A prognostic index for long‐term outcome after successful acute phase cognitive therapy and interpersonal psychotherapy for major depressive disorder

• Published in: Depression and anxiety Vol. 36; no. 3; pp. 252 - 261
• Main Authors: Bronswijk, Suzanne C., Lemmens, Lotte H.J.M., Keefe, John R., Huibers, Marcus J.H., DeRubeis, Robert J., Peeters, Frenk P.M.L.
• Format: Journal Article
• Language: English
• Published: United States Hindawi Limited 01.03.2019; John Wiley and Sons Inc
• Subjects: Adult; Affect; clinical trials; Cognitive ability; Cognitive Behavioral Therapy; Cognitive therapy; depression; Depression - diagnosis; Depression - psychology; Depression - therapy; Depressive Disorder, Major - diagnosis; Depressive Disorder, Major - psychology; Depressive Disorder, Major - therapy; empirical supported treatments; Female; Hope; Humans; interpersonal psychotherapy; Long-term depression; Male; Medical prognosis; Mental depression; Prediction models; Prognosis; Psychotherapy; Reproducibility of Results; Self Concept; Treatment Outcome; interpersonal psychotherapy; clinical trials; depression; cognitive therapy; empirical supported treatments
• ISSN: 1091-4269; 1520-6394
• DOI: 10.1002/da.22868

Background Major depressive disorder (MDD) has a highly recurrent nature. After successful treatment, it is important to identify individuals who are at risk of an unfavorable long‐term course. Despite extensive research, there is no consensus yet on the clinically relevant predictors of long‐term outcome in MDD, and no prediction models are implemented in clinical practice. The aim of this study was to create a prognostic index (PI) to estimate long‐term depression severity after successful and high quality acute treatment for MDD. Methods Data come from responders to cognitive therapy (CT) and interpersonal psychotherapy (IPT) in a randomized clinical trial (n = 85; CT = 45, IPT = 40). Primary outcome was depression severity, assessed with the Beck Depression Inventory II, measured throughout a 17‐month follow‐up phase. We examined 29 variables as potential predictors, using a model‐based recursive partitioning method and bootstrap resampling in conjunction with backwards elimination. The selected predictors were combined into a PI. Individual PI scores were estimated using a cross‐validation approach. Results A total of three post‐treatment predictors were identified: depression severity, hopelessness, and self‐esteem. Cross‐validated PI scores evidenced a strong correlation (r = 0.60) with follow‐up depression severity. Conclusion Long‐term predictions of MDD are multifactorial, involving a combination of variables that each has a small prognostic effect. If replicated and validated, the PI can be implemented to predict follow‐up depression severity for each individual after acute treatment response, and to personalize long‐term treatment strategies.