Psychological Assessment 2013, Vol. 25, No. 3, 1037–1043
In the public domain DOI: 10.1037/a0033432
BRIEF REPORT
Psychophysiological Assessment of PTSD: A Potential Research Domain Criteria Construct Margaret R. Bauer
Anna M. Ruef
Veterans Affairs Boston Healthcare System, Boston, Massachusetts
Veterans Affairs Medical Center, Manchester, New Hampshire
Suzanne L. Pineles and Sandra J. Japuntich
Michael L. Macklin
Veterans Affairs Boston Healthcare System, Boston, Massachusetts, and Boston University School of Medicine
Veterans Affairs Medical Center, Manchester, New Hampshire
Natasha B. Lasko and Scott P. Orr Veterans Affairs Medical Center, Manchester, New Hampshire, and Massachusetts General Hospital and Harvard Medical School Most research on posttraumatic stress disorder (PTSD) relies on clinician-administered interview and self-report measures to establish the presence/absence and severity of the disorder. Accurate diagnosis of PTSD is made challenging by the presence of symptoms shared with other psychopathologies and the subjective nature of patients’ descriptions of their symptoms. A physiological assessment capable of reliably “diagnosing” PTSD could provide adjunctive information that might mitigate these diagnostic limitations. In the present study, we examined the construct validity of a potential psychophysiological measure of PTSD, that is, psychophysiological reactivity to script-driven imagery (SDI–PR), as measured against the current diagnostic “gold-standard” for PTSD, the Clinician-Administered PTSD Scale (CAPS). Convergent and predictive validity and stability were examined. Thirty-six individuals completed an SDI–PR procedure, the CAPS, and self-report measures of mental and physical health at their initial visit and approximately 6 months later. SDI–PR and the CAPS demonstrated excellent stability across measurement occasions. SDI–PR showed moderately strong convergent validity with the CAPS. After adjusting for self-reported depression, predictive validity for the CAPS, with regard to health sequelae, was reduced, whereas it remained mostly unchanged for SDI–PR. Findings support SDI–PR as a valid and stable measure of PTSD that captures a pathophysiologic process in individuals with PTSD. Results are discussed with regard to the research domain criteria framework. Keywords: posttraumatic stress disorder, PTSD, psychophysiology, reliability, depression
Identification of a biomarker capable of diagnosing posttraumatic stress disorder (PTSD) and/or assessing its severity would provide a measure of emotional experience that does not rely on clinical judgment or subjective report. According to a National Institute of Medicine (2006, p. 46) report, “No biomarkers are clinically useful or specific in diagnosing PTSD, assessing the risk
of developing it, or charting its progression.” Although the report recognized the support for a biological basis of PTSD, it failed to acknowledge a substantial body of psychophysiological PTSD research. Across more than two dozen studies, individuals with PTSD have consistently demonstrated heightened psychophysiological reactivity to trauma-related cues, compared with those
This article was published Online First July 1, 2013. Margaret R. Bauer, National Center for PTSD, Women’s Health Sciences Division, Veterans Affairs Boston Healthcare System, Boston, Massachusetts; Anna M. Ruef, Research Service, Veterans Affairs Medical Center, Manchester, New Hampshire; Suzanne L. Pineles and Sandra J. Japuntich, National Center for PTSD, Women’s Health Sciences Division, Veterans Affairs Boston Healthcare System, and Department of Psychiatry, Boston University School of Medicine; Michael L. Macklin, Research Service, Veterans Affairs Medical Center, Manchester, New Hampshire; Natasha B. Lasko and Scott P. Orr, Research Service, Veterans Affairs
Medical Center, Manchester, New Hampshire, and Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School. Partial support for this work was provided by a Veterans Affairs Career Development Award (principal investigator: Suzanne L. Pineles) and Veterans Affairs Merit Review (principal investigator: Scott P. Orr) from the Clinical Sciences Research and Development Service, Department of Veterans Affairs, Washington, DC. Correspondence concerning this article should be addressed to Scott P. Orr, Massachusetts General Hospital–East, Building 120, 2nd Avenue, Charlestown, MA 02129. E-mail:
[email protected] 1037
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without PTSD (Orr, McNally, Rosen, & Shalev, 2004; Pole, 2007). Heightened reactivity to trauma-related reminders provides reliable and compelling evidence for a biomarker of PTSD. Heightened psychophysiological reactivity to trauma-related scriptdriven imagery (SDI–PR) in individuals previously exposed to traumatic events can reliably discriminate individuals with PTSD from those without the disorder (e.g., Orr, Pitman, Lasko, & Herz, 1993; Pitman et al., 1990; Pitman, Orr, Forgue, de Jong, & Claiborn, 1987). SDI–PR provides a useful method for assessing PTSD that is not limited by an individual’s ability to accurately describe his or her subjective emotional experiences. In the current study, we examined the construct validity of SDI–PR, a possible PTSD assessment tool. Construct validation assesses whether a test measures what it intends to measure (Cronbach & Meehl, 1955). Campbell and Fiske (1959) suggested a series of comparisons to determine construct validity. These comparisons fall into two types, convergent validity (agreement between the target measure and measures of the same construct) and discriminant validity (specificity of the target measure’s relationship to theoretically related constructs). Convergent validity can be assessed by administering related measures at the same time as the measure of interest (concurrent validity) or by predicting a related construct measure at a future point in time (predictive validity). Finally, Cronbach and Meehl (1955) suggested that if a construct is assumed to be stable, then stability must also be assessed by evaluating test–retest reliability. Heightened psychophysiological reactivity to trauma reminders has shown convergent validity with other PTSD measures and discriminant validity (i.e., more strongly associated with PTSD than most other anxiety disorders; e.g. Pitman et al., 1990; Zander & McNally, 1988). However, little is known about the stability of this feature of PTSD. Predictive validity of this measure for psychological and physical health sequelae of PTSD (e.g., anxiety and depression: Kessler, Sonnega, Bromet, Hughes, & Nelson, 1995; physical health problems: Schnurr & Green, 2004) has not been assessed. Establishing that a psychophysiological measure of PTSD possesses acceptable stability would encourage its consideration as a diagnostic and treatment indicator. Finally, examining relationships among different diagnostic indices and specific measures of mental and physical health and quality of life, while adjusting for an important measure of symptoms that may be comorbid with, but not specific to, PTSD (viz., depression) could facilitate the identification of features that uniquely contribute to the PTSD profile. We examined the stability of SDI–PR and ClinicianAdministered PTSD Scale (CAPS; Blake et al., 1995), the current “gold-standard” PTSD clinical interview, over a period of several months. Convergent and predictive validity of these two diagnostic measures of PTSD and depression were assessed with regard to anxiety, physical symptoms, mental health symptoms, and quality of life. It was predicted that SDI–PR, CAPS, and self-reported depression would demonstrate relationships with the known health sequelae of PTSD. Finally, we examined the impact of removing variance explained by self-reported depression on the relationships of SDI–PR and CAPS with self-reported anxiety, mental and physical health measures, and quality of life. Because the CAPS and self-reported depression both capture symptoms shared across mood and anxiety disorders, we expected that the strength of its associations with self-reported anxiety, physical and mental health
symptoms, and quality of life would be significantly reduced after controlling for depression. Because heightened SDI–PR may reflect a symptom component that is specific to PTSD, we expected that its associations with anxiety, physical and mental health symptoms, and quality of life would not be influenced after controlling for depression.
Method Participants All participants were exposed to a traumatic event as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM– IV; American Psychiatric Association, 1994). Of the 46 individuals recruited for an initial assessment, 36 completed the initial and follow-up assessments and make up the sample. Index traumatic events included combat trauma (n ⫽ 27), motor vehicle accident (n ⫽ 3), sexual harassment or assault (n ⫽ 2), nonvehicle accident (n ⫽ 2), homicide of family members (n ⫽ 1), and medical trauma (n ⫽ 1). Mean time between the traumatic event and initial assessment was 25.9 months (SD ⫽ 43.1; range 1–188). Exclusion criteria included current victimization or psychosis, a cognitive disorder (e.g., dementia), or a condition contraindicating participation (e.g., pregnancy).
Procedures Time 1 (T1) assessment. After giving informed consent, participants completed self-report measures and structured clinical interviews on one day and returned a week later for the SDI–PR assessment. The clinical interview included the Structured Clinical Interview for DSM–IV (SCID; First, Spitzer, Williams, & Gibbon, 1996) and CAPS. Personalized-trauma scripts were also created for the index traumatic event identified during the CAPS interview. Time 2 (T2) assessment. Participants returned for their second assessment an average of 6.6 months (SD ⫽ 1.1) after the initial visit. The self-report measures, SCID, CAPS, and SDI–PR were readministered. Due to an administrative oversight, the Beck Depression Inventory (BDI; Beck, Ward, Mendelson, Mock, & Erbaugh, 1961) was not repeated at T2. Before administering the CAPS at T2, participants were asked if they had experienced any traumatic event since the T1 assessment. Because only one participant endorsed a trauma but denied distress related to this event, the CAPS and SDI–PR at T2 were anchored to the T1 index trauma for all participants.
Measures The CAPS (Blake et al., 1995) is considered to be the “gold standard” interview for diagnosing and assessing PTSD (National Center for PTSD, 2010). The 17 items on the CAPS reflect PTSD symptoms described in the DSM–IV (American Psychiatric Association, 1994) and are rated on a 5-point scale (range ⫽ 0 – 4) for both frequency and intensity. Severity scores are calculated by summing the frequency and intensity scores (range ⫽ 0 –136). Additional validated measures were used to assess depression (BDI; Beck et al., 1961), physical and mental health (Short Form–36 Health Survey [SF-36]; Ware, Snow, Kosinki, & Gan-
PSYCHOPHYSIOLOGICAL ASSESSMENT OF PTSD
dek, 1993), state and trait anxiety (State–Trait Anxiety Inventory– Trait Scale [STAI–T]; Spielberger, Gorsuch, & Lushene, 1970), and quality of life (Quality of Life and Enjoyment Satisfaction Questionnaire–Short Form [Q–LES–Q–SF]; Endicott, Nee, Harrison, & Blumenthal, 1993).
Script-Driven Imagery Task The SDI–PR was performed according to standard procedures (e.g., Orr et al., 1993; Pitman et al., 1987). At the initial visit, the diagnostician and participant created personalized scripts reflecting two distinct aspects of the individual’s index traumatic event, as well as non-trauma-related stressful, positive, and neutral scripts. Scripts were approximately 30 s in length, composed in the second person, present tense, and recorded in a neutral voice (e.g., Pitman et al., 1987). In addition, participants also heard standard scripts describing common experiences (see Lang, Levin, Miller, & Kozak, 1983; Miller et al., 1987). Only results from the personalized trauma scripts were analyzed for the present investigation. For the SDI–PR session, participants were oriented to the laboratory and seated while electrodes were attached. Participants were informed that a series of scripts would be presented after listening to a brief set of relaxation instructions. Each script consisted of four 30-s periods: baseline, read, imagery, and recovery. As the script was read, participants were asked to close their eyes and imagine each event as vividly as possible (read period). After the script concluded, they were asked to continue vividly imagining the event (imagery period) until they heard a tone, at which point they would stop imagining the event and relax (recovery period) until a second tone was heard. After the second tone, participants opened their eyes and answered questions regarding emotions they felt during the script presentation. The next trial began 1 min after the participant finished the final question or when his or her heart rate returned to within 5% of the previous trial’s baseline level, whichever took longer. Physiological recording. Heart rate (HR), skin conductance (SC), and left lateral frontalis (EMG [electromyogram]) were recorded using a Coulbourn Lablink V System (Coulbourn Instruments, Whitehall, PA) and stored on a Microsoft Windows– based computer (Microsoft Corp., Redmond, WA). HR was captured using 8-mm silver/silver chloride (Ag/AgCl) surface electrodes filled with electrolyte paste and placed on each forearm. HR was converted from interbeat interval recorded from standard limb electrocardiogram leads connected to a high-gain bioamplifier. SC was measured by an isolated skin conductance coupler (V71–23) using a 0.5 V constant direct current through 8-mm Ag/AgCl surface electrodes filled with isotonic paste and placed on the hypothenar surface of the subject’s nondominant hand (Fowles et al., 1981). Lateral frontalis EMG was measured using 4-mm Ag/ AgCl surface electrodes filled with electrolyte paste and placed on abraded skin. The EMG signal was amplified and filtered to retain the 90 –1,000 Hz frequency range and integrated using a 200-ms time constant. A sampling rate of 1,000 Hz was used throughout the procedure. Psychophysiological recordings were examined for deviant responses, which were removed prior to calculation of change scores. A change score was calculated for each physiologic measure for each script by subtracting the mean of the baseline period
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from the mean of the imagery period. Responses during the two personalized-trauma scripts were averaged. An example of a typical participant with PTSD’s response scores are 4.88 bpm for HR response, 1.24 microS for SC response, and 2.65 microV for frontalis EMG response, whereas an example of a typical participant without PTSD’s response scores are 1.21 bpm for HR response, 0.38 microS for SC response, and 0.52 microV for frontalis EMG response. Psychophysiological posterior probability score. A composite measure of SDI–PR during trauma-related imagery was obtained for each subject by applying an a priori discriminant function to their HR, SC, and lateral frontalis EMG responses, which yielded a single score reflecting the overall magnitude of psychophysiological reactivity during trauma-related imagery. The a priori discriminant function was derived from the HR, SC, and lateral frontalis EMG responses of previously studied traumaexposed individuals with and without PTSD (Carson et al., 2000; Orr et al., 1993; 1998; Pitman et al., 1987; Shalev, Orr, & Pitman, 1993). Briefly, the discriminant function analysis produced a classification criterion based on the linear combination of HR, SC, and lateral frontalis EMG responses during trauma-related imagery that best separated the clinician-determined PTSD and non-PTSD groups. The discriminant function was then applied to current study participants’ psychophysiological responses during scriptdriven imagery to produce a “posterior probability” score that estimated the likelihood that a given individual’s reactivity scores belonged to the PTSD group (SAS Institute, 2011). This measure of SDI–PR is referred to in this article as the psychophysiological posterior probability score (PPPS). The weighting scheme used for the discriminant function is .375 for HR response, .579 for SC response, and .392 for frontalis EMG response. In the previously studied sample from which the discriminant function was derived, the three psychophysiological indices were examined separately before creating the composite PPPS score. Individuals with PTSD demonstrated greater reactivity on SC, t(176) ⫽ 5.06, p ⬍ .0001; HR, t(176) ⫽ 4.97, p ⬍ .0001; and EMG, t(176) ⫽ 4.85, p ⬍ .0001. Additionally, SC, HR, and EMG were moderately intercorrelated, .31 ⱕ r(178) ⱕ .62, ps ⬍ .003; thus, each measure likely contributes unique variance to the discriminant function. The sensitivity and specificity of this discriminant function are 57% and 89%, respectively; the PPPS explained 23% of the variance in the CAPS-based PTSD diagnosis, r(36) ⫽ .48, p ⬍ .001.
Results The sample was predominantly male; the mean age and years of education were 38 (SD ⫽ 14.97) and 15 years (SD ⫽ 1.95), respectively. Psychiatric diagnoses included PTSD (58%) and major depression (36%), alcohol dependence (3%), substance dependence (6%), panic disorder (17%), phobia (14%), and dysthymia (3%). Descriptive statistics of the CAPS, PPPS, and psychometric measures are presented in Table 1. Associations among PPPS and CAPS scores at T1 and T2 are presented in Table 2. Stability was assessed as the correlation between T1 and T2 scores for each measure; both the PPPS and CAPS score showed strong stability (rtts ⫽ .75, p ⬍ .001). Convergent validity was assessed as the correlation between PPPS and CAPS scores, with moderately strong associations between PPPS
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1040 Table 1 Sample Characteristics Study variable
State–Trait Anxiety Inventory–Trait subscale Time 1 Time 2 Short Form–36 Health Survey Mental health composite Time 1 Time 2 Physical health composite Time 1 Time 2 Quality of Life Enjoyment Satisfaction Questionnaire–Short Form Time 1 Time 2 Beck Depression Inventory at Time 1 Clinician-Administered PTSD Scale Time 1 Time 2 Psychophysiological Posterior Probability Score (PPPS) Time 1 Time 2
M
SD
43.73 44.41
11.55 12.63
18.93 18.96
0.43 0.49
21.30 21.35
0.50 0.48
3.56 3.53 13.70
0.73 0.94 11.97
48.69 39.31
25.41 29.90
0.40 0.36
0.14 0.12
Note. N ⫽ 36. PTSD ⫽ posttraumatic stress disorder; PPPS ⫽ posterior probability of PTSD obtained from a priori discriminant function derived from psychophysiological responses (heart rate, skin conductance, and lateral frontalis electromyogram responses).
and CAPS scores at both time points (rs ⱖ .41, ps ⱕ .05). The associations among PPPS and CAPS scores with the BDI score adjusted are also in Table 2. The correlation between CAPS scores at T1 and T2 decreased when controlling for BDI score (rtt ⫽ .46, p ⫽ .01), whereas the correlation between PPPS at T1 and T2 increased slightly (rtt ⫽ .81, p ⬍ .001). The moderate association between PPPS and CAPS scores was maintained at both time points while controlling for BDI score (rs ⱖ .48, ps ⱕ .01). Convergent validity and predictive validity of PPPS, CAPS score, and BDI score were assessed by examining their correlations with each other and with STAI–T score, mental and physical health composite scores from the SF-36, and quality of life scores from the Q–LES–Q–SF (Table 3). Higher T1 CAPS scores were significantly correlated with higher BDI and STAI–T scores, poorer mental health, and poorer physical health at T1 (ps ⱕ .05).
In addition, higher T1 CAPS score predicted poorer mental health and physical health at T2 (ps ⱕ .01). Higher T1 PPPS was associated with poorer mental health scores at T1 (ps ⱕ .05). Higher PPPS at T1 predicted poorer mental health, physical health, and quality of life at T2 (ps ⬍ .05). Higher T1 BDI scores were associated with higher CAPS scores and STAI–T scores, poorer mental health, and poorer physical health at T1 (ps ⱕ .05) but were not associated with T1 PPPS. Higher T1 BDI scores also significantly predicted higher CAPS scores, STAI–T scores, poorer mental health, and poorer physical health at T2 (ps ⱕ .05). To assess whether PPPS and CAPS scores’ convergent validity and predictive validity were influenced by removing variance explained by self-reported depression, we examined partial correlations among the measures after adjusting for BDI score (Table 3). The magnitudes of associations between the CAPS score and
Table 2 Correlations and Partial Correlations Among Psychophysiological Posterior Probability Score and Clinician-Administered PTSD Scale Score (Controlling for Time 1 Beck Depression Inventory Score) Variable 1. 2. 3. 4. 5. 6. 7. 8.
Psychophysiological Posterior Psychophysiological Posterior Clinician-Administered PTSD Clinician-Administered PTSD Psychophysiological Posterior Psychophysiological Posterior Clinician-Administered PTSD Clinician-Administered PTSD
Probability Score T1 Probability Score T2 Scale T1 Scale T2 Probability Score T1 ctrl BDI Probability Score T2 ctrl BDI Scale T1 ctrl BDI Scale T2 ctrl BDI
1
2
3
4
— .75ⴱⴱⴱ .41ⴱⴱ .45ⴱⴱ
— .45ⴱⴱ .43ⴱⴱ
— .75ⴱⴱⴱ
—
5
6
7
8
— .81ⴱⴱⴱ .48ⴱⴱ .51ⴱⴱ
— .53ⴱⴱ .52ⴱⴱ
— .46ⴱⴱ
—
Note. N ⫽ 36. PTSD ⫽ posttraumatic stress disorder; T1 ⫽ Time 1; T2 ⫽ Time 2; psychophysiological posterior probability score (PPPS) ⫽ posterior probability of PTSD obtained from a priori discriminant function derived from psychophysiologic responses (heart rate, skin conductance, and lateral frontalis electromyogram responses); BDI ⫽ Beck Depression Inventory; ctrl BDI ⫽ scores for PPPS or Clinician-Administered PTSD Scale (CAPS) when controlling for BDI. ⴱⴱ p ⬍ .01. ⴱⴱⴱ p ⬍ .001.
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Table 3 Pearson Correlation Coefficients and Partial Pearson Correlation Coefficients for Measures (Controlling for Time 1 Beck Depression Inventory Score) CAPS Pearson correlation
PPPS Pearson correlation
Self-report measure
BDI Pearson correlation coefficient
Coefficient
Partial coefficient
Coefficient
Partial coefficient
Beck Depression Inventory T1 State–Trait Anxiety Inventory–Trait subscale T1 Short Form–36 Health Survey mental health composite T1 Short Form–36 Health Survey physical health composite T1 Quality of Life Enjoyment Satisfaction Questionnaire–Short Form T1 State–Trait Anxiety Inventory–Trait subscale T2 Short Form–36 Health Survey mental health composite T2 Short Form–36 Health Survey physical health composite T2 Quality of Life Enjoyment Satisfaction Questionnaire–Short Form T2
— .86ⴱⴱⴱ ⫺.73ⴱⴱⴱ ⫺.72ⴱⴱⴱ ⫺.80ⴱⴱⴱ .71ⴱⴱⴱ ⫺.78ⴱⴱⴱ ⫺.65ⴱⴱⴱ ⫺.73ⴱⴱⴱ
.62ⴱⴱⴱ .60ⴱⴱⴱ ⫺.64ⴱⴱⴱ ⫺.49ⴱⴱ ⫺.49ⴱⴱ .62ⴱⴱⴱ ⫺.70ⴱⴱⴱ ⫺.56ⴱⴱ ⫺.57ⴱⴱ
— .26 ⫺.61ⴱⴱ ⫺.15 ⫺.28 .58ⴱⴱ ⫺.57ⴱ ⫺.30 ⫺.32
.26 .17 ⫺.45ⴱⴱ ⫺.25 ⫺.35 .30 ⫺.50ⴱⴱ ⫺.45ⴱ ⫺.41ⴱ
— ⫺.05 ⫺.48ⴱⴱ ⫺.32 ⫺.59ⴱⴱ .24 ⫺.59ⴱⴱ ⫺.55ⴱⴱ ⫺.42
Note. Ns range from 18 –30. T1 ⫽ Time 1; T2 ⫽ Time 2; BDI ⫽ Beck Depression Inventory; PTSD ⫽ posttraumatic stress disorder; CAPS ⫽ Clinician-Administered PTSD Scale; PPPS ⫽ psychophysiological posterior probability score. Posterior probability of PTSD obtained from a priori discriminant function derived from psychophysiological responses (heart rate, skin conductance, and lateral frontalis electromyogram responses) to script-driven imagery procedures. ⴱ p ⬍ .05. ⴱⴱ p ⬍ .01. ⴱⴱⴱ p ⬍ .001.
most measures were greatly reduced after adjusting for T1 BDI score, although higher T1 CAPS scores remained significantly correlated with poorer mental health at T1 and with higher trait anxiety scores and poorer mental health at T2 (ps ⱕ .05). For PPPS, the pattern of results remained mostly unchanged after adjusting for T1 BDI score. Greater T1 PPPS was still significantly associated with poorer mental and physical health at T2 (ps ⱕ .01).
Discussion Trauma-related SDI–PR previously has been shown to reliably discriminate individuals with and without PTSD (e.g., Orr et al., 1993; Pitman et al., 1987, 1990). The present findings provide further support for the validity of SDI–PR as a PTSD assessment tool. Similar to the CAPS, the PPPS has strong stability over a period of several months. However, after controlling for depression, the stability of the CAPS decreased to a moderate level, whereas stability of the PPPS remained strong. The present results also provide evidence of convergent validity for the PPPS, with its moderately strong relationship with the CAPS score that persisted after controlling for depression. This evidence, coupled with the fact that psychophysiological measures are speculated to be less susceptible to reporting and diagnostic bias than self-report and clinician-administered measures, supports the use of PPPS as a PTSD assessment strategy. Associations among clinician-assessed PTSD symptoms and poorer mental and physical health outcomes were replicated. It is interesting that relationships between self-reported depression and current and future mental and physical health consequences paralleled or reflected stronger relationships than those observed for the CAPS score. Furthermore, after adjusting for depression, the relationships between CAPS and most self-report measures were substantially weakened. This pattern of results supports the premise that the CAPS and BDI both capture symptoms associated with an underlying dimension of negative affect that is characteristic of both mood and anxiety disorders (Brown, Chorpita, & Barlow, 1998; Clark, Watson, & Mineka, 1994). Because the CAPS and BDI appear to measure overlapping symptom profiles, conclusions
regarding the relationship between PTSD and various measures of functioning may be substantially changed after adjusting for the relationship between CAPS and self-reported depression. These diagnostic challenges support the need for reliable biomarkers of PTSD, such as the PPPS, which can provide a different diagnostic perspective and may facilitate the identification of PTSD correlates and sequelae. Although the correlation coefficients representing concurrent associations among the PPPS and measures of psychological symptoms and functioning were in the predicted direction, most were weaker than the corresponding correlations with CAPS score. Even so, higher initial PPPS predicted greater self-reported mental and physical health problems and poorer quality of life, as was found for the CAPS and BDI. Thus, the current study provides evidence supporting the concurrent and predictive validity of trauma-related SDI–PR. Additionally, unlike CAPS-measured PTSD, when depression was controlled, correlations among the PPPS and overall mental and physical consequences endured. This could be because psychophysiological reactivity to trauma-related imagery reflects fear or arousal that is specific to PTSD (and other fear-specific disorders), whereas the CAPS captures this feature of PTSD, as well as depressive symptoms or negative affect (shared with the BDI) that is not specific to PTSD. However, it is important to note that by removing variance accounted for by depression, the PTSD construct may have been altered in such a way as to be less accurately measured by the CAPS. There is also a possible methodological explanation as to why adjusting for BDI score weakened CAPS’— but not PPPS’— relationships. Specifically, adjusting for depression may have reduced variance associated with CAPS, but not with PPPS, scores due to common-method variance shared by the BDI and CAPS scores (i.e., both are self-report-based measures). Similarly, common-method variance shared by the CAPS and other selfreport-based measures may have contributed to the stronger relationships observed among clinician-diagnosed PTSD and mental and physical health scores before adjustments for depression. Consequently, there may be greater uncertainty regarding the true
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strength and nature of relationships between PTSD and self-reportbased measures of functioning when PTSD is represented by measures that rely on self-report, compared with when PTSD is represented by physiological measures such as the PPPS. Previous work examined the sensitivity and specificity of the PPPS in correctly classifying participants into PTSD and no-PTSD groups on the basis of clinician diagnoses. Keane et al. (1998) found that two thirds of their large sample was correctly classified by psychophysiological reactivity, and Orr et al. (2004) found that 70% of their combined sample was correctly categorized. When considering the less-than-perfect psychophysiologically based classification results of such studies, it is important to recognize that “correct” classification is determined by clinician diagnosis. As discussed earlier, accuracy of the clinician diagnosis depends to a large extent on the accuracy of the patient’s report of symptoms. If a patient over- or underreports symptoms, then the diagnosis may not be accurate. Thus, it is possible that the false negatives attributable to PPPS in the various classification studies may not have been true PTSD cases; the PPPS may have reflected the more accurate diagnostic determination. Taken together, these findings provide an excellent example of the insights that may be gained from using the research domain criteria (RDoC) framework. The RDoC framework is intended to promote mental health research by focusing on the identification of domains of aberrant psychological and biological processes that explain psychiatric symptoms (Sanislow et al., 2010). For example, negative valence systems, a domain highlighted in the RDoC, comprises several constructs (e.g., fear, distress) that are to be understood by several units of analyses (e.g., genes, physiology). One might assert that the heightened trauma-related SDI–PR indexed by the PPPS reflects a physiological process indicative of reactivity to acute threat or fear. Thus, the PPPS could serve as the sort of quantifiable physiological or biological trait outlined in the RDoC framework. Research with this paradigm may inform researchers’ understanding of trauma sequelae and PTSD. Individuals who do or do not show heightened psychophysiological reactivity to trauma-related cues may belong to different subtypes of PTSD: fear-based versus distress-based PTSD. The comparison of psychological traits, patterns of symptoms, and optimal treatments for the two groups could be an area for further clinical research. This is the first study to our knowledge in which the construct validity of a psychophysiological measure of PTSD is explicitly evaluated. While the results are encouraging, several limitations should be acknowledged, including the substantial number of correlations examined, relatively small sample size, failure to administer BDI at T2, and failure to include a measure of response bias. Despite these limitations, the findings provide compelling support for SDI–PR as a useful, nonsubjective measure of PTSD. This measure was found to be related to other standard measures of PTSD, to be stable over time, and to demonstrate predictive relationships with the poor mental and physical functioning often seen in individuals with PTSD. SDI–PR has consistently proven useful in traumatic stress research (Orr et al., 2004; Pole, 2007), and the present findings support its strengths as an assessment tool and valid biomarker of PTSD. While replication of the present results is necessary, we believe it is useful to reflect on the limitations of the current diagnostic tools and standards, and consider avenues for improvement.
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Received June 22, 2012 Revision received February 27, 2013 Accepted April 22, 2013 䡲
