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Results and validation of an index to measure health state of patients with depression in automated healthcare databases
Background and objective: A Depressive Health State Index (DHSI) based on 29 parameters routinely collected in an automated healthcare database (AHDB) was developed to evaluate the health state of depressive patients, and its evolution. The study objective was to describe and validate this DHSI. Met...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Routledge
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6346704/ https://www.ncbi.nlm.nih.gov/pubmed/30719242 http://dx.doi.org/10.1080/20016689.2018.1562860 |
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author | Lamy, François-Xavier Falissard, Bruno François, Clément Lançon, Christophe Llorca, Pierre Michel Tanasescu, Adrian Touya, Maëlys Verpillat, Patrice Wade, Alan G. Saragoussi, Delphine |
author_facet | Lamy, François-Xavier Falissard, Bruno François, Clément Lançon, Christophe Llorca, Pierre Michel Tanasescu, Adrian Touya, Maëlys Verpillat, Patrice Wade, Alan G. Saragoussi, Delphine |
author_sort | Lamy, François-Xavier |
collection | PubMed |
description | Background and objective: A Depressive Health State Index (DHSI) based on 29 parameters routinely collected in an automated healthcare database (AHDB) was developed to evaluate the health state of depressive patients, and its evolution. The study objective was to describe and validate this DHSI. Methods: A historical cohort of patients with at least one episode of depression was identified in the Clinical Practice Research Datalink (CPRD). The DHSI was calculated for each episode of depression. Validation was performed by comparing the DHSI between subgroups and using validated definitions of remission (proxy and PHQ-9). Robustness was studied by assessing the impact of modifying parameters of the DHSI. Results: 309,279 episodes of depression were identified in the CPRD between 1 January 2006 and 31 December 2012. Remission was observed in 8% of the patients showing the lower DHSI scores and in 88% of the patients showing the higher DHSI scores. The DHSI was robust to a modification of the most frequent variables and to the removal of rare parameters. Conclusion: The DHSI is specific to depression severity (with remission rates in accordance with the expected variations of the DHSI) and robust. It represents a promising tool for the analysis of AHDBs. |
format | Online Article Text |
id | pubmed-6346704 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Routledge |
record_format | MEDLINE/PubMed |
spelling | pubmed-63467042019-02-04 Results and validation of an index to measure health state of patients with depression in automated healthcare databases Lamy, François-Xavier Falissard, Bruno François, Clément Lançon, Christophe Llorca, Pierre Michel Tanasescu, Adrian Touya, Maëlys Verpillat, Patrice Wade, Alan G. Saragoussi, Delphine J Mark Access Health Policy Original Research Article Background and objective: A Depressive Health State Index (DHSI) based on 29 parameters routinely collected in an automated healthcare database (AHDB) was developed to evaluate the health state of depressive patients, and its evolution. The study objective was to describe and validate this DHSI. Methods: A historical cohort of patients with at least one episode of depression was identified in the Clinical Practice Research Datalink (CPRD). The DHSI was calculated for each episode of depression. Validation was performed by comparing the DHSI between subgroups and using validated definitions of remission (proxy and PHQ-9). Robustness was studied by assessing the impact of modifying parameters of the DHSI. Results: 309,279 episodes of depression were identified in the CPRD between 1 January 2006 and 31 December 2012. Remission was observed in 8% of the patients showing the lower DHSI scores and in 88% of the patients showing the higher DHSI scores. The DHSI was robust to a modification of the most frequent variables and to the removal of rare parameters. Conclusion: The DHSI is specific to depression severity (with remission rates in accordance with the expected variations of the DHSI) and robust. It represents a promising tool for the analysis of AHDBs. Routledge 2019-01-22 /pmc/articles/PMC6346704/ /pubmed/30719242 http://dx.doi.org/10.1080/20016689.2018.1562860 Text en © 2019 Lundbeck. Published by Informa UK Limited, trading as Taylor & Francis Group. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Research Article Lamy, François-Xavier Falissard, Bruno François, Clément Lançon, Christophe Llorca, Pierre Michel Tanasescu, Adrian Touya, Maëlys Verpillat, Patrice Wade, Alan G. Saragoussi, Delphine Results and validation of an index to measure health state of patients with depression in automated healthcare databases |
title | Results and validation of an index to measure health state of patients with depression in automated healthcare databases |
title_full | Results and validation of an index to measure health state of patients with depression in automated healthcare databases |
title_fullStr | Results and validation of an index to measure health state of patients with depression in automated healthcare databases |
title_full_unstemmed | Results and validation of an index to measure health state of patients with depression in automated healthcare databases |
title_short | Results and validation of an index to measure health state of patients with depression in automated healthcare databases |
title_sort | results and validation of an index to measure health state of patients with depression in automated healthcare databases |
topic | Original Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6346704/ https://www.ncbi.nlm.nih.gov/pubmed/30719242 http://dx.doi.org/10.1080/20016689.2018.1562860 |
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