Depression is associated with treatment response trajectories in adults with Prolonged Grief Disorder: A machine learning analysis.

BACKGROUND

Although evidence-based Prolonged Grief Disorder treatments (PGDT) exist, pretreatment characteristics associated with differential improvement remain unidentified. To identify clinical factors relevant to optimizing PGDT outcomes, we used unsupervised and supervised machine learning to study treatment effects from a double-blinded, placebo-controlled, randomized clinical trial.

METHODS

Patients were randomized into four treatment groups for 20 weeks: citalopram with grief-informed clinical management, citalopram with PGDT, pill placebo with PGDT, or pill placebo with clinical management. The trial included 333 PGD patients aged 18-95 years (M = 53.9; SD = 14.4). Symptom trajectories were assessed using latent growth mixture modeling based on Inventory for Complicated Grief scores collected every 4 weeks. The relationship between patient-level characteristics and assigned trajectories was examined using logistic regression with elastic net regularization based on the administration of citalopram, PGDT, and risk factors for developing PGD.

RESULTS

Three response trajectories were identified: lesser severity responders (60 %, n = 200), greater severity responders (18.02 %, n = 60), and non-responders (21.92 %, n = 73). Significant differences between greater severity responders and non-responders emerged by Week 8, persisting through the 6-month follow-up assessment. The elastic net model (AUC = 0.702; F1 = 0.777) indicated that higher baseline depression severity, grief-related functional impairment, and not receiving PGDT were associated with a decreased probability of response.

LIMITATIONS: An independent validation cohort of PGDT patients is needed to further study generalizability of findings.

CONCLUSIONS

Differential PGDT courses and the role of depression severity and grief-related functional impairment in treatment non-response were identified. These findings underscore the importance of determining clinical factors relevant to optimizing individual treatment strategies.