Treatment-resistant schizophrenia during life span : Epidemiology, outcomes and innovative M-Health treatments within M-RESIST Project

Abstract

Treatment-resistant symptoms of schizophrenia (TRS) complicate the clinical course of the illness, and a large proportion of patients do not reach functional recovery (Englisch and Zink, 2012). Out of the estimated 5 million people (0.2–2.6 %) suffering from psychotic disorders in the European Union, 30-50 % can be considered resistant to treatment, and 10–20 % ultra-resistant (Essock et al., 1996 ; Juarez-Reyes et al., 1995). The complexity of standard intervention within this population, along with the presence of persistent positive symptomatology, extensive periods of hospital care and greater risk of multi-morbidity, lead to a high degrees of suffering for the patients, family and social environment, and a high proportion of costs to the healthcare system (Kennedy et al., 2014).

At present, a uniform definition of treatment resistance in the pharmacotherapy of schizophrenia is not available (Suzuki et al., 2011), as well as generally recommendable evidence-based treatment methods (Dold and Leucht, 2014).

A recent systematic review on the topic showed that TRS is poorly a studied and understood condition, contrasted to its high prevalence, clinical importance and poor prognosis. There is lack of studies on epidemiology and risk factors of this disorder, as well as on outcomes and longitudinal course. Most of the available literature focuses on medication treatments, while very few examine efficacy of adjunctive therapeutic options (Seppala et al., in preparation).

Treatments based on information and communication technology (ICT) present novel possibilities to improve the outcomes of schizophrenia. Previous studies have indicated suitability and promising results of such intervention techniques (Granholm et al., 2012 ; Ben-Zeev et al., 2013). m-RESIST is an innovative project aimed to empower patients with resistant schizophrenia, to personalize treatment by integrating pharmacological and psychosocial approaches, and to further develop knowledge related to the illness using predictive models designed to exploit historical and real-time data based on environmental factors and treatment outcomes.

Disclosure of interest

The author has not supplied his declaration of competing interest.