Predicting treatment effects in unipolar depression: A meta-review.

Gillett, George; Tomlinson, Anneka; Efthimiou, Orestis; Cipriani, Andrea (2020). Predicting treatment effects in unipolar depression: A meta-review. Pharmacology & therapeutics, 212, p. 107557. Elsevier 10.1016/j.pharmthera.2020.107557

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There is increasing interest in clinical prediction models in psychiatry, which focus on developing multivariate algorithms to guide personalized diagnostic or management decisions. The main target of these models is the prediction of treatment response to different antidepressant therapies. This is because the ability to predict response based on patients' personal data may allow clinicians to make improved treatment decisions, and to provide more efficacious or more tolerable medications to the right patient. We searched the literature for systematic reviews about treatment prediction in the context of existing treatment modalities for adult unipolar depression, until July 2019. Treatment effect is defined broadly to include efficacy, safety, tolerability and acceptability outcomes. We first focused on the identification of individual predictor variables that might predict treatment response, and second, we considered multivariate clinical prediction models. Our meta-review included a total of 10 systematic reviews; seven (from 2014 to 2018) focusing on individual predictor variables and three focusing on clinical prediction models. These identified a number of sociodemographic, phenomenological, clinical, neuroimaging, remote monitoring, genetic and serum marker variables as possible predictor variables for treatment response, alongside statistical and machine-learning approaches to clinical prediction model development. Effect sizes for individual predictor variables were generally small and clinical prediction models had generally not been validated in external populations. There is a need for rigorous model validation in large external data-sets to prove the clinical utility of models. We also discuss potential future avenues in the field of personalized psychiatry, particularly the combination of multiple sources of data and the emerging field of artificial intelligence and digital mental health to identify new individual predictor variables.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)

UniBE Contributor:

Efthimiou, Orestis

Subjects:

600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services

ISSN:

0163-7258

Publisher:

Elsevier

Language:

English

Submitter:

Andrea Flükiger-Flückiger

Date Deposited:

27 May 2020 11:37

Last Modified:

25 Jun 2020 14:43

Publisher DOI:

10.1016/j.pharmthera.2020.107557

PubMed ID:

32437828

Uncontrolled Keywords:

Antidepressant drugs Clinical prediction model Personalized medicine Precision psychiatry Prediction Treatment response Unipolar depression

BORIS DOI:

10.7892/boris.144320

URI:

https://boris.unibe.ch/id/eprint/144320

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