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Heterogeneity of psychosis risk within individuals at clinical high risk: A meta-analytical stratification

  • King's College London (primary) and University of Pavia (secondary)

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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 I2 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.
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Copyright 2016 American Medical Association. All rights reserved.
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
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
Author Affiliations: Author
affiliations are listed at the end of this
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
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
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
). 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.
The risk of psychosis in UHR individu-
als peaks during the ensuing 2 years.
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.
The lack of reliable and valid predictive biomarkers
reflect a number of factors, including the declining transition
risks in recent years,
small sample sizes, a lack of external
and methodologic pitfalls.
However, a key
potential confounder is that the UHR category may itself be
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.
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,
which are thought to represent another separate and different
subgroup, featuring an earlier phase of prodromal psychosis
than the UHR criteria.
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.
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.
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).
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)
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
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
114 JAMA Psychiatry February 2016 Volume 73, Number 2 (Reprinted)
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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.
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
). The meta-
analysis was conducted with the metaprop package
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)
Because proportions were often expected to be
small, we used Freeman-Tukey Double Arcsine transformation
to stabilize the variances and then perform a random-effects
meta-analysis implementing the DerSimonian-Laird method.
The influence of moderators was tested using meta-regression
analyses with the metareg function,
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
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
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.
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
and with
the Egger test
in metabias
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.
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
747 Abstracts excluded
on initial review
80 Full-text articles (PDFs)
No data available
Overlapping data set
No clinical high-risk
27 Authors contacted who
provided additional data
Heterogeneity of Psychosis Risk in Clinical High-Risk Individuals Original Investigation Research (Reprinted) JAMA Psychiatry February 2016 Volume 73, Number 2 115
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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
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
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
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
APS alone at 6 mo
Subtotal (I2
64.51%, P
0.09 (0.03-0.17)
0.10 (0.07-0.13)
APS and GRD at 12 mo
Subtotal (I2
42.10%, P
APS alone at 12 mo
Subtotal (I2
65.92%, P
0.17 (0.09-0.26)
0.15 (0.12-0.18)
APS and GRD at 24 mo
Subtotal (I2
43.32%, P
APS alone at 24 mo
Subtotal (I2
80.01%, P
0.17 (0.10-0.26)
0.19 (0.15-0.23)
APS and GRD at 36 mo
Subtotal (I2
41.77%, P
APS alone at 36 mo
Subtotal (I2
68.94%, P
0.26 (0.16-0.37)
0.20 (0.16-0.24)
APS and GRD at 48 mo
Subtotal (I2
0.00%, P
APS alone at 48 mo
Subtotal (I2
73.54%, P
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
06 12 ≥4836
Mean Risk of Psychosis
Follow-up Time, mo
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.
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.
The inclusion of the BLIPS subgroup in the CHR has al-
ways been problematic because its diagnostic significance is
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
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
with consequent use of arbitrary psychosis thresh-
olds in the field.
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.
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
and therapeutic
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,
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−
Test for
(Q) PValue
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)
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)
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)
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)
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
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
). Our meta-analysis suggests that the GRD con-
struct may not qualify as a state risk criterion
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,
particularly because a re-
cent meta-analysis suggested that the impact of familial risk
was only evident after the age of 20 years,
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
and because post hoc retrospective power
analyses are not recommended.
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.
An earlier study
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.
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,
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
in CHR individuals and a recent meta-
addressing functional status in CHR patients. There
was also no effect for age, in contrast with our previous
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
and meta-analytical investigations.
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
and in a meta-
analysis of randomized clinical trials.
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
(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.
We were also unable to test additional moderators potentially
addressing the observed heterogeneity, such as treatments
other than antipsychotics, ethnicity,
substance abuse,
and comorbid affective disorders,
because these factors
had not been assessed in the original studies or were
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)
Copyright 2016 American Medical Association. All rights reserved.
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Copyright 2016 American Medical Association. All rights reserved.
Submitted for Publication: July 19, 2015; final
revision received September 21, 2013; accepted
September 23, 2013.
Published Online: December 30, 2015.
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,
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,
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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Research Original Investigation Heterogeneity of Psychosis Risk in Clinical High-Risk Individuals
120 JAMA Psychiatry February 2016 Volume 73, Number 2 (Reprinted)
Copyright 2016 American Medical Association. All rights reserved.
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... The CHR-P state consists of several subgroups, each with varying clinical profiles: Attenuated Psychotic Symptoms (APS), Brief Limited Intermittent Psychotic Symptoms (BLIPS) and/or genetic vulnerability accompanied by a deterioration in functioning (GRD) [7][8][9]. Furthermore, individuals at CHR-P have a highly variable risk enrichment [10] and substantial clinical heterogeneity in initial symptoms, functional status, transition to psychosis, and remission or persistence of symptoms [11][12][13][14][15][16]. In fact, this observed heterogeneity in clinical and outcome features has been a source of ongoing criticism of the CHR-P paradigm [17,18]. ...
... CHR-P individuals who transitioned to psychosis did not demonstrate significantly greater heterogeneity or homogeneity in regional neuroanatomical measures compared with individuals who did not transition to psychosis, as indexed by both the VR and CV (eFigs. [7][8][9][10][11][12][13][14]. ...
Full-text available
Individuals at Clinical High Risk for Psychosis (CHR-P) demonstrate heterogeneity in clinical profiles and outcome features. However, the extent of neuroanatomical heterogeneity in the CHR-P state is largely undetermined. We aimed to quantify the neuroanatomical heterogeneity in structural magnetic resonance imaging measures of cortical surface area (SA), cortical thickness (CT), subcortical volume (SV), and intracranial volume (ICV) in CHR-P individuals compared with healthy controls (HC), and in relation to subsequent transition to a first episode of psychosis. The ENIGMA CHR-P consortium applied a harmonised analysis to neuroimaging data across 29 international sites, including 1579 CHR-P individuals and 1243 HC, offering the largest pooled CHR-P neuroimaging dataset to date. Regional heterogeneity was indexed with the Variability Ratio (VR) and Coefficient of Variation (CV) ratio applied at the group level. Personalised estimates of heterogeneity of SA, CT and SV brain profiles were indexed with the novel Person-Based Similarity Index (PBSI), with two complementary applications. First, to assess the extent of within-diagnosis similarity or divergence of neuroanatomical profiles between individuals. Second, using a normative modelling approach, to assess the ‘normativeness’ of neuroanatomical profiles in individuals at CHR-P. CHR-P individuals demonstrated no greater regional heterogeneity after applying FDR corrections. However, PBSI scores indicated significantly greater neuroanatomical divergence in global SA, CT and SV profiles in CHR-P individuals compared with HC. Normative PBSI analysis identified 11 CHR-P individuals (0.70%) with marked deviation (>1.5 SD) in SA, 118 (7.47%) in CT and 161 (10.20%) in SV. Psychosis transition was not significantly associated with any measure of heterogeneity. Overall, our examination of neuroanatomical heterogeneity within the CHR-P state indicated greater divergence in neuroanatomical profiles at an individual level, irrespective of psychosis conversion. Further large-scale investigations are required of those who demonstrate marked deviation.
... UHR has been hypothesized as an imminent risk for psychosis and is identified based on the presence of either brief limited intermittent psychotic symptoms (BLIPS) and/or attenuated psychotic symptoms (APS) and/or genetic risk combined with a functional decline (Schultze-Lutter et al., 2011). BS are thought to feature an earlier at-risk phase, compared to UHR , while GRD is proportionally rare (Fusar-Poli et al., 2016). According to a recent metaanalysis, 25% of those meeting high-risk criteria developed psychosis within 3 years, and the likelihood increased over time (de Pablo et al., 2021). ...
... These studies tend to converge on the type and spatial location of WM abnormalities observed in schizophrenia and indicate their lower severity (Bernard et al., 2015;Clemm von Hohenberg et al., 2014;Krakauer et al., 2017). However, treating risk as a homogenous construct may simultaneously undermine efforts to understand the mechanisms underlying psychotic symptoms (Fusar-Poli et al., 2016). From a clinical perspective, what particularly matters is the prediction of full-blown psychosis. ...
Full-text available
Background Widespread white matter abnormalities are a frequent finding in chronic schizophrenia patients. More inconsistent results have been provided by the sparser literature on at-risk states for psychosis, i.e., emerging subclinical symptoms. However, considering risk as a homogenous construct, as characteristic of earlier studies, may impede our understanding of neuro-progression into psychosis. Methods An analysis was conducted of 3-Tesla MRI diffusion and symptom data from 112 individuals (mean age, 21.97±4.19) within two at-risk paradigm subtypes, only basic symptoms (n = 43) and ultra-high risk (n = 37), and controls (n = 32). Between-group comparisons (involving three study groups and further split based on the subsequent transition to schizophrenia) of four diffusion-tensor-imaging-derived scalars were performed using voxelwise tract-based spatial statistics, followed by correlational analyses with Structured Interview for Prodromal Syndromes responses. Results Relative to controls, fractional anisotropy was lower in the splenium of the corpus callosum of ultra-high-risk individuals, but only before stringent multiple-testing correction, and negatively correlated with General Symptom severity among at-risk individuals. At-risk individuals who transitioned to schizophrenia within 3 years, compared to those that did not transition, had more severe WM differences in fractional anisotropy and radial diffusivity (particularly in the corpus callosum, anterior corona radiata and motor/sensory tracts), which were even more extensive compared to healthy controls. Conclusions These findings align with the subclinical symptom presentation and more extensive disruptions in converters, suggestive of severity-related demyelination or axonal pathology. Fine-grained but detectable differences among ultra-high-risk subjects (i.e., with brief limited intermittent and/or attenuated psychotic symptoms) point to the splenium as a discrete site of emerging psychopathology, while basic symptoms alone were not associated with altered fractional anisotropy.
... high Se and low LR-). This limitation is in part due to the intrinsic inability to refine the current group-level prognostic estimates beyond the subgroup stratification (APS, BLIPS or GRD) [74]. To refine estimates to the individual level, CHR-P psychometric instruments should be supplemented with information from other modalities beyond symptomatology (e.g. ...
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Accurate prognostication of individuals at clinical high-risk for psychosis (CHR-P) is an essential initial step for effective primary indicated prevention. We aimed to summarise the prognostic accuracy and clinical utility of CHR-P assessments for primary indicated psychosis prevention. Web of Knowledge databases were searched until 1st January 2022 for longitudinal studies following-up individuals undergoing a psychometric or diagnostic CHR-P assessment, reporting transition to psychotic disorders in both those who meet CHR-P criteria (CHR-P + ) or not (CHR-P−). Prognostic accuracy meta-analysis was conducted following relevant guidelines. Primary outcome was prognostic accuracy, indexed by area-under-the-curve (AUC), sensitivity and specificity, estimated by the number of true positives, false positives, false negatives and true negatives at the longest available follow-up time. Clinical utility analyses included: likelihood ratios, Fagan’s nomogram, and population-level preventive capacity (Population Attributable Fraction, PAF). A total of 22 studies ( n = 4 966, 47.5% female, age range 12–40) were included. There were not enough meta-analysable studies on CHR-P diagnostic criteria (DSM-5 Attenuated Psychosis Syndrome) or non-clinical samples. Prognostic accuracy of CHR-P psychometric instruments in clinical samples (individuals referred to CHR-P services or diagnosed with 22q.11.2 deletion syndrome) was excellent: AUC = 0.85 (95% CI: 0.81–0.88) at a mean follow-up time of 34 months. This result was driven by outstanding sensitivity (0.93, 95% CI: 0.87–0.96) and poor specificity (0.58, 95% CI: 0.50–0.66). Being CHR-P + was associated with a small likelihood ratio LR + (2.17, 95% CI: 1.81–2.60) for developing psychosis. Being CHR-P- was associated with a large LR- (0.11, 95%CI: 0.06−0.21) for developing psychosis. Fagan’s nomogram indicated a low positive (0.0017%) and negative (0.0001%) post-test risk in non-clinical general population samples. The PAF of the CHR-P state is 10.9% (95% CI: 4.1–25.5%). These findings consolidate the use of psychometric instruments for CHR-P in clinical samples for primary indicated prevention of psychosis. Future research should improve the ability to rule in psychosis risk.
... Leveraging global and local models reveals the driven factors behind data heterogeneity UHR samples are known to be heterogeneous [40]. Thus, local modelling might help reveal UHR subpopulations and their respective gene signatures. ...
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Machine learning (ML) is increasingly deployed on biomedical studies for biomarker development (feature selection) and diagnostic/prognostic technologies (classification). While different ML techniques produce different feature sets and classification performances, less understood is how upstream data processing methods (e.g., normalisation) impact downstream analyses. Using a clinical mental health dataset, we investigated the impact of different normalisation techniques on classification model performance. Gene Fuzzy Scoring (GFS), an in-house developed normalisation technique, is compared against widely used normalisation methods such as global quantile normalisation, class-specific quantile normalisation and surrogate variable analysis. We report that choice of normalisation technique has strong influence on feature selection. with GFS outperforming other techniques. Although GFS parameters are tuneable, good classification model performance (ROC-AUC > 0.90) is observed regardless of the GFS parameter settings. We also contrasted our results against local modelling, which is meant to improve the resolution and meaningfulness of classification models built on heterogeneous data. Local models, when derived from non-biologically meaningful subpopulations, perform worse than global models. A deep dive however, revealed that the factors driving cluster formation has little to do with the phenotype-of-interest. This finding is critical, as local models are often seen as a superior means of clinical data modelling. We advise against such naivete. Additionally, we have developed a combinatorial reasoning approach using both global and local paradigms: This helped reveal potential data quality issues or underlying factors causing data heterogeneity that are often overlooked. It also assists to explain the model as well as provides directions for further improvement.
... Substantial heterogeneity has been previously demonstrated for the CHR-P paradigm. A meta-analysis from our group showed high variability in the level of likelihood of transitioning to psychosis across CHR-P individuals, with those presenting with a Brief and Limited Intermittent Psychotic Episode having a substantially higher risk than other subgroups [24][25][26][27][28]. There is further evidence showing that most of CHR-P clinical heterogeneity is accounted for by the recruitment and sampling phase by selecting individuals undergoing a CHR-P assessment [29][30][31]. ...
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This study aims to meta-analytically characterize the presence and magnitude of within-group variability across neurocognitive functioning in young people at Clinical High-Risk for psychosis (CHR-P) and comparison groups. Multistep, PRISMA/MOOSE-compliant systematic review (PROSPERO-CRD42020192826) of the Web of Science database, Cochrane Central Register of Reviews and Ovid/PsycINFO and trial registries up to July 1, 2020. The risk of bias was assessed using a modified version of the NOS for cohort and cross-sectional studies. Original studies reporting neurocognitive functioning in individuals at CHR-P compared to healthy controls (HC) or first-episode psychosis (FEP) patients were included. The primary outcome was the random-effect meta-analytic variability ratios (VR). Secondary outcomes included the coefficient of variation ratios (CVR). Seventy-eight studies were included, relating to 5162 CHR-P individuals, 2865 HC and 486 FEP. The CHR-P group demonstrated higher variability compared to HC (in descending order of magnitude) in visual memory (VR: 1.41, 95% CI 1.02–1.94), executive functioning (VR: 1.31, 95% CI 1.18–1.45), verbal learning (VR: 1.29, 95% CI 1.15–1.45), premorbid IQ (VR: 1.27, 95% CI 1.09–1.49), processing speed (VR: 1.26, 95% CI 1.07–1.48), visual learning (VR: 1.20, 95% CI 1.07–1.34), and reasoning and problem solving (VR: 1.17, 95% CI 1.03–1.34). In the CVR analyses the variability in CHR-P population remains in the previous neurocognitive domains and emerged in attention/vigilance, working memory, social cognition, and visuospatial ability. The CHR-P group transitioning to psychosis showed greater VR in executive functioning compared to those not developing psychosis and compared to FEP groups. Clinical high risk for psychosis subjects shows increased variability in neurocognitive performance compared to HC. The main limitation of this study is the validity of the VR and CVR as an index of variability which has received debate. This finding should be explored by further individual-participant data research and support precision medicine approaches.
... This is followed by the APD group: at 16%, 19%, and 21%, 24%, respectively. The GDR group does not appear to have an enhanced risk compared to the non-CHR-P individuals (34). ...
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Under standard care, psychotic disorders can have limited response to treatments, high rates of chronicity and disability, negative impacts on families, and wider social and economic costs. In an effort to improve early detection and care of individuals developing a psychotic illness, early intervention in psychosis services and early detection services have been set up in various countries since the 1980s. In April 2016, NHS England implemented a new ‘access and waiting times’ standard for early intervention in psychosis to extend the prevention of psychosis across England. Unfortunately, early intervention and early detection services are still not uniformly distributed in the UK, leaving gaps in service provision. The aim of this paper is to provide a business case model that can guide clinicians and services looking to set up or expand early detection services in their area. The paper also focuses on some existing models of care within the Pan-London Network for Psychosis Prevention teams.
... Moreover, UHR individuals are a very heterogeneous group that, by definition, consists of persons with subthreshold attenuated positive symptoms, transient psychotic symptoms, genetic risk, and deterioration. These groups differ not only in terms of type and severity of symptoms, but also in transition risk [75], which might possibly alter the level of white matter disturbances [76]. Due to the scarcity of studies, especially those differentiating individuals who actually developed full-blown psychosis, it is impossible to draw any definite conclusions at this moment. ...
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Some symptoms of schizophrenia might be present before full-blown psychosis, so white matter changes must be studied both in individuals with emerging psychosis and chronic schizophrenia. A total of 86 patients—12 ultra-high risk of psychosis (UHR), 20 first episode psychosis (FEP), 54 chronic schizophrenia (CS), and 33 healthy controls (HC)—underwent psychiatric examination and diffusion tensor imaging (DTI) in a 3-Tesla MRI scanner. We assessed fractional anisotropy (FA) and mean diffusivity (MD) of the superior longitudinal fasciculus (SLF) and inferior longitudinal fasciculus (ILS). We found that CS patients had lower FA than FEP patients (p = 0.025) and HC (p = 0.088), and higher MD than HC (p = 0.037) in the right SLF. In the CS group, we found positive correlations of MD in both right ILF (rho = 0.39, p < 0.05) and SLF (rho = 0.43, p < 0.01) with disorganization symptoms, as well as negative correlation of FA in the right ILF with disorganization symptoms (rho = −0.43, p < 0.05). Among UHR individuals, we found significant negative correlations between MD in the left ILF and negative (r = −0.74, p < 0.05) and general symptoms (r = −0.77, p < 0.05). However promising, these findings should be treated as preliminary, and further research must verify whether they can be treated as potential biomarkers of psychosis.
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Background: The structure of psychopathology determines how we identify people who need support services and how we can best help them. Currently, we identify those with psychopathological issues via assessments based on diagnostic manuals, such as the International Statistical Classification of Diseases (ICD) and Diagnostic and Statistical Manual of Mental Disorders (DSM). However, there is a growing literature that has raised serious concerns about these two manuals. Some have suggested that such diagnostic manuals have misguided decades of mental health studies and may have contributed to dissatisfaction among service seekers and users relating to ineffective treatments and negative experiences with service providers. This doctoral dissertation explores possible alternative approaches to our understanding of the structure of psychopathology. It considers how these approaches could contribute to future classification, diagnostic and service delivery systems. Method: We used one dataset for all four studies. It was mined from and consisted of narratives about lived experiences from people diagnosed with mental disorders. The data were analysed using Jaccard’s Coefficient to find similarity between diagnostic categories (study 1), K-Means Clustering to group symptoms into diagnostic categories (in study 2), Network Analysis to find the relationships between the co-occurring symptoms and Eigenvector Centrality to estimate which among them are co-occurring with most other symptoms (study 3), and standard correlation to find the strength of such associations (in study 4). Findings: We proposed an alternative approach for estimating the reliability of the existing system (study 1) to study the extent of diagnostic overlap (heterogeneity) because the present studies evaluating the reliability had their limitations. Study 1 (chapter 4) contributes to the literature by being the first study to exploit patient narrative data, using innovative text-mining methods in this context, to assess the diagnostic heterogeneity of the DSM categories. It provides unique evidence to reinforce existing studies of diagnostic heterogeneity using alternative approaches such as Jaccard’s coefficients. Once verified that the diagnostic heterogeneity of human-led traditional diagnostic categories is too large for practical usage, we searched for the reasons. Many studies have attributed the problem to the committee members who created the manuals. Among the several raised questions, the committee members reported a financial conflict of interests with the industry and relied more on consensus than data. So, eliminating the human component of decision making, we should be able to find homogeneous groups of disorders. Therefore, we attempted to create categories of mental illnesses using Artificial Intelligence (study 2) from patients’ reported symptoms. Study 2 (chapter 5) contributes to the literature by being the first study in this context to demonstrate how to cluster the patients using artificial intelligence based on the similarities in their reported symptoms or experiences from their illness narratives. It provides evidence to contrast the conventional idea of conceptualising “mental illnesses as categories” using unsupervised machine learning algorithms and the silhouette score elbow method. For example, in study 2, when the machine-driven approach also produced mental disorder categories with high heterogeneity, we inferred that while there might have been human biases with the traditional diagnostic manuals, the more important point is that the categorical approach is not the way forward. The findings from study 2 support the literature and state the same. The literature has proposed several alternatives, such as the dimensional and network approaches. But related to this notion of diagnosing and studying humans (and their conditions) as categories, such as depression, consisting of individual entities (e.g., symptoms), there is another serious problem with the mental health research culture - that has found its way into these new alternatives as well. This problem is related to using total scores of survey items as objects of inquiry (e.g., total depression score). This approach assumes that all the items in the questionnaire (e.g., low mood, lack of interest) contributes in equal proportions to the construct (e.g., depression), but the empirical evidence suggests otherwise. The newer dimensional approaches such as the HiTOP relies on such sum scores. Likewise, some network studies are also using such sum scores. Therefore, in doing so, such alternatives risk carrying forward some of the weaknesses of its categorical predecessors. As an alternative, we proposed the use of individual symptoms as an object of inquiry. It’s a relatively novel approach, and we hoped to advance the literature. Therefore, we created a network of psychopathological symptoms based on patients’ reports (study 3). Study 3 (chapter 6) contributes to the literature by being the first study to demonstrate how to create network graphs from pure narrative data from patients in this context and presented a new approach for exploratory analysis by finding inter-relations in their reported symptoms or experiences from patients’ illness narratives. It demonstrates a relatively novel approach to focus on individual symptoms for the object of inquiry instead of categories of mental disorders or sum-scores of scales or questionnaires. The study discovered relationships based on co-occurrences of the reported symptoms. Still, it did not communicate the strength (“numeric” degree) of such association. While finding the association has merit for preliminary exploration, for this approach of using individual symptoms as an object of inquiry to be useful for clinical and research purposes, we argue that it must provide the information related to the strength of association. So, in the final study, we attempted to find the correlations of auditory hallucination and, in doing so, demonstrated how to find correlation coefficients between pairs of symptoms from a qualitative (text-based narrative) dataset. Furthermore, the correlations were valuable to the advancement of the theoretical literature of auditory hallucination. Study 4 (chapter 7) contributes to the literature by being the first study to demonstrate how to do correlation analysis on qualitative data in this context. It suggests a new direction of conducting exploratory research using rich qualitative datasets and standard statistical methods without the limitations of a conventional survey dataset. Conclusion: The doctoral thesis found that the traditional categorical approach does not accurately reflect the complexity of people’s experiences. There might be human biases and conflict of interest, which might have influenced the creation of the diagnostic manuals. Still, even when artificial intelligence attempted to find similar patterns within the patients’ experiences, it could not indicate that psychopathological experiences cannot be categorised into homogenous groups. So, we argue that the future of mental health literature should divorce itself from using DSM and ICD categories of mental disorders as the object of investigations and as the framework for conceptualising mental illnesses. Instead, we argue that the focus should be on alternative conceptualisations of psychopathology, such as the network model of psychopathology, which focuses on the individual symptoms and the inter-relationships between them. Our preliminary network model explores the specific relationships between symptoms found that were frequently occurring but relatively less studied in the literature - opening up newer lines of investigation for future studies to build upon. Furthermore, using auditory hallucination as an object of investigation, we found the variables with the highest correlation coefficients and attempted to advance the psychosis literature. One major merit and contribution of the doctoral thesis is to demonstrate how we can do all that was mentioned above using rich qualitative data. Unlike survey data, the current data did not pose any restrictions in terms of the number or type of variables being reported. The respondents reported everything that had to report. Additionally, the thesis demonstrated how a large volume of qualitative data could be obtained and then analysed using statistical and machine learning-based approaches with minimum effort and time using advanced technologies such as Natural Language Processing, Artificial Intelligence, and Web-Scraping technologies. This thesis's second major merit and contribution are to demonstrate how to use novel data analytic procedures such as Jaccard’s Coefficient, K-Means Clustering and Network Graphs, and conventional statistics such as correlation coefficients on such qualitative datasets. No manual analysis, such as thematic analysis of the qualitative data, was done. This thesis's third merit and contribution were in terms of advancing the literature by evaluating diagnostic heterogeneity between categories of mental disorders using a novel approach (study 1); finding out symptoms that exclusive to each cluster of mental disorders (study 2); estimating the tendency of specific symptoms to co-occur with other symptoms (study 3), and finding out the symptoms associated with auditory hallucination (study 4). Future mental health studies will benefit from this contribution and are expected to produce deeper insight into mental conditions and treatment of mental ill-health.
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Self-reported and interview-based measures can be considered coprimary measures of cognitive performance. We aimed to ascertain to what extent cognitive impairment in psychotic disorders, as assessed with a neuropsychological battery, is associated with subjective cognitive complaints compared to difficulties in daily activities caused by cognitive impairment. We assessed 114 patients who had a psychotic disorder with a set of neuropsychological tests and two additional measures: the Cognitive Assessment Interview-Spanish version (CAI-Sp) and the Frankfurt Complaint Questionnaire (FCQ). Patients also underwent a clinical assessment. The CAI-Sp correlated significantly with all the clinical dimensions, while the FCQ correlated only with positive and depressive symptoms. The CAI-Sp correlated significantly with all cognitive domains, except for verbal memory and social cognition. The FCQ was associated with attention, processing speed and working memory. The combination of manic and depressive symptoms and attention, processing speed, working memory and explained 38–46% of the variance in the patients’ CAI-Sp. Education and negative symptoms, in combination with attention, processing speed, and executive functions, explained 54–59% of the CAI-Sp rater’s variance. Only negative symptoms explained the variance in the CAI-Sp informant scores (37–42%). Depressive symptoms with attention and working memory explained 15% of the FCQ variance. The ability to detect cognitive impairment with the CAI-Sp and the FCQ opens the possibility to consider these instruments to approximate cognitive impairment in clinical settings due to their ease of application and because they are less time-consuming for clinicians.
Background and Hypothesis Mapping a patient’s speech as a network has proved to be a useful way of understanding formal thought disorder in psychosis. However, to date, graph theory tools have not incorporated the semantic content of speech, which is altered in psychosis. Study Design We developed an algorithm, “ netts ”, to map the semantic content of speech as a network, then applied netts to construct semantic speech networks for a general population sample, and a clinical sample comprising patients with first episode psychosis (FEP), people at clinical high risk of psychosis (CHR-P), and healthy controls. Study Results Semantic speech networks from the general population were more connected than size-matched randomised networks, with fewer and larger connected components, reflecting the non-random nature of speech. Networks from FEP patients were smaller than from healthy participants, for a picture description task but not a story recall task. For the former task, FEP networks were also more fragmented than those from controls; showing more, smaller connected components. CHR-P networks showed fragmentation values in-between FEP patients and controls. A clustering analysis suggested that semantic speech networks captured novel signal not already described by existing NLP measures. Network features were also related to negative symptom scores and scores on the Thought and Language Index, although these relationships did not survive correcting for multiple comparisons. Conclusions Overall, these data suggest that semantic networks can enable deeper phenotyping of formal thought disorder in psychosis. We are releasing Netts as an open Python package alongside this manuscript.
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Meta-analyses have become an essential tool in synthesizing evidence on clinical and epidemiological questions derived from a multitude of similar studies assessing the particular issue. Appropriate and accessible statistical software is needed to produce the summary statistic of interest. Metaprop is a statistical program implemented to perform meta-analyses of proportions in Stata. It builds further on the existing Stata procedure metan which is typically used to pool effects (risk ratios, odds ratios, differences of risks or means) but which is also used to pool proportions. Metaprop implements procedures which are specific to binomial data and allows computation of exact binomial and score test-based confidence intervals. It provides appropriate methods for dealing with proportions close to or at the margins where the normal approximation procedures often break down, by use of the binomial distribution to model the within-study variability or by allowing Freeman-Tukey double arcsine transformation to stabilize the variances. Metaprop was applied on two published meta-analyses: 1) prevalence of HPV-infection in women with a Pap smear showing ASC-US; 2) cure rate after treatment for cervical precancer using cold coagulation. The first meta-analysis showed a pooled HPV-prevalence of 43% (95% CI: 38%-48%). In the second meta-analysis, the pooled percentage of cured women was 94% (95% CI: 86%-97%). By using metaprop, no studies with 0% or 100% proportions were excluded from the meta-analysis. Furthermore, study specific and pooled confidence intervals always were within admissible values, contrary to the original publication, where metan was used.
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Starting from the early descriptions of Kraepelin and Bleuler, the construct of schizotypy was developed from observations of aberrations in nonpsychotic family members of schizophrenia patients. In contemporary diagnostic manuals, the positive symptoms of schizotypal personality disorder were included in the ultra high-risk (UHR) criteria 20 years ago, and nowadays are broadly employed in clinical early detection of psychosis. The schizotypy construct, now dissociated from strict familial risk, also informed research on the liability to develop any psychotic disorder, and in particular schizophrenia-spectrum disorders, even outside clinical settings. Against the historical background of schizotypy it is surprising that evidence from longitudinal studies linking schizotypy, UHR, and conversion to psychosis has only recently emerged; and it still remains unclear how schizotypy may be positioned in high-risk research. Following a comprehensive literature search, we review 18 prospective studies on 15 samples examining the evidence for a link between trait schizotypy and conversion to psychosis in 4 different types of samples: general population, clinical risk samples according to UHR and/or basic symptom criteria, genetic (familial) risk, and clinical samples at-risk for a nonpsychotic schizophrenia-spectrum diagnosis. These prospective studies underline the value of schizotypy in high-risk research, but also point to the lack of evidence needed to better define the position of the construct of schizotypy within a developmental psychopathology perspective of emerging psychosis and schizophrenia-spectrum disorders. © The Author 2014. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email:
An accurate detection of individuals at clinical high risk (CHR) for psychosis is a prerequisite for effective preventive interventions. Several psychometric interviews are available, but their prognostic accuracy is unknown. We conducted a prognostic accuracy meta-analysis of psy-chometric interviews used to examine referrals to high risk services. The index test was an established CHR psychometric instrument used to identify subjects with and without CHR (CHR1 and CHR2). The reference index was psychosis onset over time in both CHR1 and CHR2 subjects. Data were analyzed with MIDAS (STATA13). Area under the curve (AUC), summary receiver operating characteristic curves, quality assessment, likelihood ratios, Fagan's nomogram and probability modified plots were computed. Eleven independent studies were included , with a total of 2,519 help-seeking, predominately adult subjects (CHR1: N51,359; CHR2: N51,160) referred to high risk services. The mean follow-up duration was 38 months. The AUC was excellent (0.90; 95% CI: 0.87-0.93), and comparable to other tests in preventive medicine, suggesting clinical utility in subjects referred to high risk services. Meta-regression analyses revealed an effect for exposure to anti-psychotics and no effects for type of instrument, age, gender, follow-up time, sample size, quality assessment, proportion of CHR1 subjects in the total sample. Fagan's nomogram indicated a low positive predictive value (5.74%) in the general non-help-seeking population. Albeit the clear need to further improve prediction of psychosis, these findings support the use of psychometric prognostic interviews for CHR as clinical tools for an indicated prevention in subjects seeking help at high risk services worldwide.
Article InformationCorresponding Author: Paolo Fusar-Poli, MD, PhD, RCPsych, Department of Psychosis Studies, Institute of Psychiatry, King’s College London, PO Box 63, De Crespigny Park, SE58AF London, United Kingdom ( Published Online: August 12, 2015. doi:10.1001/jamapsychiatry.2015.0611. Conflict of Interest Disclosures: None reported. Additional Contributions: Grazia Rutigliano, MD, helped with the preparation of the figure. She received no financial compensation.
In the research on schizophrenia, one of today's main focus is on the early detection of schizophrenia. One of the concepts addressing this problem is the German concept of basic symptoms. Basic symptoms as described by Huber [27.28.31] are mild, often subclinical, but troublesome self-experienced disturbances of drive and affect, thought, speech, perception, proprioception and motor action as well as of vegetative functions that can be found even decades before the first psychotic manifestation. They can be externally assessed in great detail with the 'Bonn Scale for the Assessment of Basic Symptoms - BSABS' [18]. For the evaluation of the BSABS as an instrument for the assessment of schizophrenia proneness, different questions have to be answered: (a) Can basic symptoms be assessed reliably? (b) Can schizophrenics be differentiated from other psychiatric disorders by basic symptoms? (c) Do basic symptoms indicate a liability to schizophrenia? (d) Can schizophrenia be predicted by basic symptoms? The article reviews and discusses studies of this four points. Whereas to the first question, a positive answer can be easily and unambiguously given - the interrater reliability between trained raters was found to be satisfactory, the other three have to be addressed in more detail. Even though basic symptoms spread over the whole range of psychic disorders and occur also in psychic healthy persons without a liability to schizophrenia, they can not be generally regarded as the expression of an overall psychophysiologic impairment. Basic symptoms of the BSABS subsyndromes 'information processing disturbances', including cognitive thought, perception and motor disturbances, and 'interpersonal irritation', consistent of basic symptoms describing feelings of discomfort and insecurity in social situations, were found to be of significance in all three studies conducted on these questions. Because the designs and samples of these studies were quite different, this general result can be regarded as well founded and stable. Thus, basic symptoms of these two BSABS subsyndromes seem to be not only of diagnostic validity and specific for schizophrenia, but also able to indicate a liability to schizophrenia and even to predict schizophrenia. Nevertheless, further studies are needed to strengthen this result, before deeply intervening primarily preventive interventions based on the presence of basic symptoms are justified.
It is well known that statistical power calculations can be valuable in planning an experiment. There is also a large literature advocating that power calculations be made whenever one performs a statistical test of a hypothesis and one obtains a statistically nonsignificant result. Advocates of such post-experiment power calculations claim the calculations should be used to aid in the interpretation of the experimental results. This approach, which appears in various forms, is fundamentally flawed. We document that the problem is extensive and present arguments to demonstrate the flaw in the logic.
This meta-analysis discusses the speed of psychosis progression in patients at ultra-high risk. The transition to psychosis in patients at ultra-high clinical risk (UHR; as defined elsewhere¹) is most likely to occur within the first 2 years after presentation to clinical services (risk estimate, 29%; 95% CI, 23-36).² After this phase, the speed of psychosis progression tends to plateau from the third year,² reaching approximately 35% after 10 years.³ However, the exact speed of psychosis progression at a particular point during the critical first 2 years is unclear, preventing clinical advancements in the field.