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Psychosis Incident Cohort Outcome Study (PICOS). A multisite study of clinical, social and biological characteristics, patterns of care and predictors of outcome in first-episode psychosis. Background, methodology and overview of the patient sample

Department of Public Health and Community Medicine, Section of Psychiatry, University of Verona, Verona, Italy.
Epidemiology and Psychiatric Sciences (Impact Factor: 3.36). 06/2012; 21(3):281-303. DOI: 10.1017/S2045796012000315
Source: PubMed

ABSTRACT This paper aims at providing an overview of the background, design and initial findings of Psychosis Incident Cohort Outcome Study (PICOS).
PICOS is a large multi-site population-based study on first-episode psychosis (FEP) patients attending public mental health services in the Veneto region (Italy) over a 3-year period. PICOS has a naturalistic longitudinal design and it includes three different modules addressing, respectively, clinical and social variables, genetics and brain imaging. Its primary aims are to characterize FEP patients in terms of clinical, psychological and social presentation, and to investigate the relative weight of clinical, environmental and biological factors (i.e. genetics and brain structure/functioning) in predicting the outcome of FEP.
An in-depth description of the research methodology is given first. Details on recruitment phase and baseline and follow-up evaluations are then provided. Initial findings relating to patients' baseline assessments are also presented. Future planned analyses are outlined.
Both strengths and limitations of PICOS are discussed in the light of issues not addressed in the current literature on FEP. This study aims at making a substantial contribution to research on FEP patients. It is hoped that the research strategies adopted in PICOS will enhance the convergence of methodologies in ongoing and future studies on FEP.

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    • "This study was conducted in the frame of Psychosis Incident Cohort Outcome Study (PICOS), a multi-site naturalistic research, aimed at analyzing clinical, social, cognitive, genetic and imaging features of FEP patients in a large epidemiological area of the Veneto region, Italy (Lasalvia et al., 2012). PICOS inclusion criteria, based on the overinclusive screening methodology adopted in the WHO ten-country study (Jablensky et al., 1992), were: (1) age 15–54 years; (2) residence in the Veneto region; (3) presence of (a) at least one of the following symptoms: hallucinations, delusions, qualitative speech disorder, qualitative psychomotor disorder, and bizarre or grossly inappropriate behavior, or (b) at least two of the following symptoms: loss of interest, initiative and drive, social withdrawal, episodic severe excitement, purposeless destructiveness, overwhelming fear, and marked self-neglect; and (4) first lifetime contact with any mental health service located in PICOS area during the study period occasioned by symptoms enumerated in (3). "
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    ABSTRACT: Hemodynamic changes in the brain have been reported in major psychosis in respect to healthy controls, and could unveil the basis of structural brain modifications happening in patients. The study of first episode psychosis is of particular interest because the confounding role of chronicity and medication can be excluded. The aim of this work is to automatically discriminate first episode psychosis patients and normal controls on the basis of brain perfusion employing a support vector machine (SVM) classifier. 35 normal controls and 35 first episode psychosis underwent dynamic susceptibility contrast magnetic resonance imaging, and cerebral blood flow and volume, along with mean transit time were obtained. We investigated their behavior in the whole brain and in selected regions of interest, in particular the left and right frontal, parietal, temporal and occipital lobes, insula, caudate and cerebellum. The distribution of values of perfusion indexes were used as features in a support vector machine classifier. Mean values of blood flow and volume were slightly lower in patients, and the difference reached statistical significance in the right caudate, left and right frontal lobes, and in left cerebellum. Linear SVM reached an accuracy of 83% in the classification of patients and normal controls, with the highest accuracy associated with the right frontal lobe and left parietal lobe. In conclusion, we found evidence that brain perfusion could be used as a potential marker to classify patients with psychosis, who show reduced blood flow and volume in respect to normal controls. Copyright © 2015 Elsevier B.V. All rights reserved.
    Schizophrenia Research 04/2015; 165(1). DOI:10.1016/j.schres.2015.03.017 · 4.43 Impact Factor
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    • "First-episode psychosis patients were recruited from the PICOS study; see Lasalvia et al. (Lasalvia et al. 2012) for a detailed description of the dataset. To exploit MRI multimodality , we selected only subjects with both T1 and DTI sequences. "
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    ABSTRACT: Currently, most of the classification studies of psychosis focused on chronic patients and employed single machine learning approaches. To overcome these limitations, we here compare, to our best knowledge for the first time, different classification methods of first-episode psychosis (FEP) using multi-modal imaging data exploited on several cortical and subcortical structures and white matter fiber bundles. 23 FEP patients and 23 age-, gender-, and race-matched healthy participants were included in the study. An innovative multivariate approach based on multiple kernel learning (MKL) methods was implemented on structural MRI and diffusion tensor imaging. MKL provides the best classification performances in comparison with the more widely used support vector machine, enabling the definition of a reliable automatic decisional system based on the integration of multi-modal imaging information. Our results show a discrimination accuracy greater than 90 % between healthy subjects and patients with FEP. Regions with an accuracy greater than 70 % on different imaging sources and measures were middle and superior frontal gyrus, parahippocampal gyrus, uncinate fascicles, and cingulum. This study shows that multivariate machine learning approaches integrating multi-modal and multisource imaging data can classify FEP patients with high accuracy. Interestingly, specific grey matter structures and white matter bundles reach high classification reliability when using different imaging modalities and indices, potentially outlining a prefronto-limbic network impaired in FEP with particular regard to the right hemisphere.
    Journal of Neural Transmission 10/2014; 122(6). DOI:10.1007/s00702-014-1324-x · 2.87 Impact Factor
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    • "In Veneto, the majority of psychotic patients is treated within the public sector: a negligible fraction of psychotic patients in treated in private hospitals or in private practice alone, and it is standard practice for general practitioners to refer all psychosis cases to public mental health services (Lasalvia et al., 2012). Standard care for FEP patients generally comprises personalized outpatient psychopharmacological treatment, combined with nonspecific supportive clinical management at the Community Mental Health Center level or, when required, at the patient's home (Lasalvia et al., 2007). "
    Journal of Psychiatric Research 01/2013; · 4.09 Impact Factor
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