P433. Gender Differences in Adult ADHD Symptoms: A Network Analysis of Real-World Data

Kira Griffiths, Gunjan Batra, Li Tong Low, Emily Palmer, Miguel E Rentería, Rashmi Patel, Scott H. Kollins

Research output: Contribution to journalMeeting abstractpeer-review

1 Citation (Scopus)


Background: Symptoms of ADHD can be classified as inattentive (IA) or hyperactive/impulsive (H/I). However, significant heterogeneity in the clinical presentation of ADHD has been reported across development and between genders. Network analysis offers a more detailed understanding of symptom structure compared to traditional factor analytic approaches. Electronic health record (EHR) data captures real-world symptom presentation in large and representative cohorts. The current study applied network analysis to real-world symptom data from adults with ADHD. This study aimed to explore and compare ADHD symptom networks in adult males and females.

Methods: Symptoms recorded by mental healthcare professionals as part of clinical assessment were obtained from a de-identified EHR dataset from 25 US mental healthcare systems (WCG Institutional Review Board Ref: WCG-IRB 1-1470336-1).The cohort included adult patients (>18 years) with a diagnosis of ADHD (ICD9/10: 314.00, 314.01, F90.0, F90.1, F90.2, F90.8 and F90.9) and symptom data within the first six months of diagnosis. Symptoms were defined according to the 18 symptoms outlined in the DSM-5. Networks for males and females were constructed separately, with each symptom reflecting a unique node within a network. Symptoms were entered as binary variables indicated by their presence or absence within individual patients. Analyses were conducted using R version 4.1.3, Bootstrap package 1.5 and Isingfit package v0.3.1. Networks were estimated using the eLASSO procedure and then visualized. In addition to the network graphs the centrality measures of betweenness, closeness and strength were calculated. In networks of psychiatric symptoms, centrality measures allow the identification of symptoms of high importance within a network and may therefore highlight key symptoms to target for intervention.

Results: Data were available for 2,398 patients (53% female) with a diagnosis of ADHD. Networks clustered into H/I and IA symptoms. In adult males, hyperactive/impulsive symptoms form a relatively tight network, whereas inattentive symptoms are less central. H/I and IA symptom clusters in females were relatively more dispersed than in males. In terms of strength, the top three unique symptoms for males and females both included two symptoms from the H/I cluster and one from the IA cluster. However, these differed by gender with the strongest symptoms associations in males being excessive/inappropriate movement, being “on the go” and failure of close attention. Whereas for females’ symptoms of difficulty remaining seated, frequently interrupting and being easily distracted had the highest strength in the network. In terms of closeness, the most central symptom was in the IA cluster for females (does not seem to listen) and the H/I cluster for males (difficulty waiting their turn). H/I symptoms were the most central in terms of betweenness for males and females, both of which reflected inappropriate movement.

Conclusions: Clustering of H/I and IA symptoms is generally consistent with the current conceptualisation of ADHD. Differences in unique symptoms identified as most central in each network add further evidence that symptoms may vary in terms of clinical relevance for males and females, which may impact the identification, diagnosis, and treatment of ADHD between genders. These differing networks may also be important for identifying unique treatment targets for males and females. In addition, the general approach to characterizing networks could also be important for more precisely defining ADHD symptoms in other subgroups (eg., different racial/ethnic groups, different developmental levels).
Original languageEnglish
Issue numberS1
Publication statusPublished - 1 Dec 2022


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