TY - JOUR
T1 - Predicting the naturalistic course in anxiety disorders using clinical and biological markers
T2 - A machine learning approach
AU - Bokma, Wicher A.
AU - Zhutovsky, Paul
AU - Giltay, Erik J.
AU - Schoevers, Robert A.
AU - Penninx, Brenda W.J.H.
AU - Van Balkom, Anton L.J.M.
AU - Batelaan, Neeltje M.
AU - Van Wingen, Guido A.
N1 - Funding Information: This study was supported by the Netherlands Organization for Scientific Research (NWO/ZonMW Vidi 016.156.318) and the AMC Research Council (150622). The infrastructure for the NESDA study ( Publisher Copyright: Copyright © The Author(s), 2020. Published by Cambridge University Press
PY - 2022/1/11
Y1 - 2022/1/11
N2 - 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.
AB - 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.
KW - agoraphobia
KW - anxiety disorders
KW - classification
KW - generalized anxiety disorder
KW - machine learning
KW - panic disorder
KW - random forest classification
KW - social phobia
UR - http://www.scopus.com/inward/record.url?scp=85086729150&partnerID=8YFLogxK
U2 - https://doi.org/10.1017/S0033291720001658
DO - https://doi.org/10.1017/S0033291720001658
M3 - Article
C2 - 32524918
SN - 0033-2917
VL - 52
SP - 57
EP - 67
JO - Psychological Medicine
JF - Psychological Medicine
IS - 1
ER -