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Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population
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 usi...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8414584/ https://www.ncbi.nlm.nih.gov/pubmed/34483986 http://dx.doi.org/10.3389/fpsyt.2021.689026 |
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author | Morelli, Davide Dolezalova, Nikola Ponzo, Sonia Colombo, Michele Plans, David |
author_facet | Morelli, Davide Dolezalova, Nikola Ponzo, Sonia Colombo, Michele Plans, David |
author_sort | Morelli, Davide |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8414584 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84145842021-09-04 Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population Morelli, Davide Dolezalova, Nikola Ponzo, Sonia Colombo, Michele Plans, David Front Psychiatry Psychiatry 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. Frontiers Media S.A. 2021-08-13 /pmc/articles/PMC8414584/ /pubmed/34483986 http://dx.doi.org/10.3389/fpsyt.2021.689026 Text en Copyright © 2021 Morelli, Dolezalova, Ponzo, Colombo and Plans. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychiatry Morelli, Davide Dolezalova, Nikola Ponzo, Sonia Colombo, Michele Plans, David Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population |
title | Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population |
title_full | Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population |
title_fullStr | Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population |
title_full_unstemmed | Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population |
title_short | Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population |
title_sort | development of digitally obtainable 10-year risk scores for depression and anxiety in the general population |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8414584/ https://www.ncbi.nlm.nih.gov/pubmed/34483986 http://dx.doi.org/10.3389/fpsyt.2021.689026 |
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