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Early PTSD Symptom Trajectories: Persistence, Recovery,
and Response to Treatment: Results from the Jerusalem
Trauma Outreach and Prevention Study (J-TOPS)
Isaac R. Galatzer-Levy
2
*, Yael Ankri
1
, Sara Freedman
3
, Yossi Israeli-Shalev
1
, Pablo Roitman
1
,
Moran Gilad
1
, Arieh Y. Shalev
1,2
1 Center for Traumatic Stress Studies, Hadassah University Hospital, Jerusalem, Israel, 2 Department of Psychiatry, NYU School of Medicine, New York , New York, United
States of America, 3 School of Social Work, Bar Ilan University, Ramat Gan, Israel
Abstract
Context:
Uncovering heterogeneities in the progression of early PTSD symptoms can improve our understanding of the
disorder’s pathogenesis and prophylaxis.
Objectives:
To describe discrete symptom trajectories and examine their relevance for preventive interventions.
Design:
Latent Growth Mixture Modeling (LGMM) of data from a randomized controlled study of early treatment. LGMM
identifies latent longitudinal trajectories by exploring discrete mixture distributions underlying observable data.
Setting:
Hadassah Hospital unselectively receives trauma survivors from Jerusalem and vicinity.
Participants:
Adult survivors of potentially traumatic events consecutively admitted to the hospital’s emergency
department (ED) were assessed ten days and one-, five-, nine- and fifteen months after ED admission. Participants with data
at ten days and at least two additional assessments (n = 957) were included; 125 received cognitive behavioral therapy (CBT)
between one and nine months.
Approach:
We used LGMM to identify latent parameters of symptom progression and tested the effect of CBT on these
parameters. CBT consisted of 12 weekly sessions of either cognitive therapy (n = 41) or prolonged exposure (PE, n = 49),
starting 29.865.7 days after ED admission, or delayed PE (n = 35) starting at 151.8642.4 days. CBT effectively reduced PTSD
symptoms in the entire sample.
Main Outcome Measure:
Latent trajectories of PTSD symptoms; effects of CBT on these trajectories.
Results:
Three trajectories were identified: Rapid Remitting (rapid decrease in symptoms from 1- to 5-months; 56% of the
sample), Slow Remitting (progressive decrease in symptoms over 15 months; 27%) and Non-Remitting (persistently elevated
symptoms; 17%). CBT accelerated the recovery of the Slow Remitting class but did not affect the other classes.
Conclusions:
The early course of PTSD symptoms is characterized by distinct and diverging response patterns that are
centrally relevant to understanding the disorder and preventing its occurrence. Studies of the pathogenesis of PTSD may
benefit from using clustered symptom trajectories as their dependent variables.
Citation: Galatzer-Levy IR, Ankri Y, Freedman S, Israeli-Shalev Y, Roitman P, et al. (2013) Early PTSD Symptom Trajectories: Persistence, Recovery, and Response to
Treatment: Results from the Jerusalem Trauma Outreach and Prevention Study (J-TOPS). PLoS ONE 8(8): e70084. doi:10.1371/journal.pone.0070084
Editor: Kim Felmingham, University of Tasmania, Australia
Received February 4, 2013; Accepted June 17, 2013; Published August 22, 2013
Copyright: ß 2013 Galatzer-Levy et a l. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: Dr. YIS received an investigator-initiated grant from Lundbeck Pharmaceuticals for this study and for a collaborative study (principal investigator: Dr.
Joseph Zohar) entitled ‘‘Prevention of PTSD by Escitalopram.’’ The funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript. This does not alter the authors’ adherence to all the PLOS ONE policies on sharing data and materials.
Competing Interests: The authors have declared that no competing intere sts exist.
* E-mail: Isaac.Galatzer-Levy@nyumc.org
Introduction
Recent events repeatedly show the extent of devastation and
trauma caused by war, violence and disasters. Post-traumatic stress
disorder (PTSD) transforms survivors’ initial reactions to life-long
illness. Chronic PTSD is prevalent, debilitating, and tenacious [1–
3]. It occurs in a significant proportion of those who express acute
PTSD symptoms after trauma exposure [4–6]. Preventing PTSD
is a major humanitarian and public health challenge [7].
Numerous studies have shown that early, trauma-focused,
cognitive behavioral therapy (CBT) reduces the prevalence of
chronic PTSD among survivors with acute PTSD (e.g., [8–10]).
However, the effectiveness of this family of resource-demanding
interventions is limited by barriers to receiving care [11–13], by
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our inability to identify survivors who might remit without
treatment (up to 45% of those with Acute PTSD [3,8,9]) as well
as those who do not recover despite properly dispensed treatment
(about 20%; [9]).
Previous studies of early PTSD [4,5,14,15] used central tendency
statistics to document the progressively decreasing prevalence of
PTSD and PTSD symptoms in cohorts of survivors followed
longitudinally. Subsequent meta-analyses of risk factors for PTSD
[16,17] are based on that approach. Central tendency statistics
assess groups as a whole by examining change to their arithmetic
mean over time. Their use implies that the mean (and dispersion
around the mean) accurately and parsimoniously describes the
sample studied and its reference population.
When multiple latent sub-populations are present, however, the
progression of the mean does not provide an accurate picture
[18,19], in which case exploring heterogeneities of symptoms’
progression better discerns underlying ‘longitudinal’ phenotypes.
Uncovering these phenotypes may improve our understanding of
the pathogenesis of PTSD and its early prevention.
Latent Growth Mixture Modeling (LGMM) uses maximum-
likelihood estimation to uncover discrete longitudinal mixture
distributions and identify latent subpopulations, or classes.
Predictors of those classes, as well as the rates of change over
time, can be modeled within the same framework. Studies using
LGMM-based techniques to model latent subpopulations by their
symptom severity have identified common patterns of response to
potentially traumatic events (PTEs) and predictors of these
patterns [20–24]. They, thereby, uncovered diagnostically mean-
ingful patterns of stress response [18,20,25]. Indeed, LGMM-
based techniques are emerging as a methodology to study
treatment effects across disorders and identify distinct trajectories
of remission, placebo response, and response to active treatment
[26–28]. To date, however, no studies have modeled PTSD
symptom progression at multiple intervals across the first year that
follow trauma exposure or examined the effects of treatment in this
context. This study examines the critical period in the formation of
PTSD, namely the aftermath of trauma exposure and the effect of
preventive early intervention.
The current investigation used LGMM to examine patterns of
PTSD symptom progression during the fifteen months that follow
traumatic events in a large cohort of trauma-exposed, initially
symptomatic individuals. In an attempt to pursue the effect of
treatment, we included members of this cohort who received
cognitive behavioral therapy (CBT) and then examined them
separately. We used LGMM’s unconditional model to uncover clusters
of symptoms trajectories in the entire cohort and LGMM
conditional model to evaluate the effect of CBT on these trajectories.
Methods
Participants and Procedures
This study utilized data collected for the Jerusalem Trauma
Outreach and Prevention Study (J-TOPS; [9,13], ClinicalTrial.-
Gov identifier: NCT0014690) between 2004 and 2009. The J-
TOPS combined a large systematic outreach and follow-up study
of recent trauma survivors with an embedded, randomized,
controlled trial of early interventions for survivors with acute
PTSD. The study’s procedures and results have been fully
described in previous publications [9,13]. The study’s data is
available upon request to the primary investigator (AYS). They are
briefly reviewed here.
Screening, assessment and treatment allocation. J-
TOPS’s participants were adults (age: 18–70) consecutively
admitted to Hadassah University Hospital emergency department
(ED) following potentially traumatic events (PTEs; for full
eligibility see [9,13]). Eligible participants (n = 4,743) were
screened by short telephone interviews, and those with PTEs that
met DSM-IV PTSD criterion A (‘‘a traumatic event;’’ n = 1996)
received a structured, telephone-based interview that included an
assessment of PTSD symptoms (see below). Participants with
Acute PTSD symptoms in that assessment (n = 1502) were invited
for clinical interviews, which only n = 756 attended. Participants
with clinical-interview based Acute PTSD (save the one month
duration) in the clinical assessments (n = 397) were invited for
treatment unless they had chronic PTSD at the time of the
traumatic event, suffered current or lifetime psychosis or bipolar
disorder, or had current substance abuse or suicidal ideation.
Participants who accepted the invitation (n = 296) were random-
ized to Prolonged Exposure therapy (PE [29–31]), Cognitive
Therapy (CT [32]), a double-blinded SSRI/placebo condition,
and a waiting list that was followed by Delayed PE at five months
(for full description see [9,13]). The results of the original study
showed significant and similar efficacy for all three CBT-based
interventions (PE, Late-PE, and CT). In this work, we collectively
refer to these interventions as ‘CBT.’ The effect of the SSRI
(escitalopram) did not differ from placebo or waitlist.
Follow-up. Unrelated to treatment eligibility or participation,
the J-TOP included a large follow-up study. Participants seen at 10
days (n = 1996) were re-evaluated seven (n = 1784) and fifteen
(n = 1022) months after ED admission. Participants of the first
clinical assessment (n = 756) were re-evaluated five months after the
traumatic event (n = 604) regardless of treatment participation.
Telephone- and clinical interviewers were blind to subjects’
participation in the embedded steps (i.e., attending clinical
interviews for telephone interviewers and attending treatment for
clinical interviewers). Participants provided oral consent to be
interviewed by telephone and written informed consent for clinical
assessments, randomization, and treatment. All procedures were
approved and monitored by the Hadassah University Hospital’s
institutional review board.
Current Study Sample. We utilized individuals who had
data available at ten days and at least two additional time points.
Additionally, individuals whose data were collected at inconsistent
time intervals from the rest of the sample (as determined by being
further than two standard deviations from the mean data
collection time for each assessment) were not included. The final
sample for the current study was n = 957, with 125 receiving CBT
(PE: n = 49; CT = 41; Late PE n = 35). The mean age of the
current sample was 36.29 years (SD = 12.04). Mean length of stay
in the emergency room was 5.72 hours (SD = 6.31). Individuals in
the current sample came to the emergency room primarily due to
motor vehicle accidents (84.1%) followed by terrorist attacks
(9.4%), then work accidents (4.4%) then other types of incidents
(2.0%).
We assessed if individuals who were included in this work
differed from those excluded from the analysis. Using a Pearson’s
x
2
, we compared those who were retained from those who were
removed on gender [x
2
(1,1501) = .08, p = .78], and on reported
exposure to a PTE (with three levels indicating no exposure,
exposure to the same type of event, and exposure to another type
of event [x
2
(2,1500) = 3.80, p = .15]). Using an independent
samples t-test, we also compared those who were included with
those excluded on age [t (2, 1500) = 20.55, p = .59], general
distress at 10 days (see instruments below; [t (2, 1500) = 21.04,
p = .30], and PTSD symptoms at 10 days [t (2, 1500) = 21.78,
p = .08]. We further examined the trend difference in initial PTSD
symptoms score and found that that these groups were substan-
tively non-distinct (respectively, for those included and excluded,
Heterogeneity in the Course of Early PTSD
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mean PSS-I scores were 10.70, SD = 3.11 vs. 10.41, SD = 3.10).
We also conducted an analysis of variance (ANOVA) to estimate
effect size of the difference and found a trivial effect (g
2
= .002).
Finally, we compared the demographics and initial PTSD
symptom severity of those who were removed because they fell
more than two standard deviations outside of the mean data
collection dates (n = 40). These individuals did not significantly
differ in age [t (2, 995) = 1.15, p = .25], initial symptom levels at the
first interview [t (2, 995) = 21.64, p = .10], or gender [x
2
(1,
996) = 0.21, p = .65]. As such, we concluded that individuals who
were removed to improve the analysis were not a substantively
distinct population from those who were retained.
Timing of assessments. Successive telephone assessments
in this sample took place, respectively 9.21 SD = 3.20, 221.34
SD = 33.90 and 468.07 SD = 109.32 days after ED admission. We
refer to these time lags as ‘ten days,’ ‘seven months’ and ‘fifteen
months.’ The clinical interviews took place 29.51 SD = 4.93 and
143.00 SD = 32.33 days after ED admission (alias ‘one month’ and
‘five months’).
Instruments
The Clinician-Administered PTSD Scale (CAPS) [33] is a widely
used structured clinical interview for evalu ating the p resence of
PTSD and the severity of PTSD symptoms. In this study, the
CAPS was administered during clinical ass essments only, and
thus wa s not useful as a measure of symptom trajectories. We
use it to evaluate t he concurrent validity of the PTSD Symptom
Scale (below).
Structured Clinical Interview for DSM-IV (SCID) [34] is a widely
used structured clinical interview for evaluating the presence of
DSM-IV symptoms and diagnostic status. In this study, the SCID
was administered during the clinical assessments only. We utilized
this scale to examine the prevalence of anxiety disorders and
Major Depressive Disorder current and lifetime diagnoses broadly
in this sample and as they relate to individuals who fall into the
modeled trajectories. Because the entire sample did not receive a
clinical interview, however, this data is only presented on the
subset that did.
The PTSD Symptom Scale (PSS) quantified PTSD symptoms at all
time-points. The PSS is a structured, diagnostic instrument that
follows DSM-IV 17 PTSD symptom criteria [35]. The PSS
interviewer version (PSS-I; [35]) was used during telephone
interviews, with items dichotomized into present vs. absent
statements about each PTSD symptom criterion (score range: 1
to 17). The self-administered version of the PSS (PSS-SR; [36])
was used during clinical assessments. This version uses a 1–4
symptom severity score for each item. A score of two or more was
considered an endorsement of the presence of a symptom (score
range: 1 to 17). The PSS-SR total scores during the clinical interviews
were highly correlated with concurrent CAPS total scores (at one
month: r = .77, p,.001; at five months: r = .84, p,.001).
Measurement equivalence between telephone-based PSS-I and
clinically administered PSS-SR scales was established by examin-
ing the correlations between the proximal five months clinical
interviews and seven months telephone interviews. The Pearson’s
correlation coefficient revealed a strong relationship between the
scores (r = .75, p,.001). Additionally, telephone-based PSS-I
scores at seven months correlated significantly with the five
months CAPS total score (r = .76, p,.001). Based on this evidence
of measurement equivalence, we conducted our analysis utilizing
both PSS-I dichotomous scores and in-person PSS-R dichoto-
mized symptom scores.
The Kessler-6 (K6) is a brief 6-item self-report instrument that
measures general distress. It was administered during telephone
interviews. The K6 items are rated on a five-point scale from zero
(‘‘none of the time’’) to four (‘‘all of the time’’), yielding a total
score ranging from 0 to 24. The K6 strongly discriminates
between community cases and non-cases of DSM-IV/SCID
disorders with Receiver Operating Characteristic (ROC) curve
of 0.87–0.88 for all disorders with Global Assessment of
Functioning (GAF) scores of 0–70 and 0.95–0.96 when disorders
had GAF scores of 0–50 [37].
The occurrence of new PTEs during follow-up was evaluated by asking
subjects, during seven and fifteen months’ interviews, whether they
incurred a traumatic event since their inclusion in the study.
Responses were coded as ‘no incident’, ‘incident of the same
nature’ and ‘different incident.’ This variable was dummy coded
for trajectory analysis to indicate presence/absence of any recent
incident.
Data Analytic Plan
We utilized Mplus 6.0 [38], employing robust full information
maximum-likelihood (FIML) procedures to identify heterogeneous
latent classes of PTSD symptom severity over time using LGMM.
These modeling techniques allowed us to test whether the
population under study is composed of a mixture of discrete
distributions characterized as classes of individuals with differing
profiles of growth [39], while also allowing for the modeling of
covariates as predictors of class membership and slope parameters
[40].
Unconditional Model. We compared a progressive number
of classes characterized by linear only or linear and quadratic
parameters while accounting for non-specific psychological distress
by residualizing PTSD symptom scores at 10 days and 7 months
on K6 scores. We accounted for the effect of eventual trauma
exposure during the study by regressing PTSD symptom scores at 7
and 15 months on our dummy-coded trauma-re-exposure variable
as a time variant covariate. We compared progressive nested
trajectory models by assessing relative fit based on reductions in
the Bayesian Information Criterion (BIC), sample-size adjusted
Bayesian Information Criterion (SSBIC), Aikaike Information
Criterion (AIC), and significance indicated by the Bootstrap
Likelihood Ratio Test (BLRT), along with parsimony and
interpretability equally weighed. Entropy was also examined but
not utilized to determine the number of classes; all criteria were
consistent with recommendations from the literature [41].
Conditional Model. After establishing our best-fitting model,
we first regressed class membership and then the freely estimated
slopes within each class on a dummy-coded variable indicating the
receipt of treatment. Next, we examined further covariates as
predictors of the classes including age, gender, and ten days symptom
severity in the three clusters of PTSD symptomatology including
avoidance, arousal, and intrusions. We analyzed these separately from
the treatment variable because we wanted to examine the effect of
the treatment variable.
Results
Symptom progression in the entire sample. By exa min-
ing mean leve l PTSD symptom severity across our five
measurement point s, we found that, as mean level symptoms
decrease in the entire sample, the standard deviation
increases, indicating that the mean is characterizing an
increasingly wide distribution of symptoms and suggesting
that the distribution is becom ing increasingly non-normal
(Table 1).
Treatment allocation. We examined differences between
those who received CBT and those who did not. No significant
difference was observed between the treatment and non-treatment
Heterogeneity in the Course of Early PTSD
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Table 1. Study Groups’ Comparisons.
Non-Remitting (1) Slow Remitting (2) Rapid Remitting (3) Total Sample
F (df)/X
2 Post-hoc
M
(SD) n(%) 95% CI
M
(SD) n(%) 95% CI
M
(SD) n(%) 95% CI
M
(SD) n(%) 95% CI
(n = 163) (n = 258) n = 536 (n = 957)
Gender (% Male) 74(45%) 137 (53%) 280(52%) 491(51%) 2.80(2,954) = .25 1 = 2 = 3
Received CBT 26 (19%) 38 (17%) 61 (13%) 125 1.89(2,954) = .39 1 = 2 = 3
Ten Days (n = 163) (n = 258) (n = 536) (n = 957)
PTSD Symptoms 12.17(3.14) 11.68, 12.65 10.39(3.16) 10.00, 10.78 9.71 (3.09) 9.44, 9.97 10.31(3.24) 10.10,10.51 38.98(2,954),.001 1.2.3
One Month (n = 93) (n = 135) (n = 286) (n = 514)
PTSD symptoms 14.87(2.31) 14.38, 15.35 13.50(2.95) 13.00, 14.00 10.14 (4.22) 9.65,10.64 11.87(4.13) 10.10,10.51 80.08(2,511),.001 1.2.3
PTSD
*
(%) 97 (96%) 128 (91%) 186 (61%) 401 (73%) 72.53(2,511),.001 1,2.3
Five Months (n = 88) (n = 115) (n = 260) (n = 463)
PTSD symptoms 14.59(2.26) 14.09, 15.08 10.15(3.85) 9.43, 10.87 3.54 (3.00) 3.17, 3.92 7.27(5.44) 6.76, 7.78 404.52(2,460),.001 1.2.3
PTSD (%) 93 (98%) 82 (65%) 21 (8%) 196(40%) 280.14(2,460),.001 1.2.3
Seven Months (n = 163) (n = 255) (n = 533) (n = 951)
PTSD symptoms 13.01(2.71) 12.59, 13.43 7.95(2.77) 7.61, 8.29 2.67 (2.08) 2.50, 2.85 5.86(4.62) 5.57, 6.16 1297.17(2,948),.001 1.2.3
PTSD (%) 151 (93%) 124(49%) 16 (3%) 291(31%) 525.60(2,948),.001 1.2.3
15 Months (n = 141) (n = 225) (n = 472) (n = 838)
PTSD symptoms 12.17(2.37) 11.78, 12.56 6.19(2.87) 5.81, 6.57 1.78 (1.80) 1.61, 1.94 4.71(4.45) 4.41, 5.01 1248.97(2,835),.001 1.2.3
PTSD (%) 129 (91%) 57(25%) 6 (1%) 192 (23%) 501.34(2,835),.001 1.2.3
PSS scores among PTSD participants 12.48 (2.18) 9.54 (1.78) 8.17 (0.98) 204.35(2,189),.001 1,2.3
doi:10.1371/journal.pone.0070084.t001
Heterogeneity in the Course of Early PTSD
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groups by gender [x
2
(1, 956) = 2.44, p = .12)]. Significant
differences between these groups were observed on age, the
treatment group being slightly older (respectively, in years, 39.30,
SD = 12.25 vs. 35.77, SD = 11.79; t (1, 956) = 2 3.11, p,.01).
Participants who received treatment had higher PTSD symptom
severity at 10 days (i.e., prior to treatment initiation) (PSS-I total
score = 11.63, SD = 2.85 vs. 10.11, SD = 3.25; t (1, 956) = 24.95,
p,.001) and higher ten days’ K6 scores prior to treatment initiation
(mean = 19.15, SD = 4.45 vs. 17.60, SD = 5.26; t (1, 956) = 23.14,
p,.01). Because t-tests are sensitive to sample size, we examined
the effect size by group, using a one-way ANOVA and those were
as follows: for age (g
2
= .01), for ten days’ PSS-I scores (g
2
= .03) and
for 10 days K6 (g
2
= .01). Based on accepted psychometric
standards [42], we concluded that differences between groups
were trivial.
Unconditional Model: uncovering latent classes. Based
on the AIC, BIC, SSBIC, and BLRT, we found that successive
models continued to demonstrate improved fit through four
classes, both with linear only, and linear+quadratic parameters,
with linear+quadratic parameters consistently out-performing
linear alone (Table 2). However, both with linear only and
linear+quadratic parameters, the addition of a fourth class served
only to split a class into two parallel trajectories with no substantive
distinction in symptom levels. As a result, the four-class model was
rejected for being less parsimonious and less interpretable, and the
three-class model with linear+quadratic parameters was retained.
This model identified three substantively distinct classes. The
largest class (Rapid Remitting; 56% of the sample) displayed
a precipitous drop in symptoms from 1 to 5 months as captured by
a significant negative slope (Est = 226.72, SE = 2.28, p,.001),
indicating a significant overall drop in symptoms from 10 days to
five months, accompanied by a significant positive quadratic
parameter, indicating a curvilinear rate of change (Est = 17.16.23,
SE = 1.93, p,.001). The second largest class (Slow Remitting;
27%) demonstrated a relatively consistent rate of symptom
reduction across time points, as indicated by a significant negative
slope (Est = 28.83, SE = 2.50, p,.001) and a non-significant
quadratic parameter (Est = 1.95, SE = 0.63, p = .23). Finally, the
smallest class (Non-Remitting; 17%) demonstrated consistently
high symptom severity across time points with no significant
change over time, indicated by a non-significant slope
(Est = 21.19, SE = 1.68, p = .48) and a non-significant quadratic
parameter (Est = 22.67, SE = 1.62, p = .10; Figure 1). Members
of the rapid remitting class also reached lower PTSD symptom
levels at 15 months compared to those of slow remitting class, and
the latter had lower levels of PTSD symptoms than the non-
remitting class (Table 1). The frequency of full PTSD in the entire
sample is 21.8% while rates of sub-syndromal PTSD based on
meeting at least 2 of the three symptom cluster criteria is 15.8%
based on the PSS.
To assess trajectories while accounting for general levels of
distress, we regressed symptom levels at 10 days and 7 months on
initial K6 scores. These variables improved entropy indicating that
accounting for general distress improves identification of class
membership. Levels of general distress at 10 days were
significantly positively associated with PTSD symptom at 10 days
(Est = 0.11, SE = 0.01, p,.001), and marginally so at 7 months
(Est = 0.02, SE = 0.01, p = .07) across the entire population.
Novel trauma exposure, during the study, was not significantly
associated with differences between classes in concurrent PTSD
symptom levels at seven (Est = 0.07, SE = 0.24, p = .76) and 15
months (Est = 0.34, SE = 0.20, p = .08).
Finally, to assess if the trajectories were biased by the selection
of individuals with 3 or more time points, we conducted the same
analysis with all the participants. This analysis revealed weaker
overall fit in terms of entropy, but recovered the same classes in
roughly the same proportions.
Conditional model: effect of treatment and other
covariates on latent trajectory classes.
To examine the
effect of treatment on the LGMM parameters we first we regressed
class membership on our dummy-coded yes/no treatment
variable, conducted in the MPlus environment using a multino-
mial logistic regression. Results of these analyses did not approach
significance suggesting that receiving treatment did not affect class
membership.
Following the examination of the effects of treatment on class,
we explored further covariates as predictors of the latent classes
using the same modeling framework. We examined the following
variables: gender, age, and total levels of PTSD symptomatology at
10 days based on the three symptom domains (intrusions, avoidance,
arousal). Gender and intrusions were not significantly different
between the three identified classes and none of these covariates
differentiated the Rapid and the Slow Remitting classes. Com-
pared to the Rapid Remitting class, however, the Non-Remitting
class was significantly older (Est = 0.04, SE = 0.01, p,.001),
marginally more likely to have higher levels of avoidance
symptomatology (Est = 0.11, SE = 0.06, p = .10) and significantly
more likely to have higher levels of arousal symptomatology
(Est = 0.26, SE = 0.09, p,.01). This class was also significantly
more likely to be older then the Slow Remitting class (Est = 0.02,
SE = 0.01, p,.05) and more likely to have significantly higher
levels of both avoidance (Est = 0.19, SE = 0.08, p,.05) and arousal
symptom severity (Est = 0.22, SE = 0.10, p,.05).
Next, we regressed the random slope parameter of each class on
the treatment variable while controlling for distress at 10 days.
General distress at 10 days significantly predicted the slopes across
Table 2. Fit Indices for One- to Four-Class Growth Mixture Models of PTSD Symptom Severity (n = 957).
Linear Weights Only Linear
+
Quadratic Weights
Fit Indices 1 Class 2 Classes 3 Classes 4 Classes 1 Class 2 Classes 3 Classes 4 Classes
AIC 31356.07 31129.22 31062.93 30980.84 30994.95 30559.06 30466.79 30388.28
BIC 31448.48 31245.95 31203.98 31146.21 31092.23 30658.52 30622.43 30573.11
SSBIC 31388.15 31169.73 3111.88 31038.23 31028.71 30602.94 30520.80 30452.42
Entropy — .84 .82 .82 — .84 .78 .79
BLRT — p,.001 p,.001 p,.001 — p,.001 p,.001 p,.001
Note. AIC = Akaike information criterion; BIC = Bayesian information criterion; SSBIC = sample size adjusted Bayesian information criterion; LRT = Lo-Mendell-Rubin test;
BLRT = bootstrap likelihood ratio test.
doi:10.1371/journal.pone.0070084.t002
Heterogeneity in the Course of Early PTSD
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classes (Est = 20.42, SE = 0.04, p,.001). However, this analysis
revealed non-significant effect of treatment on the Rapid
Remitting and the Non-Remitting Classes and a significant
negative effect in the Slow Remitting Class (Table 3). These
findings indicate that individuals in the Slow Remitting Class, but
not other classes, benefit from treatment: treatment serves to
accelerate their symptom decline over time.
In the above analyses we retained individuals who received late
PE because of concerns that removing them from the analyses
could bias the sample. Hypothetically, however early and delayed
PE could have differentially affected symptom trajectories. We
therefore repeated the analysis with these individuals removed,
which resulted in similar effect of treatment on class membership
and slopes.
Post-hoc analyses: Trajectories, PTSD, other Diagnoses,
and Demographics.
We examined the relationship between
the LGMM-identified trajectories and meeting PTSD diagnostic criteria
at different time points. We also examined gender differences by class.
To conduct this analysis we saved the most probable class
assignments for analysis outside of the model and conducted a
series of x
2
comparisons in SPSS 19. The classes differed in the
likelihood of meeting PTSD diagnostic criteria at five, seven and fifteen
months (Table 1). There were no significant differences by gender
in relation to class, and no statistically significant differences the
proportion of individuals in treatment by class (Table 1). We also
examined differences in age between the classes using a two-tailed
ANOVA. The overall test was significant [F (2,954) = 11.60,
p,.001]; however, the effect size was trivial (g
2
= .02).
Next by comparing mean symptoms and the standard deviation
from the mean, we observe no noticeable reduction in PTSD
symptom levels in the Non-Remitting class from 10 days
(m = 12.17, SD = 3.14) to 15 months (m = 12.17, SD = 2.37), a
moderate reduction in total symptoms in the Slow Remitting class
from 10 days (m = 10.39, SD = 3.16) to 15 months (m = 6.19,
SD = 2.87), and a large reduction in total symptoms in the Rapid
Remitting class from 10 days (m = 9.71, SD = 3.09) to 15 months
(m = 1.78, SD = 1.80). The resulting confidence intervals indicate
separation between classes at all time-points (Table 1).
Finally, we examined the prevalence of one month DSM IV
Anxiety Disorders (i.e., any anxiety disorder other than PTSD)
and Major Depressive Disorder (MDD) among participants who
attended the first clinical interview (n = 514) and conducted a
series of pearson x
2
analyses to test if meeting these diagnoses
differed between latent trajectory classes. The prevalence of
anxiety disorders in the entire sample was 27.8% (n = 143) and
that of current MDD 38.5% (n = 198). The trajectory groups had
similar prevalence of current anxiety disorders. They differed,
however in the prevalence of current MDD [(respectively for Non
Remitting, Slow Remitting and Rapid Remitting 66.0% 47.4%
and 21.2%; x
2
(4, 423) = 76.58, p,.001] with significant differences
between every two trajectory groups (for Slow Remitting vs. Rapid
Remitting [x
2
(1, 426) = 31.65, p,.001]; for Non-Remitting vs.
Slow Remitting [x
2
(1, 225) = 8.23, p,.01] for Non-Remitting vs.
the Rapid Remitting [x
2
(1, 374) = 70.25, p,.001]).
Discussion
The current study evaluated the occurrence of latent classes
characterized by their trajectory of symptom change from 10 days
to 15 months post-trauma among a large cohort of recent trauma
survivors. Among 957 who were followed 125 (13.1%) received
efficacious CBT and we tested the relationship between receiving
treatment and the identified trajectories.
We identified three latent classes of symptom change: A large
class characterized by a precipitous drop in symptoms from one to
five month (Rapid Remitting, 56%), a class characterized by a
slow linear decline of symptoms over 15 months (Slow Remitting,
27%) and a class characterized by a failure to remit and no
reduction in symptoms (Non-remitting, 17%).
We also examined demographic and symptom levels at 10-days
as predictors of symptom trajectory classes and found that the
Non-remitting class was predictable by older age, higher levels of
initial hyperarousal symptoms and, less consistently, elevated
avoidance symptoms. Testing the robustness of these and other
putative predictors requires in-depth classifier analyses of this and
other longitudinal.
Examining the relationship between receipt of treatment and
the three classes we, firstly, found no evidence that receiving
treatment affected class membership and secondly found that,
within classes, treatment accelerated the rate of recovery in the
Slow Remitting class alone and had no effect on the two other
classes.
As such, these findings indicate the early CBT is effective – or
necessary - for a subset of symptomatic trauma survivors. The
finding concerning unnecessary CBT for rapid remitters replicates
a previous finding of our group [9] and other groups [43].
However, the occurrence of a non-remitting and treatment resistant group is a
novelty. Importantly, in both non-remitting and rapid remitting
groups, treatment was followed by an apparent improvement, but
such improvement did not differ from the spontaneous recovery of
those untreated within each group. The relatively small proportion
of subjects in the non-remitting group emphasizes the contribution
of the latent trajectory approach to discerning pertinent outcome
groups within entire cohorts. These findings have broad relevance
Table 3. Growth Factor Parameter Estimates for Treatment
on the Slope of the 3-Classes (n = 957).
Class
Est. S.E. p, =
Slow Remitting 20.96 0.49 0.05
Rapid Remitting 1.52 1.28 0.23
Non-Remitting 1.33 1.05 0.20
Note. Est = Estimat e; SE = Standard Error.
doi:10.1371/journal.pone.0070084.t003
Figure 1. Three Trajectory Model of PTSD Symptom Severity
Recovery Trajectories (n = 957).
doi:10.1371/journal.pone.0070084.g001
Heterogeneity in the Course of Early PTSD
PLOS ONE | www.plosone.org 6 August 2013 | Volume 8 | Issue 8 | e70084
for understand the natural course of PTSD, the differential effects
of treatment, and the heuristics of further discovery.
Regarding the Natural Course of PTSD, our findings indicate that
heterogeneities in individuals’ symptom trajectories following
trauma are not random events, but rather cluster into typical,
minimally overlapping subsets. Our findings also suggest that the
resulting subsets are highly informative with regard to the
occurrence and the severity of chronic PTSD. These populations
appear to be more informative and less error prone then the use of
diagnostic status as an outcome. Firstly, we find the 91% of
individuals who qualify for a PTSD diagnosis at 15-months fall
into the Non-Remitting trajectory. Further, among those who meet
PTSD criteria at 15 months those in the non remitting group have
significantly higher symptom severity Differences in symptom
severity at fifteen months suggest that individuals on the slow and
rapid remitting group who still meet PTSD symptom criteria
might be on their way to recovery.
Our work differs from previously reported studies (i.e. [22,44])
in that it does not include survivors without initial significant
elevations in symptoms. As a previous analysis of these data has
shown [9,14], such individuals are very unlikely to develop PTSD.
The current results reflect, therefore, symptom trajectories among
survivors at high risk – rather than among entire cohorts of
individuals exposed to potentially-traumatic events. In the context
of the current study, we strove to identify heterogeneous responses
among those who are initially highly symptomatic, to attempt to
predict these sub-populations, and to examine the differential
effects of treatment as it relates to these sub-populations.
From a treatment and prevention perspective, the finding of an
unremitting and treatment-resistant trajectory is equally important. First,
the majority of patients with chronic PTSD at 15 months (n = 129
of n = 192; 67.2%) come from this small group. Second, symptom
levels of those who remain with 15 months’ PTSD in the non-
remitting group are significantly and meaningfully higher than
those of the other classes (30.1% higher than in the slow remitting
group and 52.7% higher than the rapid remitting group), evoking
the question of fundamental differences between the resulting
conditions (e.g., potential for further recovery, neuro-cognitive
underpinning). It is therefore important to further explore this
group, in this and subsequent studies.
Looking at ways to predict this group, the non-remitting group
in this work separated from the other groups as early as 10 days
after the traumatic event (symptom levels and confidence intervals
do not overlap). However, this post-hoc observation is not yet
mature for clinical use as a predictor nor is it informative about
underlying neuro-behavioral mechanisms. Attaching biographical
information (e.g., prior trauma, childhood adversity) as well as
neuropsychological, biological and recovery-environment factors
to this trajectory may lead to better – and specific - understanding
of this catastrophic course of early PTSD symptoms.
The non-remitting group should also be amenable, as such, to
longitudinal neuro-cognitive and neuro-imaging studies looking
into putative changes in the ways the CNS transmutes an initial
reaction into chronic, entrenched disturbance. Finding analogous
trajectories in PTSD-related biomarkers would buttress this
‘irreversible acquisition’ trajectory in biological findings. Recent
and similar trajectories in animal models of conditioned fear
provide encouraging evidence to the existence of such analogies
[45,46]. Better understanding the dynamics of non-remission may
hold a key for further discovery other mental disorders with
identifiable onset and non-remitting course in a subset of patients.
Our unexpected finding of treatment (CBT) resistance in this
group makes these patients eager candidates for other treatment
approaches. However, even when effective, novel therapies for small
proportion of survivors are unlikely to generate a significant signal
in studies of entire affected groups. This highlights the importance
of identifying pertinent subpopulations for assessing treatment
effects: one treatment could be highly effective in the aggregate
while ineffective for a minority – and vice versa. Indeed, the use of
LGMM has already revealed informative description of distinct
courses of recovery in randomized clinical trials of depression, in
which it differentiated the effects of treatment from that of natural
recovery and placebo [27,28,47]. These efforts are in line with the
emergence of trait-sanctioned therapies for medical conditions
(e.g., receptor-specific therapies for breast cancer, multiple
myeloma).
The slow remitting trajectory is similarly interesting. The unique
effect of treatment on members of this cluster suggests a special
sensitivity to the effects of CBT, and thus might allow a better
allocation of patients to early treatment. It would be interesting as
well to explore the reasons for such responsiveness via exploring
membership in this trajectory class.
The finding of positive treatment effect in this otherwise
progressively remitting class is also in line with a previous and very
intriguing observation from epidemiological studies [3], according
to which early treatment (though studied retrospectively) acceler-
ated recovery but did not reduce the overall burden of PTSD.
Granted, accelerating recovery by months or years has profound
clinical and personal implications. Nonetheless, the putative
category of ‘recover-able’ trauma survivors is extremely interesting
to follow as it may optimally teach us about recovery mechanisms
that may not exist in the other two groups, and how to engage
them. Again – studying recovery in entire cohorts may not be
sensitive enough.
The finding of a rapidly remitting subgroup is in line with previous
CBT studies, in which patients with less than full Acute PTSD
symptoms recovered with or without treatment [8,9]. Identifying
who will follow this course has broad public health implications, as
it could lead to the better allocating survivors to therapy and better
use of treatment resources.
Further, our observation can inform the heuristics of uncovering the
pathogenesis of PTSD. The finding of pertinent classes of symptom
trajectories challenges the use of central tendency statistics to
enhance discovery in the area of nascent PTSD. Central tendency
statistics may collapse heterogeneous populations and obfuscate
the identification of relevant subpopulations. To take advantage of
the methodology presented here, future studies should collect
multiple data points at timing and intervals that are critical for
understanding the underlying problems, and with an eye towards
imputation of missing cases (e.g., by collecting enriched initial
assessments).
We found some indication that current depression differentiates
trajectories. These data are limited, however, because full clinical
assessments were not conducted on the entire cohort. This is
potentially valuable information as it indicates that depression
symptomatology in the acute phase may be predictive of chronic
posttraumatic stress. This finding is consistent with other findings
in the literature that have demonstrated that depression, in part,
influences the development and maintenance of PTSD [48].
Finally, despite evaluating the same construct (PTSD symptoms)
and establishing measurement equivalence, the use of different
versions of the PSS at different time points should be seen as
limitation of this study.
Conclusion
This work uncovered one of, possibly, several symptom
trajectory scenarios in recent trauma survivors. Rape survivors,
or victims of repeated or protracted violence, may have different
Heterogeneity in the Course of Early PTSD
PLOS ONE | www.plosone.org 7 August 2013 | Volume 8 | Issue 8 | e70084
longitudinal paths. This may also be true in deployed
combatants, who se survival in a b attlefiel d ma y require a
suppression of initial symptoms, and result in their delayed
emergence [49]. Nonetheless the approach outlined here
emphasized a robust methodology for uncovering systematic
clustering patterns within response heterogeneities. Its ultimate
challenge will be its ability to better inform clinical and
biological studies of the pathogenesis of trauma and stress-
related disorders and uncover robust predictors of symptoms
persistence and chronicity.
Author Contributions
Conceived and designed the experiments: AS. Performed the experiments:
MG. Analyzed the data: IGL. Contributed reagents/materials/analysis
tools: MG. Wrote the paper: AS IGL YA SF YIS PR.
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