Identifying relapse predictors in individual participant data with decision trees

Journal article

\ua9 The Author(s), 2025.Background: Estimating the risk of developing bipolar disorder (BD) in children and adolescents (C&A) with depressive disorders and identifying predictors for developing BD is important to optimize prevention and early intervention efforts. This systematic review and meta-analysis aimed to quantitatively examine this risk of developing BD from depressive disorders and identify factors which moderate this development. Methods: In this systematic review and meta-analysis (PROSPERO:CRD42023431301), PubMed and Web-of-Science databases were searched for longitudinal studies reporting the percentage of C&A with ICD/DSM-defined depressive disorders who developed BD during follow-up. Data extraction, random-effects meta-analysis, between-study heterogeneity analysis, quality assessment, sub-group analyses and meta-regressions were conducted. Results: 39 studies were included, including 72,371 individuals (mean age=13.9 years, 57.1% females). 14.7% of C&A with a depressive disorder developed BD after 20.4–288 months: 9.5% developed BD-I (95%CI=4.7–18.1); 7.7% developed BD-II (95%CI=3.2–17.3%). 19.8% (95%CI=9.9–35.6%) of C&A admitted into the hospital with a depressive disorder developed BD. Studies using the DSM (21.6%, 95%CI=20.2-23.1%) and studies evaluating C&A with a major depressive disorder only (19.8%, 95%CI=16.8-23.1%) found higher rates of development of BD. Younger age at baseline, a history of hospitalization and recruitment from specialized clinics were associated with an increased risk of developing BD at follow-up. Quality of included studies was good in 76.9% studies. Conclusions: There is a substantial risk of developing BD in C&A with depressive disorders. This is particularly the case for C&A with MDD, DSM-diagnosed depressive disorders, and C&A admitted into hospital. Research exploring additional predictors and preventive interventions is crucial

Authors: Böttcher, L; Breedvelt, JJF; Warren, FC; Segal, Z; Kuyken, W

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BioMed Central
• Journal: BMC Psychiatry
• Volume: 23
• Issue: 1
• Pages: 835-835
• Article number: 835
• Publication date: 2023-11-13
• DOI: 10.1186/s12888-023-05214-9
• EISSN: 1471-244X
• ISSN: 1471-244X
• Language: English
• Keywords: Statistics; Medicine; Gradient boosting; Psychological intervention; Logistic regression; Random forest; Machine learning; Regression; Artificial intelligence; Computer science; Psychology; Psychiatry; Clinical psychology; Mathematics; Decision tree