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Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population

Abstract:
The burden of depression and anxiety in the world is rising. Identification of individuals at increased risk of developing these conditions would help to target them for prevention and ultimately reduce the healthcare burden. We developed a 10-year predictive algorithm for depression and anxiety using the full cohort of over 400,000 UK Biobank (UKB) participants without pre-existing depression or anxiety using digitally obtainable information. From the initial 167 variables selected from UKB, processed into 429 features, iterative backward elimination using Cox proportional hazards model was performed to select predictors which account for the majority of its predictive capability. Baseline and reduced models were then trained for depression and anxiety using both Cox and DeepSurv, a deep neural network approach to survival analysis. The baseline Cox model achieved concordance of 0.7772 and 0.7720 on the validation dataset for depression and anxiety, respectively. For the DeepSurv model, respective concordance indices were 0.7810 and 0.7728. After feature selection, the depression model contained 39 predictors and the concordance index was 0.7769 for Cox and 0.7772 for DeepSurv. The reduced anxiety model, with 53 predictors, achieved concordance of 0.7699 for Cox and 0.7710 for DeepSurv. The final models showed good discrimination and calibration in the test datasets. We developed predictive risk scores with high discrimination for depression and anxiety using the UKB cohort, incorporating predictors which are easily obtainable via smartphone. If deployed in a digital solution, it would allow individuals to track their risk, as well as provide some pointers to how to decrease it through lifestyle changes.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.3389/fpsyt.2021.689026

Authors

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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0002-1041-6793
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Role:
Author
ORCID:
0000-0002-9665-1275
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Role:
Author
ORCID:
0000-0002-6754-5078
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Role:
Author
ORCID:
0000-0002-9827-344X
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Institution:
University of Oxford
Role:
Author
ORCID:
0000-0002-0476-3342


Publisher:
Frontiers Media
Journal:
Frontiers in Psychiatry More from this journal
Volume:
12
Pages:
689026-689026
Article number:
689026
Publication date:
2021-08-13
DOI:
EISSN:
1664-0640
ISSN:
1664-0640


Language:
English
Keywords:
Pubs id:
1175011
Local pid:
pubs:1175011
Source identifiers:
W3187364338
Deposit date:
2026-03-24
ARK identifier:
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