Your browser doesn't support javascript.
loading
Personalized relapse prediction in patients with major depressive disorder using digital biomarkers.
Vairavan, Srinivasan; Rashidisabet, Homa; Li, Qingqin S; Ness, Seth; Morrison, Randall L; Soares, Claudio N; Uher, Rudolf; Frey, Benicio N; Lam, Raymond W; Kennedy, Sidney H; Trivedi, Madhukar; Drevets, Wayne C; Narayan, Vaibhav A.
Afiliação
  • Vairavan S; Janssen Research & Development, LLC, 1125 Trenton Harbourton Road, Titusville, NJ, 08560, USA. svairava@its.jnj.com.
  • Rashidisabet H; Department of Bioengineering, University of Illinois Chicago, Chicago, IL, USA.
  • Li QS; Janssen Research & Development, LLC, 1125 Trenton Harbourton Road, Titusville, NJ, 08560, USA.
  • Ness S; Janssen Research & Development, LLC, 1125 Trenton Harbourton Road, Titusville, NJ, 08560, USA.
  • Morrison RL; Janssen Research & Development, LLC, 1125 Trenton Harbourton Road, Titusville, NJ, 08560, USA.
  • Soares CN; Department of Psychiatry, Queen's University School of Medicine, Kingston, ON, Canada.
  • Uher R; Department of Psychiatry, Dalhousie University, Halifax, Canada.
  • Frey BN; Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada.
  • Lam RW; Mood Disorders Program, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada.
  • Kennedy SH; Department of Psychiatry, University of British Columbia, Vancouver, Canada.
  • Trivedi M; Centre for Depression and Suicide Studies, Unity Health Toronto, Toronto, Canada.
  • Drevets WC; Krembil Neurosciences, University Health Network, Toronto, Canada.
  • Narayan VA; Department of Psychiatry, University of Toronto, Toronto, Canada.
Sci Rep ; 13(1): 18596, 2023 10 30.
Article em En | MEDLINE | ID: mdl-37903878
Major depressive disorder (MDD) is a chronic illness wherein relapses contribute to significant patient morbidity and mortality. Near-term prediction of relapses in MDD patients has the potential to improve outcomes by helping implement a 'predict and preempt' paradigm in clinical care. In this study, we developed a novel personalized (N-of-1) encoder-decoder anomaly detection-based framework of combining anomalies in multivariate actigraphy features (passive) as triggers to utilize an active concurrent self-reported symptomatology questionnaire (core symptoms of depression and anxiety) to predict near-term relapse in MDD. The framework was evaluated on two independent longitudinal observational trials, characterized by regular bimonthly (every other month) in-person clinical assessments, weekly self-reported symptom assessments, and continuous activity monitoring data with two different wearable sensors for ≥ 1 year or until the first relapse episode. This combined passive-active relapse prediction framework achieved a balanced accuracy of ≥ 71%, false alarm rate of ≤ 2.3 alarm/patient/year with a median relapse detection time of 2-3 weeks in advance of clinical onset in both studies. The study results suggest that the proposed personalized N-of-1 prediction framework is generalizable and can help predict a majority of MDD relapses in an actionable time frame with relatively low patient and provider burden.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transtorno Depressivo Maior Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Transtorno Depressivo Maior Limite: Humans Idioma: En Revista: Sci Rep Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Estados Unidos