TY - JOUR
T1 - Common and specific determinants of 9-year depression and anxiety course-trajectories: A machine-learning investigation in the Netherlands Study of Depression and Anxiety (NESDA).
AU - Wardenaar, Klaas J.
AU - Riese, Harriëtte
AU - Giltay, Erik J.
AU - Eikelenboom, Merijn
AU - van Hemert, Albert J.
AU - Beekman, Aartjan F.
AU - Penninx, Brenda W. J. H.
AU - Schoevers, Robert A.
N1 - Funding Information: The infrastructure for the NESDA study ( www.nesda.nl ) is funded through the Geestkracht program of the Netherlands Organisation for Health Research and Development (ZonMw, grant number 10-000-1002) and financial contributions by participating universities and mental health care organizations (Amsterdam University Medical Centers (location VUmc), GGZ inGeest, Leiden University Medical Center, Leiden University, GGZ Rivierduinen, University Medical Center Groningen, University of Groningen, Lentis, GGZ Friesland, GGZ Drenthe, Rob Giel Onderzoekscentrum). All authors had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Publisher Copyright: © 2021 Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/10/1
Y1 - 2021/10/1
N2 - Background: Given the strong relationship between depression and anxiety, there is an urge to investigate their shared and specific long-term course determinants. The current study aimed to identify and compare the main determinants of the 9-year trajectories of combined and pure depression and anxiety symptom severity. Methods: Respondents with a 6-month depression and/or anxiety diagnosis (n=1,701) provided baseline data on 152 sociodemographic, clinical and biological variables. Depression and anxiety symptom severity assessed at baseline, 2-, 4-, 6- and 9-year follow-up, were used to identify data-driven course-trajectory subgroups for general psychological distress, pure depression, and pure anxiety severity scores. For each outcome (class-probability), a Superlearner (SL) algorithm identified an optimally weighted (minimum mean squared error) combination of machine-learning prediction algorithms. For each outcome, the top determinants in the SL were identified by determining variable-importance and correlations between each SL-predicted and observed outcome (ρpred) were calculated. Results: Low to high prediction correlations (ρpred: 0.41-0.91, median=0.73) were found. In the SL, important determinants of psychological distress were age, young age of onset, respiratory rate, participation disability, somatic disease, low income, minor depressive disorder and mastery score. For course of pure depression and anxiety symptom severity, similar determinants were found. Specific determinants of pure depression included several types of healthcare-use, and of pure-anxiety course included somatic arousal and psychological distress. Limitations: Limited sample size for machine learning. Conclusions: The determinants of depression- and anxiety-severity course are mostly shared. Domain-specific exceptions are healthcare use for depression and somatic arousal and distress for anxiety-severity course.
AB - Background: Given the strong relationship between depression and anxiety, there is an urge to investigate their shared and specific long-term course determinants. The current study aimed to identify and compare the main determinants of the 9-year trajectories of combined and pure depression and anxiety symptom severity. Methods: Respondents with a 6-month depression and/or anxiety diagnosis (n=1,701) provided baseline data on 152 sociodemographic, clinical and biological variables. Depression and anxiety symptom severity assessed at baseline, 2-, 4-, 6- and 9-year follow-up, were used to identify data-driven course-trajectory subgroups for general psychological distress, pure depression, and pure anxiety severity scores. For each outcome (class-probability), a Superlearner (SL) algorithm identified an optimally weighted (minimum mean squared error) combination of machine-learning prediction algorithms. For each outcome, the top determinants in the SL were identified by determining variable-importance and correlations between each SL-predicted and observed outcome (ρpred) were calculated. Results: Low to high prediction correlations (ρpred: 0.41-0.91, median=0.73) were found. In the SL, important determinants of psychological distress were age, young age of onset, respiratory rate, participation disability, somatic disease, low income, minor depressive disorder and mastery score. For course of pure depression and anxiety symptom severity, similar determinants were found. Specific determinants of pure depression included several types of healthcare-use, and of pure-anxiety course included somatic arousal and psychological distress. Limitations: Limited sample size for machine learning. Conclusions: The determinants of depression- and anxiety-severity course are mostly shared. Domain-specific exceptions are healthcare use for depression and somatic arousal and distress for anxiety-severity course.
KW - Anxiety
KW - Course
KW - Depression
KW - Machine Learning
KW - Prediction
KW - SuperLearner
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85109668848&origin=inward
UR - https://www.ncbi.nlm.nih.gov/pubmed/34225209
UR - http://www.scopus.com/inward/record.url?scp=85109668848&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.jad.2021.06.029
DO - https://doi.org/10.1016/j.jad.2021.06.029
M3 - Article
C2 - 34225209
SN - 0165-0327
VL - 293
SP - 295
EP - 304
JO - Journal of affective disorders
JF - Journal of affective disorders
ER -