1

Mapping the Structure of the At-Risk Mental

State

MASTER-THESE

Naam: Sylvester N. de Koning

Studentnummer: 1845497

Inleverdatum: 29 juli 2013

Tel: 06 22 51 63 08

Email adres: sylvester.de.koning90@gmail.com

Opleiding: Master Klinische Psychologie,

Begeleider: drs. Tamar Kraan en drs. Helga K. Ising

Beoordelaar: drs. Robin N. Kok

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Abstract

It is paramount to map the structure of the at-risk mental state (ARMS) for

psychosis to improve identification and intervention strategies aimed at help-seeking

individuals at risk for psychosis.

The aims of this study were to define underlying dimensions of sub-clinical

psychopathology in ARMS subjects and to validate the robustness of these

dimensions.

316 participants meeting the criteria for the Early Detection and Intervention

Evaluation trial (EDIE-NL) for ARMS, were assessed with a semi-structured

interview; the Comprehensive Assessment of At Risk Mental State (CAARMS; Yung

et al., 2005) and other clinically relevant measures. Data was analyzed via principal

component analysis (PCA) and Pearson's r correlation coefficient.

The PCA of the CAARMS produced five interpretable components

("Depression" "Disorganization", "Bodily-impairment", "Manic" and "Schizo-

affective"). All but the "Schizo-affective" component proved robust when validated on

the PCA of the subsample. Of all the components, only the "Depression" cluster was

strongly related to worse global functioning and increased depressive symptoms and

negative illness appraisal.

Found components could provide a step towards a dimensional approach to the

CAARMS, as a complementary one to a categorical approach. However clear and

robust dimensions must be defined first, and further research on the subject is

warranted.

Keywords: Psychosis, At-risk mental state, Principal component analysis,

CAARMS, Dimensions

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Samenvatting

Het is van groot belang om de structuur van de At-Risk Mental State (ARMS)

in kaart te brengen om identificatie- en interventiestrategieën gericht op hulpzoekende

individuen met een verhoogd risico op een psychose te verbeteren.

Het doel van dit onderzoek was om de onderliggende dimensies van

subklinische psychopathologie bij Ultra-High-Risk (UHR) patiënten te identificeren

en om de robuustheid van deze dimensies te valideren.

316 deelnemers die aan de criteria voldeden van het Early Detection and

Evaluation onderzoek (EDIE-NL) voor UHR werden onderzocht met een semi-

gestructureerde interview; de Comprehensive Assessment of At Risk Mental State

(CAARMS; Yung et al., 2005) en andere klinisch relevante vragenlijsten. De data

werd geanalyseerd door middel van principale-componentenanalyse (PCA) en

Pearson's correlatie.

Uit de PCA van de CAARMS kwamen vijf componenten naar voren

(“Depressie”, “Disorganisatie”, “Lichamelijke-verstoringen”, “Manisch” en “Schizo-

affectief”). Behalve de “Schizo-affectieve” component waren alle componenten

robuust wanneer deze gevalideerd werden op de PCA van de subsample. Alleen de

“Depressie” component vertoonde een sterk verband met toegenomen depressieve

symptomen, negatieve ziektewaardering en verminderd globaal functioneren,

De gevonden componenten kunnen bruikbaar zijn bij een dimensionale

benadering op de CAARMS, welke complementair aan de gangbare categorische

benadering kan zijn. Echter, eerst moeten duidelijke en robuuste dimensies

geformuleerd worden en is verder onderzoek naar dit onderwerp wenselijk.

Sleutelwoorden: Psychose, At-risk mental state, Principale-componenten analyse,

CAARMS, Dimensies

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Contents

1. Introduction 5

2. Method 7

2.1. Design and Outcome 7

2.2. Recruitment and participants 7

2.3. Exclusion criteria 8

2.4. Measure instruments 8

2.5. Statistical analysis 9

3. Results 11

3.1. Sample characteristics 11

3.2. CAARMS: internal consistency and item distribution 12

3.3. Principal component analysis 13

3.4. Component consistency 15

3.5. Associations with baseline variables 15

4. Discussion 16

4.1. Main findings 16

4.2. Limitations 17

4.3. Strengths 18

4.4. Conclusions 18

References 20

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1. Introduction

Among the mental disorders, schizophrenia and related psychotic disorders are

considered to be the most severe in terms of human suffering and societal costs (Van

Os & Kapur, 2010). The prognosis of schizophrenia is generally poor, and has

increasingly worse clinical and functional outcomes over time when left untreated

(Harris et al., 2005). Also, pharmaceutical treatment of schizophrenia as of yet only

suppresses a portion of the symptoms and medication often comes with severe and

impairing side effects. Therefore, it is imperative that alternative models for treating

schizophrenia are being considered, with clinical staging being a promising concept.

The concept of clinical staging is becoming more prominent as a means of

diagnosing and treating psychosis in recent literature (McGorry, Nelson, Goldstone, &

Yung, 2010; McGorry et al., 2007; Raballo & Laroi, 2009). The clinical staging

model, applied to psychosis, defines not only the extent of progression of psychotic

disorders at a particular point in time but also in which stage an individual currently

finds itself along the continuum of the course of the disorder. This model is

particularly useful as it differentiates early milder clinical symptoms from those that

accompany illness progression and chronicity. Approaching the treatment of psychotic

disorders in this way assists clinicians to select relevant interventions at a certain

phase along the continuum where the interventions will be most effective and less

disruptive and harmful than more intensive treatments, such as heavy medication or

psychiatric commitment. With this in mind, a necessity rises for evidence-based early

intervention indicated early on the continuum (McGorry et al., 2007; McGorry et al.,

2010; Raballo & Larøi, 2009). Recent studies indicated that early intervention of

psychosis is effective (Van der Gaag et al., 2012), therefore it is paramount to have a

better understanding of the onset and structure of the various stages of psychosis to be

able to continue improving and developing interventions.

Yung and McGorry were the first to formulate criteria to identify individuals at

an early stage of psychosis based on genetic predisposition and the presence of milder

psychotic symptoms (Yung & McGorry, 1996). They identified the three following

groups, of which at least one has to be present to fulfill the now widely applied 'Ultra

High Risk' (UHR) or „At Risk Mental State (ARMS) criteria: 1) genetic risk, 2)

attenuated psychotic symptoms or 3) having Brief Limited Intermittent Psychotic

Symptoms (BLIPS; Yung et al., 2005).

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The first instrument that is developed to identify ARMS in individuals is the

„Comprehensive Assessment of At-Risk Mental States‟ (CAARMS; Yung et al.,

2005). This semi-structured interview assists with differentiating between individuals

with no psychotic symptoms, individuals who have an ARMS and individuals who are

presently experiencing clinical psychosis. These symptoms are divided over seven

chapters which cover the following domains: Positive Symptoms, Cognitive Domains,

Emotional Disturbances, Negative Symptoms, Behavioral Changes, Disturbances in

Motor-functions and General Psychopathology. To determine ARMS only the

positive-symptom scale is used and a marked decline in social functioning must be

present. The results of the CAARMS provide a categorical approach in assessing

psychotic symptoms and the CAARMS is well suited to detect psychotic symptoms at

a sub-clinical level (Yung et al., 2005). However, an exploration of the dimensional

structure can be useful as a means of gaining insight in the clinical vulnerability to

psychosis and assisting in developing tailor-made treatments for patients with ARMS

(Raballo et al., 2011).

In an attempt to improve the identification of clinical vulnerability to

psychosis in help-seeking subjects, Raballo and colleagues (2011) performed

principal component analysis on the items of the CAARMS to map the underlying

structure of the at-risk mental state in young adults. The analysis yielded three

symptom clusters, which were found stable after 12-month follow-up from baseline.

The symptom clusters consisted of a factor encompassing the negative symptoms, a

disorganized component and a perceptual-affective instability component. They also

found that the severity of the disorganized cluster was the strongest predictor of

transition into psychosis at 12-month follow-up.

To deem such symptom clusters as robust, it is necessary that similar clusters

can be found in various patients with ARMS. This study attempted to replicate the

results found by Raballo and colleagues (2011). The primary aim was to find similar

symptom clusters, providing further evidence for, and understanding of the underlying

dimensions of the at-risk mental state.

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2. Method

2.1. Design and Outcome

In this study the data set of the Dutch Early Detection and Intervention

Evaluation (EDIE-NL; Rietdijk et al., 2010) trial was used. This is a longitudinal

randomized clinical trial in which treatment-as-usual (TAU) is compared to an add-on

cognitive behavioral therapy (CBT), targeted at the prevention of psychosis in an

ARMS population. For a comprehensive description of the study, see Rietdijk et al.,

2010. Beside this, participants from the Early Detection and Intervention Team

(EDIT) in The Hague were included. EDIT is a department of Parnassia mental-health

institute created as a direct result of the EDIE-NL trial, and uses the same methods.

The main outcome measures in this study were if clear interpretable

dimensions could be extracted from the CAARMS and if such dimensions could by

validated by a subset of the sample.

2.2. Recruitment and participants

In the EDIE-NL trial 283 ARMS patients were interviewed with the complete

CAARMS. Of these 283 participants, 201 completed additional baseline measures on

clinically relevant variables. Furthermore an additional 33 participants from the EDIT

were included with complete CAARMS data. The total sample was thus comprised of

316 participants.

The participants in the EDIE-NL trial were recruited at 4 different research

sites, using two different recruitment methods. Participants included by the first

method were referred to specialized early psychosis clinics in Amsterdam by mental-

health practitioners who suspected the presence of a psychotic development. These

participants were aged 14 to 35 years.

The second method was a two-stepped screening. For this method treatment-

seeking participants in The Hague filled out the Prodromal Questionnaire (PQ-92;

Loewy et al., 2005; PQ-16; Ising et al., 2012) to reduce unnecessary interviewing of

true-negatives by measuring psychotic proneness around the time of their intake at a

secondary mental health institute. Those who scored above cut-off were interviewed

by clinical psychologists or research assistants with the Social and Occupational

Functioning Scale (SOFAS; Goldman et al. 1992) and the first chapter of the

CAARMS to determine ARMS in participants. If participants met the ARMS criteria,

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they were invited for the remaining domains of the CAARMS interview and also

completed additional baseline measures. Participants recruited by this method were

aged 18 to 35 years.

The EDIT department (which is located in The Hague) also used the two-step

screening method. The sites of Rivierduinen (Leiden and surroundings) and the

province of Friesland used both the referral and two-step screening method.

2.3. Exclusion criteria

Criteria for exclusion were: a) current or previous usage of anti-psychotic

medication over 15 mg Haloperidol equivalents; b) severe learning impairment; c)

problems due to somatic condition; d) insufficient competence in the Dutch language;

e) history of psychosis.

2.4. Measure instruments

The Comprehensive Assessment of At-Risk Mental States (CAARMS; Yung et

al., 2005) was used to determine the at-risk mental state. The CAARMS is a semi-

structured interview covering seven domains of the symptoms of schizophrenia:

Positive Symptoms, Cognitive Domains, Emotional Disturbances, Negative

Symptoms, Behavioral Changes, Disturbances in Motor-functions and General

Psychopathology. The seven domains consist of 28 sub-categories on which

symptoms can rated be on a seven-point Likert-scale for intensity, from 0 absent to 6

extreme.

Items were also rated on a seven-point Likert-scale for frequency, from 0

never to 6 continuously. Finally items can be rated on a three-point scale on the

relation between symptoms and substance use from 0 no relation to substance use to 2

noted only in relation to substance use The CAARMS produces three outcomes: Not

at-risk, At-risk mental state or Psychosis. Furthermore, individuals with an ARMS can

be categorized in one or more of the following groups: (1) experiencing sub-clinical

positive psychotic symptoms, or “State” (2) having experienced Brief intermittent

psychotic symptoms (BLIPS) or (3) being diagnosed with schizotypical personality

disorder or having a first-degree relative with a psychotic disorder, or “Trait”. The

CAARMS has been found to have good to excellent inter-rater reliability (ICC of total

CAARMS was 0.85) and good predictive validity (Yung et al., 2005).

To reduce the amount of true-negatives found by the CAARMS, the Dutch

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translation of the Prodromal Questionnaire, both 92-item version (PQ-92; Loewy et

al., 2005) and shortened 16-item version (PQ-16; Ising et al., 2012) was used prior to

interviewing. The items are statements which can be answered with “true” or “false”.

Examples of items are “My thoughts are sometimes so strong that I can almost hear

them” or “I often feel that others have it in for me”. With the PQ-92 (used up until

March 2011) a cutoff of 18 items or more answered with “true” was used, which has a

specificity and sensitivity of both 90% in this population (Ising et al., 2012). The

internal validity was excellent with a Cronbach‟s alpha of .96 (Loewy et al., 2005).

With the PQ-16 (used after March 2011) a cutoff of 6 items answered “true” was

used, which corresponded with a specificity and sensitivity of 87% for both, and has a

Cronbach‟s alpha of .77, which is between acceptable and good (Ising et al., 2012).

Social impairment was determined by the Social and Occupational

Functioning Scale (SOFAS; Goldman et al., 1992), which rates social impairment as a

result of physical and psychological disability on a scale from 0 to 100. To be

included in the study, participants either have had a 30% decline in SOFAS score

within a month or having in the past 12 months a SOFAS score lower than 55.

The Becks Depression Inventory-II, Dutch translation (BDI-II-NL; Van der

Does, 2002) was used to assess depression scores ranging from 0 - 63; a high score

reflects more severe depression. The test-retest reliability and the internal consistency

show high rates (Van der Does, 2002).

To assess the participants‟ subjective appraisal of their illness, the Personal

Beliefs about Illness Questionnaire-Revised (PBIQ-R; Birchwood et al., 1993;

Birchwood et al., 2012) was used. It is a self-report questionnaire with five subscales:

1) loss, 2) humiliation, 3) shame, 4) attribution of behavior to self or to illness and 5)

entrapment in psychosis. While not specifically designed to produce an overall score,

the sum of the subscales can provide an estimate of either positive or negative

appraisal. Higher overall scores indicate stronger negative appraisal of illness. The

scale has demonstrated good reliability and validity in individuals with schizophrenia.

2.5. Statistical analysis

The data was tested for suitability for component analysis using the Kaiser–

Meyer–Olkin measure of sampling adequacy and the Bartlett's Test of Sphericity. A

principal component analysis with Varimax rotation was used on the 28 items of the

CAARMS in order to explore the factor structure of at-risk mental state symptoms at

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baseline. This included all EDIE-NL and EDIT patients who met the ARMS criteria

that were interviewed with the complete CAARMS (N =316).

The Horn's parallel analysis method (Horn, 1965) was used to determine the

amount of components. This method generates a large number of random correlation

matrices with the same number of variables and sample size as the actual matrix, and

compares the eigenvalues in the observed matrix with mean eigenvalues in the

random matrices. This is the same method used to determine the amount of

components used by Raballo and colleagues (2011) to ensure comparability.

To test the stability of the factors a principal component analysis with Varimax

rotation has been conducted on the data of the EDIE-NL subsample of 201

participants. These components are then compared to the components found on the

total sample using Pearson's r correlation coefficient.

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3. Results

3.1. Sample characteristics

The total sample consisted of 316 participants of whom 141 were male

(44.6%) and 175 were female (55.4%). The mean age of the total sample was 23.01

years (SD = 5.68). Excluding the 82 EDIE-NL participants who did not complete the

additional measures, there were significant differences between the EDIE-NL and

EDIT samples in age (t(232) = 4.40, p = .001), sex (U=2604.00, p = .022, r = .15)

and ARMS group distribution (χ2(3, N = 234) = 9.71, p = .021). Further demographics

and clinical characteristics on the participants who completed additional measures can

be found in table 1.

Demographics and Measure Means of EDIE-NL and EDIT participants (N=234)

Characteristic

EDIT

(N=33)

EDIE-NL

(N=201)

Statistics

Mean (SD) Mean (SD)

Age in years 26.18 (4.71) 22.72 (5.54) t(232) = 4.40, p = .001

N (%) N (%)

Female 24 (72.7) 103 (51.2) U=2604.00, p = .022, r = .15

BDI-II

22.84 (10.90)

22.69 (12.33)

t(222) = 0.06, p = .953

SOFAS 45.12 (3.63) 46.03 (4.98) t(53.98) = 0.21, p = .212

PBIQ-R 74.62 (14.85) 74.23 (16.37) t(217) = 0.37, p = .709

Intake diagnosis, N (%) χ2(11, N = 234) = 11.27, p = .421

Anxiety disorder 7 (21.2) 54 (26.9)

Depression 7 (21.2) 52 (25.9)

Mixed anxiety and

Depression

1 (3.0) 10 (5.0)

Personality disorder 7 (21.2) 15 (7.5)

ADHD 2 (6.1) 13 (6.5)

Addiction problems 1 (3.0) 11 (5.5)

Eating Disorder 2 (6.1) 11 (5.5)

PTSD 4 (12.1) 10 (5.0) Oppositional Defiant

Disorder

0 (0.0) 6 (3.0)

Asperger Syndrome 0 (0.0) 5 (2.5)

V-code DSM-IV diagnosis 1 (3.0) 5 (2.5)

Other problems 1 (3.0) 9 (4.5)

CAARMS At-risk group χ2(3, N = 234) = 9.71, p = .021

“State” 28 (84.8) 193 (96.0)

“Trait” 5 (15.2) 6 (3.0)

“BLIPS” 0 (0.0) 2 (1.0) Note. CAARMS = Comprehensive Assessment of At-Risk Mental State; SOFAS = Social and Occupational Functioning Scale; BDI-II = Becks

Depression Inventory – II; PBIQ-R = Personal Beliefs about Illness Questionnaire-Revised; EDIE-NL = Early Detection and Intervention Evaluation –

Nederland; EDIT = Early Detection and Intervention Team. Intake diagnosis percentages do not sum to 100 percent due to rounding.

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3.2. CAARMS: internal consistency and item distribution

Internal consistency-analysis revealed a good internal coherence on the

CAARMS (Cronbach's α = .80), with none of the items having a relevant alpha

degrading tendency. Table 2 provides modes and medians of the 28 items. Diminished

role functioning had a mode of 5 (severe), three items (Unusual thought content,

Perceptual abnormalities and Avolition/Apathy) had a mode of 4 (moderately severe),

seven had 3 (moderate), three had 2 (mild) and 15 had 0 (absent).

Table 2

CAARMS Intensity Item Scores at first Assessment (N = 316): Descriptive Statistics and Cronbach's Alphas.

Min

Max

Mean

SD

Median

Mode

Alpha if item

deleted

Unusual thought content 0 6 2.73 1.70 3 4 .80

Non-bizarre ideas 0 6 2.56 1.47 3 2 .80

Perceptual abnormalities 0 6 3.23 1.37 3 4 .80

Disorganized speech 0 5 1.63 1.29 2 2 .79

Observed cognitive change 0 6 0.82 1.17 0 0 .80

Subjective cognitive change 0 6 2.56 1.11 3 3 .78

Subjective emotional

disturbance

0 6 2.23 1.50 2 2 .78

Observed blunter affect 0 5 1.14 1.33 0 0 .79

Observed inappropriate affect

0 4 0.24 0.74 0 0 .80

Alogia 0 5 1.19 1.26 1 0 .79

Avolition/apathy 0 6 2.98 1.48 4 4 .78

Anhedonia 0 6 2.89 1.76 3 3 .79

Social isolation 0 6 2.68 1.65 3 3 .78

Impaired role function 0 6 3.13 1.77 3 5 .78

Disorganizing/odd/stigmatiz

ing behaviour

0 6 0.78 1.31 0 0 .79

Aggression/dangerous

behaviour

0 5 2.47 1.45 3 3 .79

Subjective complaints of impaired motor funtioning

0 5 0.86 1.79 0 0 .79

Subjective complaints of

impaired bodily sensation

0 6 0.78 1.40 0 0 .79

Informant reported or

observed changes in motor

functioning

0 4 0.11 0.59 0 0 .80

Subjective complaints of

impaired autonomic

functioning

0 5 1.76 1.55 2 0 .78

Mania 0 4 0.65 1.14 0 0 .80

Depression 0 5 3.03 1.38 3 3 .78

Suicidality and self-harm 0 6 1.54 1.38 2 0 .79 Mood swings/lability 0 5 1.51 1.44 2 0 .79

Anxiety 0 6 3.18 1.65 3 3 .78

Obsessive-compulsive

symptoms

0 6 1.58 1.69 2 0 .79

Dissociative symptoms 0 5 1.24 1.57 0 0 .79

Impaired tolerance to

normal stress

0 6 2.32 1.76 3 0 .78

Note. CAARMS = Comprehensive Assessment of At-Risk Mental State. Intensity scores: 0 = Absent, 1 = Questionable, 2 = Mild, 3 =

Moderate, 4 = Moderately severe, 5 = Severe. 6 = Extreme (i.e. psychotic intensity)

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3.3. Principal component analysis

The data was suitable for factor analysis on the total sample, as were EDIE-NL

participants alone as indicated by the Kaiser-Meyer-Olkin measure of sampling

adequacy (EDIE-NL and EDIT: .79; EDIE-NL: .75) and the Bartlett's Test of

Sphericity (EDIE-NL and EDIT: χ2 (378) = 1659.79, p < .001; EDIE-NL: 1367.95, p <

.001).

Preliminary principle component analysis indicated that of the 28 items ten

components with eigenvalues above 1 could be extracted, which accounted for 62%

of variance. The Horn's parallel analysis method further reduced the amount of

components to five, which accounted for 41% of variance.

Component loadings of the principal component analysis on the CAARMS

items can be found in Table 3. Component I included high loadings on the items

“Impaired role function”, “Avolition/apathy”, “Social isolation”, “Depression”,

“Anhedonia”, “Impaired tolerance to normal stress” and “Anxiety”, and can be

considered a "Depression" cluster. Component II had high loadings on “Observed

cognitive change”, “Disorganized speech”, “Alogia” and

“Disorganized/odd/stigmatizing behavior”, a "Disorganization" cluster. Items that

loaded highly on component III were “Subjective complaints of impaired bodily

sensation”, “Subjective complaints of impaired autonomic functioning”, “Subjective

complaints of impaired motor functioning” and “Obsessive-compulsive symptoms”,

named the “Bodily-impairment" symptom-cluster. Component IV included items

“mood swings/lability”, “mania” and “aggressive/dangerous behavior”, the "Manic"

cluster. Component V included items “Subjective emotional disturbance”, “Observed

blunter affect” and “Unusual thought content”, here called the "Schizo-affective"

symptom-cluster. Similar components were found on the EDIE-NL sample, with the

exception of component V, which was replaced with a component that loaded high on

items such as “Impaired role function”, “Avolition/apathy”, “Social isolation”,

“Impaired tolerance to normal stress”, “Anxiety” and “Subjective complaints of

impaired autonomic functioning”.

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Table 3

Component Loadings on the 28 Items of the CAARMS in the total sample and the EDIE-NL

sample

Total Sample (N=316)

EDIE-NL (N=201)

1 2 3 4 5 1 2 3 4 5

Impaired role function .74 .53 .43

Avolition/apathy .74 .60 .42

Social isolation .73 .42 .39 .49

Depression .64 .37 .67

Anhedonia .57 .71

Impaired tolerance to

normal stress .53 .69

Anxiety .53 .72

Suicidality and self-harm .34 -.32 .30 .47 .60 .35

Observed cognitive change

.66 .64

Disorganized speech .63 .54 .34

Alogia .58 .48 .40

Disorganized/odd/stigmat

izing behavior

.48 .53

Observed inappropriate

affect

.35 .48

Informant reported or

observed changes in

motor functioning

.30 .34 .35 .36

Subjective complaints of

impaired bodily sensation

.62 .64

Subjective complaints of

impaired autonomic

functioning

.32 .59 .51 .48

Subjective complaints of

impaired motor

functioning

.53 .59

Obsessive-compulsive

symptoms

.45 .44

Mood swings/lability .67 .57 .37

Mania .62 .71

Aggression/dangerous behavior

.58 .55

Dissociative symptoms .34

Subjective emotional

disturbance

.31 .65 .67 .36

Observed blunter affect .59 .65

Unusual thought content .47

Subjective cognitive

change

.34 .38 .38 .43 .32

Non-bizarre ideas .32

Perceptual abnormalities .32 Note. CAARMS = Comprehensive Assessment of At-Risk Mental State; EDIE-NL = Early Detection and Intervention

Evaluation – Nederland; EDIT = Early Detection and Intervention Team. Items are sorted by decreasing size of the

coefficients of the total sample. All loadings greater than .30 are reported; loadings greater than .50 are set in bold font;

items that show the same co-aggregation pattern for the total sample and EDIE-NL are highlighted.

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3.4. Component consistency

Of the components extracted from the total sample, components II, III and IV

had very strong correlations with a single component from the EDIE-NL sample

(Pearson's r = .93, r = .92 and r = .89 respectively). Component I from the total

sample correlated highly with both component I and V (both r = .68) from the EDIE-

NL sample. Pearson's r coefficients for all components can be found on table 4.

Table 4

Pearson's r correlations between the total sample (N=316) and EDIE-NL participants (N=201)

EDIE-NL

I II III IV V

Total

sample

I .68** .21** -.12* -.22** .68**

II .06 .93** .19** .14* -.16**

III -.08* -.07 .92** .15* .29**

IV -.11 -.04 .02 .89** .38**

V .66** -.24** .19** .27** -.33**

Note: EDIE-NL = Early Detection and Intervention Evaluation – Nederland. Correlations marked with * indicate p > .05, ** indicate P > .001,

coefficients over .4 are shown in bold.

3.5. Associations with baseline variables

Pearson‟s r coefficients were calculated between found components on the

CAARMS and demographic and clinical relevant variables (see Table 5). Of the five

components, Component I showed strong significant positive relationships with

depression and negative illness appraisal and a moderate relationship with age, and a

strong inverse relationship with social functioning. On the other components

significant correlation coefficients were either weak, or negligible.

Table 5

Pearson’s r correlations between CAARMS components and baseline variables (N=201)

CAARMS Component

I II III IV V

Age .33** -.02 .18** .12* .11

SOFAS -.44** -.04 .15* .08 -.08

BDI-II .49** .07 .16* .20** .20**

PBIQ-R .44** .00 .21** .21** .15*

Note: CAARMS = Comprehensive Assessment of At-Risk Mental State; SOFAS = Social and Occupational Functioning Scale;

BDI-II = Becks Depression Inventory – II; PBIQ-R = Personal Beliefs about Illness Questionnaire-Revised. Correlations marked

with * indicate p > .05, ** indicate P > .001, coefficients over .4 are shown in bold.

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4. Discussion

4.1. Main findings

From the 28 items of the CAARMS intensity scores on the whole EDIE-NL

(N=283) and EDIT sample (N=33), five components could be extracted by PCA. Of

these components, four remained stable after validation on the components extracted

from the EDIE-NL subsample (N=201). The components which remained stable

included a "Depression" symptom-cluster (I) , a "Disorganization" symptom-cluster

(II), a "Bodily-impairment" symptom-cluster (III) and a "Manic" symptom-cluster

(IV). The fifth found symptom-clusters could be considered a "Schizo-affective"

symptom-cluster. The “Schizo-affective” component however, was not found when

analyzing the EDIE-NL sample (N=201) with PCA, instead being replaced by a

component with high loadings on many of the items of the "Depression" symptom-

cluster.

Raballo and colleagues (2011) found a "negative-interpersonal” component, a

“disorganized” component and a “perceptual-affective instability” component. These

components showed similarities to several symptom-clusters found in this study,

namely the "Depression" symptom-cluster, the "Disorganization" symptom-cluster

and the "Manic" symptom-cluster respectively, with the “negative-interpersonal”

component and the “Depression” component being nearly identical. Other studies

based on the CAARMS (Demjaha, Valmaggia, Stahl, Byrne, & McGuire, 2012) or on

the conceptually and clinically similar instrument, the Scale of Prodromal Symptoms

(SOPS; Miller et al., 1999) also produced clear negative symptom-clusters, suggesting

that it is a robust feature of the ARMS (Hawkins et al., 2004; Lemos et al., 2006). The

“Disorganized” symptom-cluster also bears similarity Disorganization/cognitive

dimension found by Demjaha and colleagues (2012).

The main difference was the absence of a component including the items

“Subjective complaints of motor-function impairment”, “Subjective complaints of

impaired autonomic functioning” and “Subjective complaints of impaired bodily

sensation” among the components found by Raballo and colleagues (2011). A finding

of note, or rather a lack thereof was that no clear component with the positive

symptoms was found. An explanation for why this study could not produce a positive

component might be that the screening instrument used focused specifically on

positive symptoms, and in fact inclusion is by definition based on the presence of

17

positive symptoms. As a result the variance on these three items may have been too

low to produce a clear positive symptom-cluster. As the study of Raballo and

colleagues (2011) also could not produce a positive component, they suggested that

this might be due to the relative low amount of items measuring positive symptoms on

the CAARMS, 3 items of the 28.

When correlating the components to clinically relevant baseline variables, the

strongest relations were found in the “Depression” component with depression and

negative illness appraisal and a moderate relationship with age, and a strong inverse

relationship with social functioning and a moderate inverse relationship with

subjective health. While these relations are not unexpected with the “Depression”

component, it is interesting to see that the other components have a weak or no

relation with these variables, unlike Raballo and colleagues (2011) found on all of

their components.

4.2. Limitations

The first limitation of this study is that the stability of the components was

tested by comparing the components of the total sample to a sub-part of the total

sample instead of testing and retesting the whole sample. Because of this, the

temporal stability of the components could not be assessed. While participants of the

EDIE-NL trial were re-interviewed with the CAARMS at six-month intervals, a part

of them received an intervention aimed at reducing ARMS symptoms, which would

have confounded the results.

Another limitation is that the Varimax rotation in the EDIE-NL subsample

took 36 iterations to get a decent fit. As a general rule of thumb, any amount higher

than 25 rotations means that interpretable results are questionable at best. An

explanation for this could be that when using the Horn's parallel analysis method to

explore the recommended amount of components on the EDIE-NL subsample, it

suggested that six components instead of five components were recommended for the

EDIE-NL subsample. However, this would have made comparisons with the five

components of the total sample impossible and choosing six components over five on

the total sample has no supporting theoretic arguments.

A third limitation is that there was a significant age difference between the

EDIT and EDIE-NL participants, with the EDIT participants being older on average.

The difference in age can be explained because the Amsterdam and Leiden research

18

site included participants from 14 years and up, while the EDIT participants were

included from age 18 and up. Taking into account that this study also found a

significant positive correlation between age and the “Depression” component, this

could have a confounding effect, skewing the component analysis of the complete

sample towards the “Depression” component, when compared to the component

analysis on the subsample.

Finally a significant difference between ARMS groups between the EDIE-NL

and EDIT samples was observed; the EDIT sample had relatively more “Trait” ARMS

patients than the EDIE-NL sample. This can be explained due to the possibility for

participants to fit into multiple categories, and the analysis only takes into account the

most prominent category. For example a person with a first degree family member

with a psychotic disorder who also exhibits sub-clinical symptoms themselves, would

be placed in the “State” group first and in the “Trait” group second.

4.3. Strengths

The main strength of this study is the use of the two-stepped screening method

of recruiting participants in the majority of cases. As Rietdijk and colleagues (2012)

have shown that the usual referral method has a higher rate of false negative ARMS

patients compared to a two-stepped screening method. Therefore it can be argued that

this sample is a better representation of the ARMS population, meaning that the found

components should lie closer to the “true” underlying dimensions.

Another strength of this study is the larger sample size compared to other

studies. 223 participants in Raballo and colleagues (2011), 122 participants in

Demjaha and colleagues (Demjaha et al., 2012) and 94 in Hawkins and colleagues

(Hawkins et al., 2004) versus 316 participants in this study.

4.4. Conclusions

In conclusion, this study found that the CAARMS interview has an underlying

five-dimensional factor structure. With the symptom grouping bearing similarities to

the groupings of comparable studies, this study provides further evidence for such an

underlying dimensional structure of the CAARMS. However, the differences in the

amount of components, and the emergence of entirely different components warrant

further research into the dimensional structure of the CAARMS. Confirmatory factor

analysis for example could provide more insight in a definite underlying dimensional

19

structure. The CAARMS is an excellent instrument to assess the subtleties of the

psychotic spectrum in patients, and this study might prove a stepping-stone to a

dimensional approach to assist in tailor-made treatments according to the clinical-

staging model.

20

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