Abstract
A reliable diagnosis of adult Attention Deficit/Hyperactivity Disorder (ADHD) is challenging as many of the symptoms of ADHD resemble symptoms of other disorders. ADHD is associated with gambling disorder and obesity, showing overlaps of about 20% with each diagnosis. It is important for clinical practice to differentiate between conditions displaying similar symptoms via established diagnostic instruments. Applying the LightGBM algorithm in machine learning, we were able to differentiate subjects with ADHD, obesity, problematic gambling, and a control group using all 26 items of the Conners’ Adult ADHD Rating Scales (CAARS-S: S) with a global accuracy of .80; precision (positive predictive value) ranged between .78 (gambling) and .92 (obesity), recall (sensitivity) between .58 for obesity and .87 for ADHD. Models with the best 5 and best 10 items resulted in less satisfactory fits. The CAARS-S seems to be a promising instrument to be applied in clinical practice also for multiclassifying disorders displaying symptoms resembling ADHD.
Highlights
Today there is general agreement that Attention Deficit/Hyperactivity Disorder (ADHD) often persists into adulthood with a prevalence rate of ~ 2.8% for adult A DHD1,2
Impulsivity as apparent in ADHD may be difficult to distinguish from characteristics like those in manic or hypo-manic episodes, or from impulsive behavior inherent to borderline personality disorder and other disorders related to poor impulse control
Means and standard deviations of Conners’ Adult ADHD Rating Scales (CAARS-S):S items and subscales of the different patient groups and the control group are displayed in Supplementary Table S1
Summary
Today there is general agreement that Attention Deficit/Hyperactivity Disorder (ADHD) often persists into adulthood with a prevalence rate of ~ 2.8% for adult A DHD1,2. According to the European Consensus Statement on diagnosing and treating adult ADHD2 the gold standard in diagnosis has four components: (a) a DSM-based clinical interview entailing the specific assessment of adult ADHD symptoms, (b) standardized questionnaires for assessing adult ADHD symptoms, (c) comorbidity assessment, and (d) the appraisal of school- or work certificates This elaborate procedure might not be readily translatable in everyday clinical practice for various factors, e.g., economic restrictions, time limits regarding the diagnostic process or too little knowledge about adult A DHD2. We highlight that the older the patient, the more difficult it might be to establish whether a patient with a history of inattention, hyperactivity, impulsivity, low self-esteem, and deficits in executive functions has ADHD, another disorder or both, since various other disorders might be associated with the deficits observed Other disorders such as obesity and pathological gambling might be comorbid with ADHD and respond to psychostimulant treatment if diagnosed correctly. Solanto et al.[16] tested the predictive value of the Brown Attention Deficit Disorder Scale and a Continuous Performance Test and concluded that sensitivity and specificity parameters provided no meaningful contribution to the differential diagnosis of ADHD and internalizing disorders
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