, ³⁶²^, ³⁶⁵^, ³⁶⁶^, ³⁶⁹^, ³⁷⁰^, ³⁷⁹^, ³⁸²^, ³⁸⁵^, ³⁸⁸^–³⁹¹^, ³⁹³^–³⁹⁵^, ³⁹⁷^, ⁴⁰⁰^–⁴⁰⁵^, ⁴⁰⁷^, ⁴⁰⁸^, ⁴¹²^, ⁴¹³^, ⁴¹⁵^–⁴¹⁷^, ⁴²⁰^–⁴²⁴^, ⁴²⁷^, ⁴²⁹^, ⁴³⁴^, ⁴³⁶^–⁴³⁸^, ⁴⁴⁵^–⁴⁵⁰^, ⁴⁶²^–⁴⁶⁵^, ⁴⁶⁷^–⁴⁷⁰^, ⁴⁷³^, ⁴⁷⁵^, ⁴⁷⁷^, ⁴⁷⁹^, ⁴⁸²^, ⁴⁸⁶^, ⁴⁸⁷^, ⁴⁹¹^, ⁴⁹³^–⁴⁹⁶^, ⁴⁹⁸^–⁵⁰²^, ⁵⁰⁶^, ⁵¹⁴^–⁵¹⁶^, ⁵¹⁸^, ⁵¹⁹^, ⁵²⁴^, ⁵²⁷^, ⁵²⁸^, ⁵³⁶^, ⁵³⁷^, ⁵⁴¹^–⁵⁴³^, ⁵⁴⁶^–⁵⁴⁹^, ⁵⁵³^, ⁵⁵⁸^, ⁵⁵⁹^, ⁵⁶³^, ⁵⁶⁴^, ⁵⁶⁶^, ⁵⁷⁰^, ⁵⁷¹^, ⁵⁷⁶^, ⁵⁸⁰^–⁵⁸⁴^, ⁵⁸⁷^, ⁵⁹¹^, ⁵⁹²^, ⁵⁹⁹^, ⁶⁰⁰^, ⁶⁰³^, ⁶⁰⁵^, ⁶⁰⁷^, ⁶¹⁴^, ⁶¹⁵^, ⁶²⁵^, ⁶²⁷^, ⁶³⁰^–⁶³³^, ⁶³⁵^, ⁶³⁸^, ⁶³⁹^, ⁶⁴¹^, ⁶⁴²^, ⁶⁴⁴^, ⁶⁴⁷ The methodological rigor and the reporting varied substantially in the identified studies. The potential for risk of bias in the studies is documented in Figure 5. The critical appraisal for the individual studies is in Appendix D.

Figure 5

Risk of bias in Key Question 1 ADHD diagnostic studies. Notes: ADHD = attention deficit hyperactivity disorder

Selection bias was likely present in two thirds of studies. Often samples were restricted and did not necessarily represent the full range of children with ADHD. For example, studies explicitly reported using a convenience sampling strategy. Index test issues were present in ten percent of studies. Although the review was restricted to studies reporting a clinical diagnosis of ADHD for participants, reference standard issues were also present in a small number of studies, in particular due to lack of details on procedures and/or diagnosticians.¹¹¹^, ¹⁴²^, ²³³^, ³⁴²^, ⁴⁰⁵^, ⁴¹²^, ⁴⁵⁰^, ⁵¹⁶^, ⁵⁵³^, ⁶⁴² Flow and timing was rated as high risk of bias in several studies.¹¹¹^, ¹²¹^, ¹⁴³^, ¹⁶²^, ¹⁷²^, ³¹²^, ³¹⁹^, ³⁵¹^, ³⁷⁹^, ⁵⁰¹ Typically this was due to an unclear participant flow (e.g., it was unclear whether the diagnosis was known before the results of the index test was known).

We also assessed possible applicability issues that could influence the generalizability of the reported data. Figure 6 shows the summary of rated applicability. The applicability for the individual studies is in Appendix D.

Figure 6

Key Question 1 applicability rating. Notes: N/A = Not applicable

In several studies, samples were employed that do not represent the general population of children with ADHD, usually because children with co-morbidities were excluded. In addition, several papers took place in specialty care settings with diagnostic and treatment options that go beyond the standard course of action for children with ADHD.

4.3. Summary ADHD Diagnosis by Tests for All Age Groups

We broadly differentiated between parental ratings, teacher ratings, tools for clinicians, teen self-reports, neuropsychological tests, imaging, EEG, biomarker, activity markers, and other (e.g., electrocardiogram [EKG] indicators). Studies evaluated a large number of different tools within the broader categories. In addition, where studies used the same diagnostic tool (e.g., a rating scale), authors used different components of the tool (e.g., specific subscales) or combined components in a variety of ways (e.g., different neuropsychological parameter). We identified 68 studies that used machine learning algorithms to determine the best diagnostic approach.²⁸^, ¹¹⁵^, ¹²⁰^, ¹²¹^, ¹⁴³^, ¹⁵²^, ¹⁵⁷^, ¹⁷²^, ¹⁷⁹^, ¹⁸¹^, ¹⁸²^, ¹⁸⁵^–¹⁸⁸^, ¹⁹¹^, ²¹¹^, ²¹⁴^, ²²³^, ²³³^, ²³⁴^, ²⁴⁵^, ²⁵³^, ²⁸²^, ²⁸³^, ²⁹⁹^, ³⁰³^, ³²²^, ³²³^, ³⁴⁰^, ³⁵⁵^, ³⁵⁶^, ³⁶⁹^, ³⁷⁰^, ³⁸⁸^, ³⁹⁴^, ⁴⁰⁰^, ⁴⁰²^, ⁴⁰³^, ⁴⁰⁷^, ⁴⁰⁸^, ⁴¹²^, ⁴²⁰^, ⁴²⁹^, ⁴³⁴^, ⁴³⁸^, ⁴⁴⁹^, ⁴⁵⁰^, ⁴⁶⁷^, ⁴⁶⁸^, ⁴⁷³^, ⁴⁹⁴^, ⁴⁹⁵^, ⁵¹⁸^, ⁵⁴¹^, ⁵⁴³^, ⁵⁷¹^, ⁵⁸¹^, ⁵⁸²^, ⁵⁹¹^, ⁵⁹²^, ⁵⁹⁹^, ⁶⁰³^, ⁶³⁰^–⁶³³^, ⁶⁴¹ Studies were published since 2012²⁸ and came from 21 different countries, but primarily the United States²⁸^, ¹⁵²^, ²²³^, ²³³^, ²³⁴^, ²⁸²^, ²⁹⁹^, ³²³^, ⁴⁰⁰^, ⁴⁰³^, ⁴¹²^, ⁴⁶⁷^, ⁴⁹⁵^, ⁵¹⁸^, ¹¹⁸⁸ and China.¹⁸⁵^, ¹⁸⁷^, ¹⁸⁸^, ¹⁹¹^, ³⁹⁴^, ⁴⁰⁷^, ⁴⁰⁸^, ⁵⁷¹^, ⁵⁸¹^, ⁶³⁰^, ⁶³²^, ⁶⁴¹ A third of identified studies used EEG markers as the data source¹¹⁵^, ¹²⁰^, ¹⁴³^, ¹⁵⁷^, ¹⁷²^, ¹⁷⁹^, ¹⁸⁷^, ¹⁸⁸^, ³²²^, ³⁴⁰^, ³⁷⁰^, ³⁹⁴^, ⁴¹²^, ⁴³⁸^, ⁴⁴⁹^, ⁴⁶⁸^, ⁴⁷³^, ⁴⁹⁴^, ⁵⁹²^, ⁸⁸³ with another third of the studies using MRI¹⁹¹^, ²⁸²^, ⁴⁹⁵^, ⁵¹⁸^, ⁵⁷¹^, ⁵⁸¹^, ⁶³⁰^, ⁶³³^, ¹¹⁸⁸ The remaining studies used neuropsychological test components, rating scale scores, activity estimates, or other sources. Some studies were able to achieve 100 percent sensitivity with the help of machine learning (corresponding specificity 100%)¹⁴³^, ¹⁵² Other studies maximized specificity, and some achieved 100 percent specificity in machine learning supported diagnostic models (corresponding sensitivities 100, 97, 75, 98, and 100% respectively).¹²¹^, ¹⁴³^, ¹⁵²^, ³⁷⁰^, ⁴⁵⁰ Across machine-learning supported studies, accuracy ranged from 61 percent²⁸² to 100 percent.¹⁴³^, ¹⁵²^, ⁴⁶⁸

Given that most studies included younger (typically 5- and 6-year-olds) and older children, the following section describes diagnostic tools relevant to all age groups. Some studies evaluated more than one test (e.g., a parental rating and a teacher rating).

4.3.1. Parental Ratings

We identified 59 studies using Parental ratings to diagnose ADHD.¹⁸^, ¹¹⁷^, ¹³⁴^, ¹⁶⁸^, ¹⁶⁹^, ¹⁹⁰^, ²¹⁸^, ²²³^, ²³⁰^, ²³³^, ²³⁴^, ²⁴¹^, ²⁴²^, ²⁴⁴^, ²⁵¹^, ²⁶³^, ²⁸⁵^, ²⁸⁷^, ²⁹⁷^, ³⁰⁰^, ³⁰¹^, ³¹¹^, ³¹⁴^, ³³¹^, ³³⁶^, ³³⁹^, ³⁴²^, ³⁴⁴^, ³⁵⁹^, ³⁶²^, ³⁹⁰^, ³⁹¹^, ⁴²³^, ⁴²⁴^, ⁴²⁷^, ⁴⁴⁷^, ⁴⁴⁸^, ⁴⁶³^, ⁴⁶⁴^, ⁴⁸²^, ⁴⁸⁷^, ⁴⁹¹^, ⁴⁹⁸^, ⁵⁰²^, ⁵¹⁴^–⁵¹⁶^, ⁵¹⁹^, ⁵²⁷^, ⁵²⁸^, ⁵⁴⁷^, ⁵⁵³^, ⁵⁵⁸^, ⁵⁵⁹^, ⁵⁸⁴^, ⁵⁸⁷^, ⁶⁰⁵^, ⁶³⁸^, ⁶⁴² The earliest study meeting inclusion criteria was published in 1985.⁵¹⁴ Evaluations of parental rating tools came from five different English-language speaking countries, but most studies were from the United States.¹³⁴^, ¹⁶⁹^, ¹⁹⁰^, ²³⁰^, ²³³^, ²³⁴^, ²⁴¹^, ²⁴²^, ²⁴⁴^, ²⁵¹^, ²⁶³^, ²⁸⁵^, ²⁹⁷^, ²⁹⁹^, ³¹¹^, ³³¹^, ³³⁶^, ³³⁹^, ³⁴²^, ³⁴⁴^, ³⁵⁹^, ³⁹⁰^, ³⁹¹^, ⁴²³^, ⁴²⁴^, ⁴²⁷^, ⁴⁴⁸^, ⁴⁶³^, ⁴⁶⁴^, ⁴⁸²^, ⁴⁸⁷^, ⁴⁹¹^, ⁴⁹⁸^, ⁵⁰²^, ⁵¹⁴^–⁵¹⁶^, ⁵¹⁹^, ⁵²⁷^, ⁵²⁸^, ⁵⁴⁷^, ⁵⁵³^, ⁵⁵⁸^, ⁵⁵⁹^, ⁵⁸⁴^, ⁶⁰⁵^, ⁶³⁸^, ⁶⁴²The populations studied were predominately males and included participants ranged between the ages of two and 18. Four studies exclusively included children younger than seven years old.³³¹^, ⁵¹⁶^, ⁵¹⁹^, ⁵⁵⁹ For studies that distinguished between ADHD presentations, most of the participants were diagnosed with the combined or inattentive presentations. In one study focusing on preschool age children who presented with disruptive behavior disorders, 57 percent of participants were diagnosed with the hyperactive/impulsive presentation.³³¹ While ADHD participants with co-occurring disorders were not excluded from most studies, only a few purposely included children with specific co-occurring disorders such as disruptive behavior disorders³³¹ or autism.²³⁴^, ⁴⁴⁷ However, about half of identified studies came from clinical samples, rather than general neurotypically developing children–- i.e., they identified children undergoing a diagnostic workup for a potential diagnosis of ADHD, conduct disorders, autism, or depression.

In half of the identified studies, White participants made up more than 70 percent of the sample. One study evaluated diagnostic accuracy a sample in which over 50 percent of participants were Black/African American,⁴⁶²^, ⁵³⁶ and one study was identified in which 85 percent of participants were Hispanic or Latino.⁵⁵³ Studies reported predominantly on the estimated sensitivity and specificity. Some studies also reported on the area under the curve (AUC) as a summary test performance, but other key outcomes were less frequent. Figure 7 plots the sensitivity and specificity for the parental rating scale evaluated in the study.

Figure 7

Sensitivity and specificity of parental rating scales. Notes: Evaluated tools: ADHD-RS-IV, ADHD-SC4-P, ADHD-SRS-H, ADHD-SRS-Im, ARS, BASC-2-EF, BASC-3, BASC-PRS, BRIEF, BRIEF2, BRIEF-P+BRIEF-T+DKEFS, CBCL, CBCL-A, CBCL-AD/H, CBCL-Ag, CBCL-SP, Conner’’, (more...)

The studies reporting sensitivity and specificity (the measures are not independent from each other, and high sensitivity can come at a cost of low specificity and vice versa) show the wide variation in diagnostic accuracy estimates. The figure also shows that studies evaluated a large range of different parental rating scales, with few studies reporting on the same tool.

The most frequently evaluated diagnostic tool was the CBCL (Child Behavior Checklist), either alone or in combination with other scales, using different cutoffs, and evaluating different subscales (the attention deficit/hyperactivity problems subscale most frequently). Reported sensitivity for the CBCL ranged from 71 percent in a study differentiating ADHD and oppositional defiance disorder³³¹ to 84 percent in two studies, one using an outpatient pediatric medical clinic, the other one a sample of children with traumatic brain injury.¹⁹⁰^, ⁶⁰⁵ Reported specificity for this parental scale ranged from 33 percent⁵⁸⁷ to 93 percent¹⁹⁰ in the pediatric medical clinic sample. The reported AUC ranged from 0.55³⁴⁴ to 0.93¹⁹⁰ with three independent studies reporting estimates of 0.83 or 0.84 for this diagnostic measure for the CBCL.²⁵¹^, ³³¹^, ⁴⁹⁸ The evidence table in the appendix shows the results for all diagnostic and psychometric outcomes of interest for all identified studies.

Table 3 shows the findings for the outcomes of interest together with the number of studies and study identifiers for parental rating scales. For the main results, we report findings from population samples that differentiated ADHD from neurotypical developing children separately from results obtained in clinical samples, given that the study population was identified as one of the sources of heterogeneity in reported results as documented in KQ1c. Results are shown across studies and tools for the main analyses. Where at least two different author groups reported on the same rating scale, we provide results for a specific scale.

Table 3

KQ1 summary of findings and strength of evidence for parental ratings.

Parental ratings reported mainly on the sensitivity and specificity. A few studies reported perfect diagnostic performance for parental ratings for either sensitivity or specificity, but not both together. Little information was provided in these diagnostic studies regarding the reliability of the measures given the large range of different measures evaluated by study authors. We downgraded the strength of evidence for study limitation (lack of detailed reporting), imprecision (large variation in reported diagnostic performance) and for inconsistency (when consistency could not be assessed because no study was identified, or only one study was identified reporting on the test and outcome of interest and results have not been replicated by another author group, or only limited data points were available). None of the included studies provided information on the effect of misdiagnosis. None of the identified studies reported the costs associated with obtaining parental ratings.

4.3.2. Teacher Ratings

We identified 23 studies using Teacher ratings to diagnose ADHD.¹⁸^, ¹¹⁹^, ¹⁸³^, ²¹⁸^, ²⁴²^, ²⁹⁹^, ³⁰¹^, ³¹⁴^, ³⁴²^, ³⁵⁹^, ³⁶²^, ³⁹¹^, ⁴⁶³^, ⁴⁷⁹^, ⁴⁸²^, ⁴⁹¹^, ⁵¹⁹^, ⁵²⁷^, ⁵²⁸^, ⁵⁵⁸^, ⁵⁵⁹^, ⁵⁸⁷^, ⁶⁴² The earliest study meeting eligibility criteria was published 1998⁴⁷⁹ from four different English-speaking countries, primarily the United States.²⁴²^, ²⁹⁹^, ³⁴²^, ³⁵⁹^, ³⁹¹^, ⁴⁶³^, ⁴⁷⁹^, ⁴⁸²^, ⁴⁹¹^, ⁵¹⁹^, ⁵²⁷^, ⁵²⁸^, ⁵⁵⁸^, ⁵⁵⁹^, ⁶⁴² The populations studied were predominately males between the ages of three and 18. Two studies exclusively included children younger than seven years old⁵¹⁹^, ⁵⁵⁹ and two exclusively in children eight years or older.¹¹⁹^, ³⁵⁹ For studies that distinguished between ADHD presentations, most of the participants were diagnosed with the combined or inattentive presentations. Almost all of the studies mention race and ethnicity demographics, with 14 studies where White participants made up greater than 70 percent of the sample, and one study in which over 85 percent of the participants were Black/African American.

ADHD participants with co-occurring disorders were not excluded from most of the studies. Studies were divided into clinical samples and those recruited from a less selective population. None of the studies included children who all had a dual diagnosis, such as ADHD and conduct disorder.

Studies reported a variety of outcomes, with sensitivity and specificity being the most frequently reported outcomes. Figure 8 plots the reported sensitivity and specificity for teacher rating scales.

Figure 8

Sensitivity and specificity of teacher rating scales. Notes: Evaluated tools: ADHD-RS, ADHD-RS-IV-I, ADHD-SC4-T, BASC-2-EF, BASC-3, BRIEF, BRIEF-P+BRIEF-T+DKEFS, CTRS, CTRS-R, DBD, ECI-4, SNAP-IV, TRF, TRF+Conners-3-T(S), TRF-A, TRF-Ag, WMRS. More information (more...)

The figure shows the large range in reported sensitivity and specificity. It also shows that studies have evaluated many different teacher rating tools.

The Teacher Report Form, alone or in combination with Conners teacher rating scales, and using the total or the subscale of attention problems, was evaluated in more than one study.²⁴²^, ³⁰¹^, ³⁴²^, ⁵⁸⁷ Reported sensitivity ranged from 72 percent³⁰¹ to 79 percent.⁵⁸⁷ Reported specificity estimates ranged from 64 percent⁵⁸⁷ to 76 percent.²⁴² Two of the studies reported on AUC and found 0.65³⁴² for the attention problem subscale and 0.77³⁰¹ in combination with the Conners 3 teacher short form. No two studies reported on rater agreement, internal consistency, or test-retest reliability for the same teacher rating scale.

Table 4 shows the findings for the outcomes of interest together with the number of studies and study identifiers.

Table 4

KQ1 summary of findings and strength of evidence for teacher ratings.

Across all teacher rating studies, reported sensitivity in individual studies were up to 97 percent in a clinical sample, but the corresponding specificity was only 26 percent.³¹⁴ We downgraded the strength of evidence for imprecision (large variation in reported diagnostic performance) and for inconsistency (when consistency could not be assessed because only one study was identified reporting on the test and outcome of interest and results had not been replicated by another author group). Identified diagnostic accuracy studies did not report on several of the other key outcomes.

4.3.3. Teen/Child Self-Reports

We identified six studies using teen/child self-reports to diagnose ADHD.¹⁴²^, ¹⁶⁸^, ²³¹^, ²⁹⁷^, ⁴⁹¹^, ⁵⁰⁶ The earliest study was published in 2002⁵⁰⁶ and data came from two countries, the United States²³¹^, ²⁹⁷^, ⁴⁹¹ and Canada,¹⁴²^, ¹⁶⁸^, ⁵⁰⁶ respectively. Self-reports were primarily completed by adolescents, however one study provided a research assistant to help read the questions for participants under 11 years old.²⁹⁷ Only one study documented the ADHD presentation: 10 percent inattentive presentation, 4 percent hyperactive/impulsive presentation, and 25 percent combined presentation.⁴⁹¹ Two studies mentioned race and ethnicity demographics. In one study, White participants made up 61 percent of the sample²⁹⁷ and one study reported 89 percent of the participants were Black/African American.⁴⁹¹

Studies reported a limited number of outcomes, with sensitivity, specificity, and AUC being the most frequently reported outcomes. No two identified studies reported on the same self-report measure. Reported diagnostic success varied widely. Table 5 shows the findings for the outcomes of interest together with the number of studies and study identifiers. None of the tools was evaluated in more than one study.

Table 5

KQ1 summary of findings and strength of evidence for self reports.

The reported diagnostic performance of teen self-reports was limited. We downgraded for the domain inconsistency (inability to judge the consistency across studies because only one study was identified reporting on the test and outcome of interest). In several cases, our searches identified no studies and the strength of evidence is insufficient for the outcome.

4.3.4. Combined Ratings

We identified 13 studies that assessed the diagnostic performance of ratings combined across informants.¹⁸^, ¹⁸⁹^, ²⁷⁷^, ²⁹⁷^, ³⁰³^, ⁴⁰⁵^, ⁴⁶⁷^, ⁴⁷⁹^, ⁵²⁷^, ⁵⁴⁸^, ⁵⁵⁹^, ⁵⁷⁰^, ⁶⁰⁰ The studies compared the information from multiple raters to the reference standard. Studies combined information sources in different ways, often selecting individual variable with the help of machine learning. Only one of these studies compared the performance when combining data from multiple informants to that of single informants: it found negligible improvement when combining youth self-report to the parent report alone using an adaptive testing questionnaire (AUC youth only 0.71; parent only 0.85; combined 0.86) in a treatment-seeking population.²⁹⁷

The studies reported only on selected accuracy measures. One study combined parent and teacher ratings on the Conners scales by requiring youth to meet diagnostic cutoffs (T-score ≥65) in one setting and substantial symptoms in the other setting (T-score ≥60). It reported a diagnostic sensitivity of 84 percent and specificity of 36 percent for the combined rating when distinguishing ADHD from other clinically referred youth.¹⁸ One study reported findings from a discriminant function analysis of mother, father, and teacher ratings on the Conners scale when distinguishing ADHD youth who were considered either intellectually gifted or not from typically developing, intellectually gifted youth. It found that the discriminant function using all three informants distinguished the typically developing youth from the two ADHD groups but did not distinguish the two ADHD groups from one another.²⁷⁷ A study in four to seven year old children used machine learning to combine parent and teacher ratings on the BRIEF in distinguishing youth with ADHD from typically developing controls. It reported an average diagnostic accuracy of 0.93, with teacher ratings being the most informative in the machine learning algorithm, though it did not formally compare accuracy for combined informants with accuracy for either informant alone. The study also found that the addition of neuropsychological test measures and cortical thickness measures to the machine learning algorithm did not meaningfully improved diagnostic performance over use of the BRIEF alone.⁴⁶⁷ The best AUC was reported by a machine learning supported study combining parent and teacher ratings (AUC 0.98).⁴⁰⁵

The studies did not report reliability measures for ratings combined across informants; studies reported only psychometric performance in individual informant groups. For example, one of the studies reported that individual ratings of the BRIEF using parent and teacher ratings found intraclass correlation coefficients (ICCs) from 0.31 to 0.59 across subscales.⁵⁷⁰Another study reported the range of Cronbach’s alpha estimates across teacher and parent ratings for individual scales, all indicating substantial internal consistency (with the lowed Cronbach’s also of 0.72, all other values were above 0.90).⁴⁶⁷

4.3.5. Clinician Tools

We identified 24 of studies evaluating additional tools that could be used by clinicians or the healthcare system (beyond neuropsychological tests; parent, teacher, or self-report ratings; biomarkers; or imaging) to aid the diagnosis of ADHD.²⁷^, ¹²¹^, ¹⁶⁷^, ¹⁸¹^, ²⁹⁸^, ²⁹⁹^, ³¹¹^, ³³⁸^, ³⁵⁵^, ³⁶²^, ³⁸⁵^, ³⁸⁸^, ³⁸⁹^, ⁴⁰⁰^, ⁴⁰³^, ⁴⁰⁷^, ⁴¹⁶^, ⁴¹⁷^, ⁴³⁴^, ⁴³⁷^, ⁴⁹⁹^, ⁵⁴²^, ⁵⁶⁶^, ⁶²⁷ The earliest identified study was published in 2009.⁶²⁷ Evaluations were published in three different countries, including eight from the United States.²⁷^, ²⁹⁹^, ³¹¹^, ³⁸⁹^, ⁴⁰⁰^, ⁴⁰³^, ⁵⁴²^, ⁵⁶⁶ The populations studied were predominately males and included youth were between the ages of three and 18. Most studies did not distinguish between ADHD presentations but three studies restricted to the combined ADHD type.¹²¹^, ⁴¹⁶^, ⁶²⁷ Where studies mentioned race and ethnicity demographics of the sample composition, the percentage of White children ranged from 52 to 100 percent, the number of Black or African American children ranged from two to 44 percent, Hispanic/Latino children three to 20 percent, and Asian children one to three percent.

Studies used different tools, including diagnostic interview guides and observation tools. Several studies measured child activity levels as an objective test, for example through an actometer or commercially available activity tracker¹²¹^, ¹⁸¹^, ²⁹⁸^, ³⁵⁵^, ⁴⁰⁰^, ⁴⁰³^, ⁴¹⁶^, ⁴³⁷^, ⁶²⁷ and two evaluated direct observation as a diagnostic tool.¹⁶⁷^, ³⁶² Three studies used insurance claim-based algorithms or medical health record indicators⁴³⁴^, ⁵⁴²^, ⁵⁶⁶ The remaining studies addressed unique interventions and questions, for example, one study focused on the clinical utility of International Classification of Diseases [ICD]-11 diagnostic guidelines⁴⁹⁹ and a clinician diagnosis combined with an assessment aid that involved integrating EEG and theta/beta ratio data.²⁷

Studies are difficult to compare since they assess different tools and approaches. Studies reported a variety of outcomes, with sensitivity and specificity being the most frequently reported outcomes. Table 6 shows the findings for the key outcomes of interest together with the number of studies and study identifiers. Where all identified studies evaluated the same tool, the first column of the study indicates the tool, otherwise estimates are reported across all tools.

Table 6

KQ1 summary of findings and strength of evidence for clinician tools.

We downgraded the strength of evidence for imprecision (very large variation in reported diagnostic performance) and for inconsistency (when consistency could not be assessed because only one study was identified reporting on the test, and outcome of interest and results had not been replicated by another author group). The tools were difficult to compare and answered study-specific questions.

4.3.6. Biomarkers

We identified seven studies using proposed biomarkers obtained from biospecimen to diagnose ADHD.³⁰⁹^, ⁵⁰¹^, ⁵⁶³^, ⁵⁸³^, ⁶⁰³^, ⁶³⁵^, ⁶⁴⁴ EEG and imaging approaches are reported in section 4.3.7 and the evidence table (Appendix C, Table C.1.) shows additional, more unique approaches using other approaches such as eye movement tracking to diagnose ADHD. Five identified studies used blood measures, including membrane potential ratio⁵⁶³ and erythropoietin/erythropoietin receptor,³⁰⁹ and three of these studies analyzed miRNA obtained from blood samples.⁶⁰³^, ⁶³⁵^, ⁶⁴⁴ The other studies evaluated urine indicators.⁵⁰¹^, ⁵⁸³ The earliest identified study was published in 2007.⁵⁰¹ Evaluations were published in five different countries, including one from the United States.⁵⁶³

The populations studied were predominately males between the ages of six and 17. Most studies required participants to not be taking stimulant medication. For studies that distinguished between ADHD presentations, most of the participants were diagnosed with the combined presentation.⁵⁶³^, ⁶³⁵^, ⁶⁴⁴ Only two studies mentioned race and ethnicity demographics, one where all of the participants were Han Chinese⁶⁰³ and the other where the majority of participants were Black/African American.⁵⁶³ None of the studies used a clinical sample or children with a consistent co-morbidity.

Table 7 shows the findings for the outcomes of interest together with the number of studies and study identifiers. Given the clinical diversity of the biomarkers (e.g., differences in invasiveness and technological requirements of tests), we include results across all biospecimen evaluations, blood markers, miRNA specifically, and urine indicators where more than one study was identified that reported on the outcome.

Table 7

KQ1 summary of findings and strength of evidence for biomarkers.

Biomarker studies reported mainly on sensitivity and specificity. Selected studies achieved very high sensitivity.³⁰⁹ Little information was provided in the studies regarding the reliability of the markers or combinations of markers. None of the included studies provided information on the effect of misdiagnosis. None of the identified studies reported the costs associated with analyzing biomarkers.

4.3.7. EEG

We identified 45 studies using EEG markers to diagnose ADHD.²⁷^, ¹¹¹^, ¹¹⁵^, ¹²⁰^, ¹²⁴^, ¹⁴³^, ¹⁵⁷^, ¹⁷²^, ¹⁷⁹^, ¹⁸²^, ¹⁸⁶^–¹⁸⁹^, ¹⁹²^, ¹⁹⁷^, ²⁴⁵^, ³¹²^, ³²²^, ³⁴⁰^, ³⁵¹^, ³⁵⁶^, ³⁶⁵^, ³⁶⁶^, ³⁷⁰^, ³⁹⁴^, ³⁹⁵^, ³⁹⁷^, ⁴⁰⁴^, ⁴⁰⁸^, ⁴¹²^, ⁴¹³^, ⁴¹⁵^, ⁴²⁰^, ⁴³⁸^, ⁴⁴⁹^, ⁴⁶⁵^, ⁴⁶⁸^, ⁴⁷³^, ⁴⁸⁷^, ⁴⁹⁴^, ⁵⁴⁶^, ⁵⁴⁸^, ⁵⁹²^, ⁶⁴¹ The earliest identified study was published in 2003.⁵⁴⁶ EEG evaluations were published in 17 different countries, primarily Iran and China, with four studies published in the United States.²⁷^, ⁴¹²^, ⁴⁸⁷^, ⁵⁴⁸ The populations studied were predominately males between the ages of six and 17, with only three studies including children as young as four years old.¹⁵⁷^, ³⁴⁰ One study included only female participants,¹⁹⁷ and seven studies included only males.¹¹¹^, ¹⁷⁹^, ⁴¹²^, ⁴¹³^, ⁴⁴⁹^, ⁴⁶⁸^, ⁴⁷³ In several studies, participants were required to demonstrate an IQ of 80 or higher and almost half of the studies required that participants not take stimulant medication or stop medication several days before testing. For studies that distinguished between ADHD presentations, most focused on the combined and inattentive presentations. Race and ethnicity demographics were not mentioned in most studies.

While ADHD participants with co-occurring disorders were not excluded from most studies, only a few studies purposely included specific co-occurring disorders to evaluate the diagnostic test performance in children with co-occurring conduct disorder or other behavioral disorders.¹⁴³ The large majority of studies had unselected samples, i.e., comparing children with ADHD to neurotypical developing children.

Studies used EEG signals obtained during a resting state with eyes closed, eyes open, while performing neuropsychological tests, and/or recording event-related potentials. Studies varied in the reported detail (e.g., number of electrodes, channels, frequency and duration of the recording); the documented information is shown in the evidence table in the appendix. Two thirds of studies used machine learning algorithms to select parameter for classification. Several studies explicitly reported combining EEG data with specific demographic variables or rating scale results.²⁷^, ¹²⁴^, ¹⁴³^, ¹⁸⁹^, ¹⁹²^, ³¹²^, ³⁵¹

Table 8 shows findings for the outcomes of interest together with the number of studies and study identifiers.

Table 8

KQ1 summary of findings and strength of evidence for EEG.

EEG studies predominantly reported accuracy estimates. Sensitivity in individual studies ranged widely from 46 percent¹⁹⁷ to perfect sensitivity (corresponding specificities 71%);¹⁴³^, ⁴¹³ the range was reduced in studies restricting to older children. Studies in clinical samples reported a reduced range of sensitivity and specificity compared to studies differentiating children with ADHD from neurotypically developing children, but the identified samples were either small or they augmented EEG predictions with demographic variables. Some studies combined EEG data with demographics; the achieved sensitivity was reported as 100 percent (corresponding specificity 100%) in one study.¹⁴³ We downgraded the strength of evidence for imprecision (large variation in performance across studies). In addition, we downgraded for study limitations as diagnostic approaches were often not well described. For some outcome measures, no study was identified that assessed it and determining the effects associated with the test was not possible.

4.3.8. Imaging

We identified 19 studies using neuroimaging.²⁸^, ¹⁹¹^, ²⁸²^, ³¹⁹^, ⁴⁰⁰^, ⁴⁶⁴^, ⁴⁶⁷^, ⁴⁹⁵^, ⁵¹⁸^, ⁵²⁴^, ⁵⁴⁹^, ⁵⁷¹^, ⁵⁸⁰^, ⁵⁸¹^, ⁵⁹¹^, ⁶³⁰^, ⁶³¹^, ⁶³³ Studies were predominantly published in the U.S. and China. A publicly available dataset (ADHD-200) produced numerous analyses.¹⁹¹^, ²⁸²^, ⁴⁹⁵^, ⁵⁸¹ The populations studied were predominately males between the ages of six and 17, with one study including only male participants.⁶³⁰ In several studies, participants were required to demonstrate an IQ of 80 or higher to be included in the sample.⁴⁹⁵^, ⁵⁴⁹^, ⁵⁷¹^, ⁶³⁰^, ⁶³¹ A quarter of the studies required participants not be taking stimulant medication or to stop medication several days before testing.⁵⁷¹^, ⁶³⁰^, ⁶³³ A third of the studies included only right-handed participants⁴⁰⁰^, ⁴⁹⁵^, ⁵⁷¹^, ⁶³⁰ In studies that distinguished between ADHD presentations, most focused on the combined and inattentive presentations. A minority specified including individuals with the hyperactive/impulsive presentation.¹⁹¹^, ²⁸²^, ⁵⁴⁹^, ⁶³³ Nearly all studies did not include race and ethnicity demographics.

While ADHD participants with co-occurring disorders were not excluded from most of the studies, no studies specifically assessed test performance in children with specific co-occurring disorders. One study differentiated children with ADHD from those with dyslexia.⁵²⁴ One evaluated the diagnostic performance of an algorithm differentiating ADHD from autism.²⁸² All studies used unselected, general samples, rather than clinical samples referred for further diagnostic workup (where a large proportion of children will either be diagnosed with ADHD, conduct disorders, autism, or depression).

All but two imaging studies used MRI to diagnose ADHD. However, studies utilized MRI in different ways. Some studies used functional MRI, some structural MRI, some used combinations of structural and functional MRI, with or without magnetic resonance spectroscopy. Two studies used near-infrared spectroscopy but the applications and diagnostic models differed.²¹¹^, ⁶³¹ Most of the imaging studies used a large number of indicators and utilized machine learning algorithms to detect markers to optimize the classifications. The reporting of the variable selection process varied, and it was often not clearly reported which exact indicators were included in the model used to determine diagnostic accuracy. Sone of the identified studies combined imaging parameter with demographic or other clinical data for the prediction model.¹⁹¹^, ²¹¹^, ²⁸²^, ⁴⁰⁰^, ⁴⁶⁷^, ⁴⁹⁵^, ⁶³¹^, ⁶³³

Reported diagnostic accuracy estimates varied widely. Table 9 shows the findings for the outcomes of interest, together with the number of studies and study identifiers. The table summarizing findings across all imaging studies, findings for MRI studies specifically, and imaging studies that combine imaging parameters with other variables (e.g., demographics) for predictions.

Table 9

KQ1 summary of findings and strength of evidence for neuroimaging.

Studies reported primarily on sensitivity, specificity, and accuracy. Across all neuroimaging studies, reported sensitivity varied widely. We downgraded the strength of evidence for imprecision (large variation in performance reported across studies). In addition, we downgraded for study limitations as the individual diagnostic models were often not well described and the number and type of predictor variables feeding into the model was unclear. For some outcomes, no study was identified, and it was not possible to determine the effects associated with the diagnostic modality. Some studies combined neuroimaging data and demographics, though the relevance is unclear, since the only demographic characteristic that is likely associated with a diagnosis of ADHD is sex, with a higher prevalence in males.

4.3.9. Neuropsychological Tests

We identified 74 studies using neuropsychological tests, assessing executive function and/or encompassing a variety of cognitive assessments, including continuous performance tests, to diagnose ADHD.¹⁸^, ²¹^, ²⁴^, ¹¹²^, ¹¹⁹^, ¹³⁵^, ¹⁴⁰^, ¹⁴¹^, ¹⁵²^, ¹⁵³^, ¹⁵⁹^, ¹⁶²^, ¹⁷⁰^, ¹⁷⁷^, ¹⁸⁴^, ¹⁸⁵^, ¹⁹⁰^, ¹⁹⁸^, ²¹³^, ²³⁷^, ²⁴⁶^, ²⁵³^, ²⁶³^, ²⁶⁷^, ²⁷⁶^, ²⁸⁴^, ²⁹³^, ²⁹⁸^, ³⁰⁷^, ³¹⁵^, ³¹⁶^, ³²³^, ³²⁷^, ³⁴⁶^, ³⁴⁷^, ³⁵¹^, ³⁵²^, ³⁷⁹^, ³⁸²^, ³⁹³^, ⁴⁰¹^, ⁴⁰²^, ⁴¹⁷^, ⁴²¹^, ⁴²²^, ⁴³⁶^, ⁴⁴⁵^, ⁴⁴⁶^, ⁴⁵⁰^, ⁴⁶²^, ⁴⁶⁷^, ⁴⁶⁹^, ⁴⁷⁰^, ⁴⁷⁵^, ⁴⁷⁷^, ⁴⁸²^, ⁴⁸⁶^, ⁴⁹³^, ⁴⁹⁶^, ⁵⁰⁰^, ⁵¹⁵^, ⁵³⁷^, ⁵⁴¹^, ⁵⁴³^, ⁵⁶⁴^, ⁵⁷⁶^, ⁶⁰⁷^, ⁶¹⁴^, ⁶¹⁵^, ⁶²⁵^, ⁶²⁷^, ⁶³²^, ⁶³⁹^, ⁶⁴⁷ Rating scales of executive function are described in the parent and teacher rating section in the beginning of the chapter.

The earliest study evaluating a neuropsychological tests as diagnostic tools was published in 1999⁴⁹⁶ and evaluations came from 18 different countries, primarily the United States. The populations studied were predominately males between the ages of six and 18. Three studies included three and four year old children.¹⁶²^, ³¹⁵^, ⁴⁶⁷ In several studies, participants were required to demonstrate an IQ of 70 or higher²⁴^, ³⁴⁶^, ³⁵²^, ³⁶⁵^, ⁴⁶⁷^, ⁴⁶⁹^, ⁵⁰⁰ with some studies requiring IQ to be at least 80²¹^, ¹⁵²^, ²⁵³^, ⁶⁴⁷ or 85.³⁷⁹^, ⁴⁴⁶^, ⁴⁸⁶ Two thirds of the studies required participants not take stimulant medication or stop medication several days before testing. For studies that distinguished between ADHD presentations, most of the participants were diagnosed with the combined or inattentive presentations. About a third of the studies mentioned race and ethnicity demographics, with seven studies where White participants made up half or more of the sample,²¹^, ¹⁶²^, ¹⁷⁰^, ²⁶³^, ⁴⁶²^, ⁶⁰⁷ one study where all of the participants were Asian,³⁹³ one study where over 50 percent were Black/African American,⁴⁶² and one study where 83 percent of the participants were Hispanic or Latino.⁴⁶⁷

ADHD participants with co-occurring disorders were not excluded from most of the studies. Some studies used clinical samples with participants who were referred for diagnostic work-up where all children presented with attention issues or other symptoms indicative of ADHD or a different clinical diagnosis.²⁴^, ¹⁵³^, ¹⁶²^, ²⁶³^, ³¹⁵ One study specifically looked at distinguishing between children with ADHD, developmental dyslexia, and those who had both disorders.⁴⁴⁶ The remaining studies used samples of neurotypically developing children as controls rather than clinical samples.

ADHD participants with co-occurring disorders were not excluded from most of the studies. Some studies used clinical samples with participants who were referred for diagnostic work-up where all children presented with attention issues or other symptoms indicative of ADHD or a different clinical diagnosis²⁴^, ¹⁵³^, ¹⁶²^, ²⁶³^, ³¹⁵ One study specifically looked at distinguishing between children with ADHD, developmental dyslexia, and those who had both disorders.⁴⁴⁶ The remaining studies used samples of neurotypically developing children as controls rather than clinical samples.

Studies described a wide range of test batteries, but over 50 studies used continuous performance testing (CPT) to diagnose children and adolescents. CPTs provide multiple behavioral outputs relevant to ADHD, including omission errors (reflecting inattention), commission errors (reflecting impulsivity), and reaction time standard deviation (or reflecting moment-to-moment response variability). Studies varied in their use of traditional visual CPTs, such as the TOVA (Test of Variables of Attention), or more novel, multifaceted CPT approaches. These latter “hybrid” CPT paradigms included CPTs that combined auditory and visual attentional processing demands together in the same task, those that monitored physical movements during task administration, and virtual reality CPTs built upon environments designed to emulate real-world distractibility in a classroom setting. The included studies used idiosyncratic combinations of individual cognitive measures to achieve the best performance. However, multiple studies reported on attention and impulsivity measures included in the continuous performance tests.

Studies reported a variety of statistical parameters to determine the accuracy of the diagnostic approach. Sensitivity, specificity, and accuracy were the most frequently reported diagnostic measures. Table 10 shows the findings for the outcomes of interest together with the number of studies and study identifiers for all key outcomes. Where we found more than one study reporting on the same test or test component, the table also summarizes the performance for those, specifically.

Table 10

KQ1 summary of findings and strength of evidence for neuropsychological tests.

Studies evaluating neuropsychological tests reported predominantly on sensitivity and specificity. Although selected studies reported perfect diagnostic performance for neuropsychological tests,¹⁵² those studies reported the diagnostic performance for composite measures (unique and study-specific combinations of individual cognitive measures), making it difficult to compare test performance across studies. The wide range in performance was narrower in studies restricting to children seven and above. Reliability measures were rarely reported in the identified studies. No study addressed the effects of misdiagnosis. Costs were reported in only one study. We downgraded the strength of evidence for imprecision (large variation in performance reported across studies). For some outcome measures, no study was identified, and it was not possible to determine the effects associated with the test.

4.4. KQ1a. What is the comparative diagnostic accuracy of approaches that can be used in the primary care practice setting or by specialists to diagnose ADHD among individuals younger than 7 years of age?

We identified only 12 studies that reported exclusively on children younger than seven years of age.¹⁶²^, ¹⁶⁷^, ¹⁸⁹^, ³¹⁶^, ³³¹^, ⁴¹²^, ⁴¹⁶^, ⁴³⁷^, ⁴⁶⁷^, ⁵¹⁶^, ⁵¹⁹^, ⁵⁵⁹ The earliest identified study was published in 2002⁵⁵⁹ and data came from the United States, Portugal, Spain, The Netherlands, Germany, Taiwan, and New Zealand. The percent female ranged from none to 41 percent, where reported, and the proportion of Caucasian children ranged from 54 to 90 percent. We identified three studies that explicitly reported on diagnostic performance data collected in primary care.¹⁶²^, ⁴⁴⁵^, ⁶⁰⁵ Several studies used clinic populations of children referred for diagnostic purposes and children often presented with multiple co-occurring disorders.

Studies evaluated parent ratings, teacher ratings, combined ratings, activity, EEG, imaging, and neuropsychological tests. Studies reported a variety of outcomes, with sensitivity and specificity being the most frequently reported outcomes. Sensitivity achieved in this age group reached up to 97 percent in a study evaluating the use of activity ratings,⁴¹⁶ while a study evaluated a continuous performance tests showed the lowest sensitivity (42%).¹⁸⁹ Reported specificity was 91 percent in a study using parental ratings to diagnose ADHD³³¹, but EEG data achieved only a specificity of 38 percent.¹⁸⁹ Few of these diagnostic studies reported reliability measures. The results across studies for the key outcomes are shown in the summary of findings table at the end of the chapter, all other measures (where reported) are shown in the evidence table in the appendix. We did not identify any study reporting on the adverse effect following a misdiagnosis (not being diagnosed or being incorrectly diagnosed) in this age group. In addition, none of the diagnostic studies mentioned costs of tests in this subsample.

The summary of findings table at the end of this chapter shows the diagnostic performance in this young age group in more detail. The table summarizes the limited available evidence across identified studies, together with the strength of evidence. Strength of evidence was either low due to the limited evidence, or insufficient due to the lack of studies in this age group reporting on the outcomes of interest.

4.5. KQ1b. What is the comparative diagnostic accuracy of EEG, imaging, or approaches assessing executive function that can be used in the primary care practice setting or by specialists to diagnose ADHD among individuals aged 7 through 17?

We identified 61 studies that reported exclusively on children aged seven and older. The earliest identified study was published in 1989. Data came from 23 different countries, most frequently U.S. and Chinese studies. Six studies restricted to boys, but one study included 75 percent girls.⁴⁴⁶ The proportion of White children ranged from 44⁴⁶⁴ to 100¹¹² percent. The proportion of Hispanic or Latino children ranged from one⁶⁰⁷ to 20⁴⁰⁰ percent. The proportion of Black or African American children ranged from five³⁵⁹ to 34⁶⁰⁷ percent. The proportion of Asian children ranged from one⁵⁷⁰ to 100⁶⁴¹ percent. The proportion of multiracial youth (where reported) ranged from eight⁴⁰⁰ to 20⁴⁶⁴ percent.

Studies evaluated parent ratings, teacher ratings, combined ratings, teen/child self-report, continuous performance, executive functioning, activity, EEG, MRI imaging, and neuropsychological tests. Studies reported a variety of outcomes, with sensitivity and specificity being the most frequently reported outcomes. Few of these diagnostic studies reported reliability measures. We did not identify any study reporting on the adverse effect following a misdiagnosis (not being diagnosed or incorrectly diagnosed) in this age group. In addition, none of the diagnostic studies mentioned costs of tests in this subsample. The results across studies for the key outcomes and interventions are shown in the summary of findings table at the end of the chapter, all other measures (where reported) and results for other interventions evaluated in this age group are shown in the Appendix C, Table C.1.

4.5.1. Diagnostic Accuracy of EEG in Youth Aged 7 Through 17

We identified 16 studies that used EEG to diagnose youth.¹¹¹^, ¹²⁰^, ¹⁷²^, ²⁴⁵^, ³¹²^, ³⁵¹^, ³⁷⁰^, ³⁹⁴^, ³⁹⁷^, ⁴⁰⁸^, ⁴³⁸^, ⁴⁴⁹^, ⁴⁶⁵^, ⁴⁹⁴^, ⁵⁴⁶^, ⁶⁴¹ The first study meeting eligibility criteria was published in 2003.¹¹¹^, ¹²⁰^, ¹⁷²^, ²⁴⁵^, ³¹²^, ³⁵¹^, ³⁷⁰^, ³⁹⁴^, ³⁹⁷^, ⁴⁰⁸^, ⁴³⁸^, ⁴⁴⁹^, ⁴⁶⁵^, ⁴⁹⁴^, ⁵⁴⁶^, ⁶⁴¹ Study locations included 11 different countries, with several studies being conducted in China³⁵¹^, ³⁹⁴^, ⁴⁰⁸^, ⁶⁴¹ and Iran²⁴⁵^, ⁴³⁸^, ⁴⁹⁴ The proportion of included girls ranged from none¹¹¹^, ⁴⁴⁹ to 56 percent³⁹⁴ Race and ethnicity was rarely reported, one study included 100% Asian youth.³⁵¹ The ADHD presentation was often not reported but where reported, but two studies reported two thirds of children with combined presentation³¹²^, ⁴⁶⁵ and one study restricted to inattentive ADHD³⁵¹ Studies did usually not exclude children with comorbidities but only one study specifically assessed the effect of ODD (oppositional defiant disorder) co-morbidity on diagnostic accuracy.³⁷⁰

Reported sensitivity, specificity, accuracy and AUC values ranged widely across studies as documented in the summary of findings table. Studies varied in how much detail they provided on the parameters that contributed to the diagnostic performance, which in combination with the wide range of reported diagnostic performance resulted in low strength of evidence statement for these outcomes of interest.

Studies did not report on rater agreement between EEG readers, internal consistency of measurements, or test-retest reliability. Identified studies also did not describe the impact of misdiagnosis and they did not mention costs. Hence, the evidence was determined to be insufficient for these outcomes of interest.

4.5.2. Diagnostic Accuracy of Imaging in Youth Aged 7 Through 17

We identified eight studies that used imagining for diagnosing in this age group, all evaluated the use of MRI.¹⁹¹^, ²⁸²^, ⁴⁰⁰^, ⁴⁶⁴^, ⁴⁹⁵^, ⁵¹⁸^, ⁵⁷¹^, ⁵⁸¹ The first studies meeting eligibility criteria published data in 2018¹⁹¹^, ⁵⁷¹ Study locations were the United States and China. The proportion of included girls ranged from 14⁵⁷¹ to 45²⁸² percent. Race and ethnicity was rarely reported, but in studies that provided a participant breakdown, the proportion of White children was 44 and 55 percent, Hispanic 19 and 20 percent, Black six and 14 percent, and Asian two and six percent in two U.S. studies.⁴⁰⁰^, ⁴⁶⁴ Several studies stated that youth with all ADHD presentations were included. Studies typically did not exclude youth with other comorbidities, but only one study assessed the effect of autism on the diagnostic accuracy.⁵¹⁸

The reported sensitivity, specificity, accuracy, and AUC values varied widely across studies. Given the wide range of reported diagnostic accuracy measures in this age group, strength of evidence was judged to be low regarding successfully diagnosing ADHD with imaging data. Rater agreement for human imaging readers, internal consistency, test-retest reliability, impact of misdiagnosis, and costs were not described. The strength of evidence was insufficient for evidence statements for these outcomes of interest.

4.5.3. Diagnostic Accuracy of Executive Function in Youth Aged 7 Through 17

While a number of studies evaluated neuropsychological tests in this age group, not all emphasized utilizing executive function characteristics for the diagnosis of ADHD. We identified 14 studies with an emphasis on executive function assessment.¹¹⁹^, ¹⁵³^, ¹⁵⁹^, ²¹³^, ²⁸⁴^, ³⁵¹^, ³⁵²^, ³⁷⁹^, ⁴⁴⁶^, ⁴⁶⁵^, ⁵⁴¹^, ⁶⁰⁷^, ⁶¹⁴^, ⁶²⁵ The earliest study was published in 1989.¹⁵⁹ Evaluations were conducted in six countries, with the United States being the most frequent country.¹⁵⁹^, ²¹³^, ⁶⁰⁷^, ⁶²⁵ The reported proportion of girls ranged from none³⁵²^, ⁶¹⁴ to 74 percent⁴⁴⁶ across studies. Race and ethnicity was rarely reported, but several identified studies included only or predominantly White youth.¹¹²^, ²¹³^, ⁶⁰⁷^, ⁶²⁵ Several studies restricted to or predominantly included youth with combined ADHD presentation,¹¹⁹^, ²⁵³^, ³⁵²^, ⁶²⁵ Studies typically did not exclude youth with comorbidities but none of the samples assessed the effect of a specific comorbidity on the diagnostic performance of the executive function test.

Sensitivity, specificity, accuracy, and AUC values ranged widely within and across the identified studies as documented in the summary of findings table. None of the identified studies assessed the performance of the same diagnostic test, and most of the studies described unique combinations of test components that were used to diagnose ADHD. All identified studies are documented in detail in the appendix. We determined the strength of evidence to be low for diagnostic outcomes of interest.

Studies did not report on rater agreement or internal consistency of the test components, but one study reported on temporal stability. The study reported correlations between tests on two occasions of 0.81 (p<0.05) for the total test score in a Tower of London–- Drexel task (assessing total move and rule violation scores), 0.79 (p<0.05) for total time violations, and 0.42 (p<0.005) for total rule violations.²¹³ Studies did not report on the impact associated with a misdiagnosis or costs of the tests. Given the lack of studies or our inability to judge consistency reported in results across studies, we determined the strength of evidence to be insufficient.

4.6. KQ1c. For both populations, how does the comparative diagnostic accuracy of these approaches vary by clinical setting, including primary care or specialty clinic, or patient subgroup, including age, sex, or other risk factors associated with ADHD?

We did not identify studies comparing the accuracy in different settings in direct, head-to-head comparisons. Hence, we had to address this KQ in indirect analyses across studies. Our analyses were further limited by studies providing insufficient details on the accuracy of performance (e.g., reporting clearly on the false positives and false negatives) and could not be based on a meta-analytic model. Instead, we used the reported summary performance measures as reported by the study authors to explore potential effect modifiers. The most common reported diagnostic performance measures were sensitivity and specificity and most analyses were only possible for these outcomes.

Figure 9 plots reported sensitivity by setting.

Figure 9

Sensitivity by setting. Notes: N/A = not available

The figure plots the sensitivity in different settings that are included in the dataset. It also shows the range within and across settings. Comparing the reported sensitivities, a simple regression analysis indicated that setting is associated with reported sensitivity (p 0.03). However, the result should be interpreted with caution, as it does not take study size or quality into account, and it was not established within a meta-analytic model. The corresponding reported specificities are shown in Figure 10.

Figure 10

Specificity by setting. Notes: N/A = not available

Reported specificity values ranged considerably, within as well as across settings. Comparing the reported specificities, a simple regression analysis did not indicate that setting is systematically associated with reported specificity (p 0.70). However, the result should be interpreted with caution, as it does not take study size or quality into account, and it was not established within a meta-analytic model. The equivalent analyses for reported accuracy (p 0.006) indicated that the reported estimate is statistically significantly associated with setting. The analysis for AUC was not significant (p 0.28).

We also evaluated whether the studies in clinical samples (i.e., referred for a clinical diagnosis, oppositional defiance disorder, or autism) and those with primarily neurotypical developing children reported different diagnostic performance values. The figure plots the sensitivity results for the two participant populations (Figure 11).

Figure 11

Sensitivity by clinical population.

Across studies, analyses detected a statistically significant difference in reported sensitivity results depending on whether a study reported on a clinical sample or children were compared to neurotypically developing children (p 0.04). On average, the sensitivity was lower in clinical samples compared to studies differentiating youth with ADHD from neurotypically developing youth (mean 75, SD 18 vs mean 81, SD 15). However, the analysis should be interpreted with caution, as it does not use a meta-analytic model for the analysis and uses reported sensitivity values as reported by the original authors.

Figure 12 plots the specificity stratified by population.

Figure 12

Specificity by clinical population.

The analysis indicated that the reported specificity was associated with the population that was used to establish diagnostic accuracy (p<0.001). On average, clinical samples reported lower specificities than studies in neurotypical samples (mean 68, SD 24 vs mean 83, SD 14). The result suggests that the clinical population appears to be a source of heterogeneity seen in the studies. However, the result should be interpreted with caution as the data were not analyzed in a meta-analytical model and used the diagnostic performance data as reported by the original authors.

Figure 13 plots the AUC values reported in included studies stratified by clinical versus neurotypical samples.

Figure 13

Specificity by clinical versus neurotypical samples.

The analyses also detected a statistically significant difference in the reported accuracy based on the population included in the evaluation sample (p<0.001). On average, the reported accuracy was lower in clinical samples than in studies that differentiated youth with ADHD from neurotypically development youth (mean 0.76, SD 0.13 versus mean 0.88, SD 0.09). However, the analysis should be interpreted with caution as it is not based on a meta-analytic model, and the number of included datapoints is smaller than for sensitivity and specificity. There were insufficient data available for analyses of other outcomes.

We further aimed to investigate whether the age of the participants is associated with the achieved diagnostic performance. Most studies included a range of ages, but studies differed in whether they included young children. Figure 14 plots sensitivity by minimum age in the sample.

Figure 14

Sensitivity by minimum age.

Across studies, we did not detect a statistically significant linear association between samples including younger children versus not on reported sensitivity (p 0.54). However, it should be noted that the number of studies that included smaller children was low and thus hindered statistical power to detect differences and this is an indirect comparison across studies that also does not take study size into account and hence should be interpreted with caution. The equivalent figure for the specificity is shown in Figure 15.

Figure 15

Specificity by minimum age.

Across studies, we did not detect a statistically significant linear association between samples including younger children or not on reported specificity (p 0.37). However, this analysis is an indirect analysis across studies which is also not based on the meta-analytic model and should therefore be interpreted with caution. We also categorized studies as younger versus older children. Using a dichotomous indicator differentiating between young (under 7) and older children (7 and over) also did not indicate a systematic effect for sensitivity (p 0.98), specificity (p 0.35), accuracy (p 0.09), or AUC (p 0.28).

We also analyzed the gender distribution in the identified studies, as the reported accuracy of a diagnosis may be associated with the gender of the participants. Figure 16 plots the percent female participants, the sensitivity, and specificity.

Figure 16

Sensitivity and specificity by proportion of female participants.

Across samples, the proportion of girls was not associated with reported sensitivity (p 0.63) or specificity (p 0.80). Analysis for reported accuracy also did not detect an effect (p 0.34) nor did an analysis of the reported AUCs (p 0.90) and there were insufficient data for further analyses. However, the number of female participants was small across studies, which lowers the statistical power to detect an effect.

There were insufficient numbers of studies to evaluate any other risk factors or participant variables on the diagnostic outcomes of interest.

4.7. KQ1d. What are the adverse effects associated with being labeled correctly or incorrectly as having ADHD?

Identified studies did not address consequence for patients correctly or not correctly receiving a diagnosis of ADHD or adverse effects associated with being labeled correctly or incorrectly as having ADHD. One study highlighted that a missed diagnosis has implications for accessing funding in the Australian healthcare system (e.g., national Disability Insurance Scheme) but provided no further empirical data.⁴⁴⁷ None of the included studies reported on stigma associated with being diagnosed or labeled with ADHD.

4.8. Summary of Findings. KQ1a–d

Table 11 provides a very broad overview of the identified research. Results of the individual studies are shown in the evidence table in Appendix C, Table C.1.

Table 11

KQ1 summary of findings and strength of evidence for the diagnosis of ADHD.

As documented in the summary of findings table, tests to diagnose ADHD were very diverse, and studies reported a large range of diagnostic and psychometric performance. Strength of evidence assessments for this group were low or insufficient for all outcomes. We downgraded results for study limitation (lack of details on the selected tests, employed machine learning algorithm used to select variables, and lack of details on the exact variables included in the final model contributing to the effect estimate), imprecision (large variation in reported diagnostic performance across studies), and/or lack of replication in more than one study assessing the same test (i.e., consistency could not be assessed). Few studies were available to diagnose ADHD in young children. More studies were available for the older children; however, studies did not report on all outcomes of interest. We downgraded the strength of evidence for study limitations where the evidence base consisted primarily of studies that provided insufficient detail on the diagnostic strategy (e.g., which cut offs, which variables exactly entered models). We downgraded for imprecision where studies reported a large range of possible diagnostic performance. The strength of evidence for other outcomes was downgraded for the domain inconsistency because consistency could not be assessed as no replication of the document effect has been identified.

Effect modifier analyses were hindered by the lack of reported detail needed to assess effects in meta-regressions. Indirect analyses using simple regression indicated that the diagnostic setting may influence diagnostic accuracy estimates. Further analyses assessing study population characteristics (e.g., whether the comparison is to neurotypical developing or was made in clinical samples) may affect estimates. Given that both aspects (e.g., clinical samples are seen in specialty care) may be associated with key outcomes for this review, we stratified the test-specific result presentation by neurotypical or clinical sample.

We did not identify studies reporting on the impact of correctly or incorrectly labeling youth as having ADHD or the impact of an incorrect diagnosis, and the strength of evidence is insufficient to make any evidence statements.

Publication Details

Copyright

Publisher

Agency for Healthcare Research and Quality (US), Rockville (MD)

NLM Citation

Peterson BS, Trampush J, Maglione M, et al. ADHD Diagnosis and Treatment in Children and Adolescents [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2024 Mar. (Comparative Effectiveness Review, No. 267.) 4, Results: Diagnosis of ADHD.

Figure 5Risk of bias in Key Question 1 ADHD diagnostic studies

Notes: ADHD = attention deficit hyperactivity disorder

Figure 6Key Question 1 applicability rating

Notes: N/A = Not applicable

Figure 7Sensitivity and specificity of parental rating scales

Notes: Evaluated tools: ADHD-RS-IV, ADHD-SC4-P, ADHD-SRS-H, ADHD-SRS-Im, ARS, BASC-2-EF, BASC-3, BASC-PRS, BRIEF, BRIEF2, BRIEF-P+BRIEF-T+DKEFS, CBCL, CBCL-A, CBCL-AD/H, CBCL-Ag, CBCL-SP, Conner’’, Conners-3-P(S)+CBCL, CPRS, CPRS-HI, CPRS-R, CPRS-R(S), DBC-HI, DBD, DSMD, MPS, Parental-concern, PRASIS-I-16, PRASIS-I-19, PRASIS-I-30, PSC, PSC-17, PSC-AS, RCI-4, SNAP-IV, SWAN, SWAN-H/I, VADPRS. More information can be found in Appendix C, Table C.1.

Table 3KQ1 summary of findings and strength of evidence for parental ratings

KQ1 Diagnostic Test	Outcome	Number of Studies and IDs	Findings	Reasons for Downgrading	SoE
KQ1 Parental Ratings	Sensitivity	36 studies¹⁸^, ¹³⁴^, ¹⁶⁸^, ¹⁹⁰^, ²¹⁸^, ²²³^, ²³⁰^, ²⁴²^, ²⁴⁴^, ²⁵¹^, ²⁸⁵^, ²⁸⁷^, ³⁰¹^, ³¹⁴^, ³³¹^, ³³⁶^, ³³⁹^, ³⁵⁹^, ³⁹⁰^, ⁴²⁴^, ⁴²⁷^, ⁴⁴⁸^, ⁴⁸²^, ⁴⁸⁷^, ⁵⁰²^, ⁵¹⁴^, ⁵¹⁵^, ⁵²⁸^, ⁵⁴⁷^, ⁵⁵³^, ⁵⁵⁸^, ⁵⁵⁹^, ⁵⁸⁴^, ⁵⁸⁷^, ⁶⁰⁵^, ⁶⁴²	Sensitivity ranged from 61% for a Maternal Perinatal Scale (corresponding specificity 73%)⁴²⁷ to 94% for the BASC-3 (corresponding specificity 51%) differentiating ADHD and neurotypical development Sensitivity showed more variation and ranged from 38% using the BRIEF (corresponding specificity 96%)³³⁹ to 100% using the CPRS-R or the SNAP-IV (corresponding specificities 92% and 4%)²⁸⁷^, ³¹⁴ differentiating ADHD in clinical samples	S, I	Low
KQ1 CBCL	Sensitivity	7 studies¹⁹⁰^, ²⁴²^, ²⁵¹^, ³³¹^, ³³⁶^, ⁵⁸⁷^, ⁶⁰⁵	Sensitivity ranged from 71% (corresponding specificity 91%)³³¹ to 84% (corresponding specificity 93 and 84)¹⁹⁰^, ⁶⁰⁵	S	Moderate for moderate sensitivity
KQ1 DSMD	Sensitivity	2 studies²⁴⁴^, ⁵⁴⁷	Sensitivity ranged from 63% (corresponding specificity 70%)⁵⁴⁷ to 77% (corresponding specificity 78%)²⁴⁴	S, I	Low
KQ1 SNAP-IV	Sensitivity	2 studies⁴⁸² ³¹⁴	Sensitivity ranged from 55% (corresponding specificity )⁵⁴⁷ to 100% (corresponding specificity 4)³¹⁴	S, I	Low
KQ1 SWAN	Sensitivity	2 studies¹⁶⁸^, ²²³	Sensitivity ranged from 67% (corresponding specificity 84%)²²³ to 82% (corresponding specificity 81%)¹⁶⁸	S, I	Low
KQ1 Parental Ratings	Specificity	36 studies¹⁸^, ¹³⁴^, ¹⁶⁸^, ¹⁹⁰^, ²¹⁸^, ²²³^, ²³⁰^, ²⁴²^, ²⁴⁴^, ²⁵¹^, ²⁸⁵^, ²⁸⁷^, ³⁰¹^, ³¹⁴^, ³³¹^, ³³⁶^, ³³⁹^, ³⁵⁹^, ³⁹⁰^, ⁴²⁴^, ⁴²⁷^, ⁴⁴⁸^, ⁴⁸²^, ⁴⁸⁷^, ⁵⁰²^, ⁵¹⁴^, ⁵¹⁵^, ⁵²⁸^, ⁵⁴⁷^, ⁵⁵³^, ⁵⁵⁸^, ⁵⁵⁹^, ⁵⁸⁴^, ⁵⁸⁷^, ⁶⁰⁵^, ⁶⁴²	Specificity ranged from 37% using the BASC-PRS (corresponding sensitivity 88%)²³⁰ to 100% using the Conners Parent Rating Scale Hyperactivity Index (corresponding sensitivity 79%)⁵¹⁵ differentiating ADHD and neurotypical development Specificity ranged from 4% using the SNAP-IV (corresponding sensitivity 100%)³¹⁴ to 96% (corresponding sensitivity 38%)³³⁹ differentiating ADHD in clinical samples	S, I	Low
KQ1 CBCL	Specificity	6 studies¹⁹⁰^, ²⁵¹^, ³³¹^, ³³⁶^, ⁵⁸⁷^, ⁶⁰⁵	Specificity ranged from 33% (corresponding sensitivity 77%)⁵⁸⁷ to 93% (corresponding sensitivity 84)¹⁹⁰	S, I	Low
KQ1 DSMD	Specificity	2 studies²⁴⁴^, ⁵⁴⁷	Specificity ranged from 70% (corresponding sensitivity 63%)⁵⁴⁷ to 88% (corresponding sensitivity 69%) in a second sample⁵⁴⁷	S, I	Low
KQ1 SNAP-IV	Specificity	2 studies³¹⁴^, ⁴⁸²	Specificity ranged from 4% (corresponding sensitivity 100%)⁷² to 66% (corresponding sensitivity 55%)⁴⁸²	S, I	Low
KQ1 SWAN	Specificity	2 studies¹⁶⁸^, ²²³	Specificity ranged from 74% (corresponding sensitivity 77%)²²³ to 84% (corresponding sensitivity 69%) in a second sample²²³	S, I	Low
KQ1 Parental Ratings	Accuracy	11 studies¹⁸^, ¹⁶⁹^, ²²³^, ²⁵¹^, ³³¹^, ³³⁹^, ⁴²⁷^, ⁴⁸⁷^, ⁵⁴⁷^, ⁵⁸⁷^, ⁶⁰⁵	Accuracy was 67% using the Maternal Perinatal Scale⁴²⁷ differentiating ADHD and neurotypical development Accuracy ranged from 59% using the CBCL to 95% using the BRIEF¹⁸ differentiating ADHD in clinical samples	S, I	Low
KQ1 CBCL	Accuracy	3 studies²⁵¹^, ³³¹^, ⁶⁰⁵	Accuracy ranged from 80%³³¹ to 84%⁶⁰⁵	S	Moderate for good accuracy
KQ1 Parental Ratings	AUC	23 studies¹⁶⁸^, ¹⁹⁰^, ²¹⁸^, ²³⁰^, ²³³^, ²³⁴^, ²⁴¹^, ²⁵¹^, ²⁶³^, ²⁸⁵^, ²⁸⁷^, ²⁹⁷^, ³⁰¹^, ³¹¹^, ³³¹^, ³³⁹^, ³⁴²^, ³⁴⁴^, ³⁵⁹^, ⁴⁶⁴^, ⁴⁹⁸^, ⁵⁰²^, ⁵⁵³	AUC ranged from 0.73 using the PSC-AS⁵⁵³ to 0.95 for a combination of BRIEF 2 and Conners 3⁴⁶⁴ differentiating ADHD and neurotypical development AUC ranged from 0.55 using the CBC attention deficit / hyperactivity problem scale³⁴⁴ to 0.97 using the SRS²³⁴ differentiating ADHD in clinical samples	S, I	Low
KQ1 CBCL	AUC	6 studies¹⁹⁰^, ²⁴¹^, ²⁵¹^, ³³¹^, ³⁴⁴^, ⁴⁹⁸	The reported AUC ranged from 0.55³⁴⁴ to 0.93¹⁹⁰ with three independent studies reporting estimates of 0.84 or 0.83²⁵¹^, ³³¹^, ⁴⁹⁸	S	Moderate for acceptable AUC
KQ1 Parental Ratings	Rater agreement	2 studies²¹⁸^, ⁴²³	ICC 0.51 for inattention, 0.56 for hyperactivity, and 0.58 for impulsivity between mother and father subscale ratings on the DSM-ADHD-Symptom Rating Scale⁴²³ in a sample of children with ADHD ICC between parent and teacher total scores using CPRS-R and CTRS-R was 0.19²¹⁸	S, I	Low
KQ1 Parental Ratings	Internal consistency	10 studies¹⁶⁸^, ²¹⁸^, ²⁸⁷^, ³³⁹^, ³⁴²^, ³⁵⁹^, ⁴²³^, ⁴²⁴^, ⁴⁴⁷^, ⁵¹⁶	Across children with ADHD, autism, and neurotypically developing Cronbach’s alpha SCQ 0.93⁴⁴⁷ In neurotypical samples: Cronbach’s alpha SWAN 0.95¹⁶⁸; Cronbach’s alpha BASC-2 Executive Function Screener parent rating global sum score 0.91³⁵⁹; Cronbach’s alpha DBDRS Inattention 0.94, hyperactivity / impulsivity 0.91⁵²⁷ In clinical samples: Cronbach’s alpha BRIEF2 global executive composite summary score 0.97³³⁹; Cronbach’s alpha CBCL Attention Problems 0.76³⁴² and CPRS-R 0.84²¹⁸; Cronbach’s alpha DBC-HI 0.93²⁸⁷; Cronbach’s alpha DIPA 0.92⁵¹⁶ Cronbach’s alpha DSM-ADHD-Symptom Rating Scale total 0.90 for mother’s rating, 0.91 for father’s rating⁴²³; Cronbach’s alpha PSC attention subscale 0.90⁴²⁴	S, I	Low
KQ1 Parental Ratings	Test-retest reliability	2 studies¹³⁴^, ³⁹¹	Test-retest correlations in a high-risk sample were .91 for inattention, .92 for hyperactive/impulsive, .95 for conduct/oppositional, .87 for anxiety/depression, .82 for performance subscales¹³⁴ CHAOS test-retest reliability ranged from 0.74 to 0.87³⁹¹ over four subscales in a clinical sample	S, C	Low
KQ1 Parental Ratings	Misdiagnosis impact	0 studies	No data	C	Insufficient
KQ1 Parental Ratings	Costs	0 studies	No data	C	Insufficient

: Notes: ADHD = attention deficit hyperactivity disorder, AUC = area under the curve, BASC = Behavior Assessment System for Children, BRIEF2 = Behavior Rating Inventory of Executive Function, Second Edition, C = inconsistency, CBCL = Child Behavior Checklist, CPRS-R = Revised Conners Parent Rating Scale, CHAOS = Conduct-Hyperactive-Attention Problem-Oppositional Symptom scale, DBC-HI = Developmental Behaviour Checklist Hyperactivity Index, DBDRS = Parent Disruptive Behavior Disorder Ratings Scale, DIPA-L = diagnostic infant and preschool assessment, I = imprecision, KQ = Key Question, PSC = Pediatric Symptom Checklist, S = study limitation, SCQ = Social Communication Questionnaire, SoE strength of evidence, SRS = Social Responsiveness Scale, SWAN = Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Rating Scale

Figure 8Sensitivity and specificity of teacher rating scales

Notes: Evaluated tools: ADHD-RS, ADHD-RS-IV-I, ADHD-SC4-T, BASC-2-EF, BASC-3, BRIEF, BRIEF-P+BRIEF-T+DKEFS, CTRS, CTRS-R, DBD, ECI-4, SNAP-IV, TRF, TRF+Conners-3-T(S), TRF-A, TRF-Ag, WMRS. More information can be found in the evidence table in the appendix

Table 4KQ1 summary of findings and strength of evidence for teacher ratings

KQ1 Diagnostic Test	Outcome	Number of Studies and IDs	Findings	Reasons for Downgrading	SoE
KQ1 Teacher Ratings	Sensitivity	17 studies¹⁸^, ¹¹⁹^, ¹⁸³^, ²¹⁸^, ²⁴²^, ²⁹⁹^, ³⁰¹^, ³¹⁴^, ³⁵⁹^, ⁴⁷⁹^, ⁴⁸²^, ⁵²⁷^, ⁵²⁸^, ⁵⁵⁸^, ⁵⁵⁹^, ⁵⁸⁷^, ⁶⁴²	Sensitivity ranged from 70% using the BASC-3 (corresponding specificity 73%)⁶⁴² to 92% using the ADHD-RS (corresponding specificity 88%)²⁹⁹ differentiating ADHD and neurotypical development Sensitivity ranged from 40% using the SNAP-IV (corresponding specificity 71%)⁴⁸² to 97% using the SNAP-IV (corresponding specificity 26%)³¹⁴ in clinical samples	S, I	Low
KQ1 TRF	Sensitivity	3 studies²⁴²^, ³⁰¹^, ⁵⁸⁷	Reported sensitivity ranged from 72% (corresponding specificity 75%)³⁰¹ to 79% (corresponding specificity 64%)⁵⁸⁷	S, C	Low
KQ1 SNAP-IV	Sensitivity	2 studies³¹⁴^, ⁴⁸²	Reported sensitivity ranged from 40% (corresponding specificity 71%)⁴⁸² to 97% (corresponding specificity 26%)³¹⁴	S, C	Low
KQ1 Teacher Ratings	Specificity	16 studies¹⁸^, ¹¹⁹^, ¹⁸³^, ²¹⁸^, ²⁴²^, ²⁹⁹^, ³⁰¹^, ³¹⁴^, ³⁵⁹^, ⁴⁸²^, ⁵²⁷^, ⁵²⁸^, ⁵⁵⁸^, ⁵⁵⁹^, ⁵⁸⁷^, ⁶⁴²	Specificity ranged from 55% using the DBD (corresponding sensitivity 82%)⁵²⁸ to 88% for the ADHD-RS (corresponding sensitivity 92%)²⁹⁹ differentiating ADHD and neurotypical development Specificity ranged from 48% using the CTRS-R (corresponding sensitivity 82%)¹⁸³ to 91% for the TRF aggressive behavior scale (corresponding sensitivity 48%)³⁰¹ in clinical samples	S, I	Low
KQ1 TRF	Specificity	4 studies²⁴²^, ³⁰¹^, ³⁴²^, ⁵⁸⁷	Reported specificity ranged from 64%⁵⁸⁷ to 76%²⁴²	S, C	Low
KQ1 SNAP-IV	Specificity	2 studies³¹⁴^, ⁴⁸²	Reported specificity ranged from 26% (corresponding sensitivity 97%)³¹⁴ to 71% (corresponding sensitivity 40%)⁴⁸²	S, C	Low
KQ1 Teacher Ratings	Accuracy	4 studies¹⁸^, ²⁹⁹^, ⁵⁵⁹^, ⁵⁸⁷	Accuracy was 91%²⁹⁹ using the ADHD-RS to differentiate ADHD and neurotypical development Accuracy ranged from 69 using the ECI-4⁵⁵⁹ to 76% using the TRF⁵⁸⁷ in clinical samples	C	Low
KQ1 Teacher Ratings	AUC	5 studies²¹⁸^, ³⁰¹^, ³⁴²^, ³⁵⁹^, ⁴⁷⁹	AUC was 0.83 using the BASC-2³⁵⁹ differentiating ADHD and neurotypical development AUC ranged from 0.65 for TRF³⁴² to 0.84 for the ADHD RS-IV teacher rating inattention scale⁴⁷⁹ in clinical samples	S, I	Low
KQ1 TRF	AUC	2 studies³⁰¹^, ³⁴²	AUC ranged from 0.65%³⁴² to 0.77 when combined with Conners-3-T(S)³⁰¹	S, C	Low
KQ1 Teacher Ratings	Rater agreement	4 studies²¹⁸^, ³⁶²^, ³⁹¹^, ⁴⁶³	In clinical samples: Correlations between teacher and parent ratings ranged from 0.17 to 0.41 over four subscales on the CHAOS scale,³⁹¹ the reported kappa range was 0.29 between teacher and parent ratings on the ADHD RS-IV,³⁶² up to 0.68 for Symptom Inventories Teacher rating⁴⁶³; ICCs comparing teacher and parent scores of the Conners rating scales were 0.19²¹⁸	S, I	Low
KQ1 Teacher Ratings	Internal consistency	6 studies²¹⁸^, ³⁴²^, ³⁵⁹^, ³⁹¹^, ⁵²⁷^, ⁵²⁸	In neurotypical samples: Cronbach’s alpha 0.94 for both teacher-rated inattention and hyperactivity symptom counts on the DBD⁵²⁸ Cronbach’s alpha was 0.95 for BASC-2³⁵⁹ Cronbach’s alpha was 0.94 for the DBD⁵²⁷ In clinical samples: Cronbach’s alpha was 0.95 for the TRF attention problems subscale³⁴²; Cronbach’s alpha ranged from 0.64 to 0.91 over four subscales of the CHAOS scale³⁹¹, Cronbach’s alpha was 0.80 for CTRS-R²¹⁸	S, I	Low
KQ1 Teacher Ratings	Test-retest reliability	1 study³⁹¹	Pearson correlations ranged from 0.74 to 0.87 over four subscales of the CHAOS scale, retest between 1 and 829 days³⁹¹ in a clinical sample	C	Low
KQ1 Teacher Ratings	Misdiagnosis impact	0 studies	No data	C	Insufficient
KQ1 Teacher Ratings	Costs	0 studies	No data	C	Insufficient

: Notes: AUC = area under the curve, KQ = Key Question, Attention-Deficit/Hyperactivity Disorder Rating Scale, ^4th edition, ASEBA = Achenbach System of Empirically Based Assessment, BASC = Behavior Assessment System for Children, C = inconsistency, CHAOS = Hyperactive-Attention Problem- Oppositional Symptom, CTRS-R = Connor Teacher Rating Scale Revised, DBD = Disruptive Behavior Disorder rating scale, I = imprecision, S = study limitation, SNAP-IV = Swanson, Nolan, and Pelham Questionnaire, SoE = strength of evidence, TRF = Teacher Report Form

Table 5KQ1 summary of findings and strength of evidence for self reports

KQ1 Diagnostic Test	Outcome	Number of Studies and IDs	Findings	Reasons for Downgrading	SoE
KQ1 Self-reports	Sensitivity	4 studies¹⁴²^, ¹⁶⁸^, ²⁹⁷^, ⁵⁰⁶	Sensitivity ranged from 57% (corresponding specificity 81%) using the SWAN self report¹⁶⁸ to 86% for the DIA-R (corresponding specificity 70%)¹⁴² differentiating ADHD and neurotypical development Sensitivity ranged from 53% using the Brown ADD Scale for Adolescents (corresponding specificity 98%)¹⁴² to 78% using the Brown ADD scale plus CWASR in clinical samples	C	Low
KQ1 Self-reports	Specificity	4 studies¹⁴²^, ¹⁶⁸^, ²⁹⁷^, ⁵⁰⁶	Specificity ranged from 70% using the DIA-R (corresponding sensitivity 86%)⁵⁰⁶ to 81% (corresponding sensitivity 57%) using the SWAN self report,¹⁶⁸ differentiating ADHD and neurotypical development Specificity was 98% for the Brown ADD Scale for Adolescents (corresponding sensitivity 53%)⁵⁰⁶ in clinical samples	C	Low
KQ1 Self-reports	Accuracy	1 study⁵⁰⁶	Accuracy ranged between 78 and 87% using the Brown ADD scale for Adolescents in samples of children with reading disabilities⁵⁰⁶	C	Insufficient
KQ1 Self-reports	AUC	4 studies¹⁴²^, ¹⁶⁸^, ²⁹⁷^, ⁴⁹¹	AUC ranged from 0.71 for the SWAN self report,¹⁶⁸ and the Kiddie-Computerized adaptive test (K-CAT)²⁹⁷ to 0.85 using the DIA-R¹⁴² differentiating ADHD and neurotypical development AUC was 0.56⁴⁹¹ for the ASEBA in clinical samples⁴⁹¹	C	Low
KQ1 Self-reports	Rater agreement	1 study⁵⁰⁶	Spearman correlation between child self-report and parent report ranged from .164 for subscales of the Conners and Child Behavior Checklist to .747 for the Brown ADD self-report vs the K-SADS parent report⁵⁰⁶	C	Low
KQ1 Self-reports	Internal consistency	1 study¹⁶⁸	Cronbach’s alpha was above 0.80 for the DIA-R¹⁴² and 0.88 for the SWAN self report¹⁶⁸ differentiating ADHD and neurotypical development	C	Low
KQ1 Self-reports	Test-retest reliability	1 study¹⁴²	Test-retest ICC was between 0.82 to 0.84 across clinical and neurotypical developing subsamples using the DIA-R¹⁴²	C	Low
KQ1 Self-reports	Misdiagnosis impact	0 studies	No data	C	Insufficient
KQ1 Self-reports	Costs	0 studies	No data	C	Insufficient

: Notes: AUC = area under the curve, KQ = Key Question, ASEBA = Achenbach System of Empirically Based Assessment, C = inconsistency, DIA-R = Dominic Interactive for Adolescents-Revised, I = imprecision, K-SADS = Kiddie Schedule for Affective Disorders and Schizophrenia, S = study limitation, SoE = strength of evidence, SWAN = Strengths and Weaknesses of ADHD Symptoms and Normal Behavior Rating Scale

Table 6KQ1 summary of findings and strength of evidence for clinician tools

KQ1 Diagnostic Test	Outcome	Number of Studies and IDs	Findings	Reasons for Downgrading	SoE
KQ1 Clinician tool	Sensitivity	14 studies²⁷^, ¹²¹^, ¹⁶⁷^, ¹⁸¹^, ²⁹⁸^, ²⁹⁹^, ³⁵⁵^, ³⁸⁸^, ⁴⁰⁰^, ⁴⁰³^, ⁴⁰⁷^, ⁴¹⁶^, ⁴¹⁷^, ⁴³⁴	Sensitivity ranged from 25 (corresponding specificity 94%) using actigraph measures taken during CPT task⁴¹⁷ to 100% (corresponding specificity 99%) using extended activity measurement³⁵⁵ differentiating ADHD and neurotypical development Sensitivity ranged from 63% (corresponding specificity 74%) using a combination of medical record indicators⁴³⁴ to 93% using smart chair data¹⁸¹ in a clinical sample	C, I	Low
KQ1 Clinician tool	Specificity	14 studies²⁷^, ¹²¹^, ¹⁶⁷^, ¹⁸¹^, ²⁹⁸^, ²⁹⁹^, ³⁵⁵^, ³⁸⁸^, ⁴⁰⁰^, ⁴⁰³^, ⁴⁰⁷^, ⁴¹⁶^, ⁴¹⁷^, ⁴³⁴	Specificity ranged from 79% using an observational assessment tool (corresponding sensitivity 87%)¹⁶⁷ to 100% (corresponding sensitivity 98%)¹²¹ using activity measures differentiating ADHD and neurotypical development Specificity ranged from 36% using interview notes and family history data (corresponding sensitivity 89%)⁴³⁴ to 95% (corresponding sensitivity 67%)¹⁸¹ using smart chair data in a clinical sample	C, I	Low
KQ1 Clinician tool	Accuracy	12 studies²⁷^, ¹²¹^, ¹⁸¹^, ²⁹⁸^, ²⁹⁹^, ³⁵⁵^, ³⁸⁸^, ⁴⁰³^, ⁴⁰⁷^, ⁴¹⁶^, ⁴³⁴^, ⁴³⁷	Accuracy ranged from 0.68⁴³⁷ to 0.99¹²¹ using activity measures to differentiate ADHD and neurotypical development Accuracy ranged from 0.61 for individual clinical impressions²⁷ to 0.92 using smart chair data¹⁸¹ in a clinical sample	S, I	Low
KQ1 Clinician tool	AUC	12 studies¹²¹^, ¹⁶⁷^, ¹⁸¹^, ³¹¹^, ³⁵⁵^, ³⁸⁵^, ³⁸⁹^, ⁴⁰⁰^, ⁴⁰⁷^, ⁴¹⁶^, ⁴³⁴^, ⁶²⁷	Activity measures ranged from AUC 0.79⁶²⁷ to 0.9996³⁵⁵ differentiating ADHD and neurotypical development AUC ranged from 0.66 using a combination of medical record indicators⁴³⁴ to 0.98 using smart chair data in a clinical sample	S, I	Low
KQ1 Clinician tools	Rater agreement	2 studies¹⁶⁷^, ⁴⁹⁹	ICC was 0.92 for raters using the DB-DOS¹⁶⁷ differentiating ADHD and neurotypical development Kappa between a clinician interviewer and clinician observing the interview was 0.46⁴⁹⁹ in a clinical sample	C	Low
KQ1 Clinician tools	Internal consistency	2 studies¹⁶⁷^, ³⁸⁵	Cronbach’s alpha was 0.82 for the DB-DOS¹⁶⁷ differentiating ADHD and neurotypical development Cronbach’s alpha was 0.86 for the HDS³⁸⁵ in a clinical sample	C	Low
KQ1 Clinician tools	Test-retest reliability	1 study¹⁶⁷	Test-retest reliability was ICC 0.64 for the DB-DOS¹⁶⁷ differentiating ADHD and neurotypical development	C	Insufficient
KQ1 Clinician tools	Misdiagnosis impact	0 studies	No data	C	Insufficient
KQ1 Clinician tools	Costs	0 studies	No data	C	Insufficient

: Notes: ADHD = attention deficit hyperactivity disorder, AUC = area under the curve, C = inconsistency, DB-DOS = Disruptive Behavior Diagnostic Observation Schedule, HDS= InterRAI child and Youth Mental Health Hyperactive/Distraction scale, I = imprecision, KQ = Key Question, S = study limitation; SoE = strength of evidence

Table 7KQ1 summary of findings and strength of evidence for biomarkers

KQ1 Diagnostic Test	Outcome	Number of Studies and IDs	Findings	Reasons for Downgrading	SoE
KQ1 Biomarkers (biospecimen)	Sensitivity	7 studies³⁰⁹^, ⁵⁰¹^, ⁵⁶³^, ⁵⁸³^, ⁶⁰³^, ⁶³⁵^, ⁶⁴⁴	Sensitivity ranged from 56% (corresponding specificity 95%)⁵⁰¹ to100% (corresponding specificity 100%) using a serum marker³⁰⁹ differentiating ADHD and neurotypical development	S, I	Low
KQ1 Blood biomarkers	Sensitivity	2 studies³⁰⁹^, ⁵⁶³	Sensitivity ranged from 79% (corresponding specificity 25%)⁵⁶³ to 100% (corresponding specificity 100%) using a serum marker³⁰⁹	S, I	Low
KQ1 miRNA biomarkers	Sensitivity	3 studies⁶⁰³^, ⁶³⁵^, ⁶⁴⁴	Sensitivity ranged from 68% (corresponding specificity 71%)⁶³⁵ to 90% (corresponding specificity 80%)⁶⁰³	S, I	Low
KQ1 urine markers	Sensitivity	2 studies⁵⁰¹^, ⁵⁸³	Sensitivity ranged from 56% (corresponding specificity 95%)⁵⁰¹ to 94% (corresponding specificity 83%)⁵⁸³	S, I	Low
KQ1 Biomarkers (biospecimen)	Specificity	7 studies³⁰⁹^, ⁵⁰¹^, ⁵⁶³^, ⁵⁸³^, ⁶⁰³^, ⁶³⁵^, ⁶⁴⁴	Specificity ranged from 25% (corresponding sensitivity 79%)⁵⁶³ to 100% (corresponding sensitivity 100%) using a serum marker³⁰⁹ differentiating ADHD and neurotypical development	S, I	Low
KQ1 Blood biomarkers	Specificity	2 studies³⁰⁹^, ⁵⁶³	Specificity ranged from 25% (corresponding sensitivity 79%)⁵⁶³ to 100% (corresponding sensitivity 100%)³⁰⁹	S, I	Low
KQ1 miRNA biomarkers	Specificity	3 studies⁶⁰³^, ⁶³⁵^, ⁶⁴⁴	Specificity ranged from 71% (corresponding specificity 68%)⁶³⁵ to 95% (corresponding sensitivity 82%)⁶³⁵	S, I	Low
KQ1 urine markers	Specificity	2 studies⁵⁰¹^, ⁵⁸³	Specificity ranged from 80% (corresponding sensitivity 88%)⁵⁰¹ to 95% (corresponding sensitivity 56%)⁵⁰¹	S, I	Low
KQ1 Biomarkers (biospecimen)	Accuracy	2 studies⁵⁶³^, ⁶⁰³	Accuracy ranged from 55% using a blood marker⁵⁶³ to 85% using miRNA⁶⁰³ differentiating ADHD and neurotypical development	S, C	Low
KQ1 Biomarkers (biospecimen)	AUC	4 studies³⁰⁹^, ⁵⁸³^, ⁶⁰³^, ⁶⁴⁴	AUC ranged from 0.68⁶⁰³ using mIRNA⁶³⁵ to 1.00³⁰⁹ using a serum marker differentiating ADHD and neurotypical development	S, C	Low
KQ1 Biomarkers (biospecimen)	Rater agreement	0 studies	No data	C	Insufficient
KQ1 Biomarkers (biospecimen)	Internal consistency	0 studies	No data	C	Insufficient
KQ1 Biomarkers (biospecimen)	Test-retest reliability	0 studies	No data	C	Insufficient
KQ1 Biomarkers (biospecimen)	Misdiagnosis impact	0 studies	No data	C	Insufficient

: Notes: ADHD = attention deficit hyperactivity disorder, AUC = area under the curve, C = inconsistency, I = imprecision, Key Question = Key Question, S = study limitation, SoE = strength of evidence

Table 8KQ1 summary of findings and strength of evidence for EEG

KQ1 Diagnostic Test	Outcome	Number of Studies and IDs	Findings	Reasons for Downgrading	SoE
KQ1 EEG	Sensitivity	27 studies²⁷^, ¹¹¹^, ¹¹⁵^, ¹²⁰^, ¹²⁴^, ¹⁴³^, ¹⁵⁷^, ¹⁷²^, ¹⁷⁹^, ¹⁸²^, ¹⁸⁶^, ¹⁸⁹^, ¹⁹⁷^, ²⁴⁵^, ³⁴⁰^, ³⁵¹^, ³⁵⁶^, ³⁷⁰^, ³⁹⁵^, ³⁹⁷^, ⁴⁰⁸^, ⁴¹³^, ⁴⁷³^, ⁴⁸⁷^, ⁵⁴⁶^, ⁵⁴⁸^, ⁵⁹²	Sensitivity ranged from 46% (corresponding specificity 74%)¹⁹⁷ to 100% (corresponding specificities 71% to 100%)¹⁴³^, ²⁴⁵^, ⁴¹³ differentiating ADHD and neurotypical development Sensitivity ranged from 82% (corresponding specificity 94%)²⁷ to 97% (corresponding specificity 100%)⁴⁸⁷ in clinical samples	S, I	Low
KQ1 EEG combined with ratings or demographics	Sensitivity	6 studies²⁷^, ¹²⁴^, ¹⁴³^, ¹⁵⁷^, ¹⁸⁹^, ¹⁹⁷	Sensitivity ranged from 76% (corresponding specificity 74%)¹²⁴ to 100% (corresponding specificity¹⁵⁷ 100%)¹⁴³	S, I	Low
KQ1 EEG	Specificity	27 studies²⁷^, ¹¹¹^, ¹¹⁵^, ¹²⁰^, ¹²⁴^, ¹⁴³^, ¹⁵⁷^, ¹⁷²^, ¹⁷⁹^, ¹⁸²^, ¹⁸⁶^, ¹⁸⁹^, ¹⁹⁷^, ²⁴⁵^, ³⁴⁰^, ³⁵¹^, ³⁵⁶^, ³⁷⁰^, ³⁹⁵^, ³⁹⁷^, ⁴⁰⁸^, ⁴¹³^, ⁴⁷³^, ⁴⁸⁷^, ⁵⁴⁶^, ⁵⁴⁸^, ⁵⁹²	Specificity ranged from 38% (corresponding sensitivity 95%)¹⁹⁷ to 100% (corresponding specificities 71% or 100%)¹⁴³^, ⁴¹³ differentiating ADHD and neurotypical development Sensitivity ranged from 83% (corresponding specificity 84%)³⁹⁵ to 100% (corresponding specificity 94%)⁴⁸⁷ in clinical samples	S, I	Low
KQ1 EEG combined with ratings or demographics	Specificity	6 studies²⁷^, ¹²⁴^, ¹⁴³^, ¹⁵⁷^, ¹⁸⁹^, ³⁵¹	Specificity ranged from 74% (corresponding sensitivity 76%)¹²⁴ to 100% (corresponding sensitivity²⁷ 100%)¹⁴³	S, I	Low
KQ1 EEG	Accuracy	35 studies²⁷^, ¹¹¹^, ¹¹⁵^, ¹²⁰^, ¹⁴³^, ¹⁵⁷^, ¹⁷²^, ¹⁸²^, ¹⁸⁶^–¹⁸⁹^, ¹⁹⁷^, ²⁴⁵^, ³¹²^, ³²²^, ³⁴⁰^, ³⁵¹^, ³⁵⁶^, ³⁶⁶^, ³⁷⁰^, ³⁹⁴^, ³⁹⁷^, ⁴⁰⁸^, ⁴²⁰^, ⁴³⁸^, ⁴⁴⁹^, ⁴⁶⁸^, ⁴⁷³^, ⁴⁸⁷^, ⁴⁹⁴^, ⁵⁴⁶^, ⁵⁴⁸^, ⁵⁹²^, ⁶⁴¹	Accuracy ranged from 58%¹⁹⁷ to 100%¹⁴³^, ²⁴⁵^, ³⁴⁰^, ⁴⁶⁸^, ⁴⁹⁴ differentiating ADHD and neurotypical development Accuracy ranged from 88%²⁷ to 98%³⁷⁰ in clinical samples	S, I	Low
KQ1 EEG combined with ratings or demographics	Accuracy	5 studies²⁷^, ¹⁴³^, ¹⁸⁹^, ³¹²^, ³²²^, ³⁵¹	Accuracy ranged from 86%¹⁸⁹ to 98%³¹²	S, I	Low
KQ1 EEG	AUC	13 studies¹²⁰^, ¹⁷⁹^, ¹⁸⁶^, ¹⁸⁷^, ¹⁸⁹^, ¹⁹⁷^, ²⁴⁵^, ³⁴⁰^, ⁴⁰⁴^, ⁴¹²^, ⁴¹³^, ⁴¹⁵^, ⁴³⁸	AUC ranged from 0.63¹⁹⁷ to 1.00²⁴⁵ differentiating ADHD from neurotypical development	S, I	Low
KQ1 EEG	Rater agreement	1 study¹²⁰	Kappa for classifiers ranged from 0.73 to 0.78¹²⁰ across different models differentiating ADHD and neurotypical development	C	Insufficient
KQ1 EEG	Internal consistency	0 studies	No data	C	Insufficient
KQ1 EEG	Test-retest reliability	1 study²⁷	ICC was 0.83 for Theta/Beta ratio; repeated measures collected on two different visits in a clinical sample²⁷	C	Low
KQ1 EEG	Misdiagnosis impact	0 studies	No data	C	Insufficient
KQ1 EEG	Costs	0 studies	No data	C	Insufficient

: Notes: ADHD = attention deficit hyperactivity disorder, AUC = area under the curve, C = inconsistency, EEG = electroencephalogram; I = imprecision, KQ = Key Question, S = study limitation, SoE strength of evidence

Table 9KQ1 summary of findings and strength of evidence for neuroimaging

KQ1 Diagnostic Test	Outcome	Number of Studies and IDs	Findings	Reasons for Downgrading	SoE
KQ1 Imaging (MRI, NIRS) to diagnose ADHD	Sensitivity	15 studies²⁸^, ¹⁹¹^, ²¹¹^, ²⁸²^, ³¹⁹^, ⁴⁰⁰^, ⁴⁶⁷^, ⁴⁹⁵^, ⁵¹⁸^, ⁵⁴⁹^, ⁵⁷¹^, ⁵⁸¹^, ⁵⁹¹^, ⁶³⁰^, ⁶³¹	Sensitivity ranged from 42% (corresponding specificity 95%) using morphometric MRI⁵⁴⁹ to 99% (corresponding specificity 100%) utilizing fMRI in a complex machine learning approach⁵⁸¹ differentiating ADHD and neurotypical development	S, I	Low
KQ1 MRI to diagnose ADHD	Sensitivity	13 studies²⁸^, ¹⁹¹^, ²⁸²^, ³¹⁹^, ⁴⁰⁰^, ⁴⁶⁷^, ⁴⁹⁵^, ⁵¹⁸^, ⁵⁴⁹^, ⁵⁷¹^, ⁵⁸¹^, ⁵⁹¹^, ⁶³⁰	Sensitivity ranged from 42% (corresponding specificity 95%) using morphometric MRI⁵⁴⁹ to 99% (corresponding specificity 100%) utilizing fMRI in a complex machine learning approach⁵⁸¹ differentiating ADHD and neurotypical development	S, I	Low
KQ1 NIRS to diagnose ADHD	Sensitivity	2 studies²¹¹^, ⁶³¹	Sensitivity ranged from 73% (corresponding specificity 87%)²¹¹ to 89% (corresponding specificity 84%)⁶³¹	S, I	Low
KQ1 Imaging combining data with non-imaging variables	Sensitivity	3 studies²¹¹^, ²⁸²^, ⁶³¹	Sensitivity ranged from 73% (corresponding specificity 65%) using near-infrared spectroscopy for functional measures in a multi-domain profile of measures to 93% (corresponding specificity 95%) in a complex machine learning model based on fMRI²⁸²	S, I	Low
KQ1 Imaging (MRI, NIRS) to diagnose ADHD	Specificity	14 studies²⁸^, ¹⁹¹^, ²¹¹^, ²⁸²^, ³¹⁹^, ⁴⁰⁰^, ⁴⁹⁵^, ⁵¹⁸^, ⁵⁴⁹^, ⁵⁷¹^, ⁵⁸¹^, ⁵⁹¹^, ⁶³⁰^, ⁶³¹	Specificity ranged from 55% (corresponding sensitivity 95%) in a model using resting state fMRI⁵¹⁸ to 100% (corresponding sensitivity99%) utilizing fMRI in complex machine learning approaches⁵⁸¹ differentiating ADHD and neurotypical development	S, I	Low
KQ1 MRI to diagnose ADHD	Specificity	12 studies²⁸^, ¹⁹¹^, ²⁸²^, ³¹⁹^, ⁴⁰⁰^, ⁴⁹⁵^, ⁵¹⁸^, ⁵⁴⁹^, ⁵⁷¹^, ⁵⁸¹^, ⁵⁹¹^, ⁶³⁰	Specificity ranged from 55% (corresponding sensitivity 95%) in a model using resting state fMRI⁵¹⁸ to 100% (corresponding sensitivity 100%) utilizing fMRI in a complex machine learning approach⁵⁸¹	S, I	Low
KQ1 Imaging combining data with non-imaging variables	Specificity	3 studies²¹¹^, ²⁸²^, ⁶³¹	Specificity ranged from 84% (corresponding sensitivity 89%) using a combination of NIRS and other data⁶³¹ to 95% (corresponding sensitivity 93%) utilizing fMRI and non-imaging data²⁸²	S, I	Low
KQ1 NIRS to diagnose ADHD	Specificity	2 studies²¹¹^, ⁶³¹	Specificity ranged from 84% (corresponding specificity 89%)⁶³¹ to 87% (corresponding specificity 73%)²¹¹	S, I	Low
KQ1 Imaging (MRI, NIRS) to diagnose ADHD	Accuracy	14 studies¹⁹¹^, ²¹¹^, ²⁸²^, ³¹⁹^, ⁴⁰⁰^, ⁴⁶⁷^, ⁴⁹⁵^, ⁵¹⁸^, ⁵⁷¹^, ⁵⁸¹^, ⁵⁹¹^, ⁶³⁰^, ⁶³¹^, ⁶³³	Accuracy ranged from 61% using sMRI⁴⁶⁷ to 99.6% utilizing resting state fMRI in complex machine learning approaches¹⁹¹^, ⁵⁸¹ differentiating ADHD and neurotypical development	S, I	Low
KQ1 MRI to diagnose ADHD	Accuracy	12 studies¹⁹¹^, ²⁸²^, ³¹⁹^, ⁴⁰⁰^, ⁴⁶⁷^, ⁴⁹⁵^, ⁵¹⁸^, ⁵⁷¹^, ⁵⁸¹^, ⁵⁹¹^, ⁶³⁰^, ⁶³³	Accuracy ranged from 61% using sMRI⁴⁶⁷ to 99.6% utilizing resting state fMRI in complex machine learning approaches¹⁹¹^, ⁵⁸¹ differentiating ADHD and neurotypical development	S, I	Low
KQ1 NIRS to diagnose ADHD	Accuracy	2 studies²¹¹^, ⁶³¹	Accuracy ranged from 81%²¹¹ to 86%⁶³¹	S, I	Low
KQ1 Imaging combining data with non-imaging variables	Accuracy	6 studies²¹¹^, ²⁸²^, ⁴⁹⁵^, ⁵¹⁸^, ⁶³¹	Accuracy ranged from 68% in a model using resting state fMRI⁵¹⁸ to 95% utilizing fMRI in combination with phenotypic data in a complex machine learning procedure⁶³¹	S, I	Low
KQ1 Imaging (MRI, NIRS) to diagnose ADHD	AUC	13 studies¹⁹¹^, ²¹¹^, ³¹⁹^, ⁴⁰⁰^, ⁴⁶⁴^, ⁴⁶⁷^, ⁵¹⁸^, ⁵⁴⁹^, ⁵⁸⁰^, ⁵⁸¹^, ⁵⁹¹^, ⁶³¹^, ⁶³³	AUC ranged from 0.58 in a multimodal imaging model⁴⁰⁰ to 0.997 in a model based on fMRI⁵⁸¹ differentiating ADHD and neurotypical development	S, I	Low
KQ1 MRI to diagnose ADHD	AUC	10 studies¹⁹¹^, ³¹⁹^, ⁴⁰⁰^, ⁴⁶⁷^, ⁵¹⁸^, ⁵⁴⁹^, ⁵⁸⁰^, ⁵⁸¹^, ⁵⁹¹^, ⁶³³	AUC ranged from 0.58⁴⁰⁰ in a multimodeal imaging model to 0.997 in a model based on fMRI⁵⁸¹	S, I	Low
KQ1 Imaging combining data with non-imaging variables	AUC	3 studies²¹¹^, ⁶³¹^, ⁶³³	AUC ranged from 0.70 using sMRI, fMRI, and diffusion-tensor MRI plus age, sex, and IQ⁶³³ to 0.898 in a model based on resting state fMRI⁶³¹	S, I	Low
KQ1 NIRS to diagnose ADHD	AUC	2 studies²¹¹^, ⁶³¹	AUC ranged from 0.80²¹¹to 0.90⁶³¹	S, I	Low
KQ1 Imaging to diagnose ADHD	Rater agreement	0 studies	No data	C	Insufficient
KQ1 Imaging to diagnose ADHD	Internal consistency	0 studies	No data	C	Insufficient
KQ1 Imaging to diagnose ADHD	Test-retest reliability	0 studies	No data	C	Insufficient
KQ1 Imaging to diagnose ADHD	Misdiagnosis impact	0 studies	No data	C	Insufficient
KQ1 Imaging to diagnose ADHD	Costs	0 studies	No data	C	Insufficient

: Notes: ADHD = attention deficit hyperactivity disorder, AUC = area under the curve, C = inconsistency, fMRI = functional magnetic resonance imagining, I = imprecision, KQ = Key Question, MRI = magnetic resonance imaging, sMRI = structural MRI, NIRS = near-infrared spectroscopy, S = study limitation, SoE strength of evidence

Table 10KQ1 summary of findings and strength of evidence for neuropsychological tests

KQ1 Diagnostic Test	Outcome	Number of Studies and IDs	Findings	Reasons for Downgrading	SoE
KQ1 Neuropsychological tests	Sensitivity	52 studies¹⁸^, ²¹^, ¹¹²^, ¹¹⁹^, ¹⁴¹^, ¹⁵²^, ¹⁵³^, ¹⁶²^, ¹⁷⁰^, ¹⁷⁷^, ¹⁹⁰^, ¹⁹⁸^, ²¹³^, ²⁴⁶^, ²⁵³^, ²⁶⁷^, ²⁷⁶^, ²⁹³^, ²⁹⁸^, ³⁰⁷^, ³²³^, ³²⁷^, ³⁴⁶^, ³⁴⁷^, ³⁵¹^, ³⁵²^, ³⁷⁹^, ³⁹³^, ⁴¹⁷^, ⁴²¹^, ⁴²²^, ⁴³⁶^, ⁴⁴⁵^, ⁴⁴⁶^, ⁴⁵⁰^, ⁴⁶²^, ⁴⁶⁷^, ⁴⁷⁰^, ⁴⁷⁵^, ⁴⁷⁷^, ⁴⁸²^, ⁴⁸⁶^, ⁴⁹³^, ⁴⁹⁶^, ⁵¹⁵^, ⁵³⁷^, ⁵⁴³^, ⁵⁶⁴^, ⁶¹⁴^, ⁶¹⁵^, ⁶³⁹^, ⁶⁴⁷	Sensitivity ranged from 28% (corresponding specificity 95%)⁴⁴⁵ to 100% (corresponding specificity up to 100%)¹⁴¹^, ¹⁵² differentiating ADHD and neurotypical development Sensitivity ranged from 22% (corresponding specificity 96%)⁵⁶⁴ to 91% (corresponding specificity 22%)⁶³⁹ in clinical samples	S, I	Low
KQ1 CPT	Sensitivity	35 studies²¹^, ¹¹²^, ¹¹⁹^, ¹⁴¹^, ¹⁵²^, ¹⁵³^, ¹⁶²^, ¹⁸⁹^, ¹⁹⁰^, ¹⁹⁸^, ²⁴⁶^, ²⁵³^, ²⁷⁶^, ²⁹⁸^, ³⁰⁷^, ³²³^, ³⁴⁶^, ³⁴⁷^, ⁴⁰⁷^, ⁴¹⁷^, ⁴²¹^, ⁴³⁶^, ⁴⁵⁰^, ⁴⁶²^, ⁴⁶⁹^, ⁴⁷⁰^, ⁴⁷⁵^, ⁴⁸²^, ⁴⁹⁶^, ⁵¹⁵^, ⁵³⁷^, ⁵⁴³^, ⁵⁶⁴^, ⁶³⁹^, ⁶⁴⁷	Sensitivity ranged from 22% (corresponding specificity 96%)⁵⁶⁴ to 100% (corresponding specificity 100%) for a brief neuropsychological measure supported by machine learning	S, I	Low
KQ1 CPT Attention	Sensitivity	3 studies²¹^, ²⁴^, ¹⁶²	Sensitivity ranged from 48% (corresponding specificity 83%)²⁴ to 68% (corresponding specificity 76%,)²¹	S, I	Low
KQ1 CPT Impulsivity	Sensitivity	2 studies²⁴^, ¹⁶²	Sensitivity ranged from 48% (corresponding specificity 83%)²⁴ to 55% (corresponding specificity 64%)¹⁶²	S, I	Low
KQ1 Neuropsychological tests	Specificity	54 studies¹⁸^, ²¹^, ¹¹²^, ¹¹⁹^, ¹²⁴^, ¹⁵²^, ¹⁵³^, ¹⁶²^, ¹⁷⁰^, ¹⁷⁷^, ¹⁹⁰^, ¹⁹⁸^, ²¹³^, ²⁴⁶^, ²⁵³^, ²⁶⁷^, ²⁷⁶^, ²⁸⁴^, ²⁹⁸^, ³⁰⁷^, ³²³^, ³²⁷^, ³⁴⁶^, ³⁵¹^, ³⁵²^, ³⁷⁹^, ³⁸⁸^, ³⁹³^, ⁴⁰²^, ⁴⁰⁷^, ⁴¹⁷^, ⁴²¹^, ⁴²²^, ⁴³⁶^, ⁴⁴⁵^, ⁴⁴⁶^, ⁴⁵⁰^, ⁴⁶²^, ⁴⁶⁹^, ⁴⁷⁰^, ⁴⁷⁵^, ⁴⁷⁷^, ⁴⁸²^, ⁴⁸⁶^, ⁴⁹³^, ⁴⁹⁶^, ⁵¹⁵^, ⁵³⁷^, ⁵⁴³^, ⁵⁶⁴^, ⁶¹⁴^, ⁶¹⁵^, ⁶³⁹^, ⁶⁴⁷	Specificity ranged from 46% (corresponding sensitivity 85%)⁴⁶⁹ to 100% (corresponding sensitivity 100% and 75% respectively)¹⁵²^, ⁴⁵⁰ differentiating ADHD and neurotypical development Specificity ranged from 22% (corresponding sensitivity 91%)⁶³⁹ to 85% (corresponding sensitivity 63%)¹⁵³ in clinical samples	S, I	Low
KQ1 CPT	Specificity	33 studies²¹^, ¹¹²^, ¹¹⁹^, ¹⁵²^, ¹⁵³^, ¹⁶²^, ¹⁸⁹^, ¹⁹⁰^, ¹⁹⁸^, ²⁴⁶^, ²⁵³^, ²⁷⁶^, ²⁹⁸^, ³²³^, ³⁴⁶^, ³⁴⁷^, ⁴⁰⁷^, ⁴¹⁷^, ⁴²¹^, ⁴³⁶^, ⁴⁵⁰^, ⁴⁶²^, ⁴⁶⁹^, ⁴⁷⁰^, ⁴⁷⁵^, ⁴⁸²^, ⁴⁹⁶^, ⁵¹⁵^, ⁵³⁷^, ⁵⁴³^, ⁵⁶⁴^, ⁶³⁹^, ⁶⁴⁷	Specificity ranged from 22% (corresponding sensitivity 91%) using TOVA⁶³⁹ to 100% (corresponding sensitivity 89%) using the PANDAS⁴⁵⁰	S, I	Low
KQ1 CPT Attention	Specificity	3 studies²¹^, ²⁴^, ¹⁶²	Specificity ranged from 64% (corresponding sensitivity 55%)¹⁶² to 83% (corresponding sensitivity 48%)²⁴	S, I	Low
KQ1 CPT Impulsivity	Specificity	2 studies²⁴^, ¹⁶²	Specificity ranged from 64% (corresponding sensitivity 55%)¹⁶² to 83% (corresponding sensitivity 48%)²⁴	S, I	Low
KQ1 Neuropsychological tests	Accuracy	40 studies¹⁸^, ¹¹²^, ¹⁴¹^, ¹⁵²^, ¹⁵⁹^, ¹⁶²^, ¹⁷⁰^, ¹⁸⁴^, ¹⁸⁵^, ¹⁹⁸^, ²¹³^, ²⁵³^, ²⁸⁴^, ²⁹³^, ²⁹⁸^, ³⁰⁷^, ³¹⁶^, ³²³^, ³²⁷^, ³⁴⁶^, ³⁵¹^, ³⁸⁸^, ⁴⁰²^, ⁴⁰⁷^, ⁴¹⁷^, ⁴²¹^, ⁴²²^, ⁴⁵⁰^, ⁴⁶²^, ⁴⁶⁷^, ⁴⁶⁹^, ⁴⁷⁰^, ⁴⁷⁵^, ⁴⁹³^, ⁵⁰⁰^, ⁵³⁷^, ⁵⁴¹^, ⁵⁴³^, ⁶⁰⁷^, ⁶³²	Accuracy ranged from 34% using the TOVA⁵⁰⁰ to 100%¹⁵² for a brief neuropsychological measure supported by machine learning differentiating ADHD and neurotypical development Accuracy ranged from 67%¹⁶² in children with co-occurring oppositional defiance disorder to 95% for a combination measure¹⁸	S, I	Low
KQ1 CPT	Accuracy	26 studies¹¹²^, ¹⁴¹^, ¹⁵²^, ¹⁶²^, ¹⁸⁵^, ¹⁸⁹^, ¹⁹⁸^, ²⁵³^, ²⁹⁸^, ³⁰⁷^, ³¹⁶^, ³²³^, ³⁴⁶^, ⁴⁰⁷^, ⁴¹⁷^, ⁴²¹^, ⁴⁵⁰^, ⁴⁶²^, ⁴⁶⁵^, ⁴⁶⁹^, ⁴⁷⁰^, ⁴⁷⁵^, ⁵⁰⁰^, ⁵³⁷^, ⁵⁴³^, ⁶³²	Accuracy ranged from 57% using a virtual reality CPT⁵⁰⁰ to 95% using TOVA¹⁴¹	S, I	Low
KQ1 Neuropsychological tests	AUC	26 studies¹⁴⁰^, ¹⁷⁰^, ¹⁷⁷^, ¹⁹⁰^, ¹⁹⁸^, ²⁴⁶^, ²⁶³^, ²⁶⁷^, ³¹⁶^, ³⁴⁶^, ³⁴⁷^, ³⁵²^, ³⁸²^, ³⁹³^, ⁴⁰¹^, ⁴⁰⁷^, ⁴⁴⁵^, ⁴⁴⁶^, ⁴⁶⁷^, ⁴⁶⁹^, ⁴⁷⁷^, ⁴⁸⁶^, ⁴⁹³^, ⁵⁶⁴^, ⁵⁷⁶^, ⁶²⁷	AUC ranged from 0.65⁴⁶⁹ to 0.93 for individual Go/No-Go task measures³⁹³ differentiating ADHD and neurotypical development AUC ranged from 0.59⁵⁶⁴ to 0.87²⁶³ in clinical samples	S, I	Low
KQ1 CPT	AUC	15 studies¹⁴⁰^, ¹⁸⁹^, ¹⁹⁰^, ¹⁹⁸^, ²⁴⁶^, ²⁶³^, ³¹⁶^, ³⁴⁶^, ³⁴⁷^, ³⁸²^, ⁴⁰¹^, ⁴⁰⁷^, ⁴⁶⁹^, ⁵⁶⁴^, ⁵⁷⁶	AUC ranged from 0.65 using the Advanced Test of Attention⁴⁶⁹ to 0.92 using the MOXO CPT¹⁴⁰	S, I	Low
KQ1 Neuropsychological tests	Rater agreement	3 studies¹⁷⁰^, ²⁶³^, ⁶³⁹	Neurotypical samples: Kappa was 0.55 between Cognitive Assessment System discriminant function analysis classifications and a priori diagnosis¹⁷⁰ Clinical samples: Kappa 0.15 between Groundskeeper game and Conners subscales, 0.18 between Groundskeeper game and CPT, and 0.3 between Conners subscales and Conners CPT²⁶³ Kappa 0.15 between Test of Variables of Attention and diagnosis by clinical assessment⁶³⁹	S, I	Low
KQ1 Neuropsychological tests	Internal consistency	2 studies¹⁹⁸^, ⁵⁰⁰	Cronbach’s alpha ranged from 0.906 to 0.987 across 15 variables in the diagnosis-supported decision support system (DS-ADHD) across all children¹⁹⁸ Cronbach’s alpha for a virtual reality instrument was 0.72⁵⁰⁰	C	Low
KQ1 Neuropsychological tests	Test-retest reliability	1 study²¹³	ICC less than 0.5 for the ADHD group on all visual and auditory test variables on The Advanced Test of Attention repeated after 2 weeks⁴⁶⁹	C	Insufficient
KQ1 Neuropsychological tests	Misdiagnosis impact	0 studies	No data	C	Insufficient
KQ1 Neuropsychological tests	Costs	1 study³¹⁵	£31 [~$42] for QbTest including 30-minute appointment, £108 a consultation within the UK Medway NHS Trust at the time of audit³¹⁵ in a clinical sample	C	Insufficient

: Notes: ADHD = attention deficit hyperactivity disorder, AUC = area under the curve, C = inconsistency, CPT = continuous performance test, I = imprecision, KQ = Key Question, QB test = Quantified Behavioral Test, S = study limitation, TOVA = Test of Variables of Attention, SoE = strength of evidence

Figure 9Sensitivity by setting

Notes: N/A = not available

Figure 10Specificity by setting

Notes: N/A = not available

Figure 11Sensitivity by clinical population

Figure 12Specificity by clinical population

Figure 13Specificity by clinical versus neurotypical samples

Figure 14Sensitivity by minimum age

Figure 15Specificity by minimum age

Figure 16Sensitivity and specificity by proportion of female participants

Table 11KQ1 summary of findings and strength of evidence for the diagnosis of ADHD

Tests To Diagnose ADHD	Outcome	Number of Studies; Study Design; IDs	Findings	Reasons for Downgrading	SoE
KQ1a Diagnostic tests for under 7 year olds	Sensitivity	7 studies¹⁶²^, ¹⁶⁷^, ¹⁸⁹^, ⁴¹⁶^, ⁴⁶⁷^, ⁵⁵⁹	Sensitivity ranged from 64% (corresponding specificity 75%) for a neuropsychological test¹⁶² to 97% (corresponding specificity 84%) for an activity measure⁴¹⁶	S, I	Low
KQ1a Diagnostic tests for under 7 year olds	Specificity	6 studies¹⁶²^, ¹⁶⁷^, ¹⁸⁹^, ³³¹^, ⁴¹⁶^, ⁵⁵⁹	Specificity ranged from 38% (corresponding sensitivity 95) using EEG data¹⁸⁹ to 91% (corresponding sensitivity 71%) for the Child Behavior Checklist for ages 1.5 to 5 Attention-Deficit/Hyperactivity Problems scale³³¹	S, I	Low
KQ1a Diagnostic tests for under 7 year olds	Accuracy	6 studies¹⁶²^, ¹⁸⁹^, ³³¹^, ⁴¹⁶^, ⁴⁶⁷^, ⁵⁵⁹	Accuracy ranged from 64%⁴⁶⁷ combining different executive function tasks to 93%⁴⁶⁷ combining teacher and parent ratings, both in a model supported by machine learning	S, I	Low
KQ1a Diagnostic tests for under 7 year olds	AUC	7 studies¹⁶⁷^, ¹⁸⁹^, ³¹⁶^, ³³¹^, ⁴¹²^, ⁴¹⁶^, ⁴⁶⁷	AUC ranged from 0.68¹⁸⁹ using EEG data to 0.98⁴⁶⁷ for combined teacher and parent ratings	S, I	Low
KQ1a Diagnostic tests for under 7 year olds	Rater agreement	1 study¹⁶⁷	ICC was 0.92 between researchers administering the Disruptive Behavior Diagnostic Observation Schedule¹⁶⁷	C	Insufficient
KQ1a Diagnostic tests for under 7 year olds	Internal consistency	3 studies¹⁶⁷^, ⁴⁶⁷^, ⁵¹⁶	Cronbach’s alpha 0.92 for parent ratings on the DIPA-L⁵¹⁶ Cronbach’s alpha Behavior Rating Inventory of Executive Function preschool version 0.976 for teacher ratings and 0.970 for parent ratings; child version 0.724 for teacher ratings and 0.978 for parent ratings⁴⁶⁷ Cronbach’s alpha was 0.83 for the K-BDS in the sample of ADHD children¹⁶⁷	S, I	Low
KQ1a Diagnostic tests for under 7 year olds	Test-retest reliability	1 study⁵¹⁶	ICC 0.91 and Kappa 0.84 for parent ratings on the Diagnostic Infant and Preschool Assessment Likert version (DIPA-L), 30 days or less between interviews⁵¹⁶	C	Insufficient
KQ1a Diagnostic tests for under 7 year olds	Misdiagnosis consequences	0 studies	No data	C	Insufficient
KQ1a Diagnostic tests for under 7 year olds	Costs	0 studies	No data	C	Insufficient
KQ1b Diagnostic tests for 7-18 year olds	Sensitivity	EEG: 9 studies¹¹¹^, ¹²⁰^, ¹⁷²^, ²⁴⁵^, ³⁵¹^, ³⁷⁰^, ³⁹⁷^, ⁴⁰⁸^, ⁵⁴⁶ Imaging: 8 studies¹⁹¹^, ²⁸²^, ⁴⁰⁰^, ⁴⁹⁵^, ⁵¹⁸^, ⁵⁷¹^, ⁵⁸¹ Executive function tests:7 studies¹¹⁹^, ²¹³^, ³⁵¹^, ³⁵²^, ³⁷⁹^, ⁴⁴⁶^, ⁶¹⁴	EEG: Sensitivity ranged from 57% (corresponding specificity 63%)⁵⁴⁶ to 100% (corresponding specificity 100%)²⁴⁵ MRI imaging: Sensitivity ranged from 57% (corresponding specificity 65%)⁴⁰⁰ to 99% (corresponding specificity 100%)⁵⁸¹ Neuropsychological test with executive function component: Sensitivity ranged from 41% (corresponding specificity 65%) using Conners K test¹¹⁹ to 84% (corresponding specificity 72%) using the CCTT³⁵²	S, I	Low
KQ1b Diagnostic tests for 7-18 year olds	Specificity	EEG: 9 studies¹¹¹^, ¹²⁰^, ¹⁷²^, ²⁴⁵^, ³⁵¹^, ³⁷⁰^, ³⁹⁷^, ⁴⁰⁸^, ⁵⁴⁶ Imaging: 7 studies¹⁹¹^, ²⁸²^, ⁴⁰⁰^, ⁴⁹⁵^, ⁵¹⁸^, ⁵⁷¹^, ⁵⁸¹ Executive function tests: 8 studies¹¹⁹^, ²¹³^, ²⁸⁴^, ³⁵¹^, ³⁵²^, ³⁷⁹^, ⁴⁴⁶^, ⁶¹⁴	EEG Specificity ranged from 63% (corresponding sensitivity 57%)⁵⁴⁶ to 100% (corresponding sensitivity 94-100%)²⁴⁵^, ³⁷⁰ MRI imaging: Specificity ranged from 55% (corresponding sensitivity 95%)⁵¹⁸ to 100% (corresponding sensitivity 100%)¹⁹¹ Neuropsychological test with executive function component: Specificity ranged from 62% (corresponding sensitivity 63%) using the BANC⁴⁴⁶ to 94% (corresponding sensitivity 74%) using a combination of reaction time and visuo-spatial working memory tests⁶¹⁴	S, I	Low
KQ1b Diagnostic tests for 7-18 year olds	Accuracy	EEG: 15 studies¹¹¹^, ¹²⁰^, ¹⁷²^, ²⁴⁵^, ³¹²^, ³⁵¹^, ³⁷⁰^, ³⁹⁴^, ³⁹⁷^, ⁴⁰⁸^, ⁴³⁸^, ⁴⁴⁹^, ⁴⁹⁴^, ⁵⁴⁶^, ⁶⁴¹ Imaging: 7 studies¹⁹¹^, ²⁸²^, ⁴⁰⁰^, ⁴⁹⁵^, ⁵¹⁸^, ⁵⁷¹^, ⁵⁸¹ Executive function tests: 7 studies¹⁵⁹^, ²¹³^, ²⁸⁴^, ³⁵¹^, ⁴⁶⁵^, ⁵⁴¹^, ⁶⁰⁷	EEG: Accuracy ranged from 59%⁵⁴⁶ to 100%²⁴⁵^, ⁴⁹⁴ MRI imaging: Accuracy ranged from 64⁴⁰⁰ to 99.6¹⁹¹ Neuropsychological test with executive function component: Accuracy ranged from 65% using components of a test battery developed to assess right hemisphere function²⁸⁴ to 88 using NCAT³⁵¹	S, I	Low
KQ1b Diagnostic tests for 7-18 year olds	AUC	EEG: 3 studies¹²⁰^, ²⁴⁵^, ³¹² Imaging: 5 studies¹⁹¹^, ⁴⁰⁰^, ⁴⁶⁴^, ⁵¹⁸^, ⁵⁸¹ Executive function tests: 2 studies³⁵²^, ⁴⁴⁶	EEG: AUC values ranged from 0.89¹²⁰ to 1.0²⁴⁵ MRI imaging: AUC ranged from 0.58⁴⁰⁰ to 0.997⁵⁸¹ Neuropsychological test with executive function component: AUC values ranged from 0.73 for the Coimbra Neuropsychological Assessment Battery⁴⁴⁶ to 0.80 for part 2 of the Children’s Color Trail Test³⁵²	S, I	Low
KQ1b Diagnostic tests for 7-18 year olds	Rater agreement	0 studies	No data	C	Insufficient
KQ1b Diagnostic tests for 7-18 year olds	Internal consistency	0 studies	No data	C	Insufficient
KQ1b Diagnostic tests for 7-18 year olds	Test-retest reliability	1 study²¹³	EEG: No data MRI imaging: No data Neuropsychological test with executive function component: test-retest correlation of 0.81 (p<0.05) for the total test score in a Tower of London–- Drexel	C	Insufficient
KQ1b Diagnostic tests for 7-18 year olds	Misdiagnosis consequences	0 studies	No data	C	Insufficient
KQ1b Diagnostic tests for 7-18 year olds	Costs	0 studies	No data	C	Insufficient
KQ1c (effect modifier) setting	Sensitivity	N/A	Indirect analyses (regression) indicated that the setting may be associated with reported results (p 0.03)	D	Low
KQ1c (effect modifier) setting	Specificity	N/A	Indirect analyses (regression) did not indicate that the setting is associated with reported results (p 0.70)	D	Low
KQ1c (effect modifier) setting	Accuracy	N/A	Indirect analyses (regression) indicated that the setting may be associated with reported results (p<0.01)	D	Low
KQ1c (effect modifier) setting	AUC	N/A	Indirect analyses (regression) did not indicate that the setting is associated with reported results (p 0.22)	D	Low
KQ1c (effect modifier) population	Sensitivity	N/A	Indirect analyses (regression) indicated that the population may be associated with reported results (p 0.04)	D	Low
KQ1c (effect modifier) population	Specificity	N/A	Indirect analyses (regression) indicated that the population may be associated with reported results (p<0.001)	D	Low
KQ1c (effect modifier) population	Accuracy	N/A	Indirect analyses (regression) indicated that the population may be associated with reported results (p<0.001)	D	Low
KQ1c (effect modifier) age	Sensitivity	N/A	Indirect analyses (regression) did not detect an association (p 0.90, p 0.58)	D	Low
KQ1c (effect modifier) age	Specificity	N/A	Indirect analyses (regression) did not detect an association (p 0.35, 0.45)	D	Low
KQ1c (effect modifier) gender	Sensitivity and specificity	N/A	Indirect analyses (regression) did not detect an association (p 0.80) but the number of female participants was small	D	Insufficient
KQ1d (labeling)	Any outcome	0 studies	No data	C	Insufficient

: Notes: ADHD = attention deficit hyperactivity disorder, AUC = area under the curve, BANC = Coimbra Neuropsychological Assessment Battery, C = inconsistency, CCTT = children’s color trails test; D = indirectness, DIPA-L = Diagnostic Infant and Preschool Assessment Likert version, EEG = Electroencephalogram, I = imprecision, KQ = Key Question, ICC = intraclass correlation coefficient, K-DBDS = Kiddie-Disruptive Behavior Disorder Schedule, MRI = magnetic resonance imaging, N/A = not applicable, NCAT = neurocognitive assessment tool, S = study limitation, SoE = strength of evidence