Towards personalized medicine for persistent depressive disorder

• Verfasst von: Serbanescu, Ilinca [VerfasserIn]
• Titel: Towards personalized medicine for persistent depressive disorder
• Titelzusatz: moving from “one size fits all” to “what works for whom?”
• Mitwirkende: Backenstraß, Matthias [AkademischeR BetreuerIn]
• Verf.angabe: presented by Ilinca-Draga Serbanescu ; advisor: Prof. Dr. Matthias Backenstraß
• Verlagsort: Heidelberg
• E-Jahr: 2023
• Jahr: 06 Jun. 2023
• Umfang: 1 Online-Ressource (146 Seiten)
• Illustrationen: Illustrationen, Diagramme
• Hochschulschrift: Dissertation, Heidelberg University, 2023
• DOI: doi:10.11588/heidok.00033283
• URN: urn:nbn:de:bsz:16-heidok-332831
• Schlagwörter: (s)Depression / (s)Chronische Depression / (s)Individualisierte Medizin / (s)Psychotherapie / (s)Cognitive Behavioral Analysis System of Psychotherapy / (s)Pharmakotherapie
• Datenträger: Online-Ressource
• Dokumenttyp: Hochschulschrift
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe: Serbanescu, Ilinca, 1990 - : Towards personalized medicine for persistent depressive disorder. - Heidelberg, 2022. - 145 Seiten
• K10plus-PPN: 1847494889
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

Persistent Depressive Disorder (PDD) is, by definition, a chronic mental disorder that severely affects the quality of life of those affected. Despite numerous available treatment options, response and remission rates are generally scarce in patients with PDD, with effectiveness of different treatments varying between individual patients. However, empirical evidence predicting and understanding the individual treatment benefit is largely lacking. Personalized medicine aims to match patients with the most promising treatment on an individual basis by identifying pre-treatment characteristics that predict the outcome of a particular treatment for an individual patient. While other medical disciplines have achieved great progress in the field of personalized medicine, psychiatry still lags far behind, holding on to the ‘one size fits all’ concept, which assumes that a certain treatment will work equally well for all patients diagnosed with a particular disorder. This is also broadly applicable to the research field on PDD. The overarching aim of this publication-based dissertation is to advance the field of personalized medicine for PDD by providing evidence for the effectiveness of certain psychotherapeutic and pharmacological treatments for specific subgroups of patients with PDD based on their multivariable pre-treatment profile. Beginning with an introduction, this thesis will first provide a theoretical framework for the two main thematical concepts of this work, namely PDD and personalized medicine, as well as an overview of previous evidence on treatment prediction in patients with PDD. Afterwards, the main objectives and research questions of this thesis are presented with respect to two specific clinical decision-making scenarios that have been studied in the three scientific papers presented thereafter, namely the selection of and between two psychotherapies (Paper 1 and Paper 2) and the choice between psychotherapy and antidepressant medication (Paper 3). Paper 1 identified and combined pretreatment characteristics of patients with early-onset PDD that moderate their benefit from disorder-specific Cognitive Behavioral Analysis System of Psychotherapy (CBASP) versus nonspecific Supportive Psychotherapy (SP), thereby detecting two subgroups with differential treatment benefits. Paper 2 investigated treatment predictors and identified several subgroups of patients experiencing a comparable treatment effectiveness of CBASP and SP. Finally, following the same question and methodology as Paper 1 for the comparison of CBASP and pharmacotherapy with Escitalopram plus Clinical Management (ESC/CM), Paper 3 identified two subgroups of patients with differential benefit from these two treatment options. Altogether, the main findings of the three papers extend the body of evidence for treatment prediction in patients with PDD in several aspects: first, they show that behind the general cross-sample effects reported in the main studies, there exist underlying subgroup effects, suggesting that the effectiveness of the investigated treatments varies greatly depending on the patient’s pre-treatment profile. Second, they present novel methodological approaches together with advantages of a multivariable consideration of the pre-treatment profile and its prediction of treatment response. Third and lastly, they provide new evidence for whom the treatments studied are more or less likely to work, possible underlying reasons, and other research questions that arise and need to be investigated by future research.