Treatment profiles and trajectories surrounding the diagnosis of major depressive disorder: a research protocol for a Danish register-based study. [version 1; peer review: 1 approved]
Background: Major depressive disorder (MDD) is a prevalent illness that causes significant suffering and expenses at the personal and societal levels. The disorder is subject to heterogeneity reflected by diverse clinical phenotypes and assorted responses to treatment. Research on MDD treatments hav...
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| Abstract | Background: Major depressive disorder (MDD) is a prevalent illness that causes significant suffering and expenses at the personal and societal levels. The disorder is subject to heterogeneity reflected by diverse clinical phenotypes and assorted responses to treatment. Research on MDD treatments have focused on one treatment at a time, however many patients receive several different treatments. Considering the number of available treatment options, we hypothesize that it is possible to identify clinically meaningful groups of patients based on their psychiatric treatment. The objective of this study is therefore to identify psychiatric treatment profiles and trajectories of patients with major depressive disorder and, for the identified profiles and trajectories, to assess clinical and sociodemographic factors.
Method: The study will be a population-based register study of patients with major depressive disorder in the Danish National Patient Register between 2011 and 2015. Using latent class analyses, we will identify homogenous groups of patients based on their psychiatric treatment patterns. These patterns constitute psychiatric treatment profiles which will be identified at six time-intervals, from 1.5 years before to 3 years after diagnosis of major depressive disorder. By cross-tabulating the identified treatment profiles, we will establish psychiatric treatment trajectories. Patients sharing profiles and trajectories will be characterized.
Discussion: Identification of psychiatric treatment profiles and trajectories based on an unsupervised learning algorithm have the potential to reveal hidden patterns of psychiatric treatment. This will potentially pave the way for future studies of treatment combinations and a larger insight into the different courses of treatment. Furthermore, the assessment of clinical and sociodemographic factors may indicate different patient characteristics across treatment profiles and trajectories. |
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| AbstractList | Background:
Major depressive disorder (MDD) is a prevalent illness that causes significant suffering and expenses at the personal and societal levels. The disorder is subject to heterogeneity reflected by diverse clinical phenotypes and assorted responses to treatment. Research on MDD treatments have focused on one treatment at a time, however many patients receive several different treatments. Considering the number of available treatment options, we hypothesize that it is possible to identify clinically meaningful groups of patients based on their psychiatric treatment. The objective of this study is therefore to identify psychiatric treatment profiles and trajectories of patients with major depressive disorder and, for the identified profiles and trajectories, to assess clinical and sociodemographic factors.
Method:
The study will be a population-based register study of patients with major depressive disorder in the Danish National Patient Register between 2011 and 2015. Using latent class analyses, we will identify homogenous groups of patients based on their psychiatric treatment patterns. These patterns constitute psychiatric treatment profiles which will be identified at six time-intervals, from 1.5 years before to 3 years after diagnosis of major depressive disorder. By cross-tabulating the identified treatment profiles, we will establish psychiatric treatment trajectories. Patients sharing profiles and trajectories will be characterized.
Discussion:
Identification of psychiatric treatment profiles and trajectories based on an unsupervised learning algorithm have the potential to reveal hidden patterns of psychiatric treatment. This will potentially pave the way for future studies of treatment combinations and a larger insight into the different courses of treatment. Furthermore, the assessment of clinical and sociodemographic factors may indicate different patient characteristics across treatment profiles and trajectories. Background: Major depressive disorder (MDD) is a prevalent illness that causes significant suffering and expenses at the personal and societal levels. The disorder is subject to heterogeneity reflected by diverse clinical phenotypes and assorted responses to treatment. Research on MDD treatments have focused on one treatment at a time, however many patients receive several different treatments. Considering the number of available treatment options, we hypothesize that it is possible to identify clinically meaningful groups of patients based on their psychiatric treatment. The objective of this study is therefore to identify psychiatric treatment profiles and trajectories of patients with major depressive disorder and, for the identified profiles and trajectories, to assess clinical and sociodemographic factors. Method: The study will be a population-based register study of patients with major depressive disorder in the Danish National Patient Register between 2011 and 2015. Using latent class analyses, we will identify homogenous groups of patients based on their psychiatric treatment patterns. These patterns constitute psychiatric treatment profiles which will be identified at six time-intervals, from 1.5 years before to 3 years after diagnosis of major depressive disorder. By cross-tabulating the identified treatment profiles, we will establish psychiatric treatment trajectories. Patients sharing profiles and trajectories will be characterized. Discussion: Identification of psychiatric treatment profiles and trajectories based on an unsupervised learning algorithm have the potential to reveal hidden patterns of psychiatric treatment. This will potentially pave the way for future studies of treatment combinations and a larger insight into the different courses of treatment. Furthermore, the assessment of clinical and sociodemographic factors may indicate different patient characteristics across treatment profiles and trajectories. |
| Author | Almarsdóttir, Anna Birna Petersen, Janne Herold Jeberg, Pernille Osler, Merete Jacobsen, Ramune Kim Wirum-Andersen, Marie Jarlov Jensen, Kristoffer Overgaard Nielsen, Anne Marije Christina |
| Author_xml | – sequence: 1 givenname: Pernille orcidid: 0000-0001-7075-827X surname: Herold Jeberg fullname: Herold Jeberg, Pernille email: pernille.herold.jeberg@regionh.dk organization: Section of Social and Clinical Pharmacy, Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark – sequence: 2 givenname: Anne Marije Christina orcidid: 0000-0003-1676-3312 surname: Overgaard Nielsen fullname: Overgaard Nielsen, Anne Marije Christina organization: Section of Social and Clinical Pharmacy, Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark – sequence: 3 givenname: Merete surname: Osler fullname: Osler, Merete organization: Section of Epidemiology, Department of Public Health, University of Copenhagen, Copenhagen, Denmark – sequence: 4 givenname: Marie surname: Kim Wirum-Andersen fullname: Kim Wirum-Andersen, Marie organization: Center for Clinical Research and Prevention, Copenhagen, Denmark – sequence: 5 givenname: Ramune orcidid: 0000-0002-8142-9807 surname: Jacobsen fullname: Jacobsen, Ramune organization: Section of Social and Clinical Pharmacy, Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark – sequence: 6 givenname: Anna Birna orcidid: 0000-0002-5354-2976 surname: Almarsdóttir fullname: Almarsdóttir, Anna Birna organization: Section of Social and Clinical Pharmacy, Department of Pharmacy, University of Copenhagen, Copenhagen, Denmark – sequence: 7 givenname: Kristoffer surname: Jarlov Jensen fullname: Jarlov Jensen, Kristoffer organization: Center for Clinical Research and Prevention, Copenhagen, Denmark – sequence: 8 givenname: Janne surname: Petersen fullname: Petersen, Janne organization: Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark |
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| Cites_doi | 10.1177/1403494811401482 10.1016/j.jphys.2016.05.018 10.1111/acps.13258 10.1177/0095798420930932 10.2147/CLEP.S91125 10.1016/j.ajp.2017.01.025 10.1093/ije/dyw213 10.1111/cdep.12163 10.1590/1516-4446-2016-2107 10.1177/1403494810394718 10.2147/PGPM.S198225 10.1146/annurev-publhealth-031912-114409 10.1038/s41380-019-0585-z 10.1177/1403494810387965 10.18773/austprescr.2016.039 10.1177/1403494815575193 10.1097/CCM.0000000000004710 10.3389/fpsyt.2019.00101 10.2147/CLEP.S179083 10.1080/10705510701575396 10.1007/s11920-012-0283-x 10.1186/1745-0179-5-4 10.1016/j.jad.2019.10.005 10.1177/1403494811399956 10.1007/s10654-014-9930-3 10.1177/1403494810395825 |
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| Copyright | Copyright: © 2022 Herold Jeberg P et al. |
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| Keywords | Denmark treatment profile Depression latent class analysis treatment trajectory |
| Language | English |
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| Title | Treatment profiles and trajectories surrounding the diagnosis of major depressive disorder: a research protocol for a Danish register-based study. [version 1; peer review: 1 approved] |
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