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Heterogeneity of Psychosis Risk Within Individuals
at Clinical High Risk
A Meta-analytical Stratification
Paolo Fusar-Poli, MD, PhD; Marco Cappucciati, MD; Stefan Borgwardt, MD, PhD; Scott W. Woods, MD; Jean Addington, PhD; Barnaby Nelson, PhD;
Dorien H. Nieman, PhD; Daniel R. Stahl, PhD; Grazia Rutigliano, MD; Anita Riecher-Rössler, MD, PhD; Andor E. Simon, MD; Masafumi Mizuno, MD,PhD;
Tae Young Lee, MD; Jun Soo Kwon, MD, PhD; May M. L. Lam, MBBS; Jesus Perez, PhD; Szabolcs Keri, MD, PhD; Paul Amminger, MD, PhD, FRANZCP;
Sibylle Metzler, PhD; WolframKawohl, MD; Wulf Rössler, MSc, MD; Jimmy Lee, MBBS, MMed(Psychiatry), MCI; Javier Labad, MD, PhD;
Tim Ziermans, PhD; Suk Kyoon An, MD,PhD; Chen-Chung Liu, MD, PhD; Kristen A. Woodberry, MSW,PhD; Amel Braham, MD; Cheryl Corcoran, MD;
Patrick McGorry, MD, PhD, FRCP, FRANZCP; Alison R. Yung, MD; Philip K. McGuire, MD, PhD
IMPORTANCE Individuals can be classified as being at clinical high risk (CHR) for psychosis if
they meet at least one of the ultra–high-risk (UHR) inclusion criteria (brief limited intermittent
psychotic symptoms [BLIPS] and/or attenuated psychotic symptoms [APS] and/or genetic
risk and deterioration syndrome [GRD]) and/or basic symptoms [BS]. The meta-analytical risk
of psychosis of these different subgroups is still unknown.
OBJECTIVE To compare the risk of psychosis in CHR individuals who met at least one of the
major inclusion criteria and in individuals not at CHR for psychosis (CHR−).
DATA SOURCES Electronic databases (Web of Science, MEDLINE, Scopus) were searched until
June 18, 2015, along with investigation of citations of previous publications and a manual
search of the reference lists of retrieved articles.
STUDY SELECTION We included original follow-up studies of CHR individuals who reported
the risk of psychosis classified according to the presence of any BLIPS, APS and GRD, APS
alone, GRD alone, BS, and CHR−.
DATA EXTRACTION AND SYNTHESIS Independent extraction by multiple observers and
random-effects meta-analysis of proportions. Moderators were tested with meta-regression
analyses (Bonferroni corrected). Heterogeneity was assessed with the I
2
index. Sensitivity
analyses tested robustness of results. Publication biases were assessed with funnel plots and
the Egger test.
MAIN OUTCOMES AND MEASURES The proportion of each subgroup with any psychotic
disorder at 6, 12, 24, 36, and 48 or more months of follow-up.
RESULTS Thirty-three independent studies comprising up to 4227 individuals were included. The
meta-analytical proportion of individuals meeting each UHR subgroup at intake was: 0.85 APS
(95%CI, 0.79-0.90), 0.1 BLIPS (95%CI, 0.06-0.14), and 0.05 GRD (95%CI, 0.03-0.07). There
were no significant differences in psychosis risk at any time point between the APS and GRD and
the APS-alone subgroups. There was a higher risk of psychosis in the any BLIPS greater than APS
greater than GRD-alone subgroups at 24, 36, and 48 or more months of follow-up. There was no
evidence that the GRD subgroup has a higher risk of psychosis than the CHR− subgroup. There
were too few BS or BS and UHR studies to allow robust conclusions.
CONCLUSIONS AND RELEVANCE There is meta-analytical evidence that BLIPS represents
separate risk subgroup compared with the APS. The GRD subgroup is infrequent and not
associated with an increased risk of psychosis. Future studies are advised to stratify their
findings across these different subgroups. The CHR guidelines should be updated to reflect
these differences.
JAMA Psychiatry. 2016;73(2):113-120. doi:10.1001/jamapsychiatry.2015.2324
Published online December 30, 2015.
Editorial page 105
Supplemental content at
jamapsychiatry.com
Author Affiliations: Author
affiliations are listed at the end of this
article.
Corresponding Author: Paolo
Fusar-Poli, MD, PhD, Institute of
Psychiatry,Psychology, and
Neuroscience, King’s College,
PO Box 63, De Crespigny Park,
SE58AF London, United Kingdom
(paolo.fusar-poli@kcl.ac.uk).
Research
Original Investigation |META-ANALYSIS
(Reprinted) 113
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The first clinical service for individuals potentially pro-
dromal for psychosis (Personal Assessment and Crisis
Evaluation Clinic) was set up in 1995 by Yung et al
1
in
Melbourne, Australia, on the basis of the ultra–high-risk (UHR)
criteria. Inclusion required the presence of one or more of the
following: attenuated psychotic symptoms (APS), brief lim-
ited intermittent psychotic symptoms (BLIPS), and/or ge-
netic risk and deterioration (GRD) criteria (for historical de-
tails, see the article by Fusar-Poli et al
2
). These subgroups were
defined a priori as independent entry criteria, and they are in-
dependently operationalized on the psychometric assess-
ment tools that are used to ascertain the UHR state. Adoles-
cents and young adults at increased risk of developing
psychotic disorders can thus be identified using standard-
ized psychometric instruments with consistent reliability and
good predictive value.
3
The risk of psychosis in UHR individu-
als peaks during the ensuing 2 years.
4
However, despite a great
deal of research for reliable clinical, behavioral, or neurobio-
logical measures that can predict the subsequent onset of psy-
chosis, researchers have yet to discover such a holy grail.
5
The lack of reliable and valid predictive biomarkers
6
may
reflect a number of factors, including the declining transition
risks in recent years,
7,8
small sample sizes, a lack of external
validation,
6
and methodologic pitfalls.
9
However, a key
potential confounder is that the UHR category may itself be
heterogeneous.
10
When the UHR paradigm was devised, the
founders suggested that there may be different UHR sub-
groups, each associated with different levels of risk. In par-
ticular, it was hypothesized that the group with the presence
of any BLIPS (ie, BLIPS alone, BLIPS and APS, or BLIPS and
APS and GRD) would have the highest level of risk, followed
by the group with APS and GRD (additive clinical and genetic
effect on psychosis risk), the group with APS alone, and then
the GRD-alone group.
11
However, to our knowledge, this
assumption has not previously been systematically tested
using a meta-analytical approach. A further complication is
that a comparably high risk of psychosis has been indepen-
dently associated with the basic symptoms (BS) criteria,
12
which are thought to represent another separate and different
subgroup, featuring an earlier phase of prodromal psychosis
than the UHR criteria.
2
Many high-risk centers now include
individuals with UHR and/or BS in their studies, and this com-
bination can be termed as defining a clinical high-risk (CHR)
state for psychosis. The extent to which all these different
subgroups can be considered as belonging to a single CHR
group is unclear. However, if the CHR category is heteroge-
neous, this may hamper ongoing efforts to understand the
mechanisms underlying the risk of psychosis and the devel-
opment of preventive treatments.
In the present study, we investigated this issue by con-
ducting, to our knowledge, the first robust meta-analytical in-
vestigation of risk stratification across different CHR sub-
groups. We test the hypothesisof heterogeneous risk levels in
UHR, stratified as any BLIPS greater than APS and GRD,greater
than APS, alone greater than GRD alone.
11
To test the actual
risk of psychosis, these subgroups are additionally compared
with individuals assessed for suspicion of psychosis risk but
not meeting CHR criteria (hereafter CHR−). This analysis is
complemented by meta-regressions, investigating the effect
of potential confounders on the meta-analytical estimates, and
by secondary analyses on BS subgroups.
Methods
Search Strategy
Two investigators (M.C., G.R.) conducted 2-step literature
searches. First, the Web of Knowledgedatabase was searched,
incorporating both the Web of Science and MEDLINE. The
search was extended until June 18, 2015, including abstracts
in the English language only. The electronic research used sev-
eral combinations of the following keywords: at risk mental
state,psychosis risk,prodrome,prodromal psychosis,ultra high
risk,high risk,help seeking patients,psychosis prediction,psy-
chosis onset, and the names of the diverse CHR assessment in-
struments. Second, Scopus was used to investigate citations
of possible previous reviews and meta-analyses on transition
to psychosis in CHR individuals and a manual search of the ref-
erence lists of retrieved articles. Articles identified through
these 2 steps were then screened in relation to the selection
criteria on the basis of reading their abstracts. Discrepancies
were discussed with another author (P.F.-P.) and resolved
through consensus. The articles surviving this selection were
assessed for eligibility on the basis of full-text reading, follow-
ing the Meta-Analyses and Systematic Reviews of Observa-
tional Studies (MOOSE) checklist (eTable 1 in the Supplement).
13
Selection Criteria
Studies were eligible for inclusion when the following criteria
were fulfilled: (1) an original article, written in English;
(2) inclusion of CHR individuals, defined according to estab-
lished international UHR criteria (ie, Comprehensive Assess-
ment of at Risk Mental States, Structured Interview for
Psychosis–Risk Syndromes, Basel Screening Instrument for
Psychosis) and/or BS criteria (Schizophrenia Proneness Instru-
ments, Bonn Scale for the Assessment of Basic Symptoms)
instruments
14-19
or CHR− individuals; (3) prospective assess-
ment of risk of psychosis onset with at least one follow-up
time point (6, 12, 24, 36, and/or ≥48 months); (4) reported risk
of psychosis stratified across the following CHR subgroups:
any BLIPS, APS and GRD, APS alone, GRD alone (individuals
meeting multiple UHR criteria were stratified for symptom
severity as previously suggested: any BLIPS greater than APS
and GRD, greater than APS alone, greater than GRD alone
11
),
BS, and/or across the CHR− subgroup. CHR− individuals were
defined as help-seeking individuals referred to ultra-high-risk
services (UHR) and/or to expert clinicians (BS) for suspicion of
psychosis risk and assessed with the standardized CHR instru-
ments but not meeting CHR criteria. This comparison group
was thus drawn from the same pool of referrals that provided
the individuals who met the CHR criteria.
When studies had not already subdivided the CHR sample
and assessed risk of psychosis in each subgroup, the corre-
sponding author was contacted and invited to use the origi-
nal raw data to stratify the samples. A similar approach was
adopted with respect to collection of potential moderators for
Research Original Investigation Heterogeneity of Psychosis Risk in Clinical High-Risk Individuals
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each subgroup. Exclusion criteria were (1) abstracts, pilot data
sets, and articles in languages other than English; (2) articles
that did not use internationally validated definitions for CHR
(ie, UHR and/or BS); (3) articles with overlapping data sets; and
(4) studies that could not provide data on transition risk in re-
lation to these subgroups. In the case of multiple publica-
tions deriving from the same study population, we selected
the articles that reported the longest follow-up data set. The
literature search was summarized according to the Preferred
Reporting Items for Systematic Reviews and Meta-analyses
(PRISMA) guidelines.
20
Recorded Variables
Data extraction was independently performed by 2 investiga-
tors (M.C., G.R.). To estimate the primary outcome variable,
we extracted the baseline sample size and the number of in-
dividuals with psychosis at each follow-up time point across
each UHR subgroup. To estimate the secondary outcome, we
further collected number of transitions across the UHR only,
BS only, and BS and UHR subgroups. Additional moderators
tested in meta-regression analyses are listed in the statistical
analysis below. Quality assessment is described in the
eMethods in the Supplement.
Statistical Analysis
The primary outcome was the risk of psychosis onset in CHR
individuals, stratified according to the initial UHR subgroups,
with the following order: any BLIPS greater than APS and GRD,
greater than APS alone, greater than GRD alone, greater than
CHR−. This was calculated as the proportion of baseline indi-
viduals across each subgroup with any psychotic diagnosis at
6, 12, 24, 36, and 48 or more months of follow-up.The baseline
sample size was conservatively used to avoid a bias toward
overly high transition risks at longer follow-ups resulting from
an increase of dropouts over time. In case of a lack of meta-
analytical differences between the APS alone and APS and GRD
subgroups, it was planned a priori to repeat the analyses with
these 2 subgroups combined in a single group (ie, BLIPS greater
than APS, greater than GRD, greater than CHR−
11
). The meta-
analysis was conducted with the metaprop package
21
of STATA
statistical software, version 13.1 (StataCorp), which has been spe-
cifically developed for pooling proportions in a meta-analysis
of multiple studies. The 95% CIs were based on score (Wilson)
procedures.
22
Because proportions were often expected to be
small, we used Freeman-Tukey Double Arcsine transformation
23
to stabilize the variances and then perform a random-effects
meta-analysis implementing the DerSimonian-Laird method.
24
The influence of moderators was tested using meta-regression
analyses with the metareg function,
25
and the metareg permu-
tation test option was used to estimate the 95% CIs. The slope
of the meta-regression line (β-coefficient: direct or inverse) in-
dicates the strength of an association between moderator and
outcome. The meta-regressions were conducted when at
least 10 studies were available for each moderator
26
and were
Bonferroni corrected for multiple testing. Heterogeneity among
study point estimates was assessed using Q statistics. The pro-
portion of the total variability in the effect size estimates was
evaluated with the I
2
index,
27
which does not depend on the
number of studies included. Because meta-analyses of obser-
vational studies are expected to be characterized by signifi-
cant heterogeneity, random-effects models were used. In ad-
dition, we conducted sensitivity analyses to investigate the
influence of each single study on the overall risk estimate by
omitting one study at a time, using Stata’s user-written func-
tion metainf.
28,29
A study was considered to be influential if the
pooled mean estimate without it was not within the 95% CI of
the overall mean. Publication biases were assessed with the
metafunnel function of Stata that produced funnel plots for as-
sessing small-study reporting bias in meta-analysis
30
and with
the Egger test
31
in metabias
32
function of Stata. We investi-
gated as secondary outcomes the risk of psychosis in individu-
als who met the original UHR criteria only, in individuals who
met the BS criteria only, and in individuals who met both the
BS and UHR criteria.
Results
Database
The literature search (Figure 1) identified 33 independent ar-
ticles, most of which contributed more than one UHR or BS sub-
group. The details of the included studies and types of samples
provided are detailed in eTable 2 in the Supplement. The age
and sex of the CHR samples, psychometric CHR instruments,
diagnostic instrument used to assign the psychotic diagno-
sis, duration of follow-up, and exposure to antipsychotics at
baseline and baseline to follow-up, quality assessment, and
baseline sample sizes of the CHR and CHR− patient sub-
groups are detailed in eTable 2 in the Supplement.
The overall characteristics of the UHR samples are de-
tailed in the eResults in the Supplement. Across the studies using
the UHR criteria (n = 3624), the baseline meta-analytical pro-
portion of individuals meeting the 3 subgroups was as follows:
Figure 1. Preferred Reporting Items for Systematic Reviews
and Meta-analyses (PRISMA) Diagram
1896 Abstracts identified
through database searching
(Web of Knowledge)
92 Abstracts identified
through manual search
1468 Abstracts after
duplicates removed
860 Abstracts screened
113 Full-text articles (PDFs)
assessed for eligibility
33 Studies included in the
meta-analysis
747 Abstracts excluded
on initial review
80 Full-text articles (PDFs)
excluded
32
44
4
No data available
Overlapping data set
No clinical high-risk
sample
27 Authors contacted who
provided additional data
Heterogeneity of Psychosis Risk in Clinical High-Risk Individuals Original Investigation Research
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APS, 0.85 (95% CI, 0.79-0.90); BLIPS, 0.10 (95% CI, 0.06-
0.14); and GRD, 0.05 (95% CI, 0.03-0.07) (eFigure 1A, B, and C
in the Supplement) (individuals who met multiple intake cri-
teria were categorized as planned: BLIPS greater than APS,
greater than GRD
11
).
Meta-analytical Stratification of Individuals
at Ultra–High-Risk for Psychosis
There were no significant meta-analytical differences be-
tween the APS and GRD and the APS-alone subgroups at any
time point (Figure 2). We therefore combined these 2 sub-
groups into a single APS subgroup and contrasted it with the
BLIPS and GRD subgroups (BLIPS greater than APS, greater than
GRD
11
).
The 33 independent studies reported primary outcome
data at a variety of different follow-uptime points, with an over-
all sample size of up to 4227 participants (Figure 3 and Table).
There was meta-analytical evidence of higher risk of psycho-
sis in the BLIPS greater than APS, greater than GRD after 24
months of follow-up, but this effect was not evident at 6 or 12
months. Across the BLIPS and APS subgroups, the psychosis
risk peaked at 24 months and then plateaued. There was no
meta-analytical evidence that the GRD subgroup had higher
risk of psychosis than the CHR− subgroup at any time point.
Sensitivity Analyses, Publication Biases, and Meta-regressions
Meta-regressions that investigated year of publication, mean
age of subgroup, proportion of females in each UHR sub-
group, baseline functional level in each subgroup, duration of
untreated attenuated psychotic symptoms, exposure to anti-
psychotics from baseline to follow-up, psychometricUHR c ri-
teria, diagnostic criteria used to assess transition to psycho-
sis at follow-up,and quality assessment are appended in eTable
3intheSupplement. There was a significant effect for publi-
cation year on risk of psychosis onset at 24 months, with the
most recent studies reporting a lower risk than the oldest stud-
ies (eFigure 2A in the Supplement). A higher proportion of an-
tipsychotic agent exposure was associated with an increased
risk of psychosis at 36 months (eFigure 2B in the Supple-
ment). All the other meta-regressions did not produce signifi-
cant effects.
Sensitivity analyses (results available from the authors on
request) confirmed the robustness of the results at all time
points. Removal of an outlier identifiedat 12, 24, or 36 months
33
did not alter the main findings of significant between-groups
heterogeneity (P< .001). There was no evidence of publica-
tion biases as indicated by visual inspections of the funnel plots
and by the Egger test for small study effects (eFigure 3A-E in
the Supplement).
Figure 2. Risk of Psychosis Over Time in the AttenuatedPsychotic
Symptoms (APS) and Genetic Risk and Deterioration Syndrome (GRD)
vs APS-Alone Groups
0 0.37.03
Study Effect Size (95% CI)
APS and GRD at 6 mo
Subtotal (I2
=
32.26%, P
=
.11)
APS alone at 6 mo
Subtotal (I2
=
64.51%, P
<
.001)
0.09 (0.03-0.17)
0.10 (0.07-0.13)
APS and GRD at 12 mo
Subtotal (I2
=
42.10%, P
=
.03)
APS alone at 12 mo
Subtotal (I2
=
65.92%, P
<
.001)
0.17 (0.09-0.26)
0.15 (0.12-0.18)
APS and GRD at 24 mo
Subtotal (I2
=
43.32%, P
=
.03)
APS alone at 24 mo
Subtotal (I2
=
80.01%, P
<
.001)
0.17 (0.10-0.26)
0.19 (0.15-0.23)
APS and GRD at 36 mo
Subtotal (I2
=
41.77%, P
=
.09)
APS alone at 36 mo
Subtotal (I2
=
68.94%, P
<
.001)
0.26 (0.16-0.37)
0.20 (0.16-0.24)
APS and GRD at 48 mo
Subtotal (I2
=
0.00%, P
=
.43)
APS alone at 48 mo
Subtotal (I2
=
73.54%, P
<
.001)
0.28 (0.19-0.37)
0.24 (0.17-0.32)
Test forbe tween-groupheterogeneity (P> .05 at all time points).
Figure 3. Meta-analytical Stratification of Ultra–High-Risk Individuals
0.5
0.4
0.6
0.3
0.2
0.1
06 12 ≥4836
Mean Risk of Psychosis
Follow-up Time, mo
24
BLIPS or BIPS
APS
GRD
CHR−
APS indicates attenuated psychosis
symptoms; BIPS, brief intermittent
psychotic symptoms; BLIPS, brief
limited intermittent psychotic
symptoms; GRD, genetic risk and
deterioration syndrome; CHR−, not at
clinical high risk for psychosis. Error
bars indicate 95% CI.
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Secondary Outcomes
The secondary analyses (UHR alone vs BS alone vs UHR and
BS) revealed that, compared with the UHR criteria alone, there
was a higher psychosis risk in the UHR and BS subgroup at 36
months and in the UHR and BS, and BS-alone subgroups at 48
months (eFigure 4 and eTable 4 in the Supplement). How-
ever, these results should be considered exploratory because
there were only very few individual studies included.
Discussion
The current study provided, to our knowledge, the first ro-
bust meta-analytical support for the existence of heteroge-
neous subgroups within the CHR samples. Most of the UHR
individuals were included at intake because of APS (85%), with
BLIPS (10%) and GRD (5%) less frequent (eDiscussion 1 in the
Supplement). The meta-analysis indicated that these sub-
groups differed according to the level of risk, with BLIPS hav-
ing a higher transition risk than APS, and APS having a higher
transition than GRD. There was no evidence of enhanced risk
in the GRD subgroup compared with the CHR− subgroup.
We found no evidence supporting additive risk for
comorbid APS and GRD, operationalized as independent
constructs in the psychometric interviews, compared with
APS alone (Figure 2), suggesting that it is the presence of APS
that increases psychosis risk. We therefore combined these
two subgroups to form a joint APS subgroup for the analyses.
The results supported our main hypothesis: there was sub-
stantial between-group (BLIPS vs APS vs GRD vs CHR−)
meta-analytical heterogeneity across all time points
(Figure 3). Post hoc analyses revealed that this was due to a
significantly higher transition risk in the BLIPS subgroup
compared with the other 2 UHR subgroups (eg, 39% vs 19%
in the APS at 24 months) and with the CHR− subgroup. This
was evident at 24-month follow-up and remained significant
in the longer term. Significant differences may not have
been evident at 6 and 12 months because the proportion of
transitions to psychosis at these time points was smaller
than at 24 months.
4
The inclusion of the BLIPS subgroup in the CHR has al-
ways been problematic because its diagnostic significance is
unclear
34
as it overlays with the established DSM/ICD catego-
ries of brief psychotic disorders. Indeed, some authors have
acknowledged that “patients whose fully psychotic experi-
ence is of sufficient short duration to meet DSM criteria for brief
psychotic disorder could potentially meet prodromal
criteria.”
35(p 707)
Competing availability of concurrent high risk
(ie, BLIPS or Brief Intermittent PsychoticSymptoms [BIPS]) and
established psychosis labels of similar diagnostic signifi-
cance (eg, Acute and Transient Psychotic Disorder or Brief
Psychotic Disorder) may be a major source of diagnostic
confusion,
36
with consequent use of arbitrary psychosis thresh-
olds in the field.
37,38
Whether the BLIPS should be considered
a feature of a high-risk state or an established psychotic dis-
order has been addressed in a separate study.
38
Our meta-
analysis clearly reveals that the BLIP subgroup has a distinc-
tive prognosis (with higher risk of psychosis) compared with
the APS subgroup. Our finding concurs with the distinctive
baseline psychopathological presentation
2
and therapeutic
needs
39
as external validators of BLIPS as a separate clinical
entity from APS.
This finding has a number of potential implications. For
example, it may be possible for future CHR studies to limit the
recruitment to the APS subgroup to reduce sample heteroge-
neity across subgroups,
40
which might otherwise confound the
assessment of genetic, demographic, and cognitivefeatures and
neurobiological measures, as well as clinical outcomes. To date,
Table. Risk of PsychosisAcross Ultra–High-Risk Subgroups
Follow-up Time, mo BLIPS/BIPS APS GRD CHR−
Total
Sample
Test for
Between-Group
Heterogeneity
(Q) PValue
6
No. of studies (No. of individuals) 19 (219) 19 (1839) 19 (154) 8 (1021) 65 (3233)
119.32 <.001
Mean (95% CI) 0.10 (0.02-0.20) 0.10 (0.08-0.13 0 (0-0.01) 0 (0-0.02)
12
No. of studies (No. of individuals) 24 (294) 24 (2093) 24 (161) 7 (879) 79 (3472)
145.65 <.001
Mean (95% CI) 0.22 (0.14-0.32) 0.16 (0.13-0.19) 0.01 (0-0.05) 0 (0-0.01)
24
No. of studies (No. of individuals) 22 (285) 22 (2694) 22 (196) 8 (1052) 74 (4227)
124.31 <.001
Mean (95% CI) 0.39 (0.7-0.51) 0.19 (0.15-0.23) 0.03 (0-0.08) 0.01 (0-0.03)
36
No. of studies (No. of individuals) 12 (180) 12 (1533) 12 (122) 7 (863) 43 (2698)
62.13 <.001
Mean (95% CI) 0.38 (0.26-0.49) 0.21 (0.16-0.25) 0.05 (0-0.12) 0.01 (0-0.05)
≥48
No. of studies (No. of individuals) 6 (137) 6 (734) 6 (64) 3 (134) 21 (1069)
32.75 <.001
Mean (95% CI) 0.38 (0.28-0.48) 0.24 (0.21-0.27) 0.08 (0-0.19) 0.04 (0-0.13)
Abbreviations: APS, attenuated psychotic symptoms; BIPS, brief intermittent psychotic symptoms; BLIPS, brief limited intermittent psychoticsymptoms;
GRD, genetic risk and deterioration syndrome; CHR−, help-seeking individuals not at clinical high risk for psychosis.
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there have been relatively few attempts to compare the fea-
tures of subgroups within CHR samples because this requires
large samples. This issue can be addressed in multicenter stud-
ies. However, another possibility would be to retain the BLIPS
in the CHR paradigm but as a distinct and separate subgroup
to facilitate prediction of persisting psychotic disorders
38
.In
addition, data from our meta-analysis may be useful for fu-
ture designation of CHR programs. Health care professionals
may be able to inform patients and caregivers about relative
risks at a particular time point given their initial intake crite-
ria. Interventions may thus be tailored to the different sub-
groups according to their prognosis. With our meta-analysis
available, it is also arguable that training manuals and psycho-
metric assessments for CHR individuals be updated to explic-
itly acknowledge the heterogeneity of risk levels associated
with an initial CHR diagnosis.
Post hoc analyses revealed no statistically significant dif-
ferences between the GRD and the CHR− subgroups (Tableand
Figure 3). This finding raises important concerns regarding the
validity of the GRD subgroup as a true clinical high-risk syn-
drome, in particular given the lack of additive value for the APS
designation (Figure 2) and concurrent lack of epidemiologic
validation of this subgroup (prevalence for the APS and BLIPS
subgroups, but not GRD, has been reported in the general
population
41
). Our meta-analysis suggests that the GRD con-
struct may not qualify as a state risk criterion
42
in that it was
not associated with an impending risk for psychosis in the short
term (ie, in the first 4 years). However, we cannot exclude the
possibility that GRD is associated with an increased risk of psy-
chosis during longer intervals,
42
particularly because a re-
cent meta-analysis suggested that the impact of familial risk
was only evident after the age of 20 years,
43
which was simi-
lar to the mean age in our GRD subgroup. Interpreting nega-
tive results is complex because absence of evidence is not evi-
dence of absence
44
and because post hoc retrospective power
analyses are not recommended.
45-47
The meta-analytical es-
timates for the GRD subgroup were based on a small sample
(n < 200) and thus yielded a large CI (Figure 3). On the other
hand, similar widths of CIs (and similar samples of <200 at 36
and ≥48 months) were observed in the BLIPS subgroup
(Figure 3), for which significant meta-analytical differences
were found. It is also possible that the decrease in function cri-
terion required for the GRD syndrome is too low or that the in-
struments used to assess functional deterioration may not be
the most suitable. The GRD subgroup is also heterogeneous
itself, including individuals with schizotypal personality dis-
orders and functional decline in addition to familial risk for psy-
chosis. The risk of psychosis in people with a schizotypal per-
sonality disorder is unclear.
42
An earlier study
48
in 100 CHR
individuals found that schizotypal personality disorder was in-
frequent and did not predict conversion. GRD may be more use-
ful as a distal marker. In the long term (eg, after 5 years), state
markers may be traded for trait markers, and thus GRD may
reveal better predictive value during longer intervals.
42
Given
that assessing each UHR entry criterion is demanding and chal-
lenging for clinicians and patients, additional research is ur-
gently required to ascertain the actual clinical benefit of evalu-
ating GRD features during CHR psychometric interviews.
We additionally tested, for the first time to our knowl-
edge, the specific effect of several moderators of psychosis
risk across each UHR subgroup (eTable 3 in the Supplement).
Sex, quality of studies, type of UHR criteria, and diagnostic
criteria used to assess transition to psychosis did not affect
the level of risk. We also tested for the first time, to our knowl-
edge, via meta-analytical analyses the potential impact of
duration of untreated attenuated psychotic symptoms before
contact with high-risk services,
8,49
finding no effect on risk of
psychosis. Level of functioning at baseline similarly had no
impact on risk, in contrast with data from the longest
follow-up study
8
in CHR individuals and a recent meta-
analysis
50
addressing functional status in CHR patients. There
was also no effect for age, in contrast with our previous
meta-analysis.
7
These negative findings may be secondary to
lower statistical power of meta-regressions and limited vari-
ability of moderators included in the current data set, which
was stratified for different subgroups. However, we did con-
firm the decreasing transition risk in the most recent years
(eFigure 2A in the Supplement), as previously described in
original studies
51,52
and meta-analytical investigations.
7,53
We
also found that increased exposure to antipsychotic treat-
ments was associated with a higher risk of psychosis (eFigure
2B in the Supplement). Such an effect may be confounded by
an increase of symptoms severity, as previously observed in
naturalistic studies of CHR samples
39,54
and in a meta-
analysis of randomized clinical trials.
55
Overall, this is the first robust meta-analysis to indicate that
the CHR state comprises subgroups with heterogeneous lev-
els of psychosis risk. Our meta-analysis overcomes the limi-
tations of a previous pilot attempt
56
(eDiscussion 2 in the
Supplement) by following the standard recommended guide-
lines and involving data from studies across the globe
(Europe, United States, Asia, Africa, and Australia), with most
studies providing access to additional data as necessary
(27 authors sent additional meta-analytical data).
However, because of limited statistical power associated
with the small number of BS studies, we were unable to pro-
vide conclusive estimates of psychosis risk in this subgroup.
Because the total number of transitions was limited, we were
similarly unable to differentiate the risk of transition toward
schizophrenia spectrum or affective psychotic disorders.
57
We were also unable to test additional moderators potentially
addressing the observed heterogeneity, such as treatments
other than antipsychotics, ethnicity,
58
substance abuse,
59
and comorbid affective disorders,
53,60
because these factors
had not been assessed in the original studies or were
infrequent.
Conclusions
There is meta-analytical evidence of heterogeneous levels of
risk of psychosis in CHR samples. The risk in the BLIPS sub-
group is higher than in the APS subgroup. The GRD subgroup
is rare and not associated with an increased risk of psychosis.
Authors of future CHR studies are advised to stratify their find-
ings across these different subgroups.
Research Original Investigation Heterogeneity of Psychosis Risk in Clinical High-Risk Individuals
118 JAMA Psychiatry February 2016 Volume 73, Number 2 (Reprinted) jamapsychiatry.com
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ARTICLE INFORMATION
Submitted for Publication: July 19, 2015; final
revision received September 21, 2013; accepted
September 23, 2013.
Published Online: December 30, 2015.
doi:10.1001/jamapsychiatry.2015.2324.
Author Affiliations: Institute of Psychiatry,
Psychology,and Neuroscience, King’s College,
London, United Kingdom (Fusar-Poli,Cappucciati,
Stahl, Rutigliano, McGuire); OASIS Clinic, South
London and Maudsley National Health Service
Foundation Trust,London, United Kingdom
(Fusar-Poli); Department of Brain and Behavioral
Sciences, University of Pavia, Pavia, Italy
(Cappucciati); University of Basel Psychiatric
Clinics, Basel, Switzerland (Borgwardt,
Riecher-Rössler,Simon); Department of Psychiatry,
Yale University, New Haven, Connecticut (Woods);
Department of Psychiatry, Universityof Calgary,
Calgary, Alberta, Canada (Addington); Orygen, the
National Centre of Excellence in YouthMental
Health, and Centre for Youth Mental Health, the
University of Melbourne, Parkville, Australia
(Nelson, Amminger, McGorry); Department of
Psychiatry,Academic Medical Center, University of
Amsterdam, Amsterdam, the Netherlands
(Nieman); Specialized Early Psychosis Outpatient
Service for Adolescents and Young Adults,
Department of Psychiatry, Bruderholz, Switzerland.
(Simon); Department of Neuropsychiatry, Toho
University School of Medicine, Tokyo, Japan
(Mizuno); Department of Psychiatry, Seoul National
University College of Medicine, Seoul, Republic of
Korea (T. Y.Lee, Kwon); Kwai Chung Hospital, New
Territories, Hong Kong, People’s Republic of China
(Lam); Department of Psychiatry, Universityof
Cambridge, Cambridge, United Kingdom (Perez);
Nyiro Gyula Hospital, National Institute of
Psychiatry and Addictions, Budapest, Hungary
(Keri); Centre for Social Psychiatry, Department of
Psychiatry,Psychotherapy and Psychosomatics,
University Hospital of Psychiatry Zurich, Zurich,
Switzerland (Metzler, Kawohl,Rössler); Department
of General Psychiatry,Institute of Mental Health,
Singapore, Singapore (J. Lee); Department of
Psychiatry,Corporacio Sanitaria Parc Tauli Sabadell,
Barcelona, Spain (Labad); Department of Clinical
Child and Adolescent Studies, Leiden University,
Leiden, the Netherlands (Ziermans); Department of
Psychiatry,Yonsei University College of Medicine,
Severance Hospital, Seoul, South Korea (An);
Department of Psychiatry, National Taiwan
University Hospital and College of Medicine,
National TaiwanUniversity, Taipei, Taiwan (Liu);
Center for Psychiatric Research, Maine Medical
Center, Portland, Maine (Woodberry); Departments
of Psychiatry,Beth Israel Deaconess Medical Center
and Harvard Medical School, Boston,
Massachusetts (Woodberry); Psychiatry
Department, University Hospital Farhat Hached,
Sousse, Tunisia (Braham); Department of
Psychiatry,Columbia University, New York, New
York (Corcoran); Instituteof Brain, Behaviour and
Mental Health, University of Manchester,and
Greater Manchester West National Health Service
Mental Health Foundation Trust,Manchester,
United Kingdom (Yung).
Author Contributions: Dr Fusar-Poli had full access
to all the data in the study and takes responsibility
for the integrity of the data and the accuracy of the
data analysis.
Study concept and design: Fusar-Poli,Cappucciati,
McGuire.
Acquisition, analysis, or interpretation of data:
Fusar-Poli, Cappucciati, Borgwardt, Woods,
Addington, Nelson, Nieman, Stahl, Rutigliano,
Riecher-Rössler,Simon, Mizuno, T. Y. Lee, Kwon,
Perez, Keri, Amminger, Metzler, Kawohl, Rössler,
J. Lee, Labad, Ziermans, An, Liu, Woodberry,
Braham, Corcoran, McGorry, Yung, McGuire.
Drafting of the manuscript: Fusar-Poli,Cappucciati,
Nelson, T.Y. Lee, Amminger,J. Lee, Braham, Yung.
Critical revision of the manuscript for important
intellectual conte nt: Fusar-Poli, Cappucciati,
Borgwardt, Woods, Addington, Nelson, Nieman,
Stahl, Rutigliano, Riecher-Rössler, Simon, Mizuno,
T.Y. Lee, Kwon, Lam, Perez, Keri,Amminger,
Metzler, Kawohl, Rössler, J. Lee, Labad, Ziermans,
An, Liu, Woodberry,Corcoran, McGorr y, Yung,
McGuire.
Statistical analysis: Fusar-Poli,Cappucciati, Nelson,
Nieman, Stahl, T.Y. Lee, Amminger,Me tzler.
Obtained funding: Fusar-Poli, Addington,
Riecher-Rössler,Simon, Rössler, J. Lee, Braham,
Corcoran, McGuire.
Administrative, technical, or material support:
Fusar-Poli, Addington, Riecher-Rössler, Simon, Lam,
Perez, Keri, Kawohl, Rössler, J. Lee, McGuire.
Study supervision: Fusar-Poli, Borgwardt,
Addington, Riecher-Rössler,T. Y.Lee, Kwon,
Kawohl, Rössler,McGorr y, Mc Guire.
Conflict of Interest Disclosures: None reported.
Funding/Support: This study was supported in
part by a 2014 NARSAD YoungInvestigator Award
(Dr Fusar-Poli). There search leadingto the se
results has also received funding from the European
Community’s Seventh FrameworkProgramme
under grant agreement HEALTH-F2-2013-603196
(Project PSYSCAN [Translating Neuroimaging
Findings from Research into Clinical Practice]).
Role of the Funder/Sponsor:The funding sources
had no role in the design and conduct of the study;
collection, management, analysis, and
interpretation of the data; preparation, review, or
approval of the manuscript; and decision to submit
the manuscript for publication.
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