Prediction models for longitudinal trajectories of depression and anxiety: a systematic review

Clinical Snapshot

Key Findings

• Effect Size: Not applicable - systematic review without meta-analysis

• Primary Outcome: Identification of 3-4 common trajectory groups: chronic/persistent-high, stable-low, increasing/worsening, and improved/remitted

• Nnt Or Sensitivity: Model predictors ranged from 3-152 variables; family psychiatric history and personal psychiatric history most common predictors

• Confidence Interval: Not reported

Clinical Application

Low feasibility for immediate clinical implementation due to lack of validated, clinically tested models Relevant to Australian mental health services but requires local validation studies; aligns with RACGP mental health guidelines emphasising early intervention, particularly for young people Limited applicability due to high bias risk and lack of validation; primarily applicable to adult populations with existing depression/anxiety diagnoses

Abstract

BACKGROUND: Prediction of atypical health trajectories may enable early intervention. We systematically reviewed the existing literature on models for predicting longitudinal depression and/or anxiety trajectories. METHODS: MEDLINE, Embase and APA PsycINFO were searched (from inception to 31-Jan-2025). We included population-based studies of children and adults (aged 3-65 years). Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST-AI) tool. RESULTS: Seven of the nine included studies were in adult populations with a diagnosis of depression or anxiety at baseline; two focused on child and adolescent populations. Only one study included anxiety trajectories. Identified trajectories typically comprised three to four groups including: chronic/persistent-high, stable-low, increasing/worsening, and improved/remitted groups. Various supervised predictive modelling methods were used. The number of final predictors included in models ranged from three to 152. Family and own/personal psychiatric history were the most common predictors but were not always important for model performance. Models including more predictors did not always perform better. Overall risk of bias was high in all studies. No studies were externally validated and no studies assessed the clinical utility of models. CONCLUSION: This review highlights a need for robust, validated models that can forecast future risk of persistent or worsening anxiety and depression, especially in young people where early intervention is possible.

References

1. 1.Fairweather, S. J., Fraser, H., Lam, N., Gilbody, S., Paton, L. W., Jones, H. J., & Khandaker, G. M. (2026). Prediction models for longitudinal trajectories of depression and anxiety: a systematic review. Journal of Affective Disorders, 121255. https://doi.org/10.1016/j.jad.2026.121255