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Not Only Mania or Depression: Mixed States/Mixed Features in Paediatric Bipolar Disorders.

Delfina JaniriEliana ConteIlaria De LucaMaria Velia SimoneLorenzo MocciaAlessio SimonettiMarianna MazzaElisa MarconiLaura MontiDaniela Pia Rosaria ChieffoGeorgios KotzalidisLuigi JaniriGabriele Sani
Published in: Brain sciences (2021)
Background: early onset is frequent in Bipolar Disorders (BDs), and it is characterised by the occurrence of mixed states (or mixed features). In this systematic review, we aimed to confirm and extend these observations by providing the prevalence rates of mixed states/features and data on associated clinical, pharmacological and psychopathological features. Methods: following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched from inception to 9 February 2021 for all studies investigating mixed states/mixed features in paediatric BD. Data were independently extracted by multiple observers. The prevalence rates of mixed states/features for each study were calculated. Results: eleven studies were included in our review, involving a total patient population of 1365 individuals. Overall, of the patients with paediatric age BD, 55.2% had mixed states/features (95% CI 40.1-70.3). Children with mixed states/features presented with high rates of comorbidities, in particular, with Attention Deficit Hyperactivity Disorder (ADHD). Evidences regarding the psychopathology and treatment response of mixed states/features are currently insufficient. Conclusions: our findings suggested that mixed states/features are extremely frequent in children and adolescents with BD and are characterised by high levels of comorbidity. Future investigations should focus on the relationship between mixed states/features and psychopathological dimensions as well as on the response to pharmacological treatment.
Keyphrases
  • systematic review
  • meta analyses
  • early onset
  • bipolar disorder
  • randomized controlled trial
  • attention deficit hyperactivity disorder
  • risk factors
  • young adults
  • late onset
  • big data
  • replacement therapy
  • deep learning