Table of Contents Table of Contents
Previous Page  71 / 916 Next Page
Information
Show Menu
Previous Page 71 / 916 Next Page
Page Background

25th European Congress of Psychiatry / European Psychiatry 41S (2017) S53–S68

S67

Aims

To present preliminary findings from the EUGEI European

Network of National Schizophrenia Networks Studying Gene Envi-

ronment Interactions study.

Methods

Population based FEP incidence/case control study.

Comparison of the incidence rate of FEP and of the distribution

of several risk factors (e.g. substance abuse, neighborhood depri-

vation, urbanicity and trauma) in natives and migrants in different

countries across Europe.

Results

Preliminary results of the EUGEI study will be discussed

in comparison with previous evidences.

Conclusion

The EUGEI study allows a deeper understanding of

the excess of FEP found among migrants in Europe.

Disclosure of interest

The authors have not supplied their decla-

ration of competing interest.

Further reading

European Network of National Networks studying Gene-

Environment Interactions in Schizophrenia (EU-GEI), van Os

J, Rutten BP, et al. Identifying gene-environment interactions in

schizophrenia: contemporary challenges for integrated, large-scale

investigations. Schizophr Bull. 2014 Jul;40(4):729–36.

Tarricone I, Boydell J, Kokona A, Triolo F, Gamberini L, Sutti E,

Marchetta M, Menchetti M, Di Forti M, Murray RM, Morgan C,

Berardi D. Risk of psychosis and internal migration: Results from

the Bologna First Episode Psychosis study. Schizophr Res. 2016

May;173(1-2):90–3.

http://dx.doi.org/10.1016/j.eurpsy.2017.01.069

Workshop: Treatment-resistant schizophrenia:

Epidemiology, clinical course and innovative

treatments, with special reference to m-RESIST

project

W047

Definition, epidemiology, clinical

course and outcomes in

treatment-resistant schizophrenia

J. Seppala

1 , 2 ,

, J. Miettunen

1 , 3 , 4

, E. Jääskeläinen

1 , 4

,

M. Isohanni

1 , 4 , 5

, A. Seppälä

1

,

H. Koponen

6

, M.R.G. M.-Resist Grou

p 7

1

University of Oulu, center for life course health research, Oulu,

Finland

2

Carea - Kymenlaakso social and health services, psyciatric services,

Kotka, Finland

3

University of Oulu, research unit for clinical neuroscience-

department of psychiatry, Oulu, Finland

4

Oulu university hospital and university of Oulu, medical research

center, Oulu, Finland

5

Oulu university hospital, department of psychiatry, Oulu, Finland

6

University of Helsinki and Helsinki university hospital, psychiatry,

Helsinki, Finland

7

TIC SALUT foundation, TIC SALUT foundation, Mataro, Spain

Corresponding author.

Based on a systematic review on TRS 285 studies were included

regarding definitions of TRS (

n

= 11), genetics (18), brain structure

and functioning (18), cognition (8), other neurobiological studies

(16), medication (158), psychotherapy and cognitive rehabilitation

(12), electroconvulsive therapy (ECT) and repetitive transcranial

magnetic stimulation (rTMS) (15), prognosis (21), and other mis-

cellaneous studies (8). Definitions of TRS varied notably. TRS

was associated with 3 to 11-fold higher healthcare costs than

schizophrenia in general. One-fifth to one-third of all patients with

schizophrenia were considered to be resistant to treatment. Based

on limited evidence of genetics, brain structure and functioning and

cognition, TRS may present as a different disorder with different

etiology compared to non-TRS. Clozapine, olanzapine, risperidone,

ECT and cognitive-behavioral therapy have shown effectiveness,

although the number of studies and quality of research on interven-

tions is limited. About 40% to 70% of TRS patients had anunfavorable

prognosis. Younger age, living in a rural or less urban area, pri-

mary education level, more psychiatric hospital treatment days

in the year before first schizophrenia diagnosis, inpatient at first

schizophrenia diagnosis, paranoid subtype, comorbid personality

disorder and previous suicide attempt may be risk factors associ-

ated with TRS.

TRS is a poorly defined, studied and understood condition. To create

a framework of knowledge for TRS, as a basis to develop innovative

studies on treatment, there is a need for a consensus on the defini-

tion of TRS. Prospective long-term prognostic and novel treatment

intervention studies are needed

[1] .

Disclosure of interest

The authors have not supplied their decla-

ration of competing interest.

Reference

[1] Seppälä A, et al. Psychiatria Fennica 2016.

http://dx.doi.org/10.1016/j.eurpsy.2017.01.070

W048

Emerging sensor-based m-health

interventions in the assessment of

psychotic symptoms

M. Bulgheroni

Ab.Acus srl, R&D, Milan, Italy

This speech aims to overview ongoing research trends on the inte-

gration of mobile health and sensors based behavioral analysis

in therapeutics programs for subjects with mental health symp-

toms or disorders. The variety of easily acquirable personal data

by smartphones and wearables in a transparent and unobtru-

sive way, offers the opportunity to describe the person in terms

of his/her lifestyle and behavior at physical, cognitive and envi-

ronmental level. An appropriate management of these data may

initiate a new line in healthcare management characterized by tail-

ored and timely interventions. However, despite the huge amount

of data that could be acquired, an effective contribution of such

information to the improvement of the quality of care in mental-

health is still not sufficiently explored. The sensors and data which

have been used in studies on mental status include accelerometer,

gyroscope, GPS, microphone, calls, messages, screen, apps usage,

environmental light, heart rate, skin conductance, and tempera-

ture. The primary outcomes build on correlations between sensor

data andmental health status/severity of symptoms. These data are

provided from studies on bipolar disorders and depression, using

validated clinical scales (Patient Health Questionnaire-9; Hamilton

Rating Scale for Depression; Young Mania Rating Scale; Center for

Epidemiologic Studies Depression Scale; etc.).

m-RESIST consortium is fully aware of the importance to describe

behavioral patterns of patients with schizophrenia that could be

used to setup remotely based therapeutic tool. m-RESIST is set-

ting up a framework for the creation of a Clinical Decision Support

System based on a mobile therapeutic intervention for treatment-

resistant schizophrenia.

Disclosure of interest

The author has not supplied his declaration

of competing interest.

http://dx.doi.org/10.1016/j.eurpsy.2017.01.071