Machine Learning Prediction of Quality of Life Improvement During Antidepressant Treatment of Patients With Major Depressive Disorder

Tejas Phaterpekar, John Jose Nunez, Emma Morton, Yang S. Liu, Bo Cao, Benicio N. Frey, Roumen V. Milev, Daniel J. Müller, Susan Rotzinger, Claudio N. Soares, Valerie H. Taylor, Rudolf Uher, Sidney H. Kennedy, Raymond W. Lam

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Background: Quality of life (QoL) is an important patient-centric outcome to evaluate in treatment of major depressive disorder (MDD). This work sought to investigate the performance of several machine learning methods to predict a return to normative QoL in patients with MDD after antidepressant treatment. Methods: Several binary classification algorithms were trained on data from the first 2 weeks of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study (n=651, conducted from 2001 to 2006) to predict week 9 normative QoL (score≥67, based on a community normative sample, on the Quality of Life Enjoyment and Satisfaction Questionnaire–Short Form [Q-LES-QSF]) after treatment with citalopram. Internal validation was performed using a STAR*D holdout dataset, and external validation was performed using the Canadian Biomarker Integration Network in Depression-1 (CAN-BIND-1) dataset (n=175, study conducted from 2012 to 2017) after treatment with escitalopram. Feature importance was calculated using SHapley Additive exPlanations (SHAP). Results: Random Forest performed most consistently on internal and external validation, with balanced accuracy (area under the receiver operator curve) of 71% (0.81) on the STAR*D dataset and 69% (0.75) on the CAN-BIND-1 dataset. Random Forest Classifiers trained on Q-LES-Q-SF and Quick Inventory of Depressive Symptomatology–Self-Rated variables had similar performance on both internal and external validation. Important predictive variables came from psychological, physical, and socioeconomic domains. Conclusions: Machine learning can predict normative QoL after antidepressant treatment with similar performance to that of prior work predicting depressive symptom response and remission. These results suggest that QoL outcomes in MDD patients can be predicted with simple patient-rated measures and provide a foundation to further improve performance and demonstrate clinical utility.

Original languageEnglish
Article number23m14864
Number of pages14
JournalJournal of Clinical Psychiatry
Volume85
Issue number1
DOIs
Publication statusPublished - Mar 2024
Externally publishedYes

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