Predicting the naturalistic course in anxiety disorders using clinical and biological markers: a machine learning approach. Issue 1 (11th January 2022)

• Record Type: Journal Article
• Authors: Bokma, Wicher A.; Zhutovsky, Paul; Giltay, Erik J.; Schoevers, Robert A.; Penninx, Brenda W.J.H.; van Balkom, Anton L.J.M.; Batelaan, Neeltje M.; van Wingen, Guido A.
• Is Part Of: Psychological medicine. Volume 52:Issue 1(2022)
• Page Start: 57
• Page End: 67
• Publication Date: 2022-01-11
• Subjects: agoraphobia -- anxiety disorders -- classification -- generalized anxiety disorder -- machine learning -- panic disorder -- random forest classification -- social phobia; Psychiatry -- Periodicals; Medicine and psychology -- Periodicals; Clinical psychology -- Periodicals; 616.89
• DOI: 10.1017/S0033291720001658
• Languages: English
• ISSNs: 0033-2917

Abstract

Abstract: Background: Disease trajectories of patients with anxiety disorders are highly diverse and approximately 60% remain chronically ill. The ability to predict disease course in individual patients would enable personalized management of these patients. This study aimed to predict recovery from anxiety disorders within 2 years applying a machine learning approach. Methods: In total, 887 patients with anxiety disorders (panic disorder, generalized anxiety disorder, agoraphobia, or social phobia) were selected from a naturalistic cohort study. A wide array of baseline predictors ( N = 569) from five domains (clinical, psychological, sociodemographic, biological, lifestyle) were used to predict recovery from anxiety disorders and recovery from all common mental disorders (CMDs: anxiety disorders, major depressive disorder, dysthymia, or alcohol dependency) at 2-year follow-up using random forest classifiers (RFCs). Results: At follow-up, 484 patients (54.6%) had recovered from anxiety disorders. RFCs achieved a cross-validated area-under-the-receiving-operator-characteristic-curve (AUC) of 0.67 when using the combination of all predictor domains (sensitivity: 62.0%, specificity 62.8%) for predicting recovery from anxiety disorders. Classification of recovery from CMDs yielded an AUC of 0.70 (sensitivity: 64.6%, specificity: 62.3%) when using all domains. In both cases, the clinical domain alone provided comparable performances. Feature analysis showed that prediction of recovery from anxiety disorders was primarily driven by anxiety features, whereas recovery from CMDs was primarily driven by depression features. Conclusions: The current study showed moderate performance in predicting recovery from anxiety disorders over a 2-year follow-up for individual patients and indicates that anxiety features are most indicative for anxiety improvement and depression features for improvement in general.