Treatment patterns in inpatient depression care

Abandoning personalization to get to precision in the pharmacotherapy of depression

Effectiveness studies and analyses of naturalistic cohorts demonstrate that many patients with major depressive disorder do not experience symptomatic remission with antidepressant treatments. In an effort to better match patients with effective treatments, numerous investigations of predictors or moderators of treatment response have been reported over the past five decades, including clinical features as well as biological measures. However, none of these have entered routine clinical practice; instead, clinicians typically personalize treatment on the basis of patient preferences as well as their own. Here, we review the reasons why it has been challenging to identify and deploy treatment-specific predictors of response, and suggest strategies that may be required to achieve true precision in the pharmacotherapy of depression. We emphasize the need for changes in how depression care is delivered, measured, and used to inform future practice.

'Big data' in mental health research: current status and emerging possibilities

PURPOSE

'Big data' are accumulating in a multitude of domains and offer novel opportunities for research. The role of these resources in mental health investigations remains relatively unexplored, although a number of datasets are in use and supporting a range of projects. We sought to review big data resources and their use in mental health research to characterise applications to date and consider directions for innovation in future.

METHODS

A narrative review.

RESULTS

Clear disparities were evident in geographic regions covered and in the disorders and interventions receiving most attention.

DISCUSSION

We discuss the strengths and weaknesses of the use of different types of data and the challenges of big data in general. Current research output from big data is still predominantly determined by the information and resources available and there is a need to reverse the situation so that big data platforms are more driven by the needs of clinical services and service users.

Treatment patterns in inpatient depression care

This study aimed to identify latent patterns of treatment combinations in inpatient depression care. A secondary analysis of routinely collected data on inpatient depression treatment from 2133 patients was conducted. Exploratory latent class modeling was used to identify distinct classes of treatment combinations based on antidepressant medication, psychotherapeutic interventions, and additional treatments. The classes were compared with regard to patient characteristics and treatment outcomes. Eight different classes of inpatient treatment combinations could be identified: 22.8% of the patients were treated with a combination labelled "standard modern antidepressants", 14.6% with "standard tricyclic antidepressants", 12.2% with "high intensity innovative strategies", 12.1% with "standard selective-reuptake-inhibitors", and 11.6% with "low intensity", 9.6% with "somatic", 8.8% with "high intensity traditional", and 8.3% with "high intensity psychosocial" care, respectively. Patients treated with different patterns of interventions differed statistically significantly regarding demographic and clinical characteristics. Responder rates ranged from 68.4% to 86.6% across treatment classes. The presented attempt of empirical modeling of a complex multifactorial intervention by means of latent class analysis proved to be a promising way of capturing the complexity of routine inpatient depression treatment. The identified classes of treatment combinations may provide relevant information for a re-evaluation and improvement of inpatient depression treatment strategies.