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Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population – a machine learning approach
BACKGROUND: The proportion of patients with post-traumatic stress disorder (PTSD) that remain undiagnosed may be substantial. Without an accurate diagnosis, these patients may lack PTSD-targeted treatments and experience adverse health outcomes. This study used a machine learning approach to identif...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9519190/ https://www.ncbi.nlm.nih.gov/pubmed/36171558 http://dx.doi.org/10.1186/s12888-022-04267-6 |
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author | Gagnon-Sanschagrin, Patrick Schein, Jeff Urganus, Annette Serra, Elizabeth Liang, Yawen Musingarimi, Primrose Cloutier, Martin Guérin, Annie Davis, Lori L. |
author_facet | Gagnon-Sanschagrin, Patrick Schein, Jeff Urganus, Annette Serra, Elizabeth Liang, Yawen Musingarimi, Primrose Cloutier, Martin Guérin, Annie Davis, Lori L. |
author_sort | Gagnon-Sanschagrin, Patrick |
collection | PubMed |
description | BACKGROUND: The proportion of patients with post-traumatic stress disorder (PTSD) that remain undiagnosed may be substantial. Without an accurate diagnosis, these patients may lack PTSD-targeted treatments and experience adverse health outcomes. This study used a machine learning approach to identify and describe civilian patients likely to have undiagnosed PTSD in the US commercial population. METHODS: The IBM® MarketScan® Commercial Subset (10/01/2015–12/31/2018) was used. A random forest machine learning model was developed and trained to differentiate between patients with and without PTSD using non–trauma-based features. The model was applied to patients for whom PTSD status could not be confirmed to identify individuals likely and unlikely to have undiagnosed PTSD. Patient characteristics, symptoms and complications potentially related to PTSD, treatments received, healthcare costs, and healthcare resource utilization were described separately for patients with PTSD (Actual Positive PTSD cohort), patients likely to have PTSD (Likely PTSD cohort), and patients without PTSD (Without PTSD cohort). RESULTS: A total of 44,342 patients were classified in the Actual Positive PTSD cohort, 5683 in the Likely PTSD cohort, and 2,074,471 in the Without PTSD cohort. While several symptoms/comorbidities were similar between the Actual Positive and Likely PTSD cohorts, others, including depression and anxiety disorders, suicidal thoughts/actions, and substance use, were more common in the Likely PTSD cohort, suggesting that certain symptoms may be exacerbated among those without a formal diagnosis. Mean per-patient-per-6-month healthcare costs were similar between the Actual Positive and Likely PTSD cohorts ($11,156 and $11,723) and were higher than those of the Without PTSD cohort ($3616); however, cost drivers differed between cohorts, with the Likely PTSD cohort experiencing more inpatient admissions and less outpatient visits than the Actual Positive PTSD cohort. CONCLUSIONS: These findings suggest that the lack of a PTSD diagnosis and targeted management of PTSD may result in a greater burden among undiagnosed patients and highlights the need for increased awareness of PTSD in clinical practice and among the civilian population. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12888-022-04267-6. |
format | Online Article Text |
id | pubmed-9519190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-95191902022-09-29 Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population – a machine learning approach Gagnon-Sanschagrin, Patrick Schein, Jeff Urganus, Annette Serra, Elizabeth Liang, Yawen Musingarimi, Primrose Cloutier, Martin Guérin, Annie Davis, Lori L. BMC Psychiatry Research BACKGROUND: The proportion of patients with post-traumatic stress disorder (PTSD) that remain undiagnosed may be substantial. Without an accurate diagnosis, these patients may lack PTSD-targeted treatments and experience adverse health outcomes. This study used a machine learning approach to identify and describe civilian patients likely to have undiagnosed PTSD in the US commercial population. METHODS: The IBM® MarketScan® Commercial Subset (10/01/2015–12/31/2018) was used. A random forest machine learning model was developed and trained to differentiate between patients with and without PTSD using non–trauma-based features. The model was applied to patients for whom PTSD status could not be confirmed to identify individuals likely and unlikely to have undiagnosed PTSD. Patient characteristics, symptoms and complications potentially related to PTSD, treatments received, healthcare costs, and healthcare resource utilization were described separately for patients with PTSD (Actual Positive PTSD cohort), patients likely to have PTSD (Likely PTSD cohort), and patients without PTSD (Without PTSD cohort). RESULTS: A total of 44,342 patients were classified in the Actual Positive PTSD cohort, 5683 in the Likely PTSD cohort, and 2,074,471 in the Without PTSD cohort. While several symptoms/comorbidities were similar between the Actual Positive and Likely PTSD cohorts, others, including depression and anxiety disorders, suicidal thoughts/actions, and substance use, were more common in the Likely PTSD cohort, suggesting that certain symptoms may be exacerbated among those without a formal diagnosis. Mean per-patient-per-6-month healthcare costs were similar between the Actual Positive and Likely PTSD cohorts ($11,156 and $11,723) and were higher than those of the Without PTSD cohort ($3616); however, cost drivers differed between cohorts, with the Likely PTSD cohort experiencing more inpatient admissions and less outpatient visits than the Actual Positive PTSD cohort. CONCLUSIONS: These findings suggest that the lack of a PTSD diagnosis and targeted management of PTSD may result in a greater burden among undiagnosed patients and highlights the need for increased awareness of PTSD in clinical practice and among the civilian population. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12888-022-04267-6. BioMed Central 2022-09-29 /pmc/articles/PMC9519190/ /pubmed/36171558 http://dx.doi.org/10.1186/s12888-022-04267-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Gagnon-Sanschagrin, Patrick Schein, Jeff Urganus, Annette Serra, Elizabeth Liang, Yawen Musingarimi, Primrose Cloutier, Martin Guérin, Annie Davis, Lori L. Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population – a machine learning approach |
title | Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population – a machine learning approach |
title_full | Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population – a machine learning approach |
title_fullStr | Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population – a machine learning approach |
title_full_unstemmed | Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population – a machine learning approach |
title_short | Identifying individuals with undiagnosed post-traumatic stress disorder in a large United States civilian population – a machine learning approach |
title_sort | identifying individuals with undiagnosed post-traumatic stress disorder in a large united states civilian population – a machine learning approach |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9519190/ https://www.ncbi.nlm.nih.gov/pubmed/36171558 http://dx.doi.org/10.1186/s12888-022-04267-6 |
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