Alessandro Pigoni1, Giuseppe Delvecchio2, Domenico Madonna1, Cinzia Bressi1, Jair Soares3, Paolo Brambilla4. 1. Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Department of Neurosciences and Mental Health, Milan, Italy; University of Milan, Department of Pathophysiology and Transplantation, Milan, Italy. 2. University of Milan, Department of Pathophysiology and Transplantation, Milan, Italy. 3. Department of Psychiatry and Behavioural Sciences, UT Houston Medical School, Houston, TX, USA. 4. Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Department of Neurosciences and Mental Health, Milan, Italy; University of Milan, Department of Pathophysiology and Transplantation, Milan, Italy. Electronic address: paolo.brambilla1@unimi.it.
Abstract
BACKGROUND: About one third of patients treated with antidepressant do not show sufficient symptoms relief and up to 15% of patients remain symptomatic even after multiple trials are applied, configuring a state called treatment resistant depression (TRD). A clear definition of this state and the understanding of underlying mechanisms contributing to chronic disability caused by major depressive disorder is still unknown. Therefore, Machine Learning (ML) techniques emerged in the last years as interesting approaches to deal with such complex problems. METHODS: We performed a bibliographic search on Pubmed, Google Scholar and Medline of clinical, imaging, genetic and EEG ML classification studies on treatment-responding depression and TRD as well as studies trying to predict response to a specific treatment in already established TRD. The inclusion criteria were met by eleven studies. Seven focused on the definition of predictors of TRD onset while four attempted to predict the response to specific treatments in TRD. RESULTS: The results showed that it seems possible to classify between responders MDD and TRD with good accuracies based on clinical variables. Moreover, some studies reported the possibility of using EEG measures to predict response to different pharmacological and non-pharmacological treatments in established TRD. LIMITATIONS: The definition of TRD, the selection of variables together with ML algorithms and pipelines varies across the studies, ultimately determining the unfeasibility to implement these models in clinical practice. CONCLUSIONS: The findings suggest that ML could be a valid approach to increase our understanding of TRD and to better classify and stratify this disorder, which may ultimately help clinicians in the assessment of major depressive disorders.
BACKGROUND: About one third of patients treated with antidepressant do not show sufficient symptoms relief and up to 15% of patients remain symptomatic even after multiple trials are applied, configuring a state called treatment resistant depression (TRD). A clear definition of this state and the understanding of underlying mechanisms contributing to chronic disability caused by major depressive disorder is still unknown. Therefore, Machine Learning (ML) techniques emerged in the last years as interesting approaches to deal with such complex problems. METHODS: We performed a bibliographic search on Pubmed, Google Scholar and Medline of clinical, imaging, genetic and EEG ML classification studies on treatment-responding depression and TRD as well as studies trying to predict response to a specific treatment in already established TRD. The inclusion criteria were met by eleven studies. Seven focused on the definition of predictors of TRD onset while four attempted to predict the response to specific treatments in TRD. RESULTS: The results showed that it seems possible to classify between responders MDD and TRD with good accuracies based on clinical variables. Moreover, some studies reported the possibility of using EEG measures to predict response to different pharmacological and non-pharmacological treatments in established TRD. LIMITATIONS: The definition of TRD, the selection of variables together with ML algorithms and pipelines varies across the studies, ultimately determining the unfeasibility to implement these models in clinical practice. CONCLUSIONS: The findings suggest that ML could be a valid approach to increase our understanding of TRD and to better classify and stratify this disorder, which may ultimately help clinicians in the assessment of major depressive disorders.
Authors: Carl B Roth; Andreas Papassotiropoulos; Annette B Brühl; Undine E Lang; Christian G Huber Journal: Int J Environ Res Public Health Date: 2021-08-05 Impact factor: 3.390