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- Title
Personalized relapse prediction in patients with major depressive disorder using digital biomarkers.
- Authors
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.
- 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.
- Subjects
MENTAL depression; DISEASE relapse; ALARMS; FALSE alarms; WEARABLE technology; BIOMARKERS; CLINICAL medicine
- Publication
Scientific Reports, 2023, Vol 13, Issue 1, p1
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-023-44592-8