Personalized relapse prediction in patients with major depressive disorder using digital biomarkers

Autor: Srinivasan Vairavan, Homa Rashidisabet, Qingqin S. Li, Seth Ness, Randall L. Morrison, Claudio N. Soares, Rudolf Uher, Benicio N. Frey, Raymond W. Lam, Sidney H. Kennedy, Madhukar Trivedi, Wayne C. Drevets, Vaibhav A. Narayan
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Scientific Reports, Vol 13, Iss 1, Pp 1-14 (2023)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-023-44592-8
Popis: Abstract 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.
Databáze: Directory of Open Access Journals
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