Abstract Objective Extant literature has demonstrated that symptoms of postconcussive syndrome (PCS) persist well beyond the expected 3-month post-injury recovery period in a minority of individuals with mild traumatic brain injury (mTBI). Suboptimal performance on validity measures and pre- and post-injury psychosocial stressors – rather than actual mTBI or current cognitive functioning – have been identified as predictors of chronic PCS. Whether premorbid IQ has any influence on chronic PCS has been understudied, in the context of established psychogenic etiologies. Method The sample included 31 veterans, who underwent mTBI neuropsychological evaluations six or more months post-injury in a VA outpatient neuropsychology clinic. A two-step multiple linear regression was conducted to examine the effects on the outcome variable, PCS (Neurobehavioral Symptom Inventory), of the following predictors: cognitive functioning (Repeatable Battery for the Assessment of Neuropsychological Status; Attention, Immediate Memory, and Delayed Memory Indices), performance validity, depression (Beck Depression Inventory-Second Edition), posttraumatic stress disorder (PTSD Checklist, Civilian Version), quality of sleep (Pittsburgh Sleep Quality Index), pain (Brief Pain Inventory), education, and Premorbid IQ (Wechsler Test of Adult Reading). Results The overall regression model containing all nine predictor variables was statistically significant. Depression (p < .05) and premorbid IQ (p < .05) were the most salient predictors of chronic PCS; in that lower premorbid IQ and greater endorsed symptoms of depression were associated with higher PCS scores. In Step 2 of the multiple linear regression, the WTAR explained an additional 6.7% of the variance in PCS after controlling for psychosocial stressors and current cognitive ability. Conclusion The findings support premorbid IQ as a unique and relevant predictor of chronic PCS, with significance variance accounted for beyond education, cognitive functioning, and psychosocial variables. Given the predictive relationship between premorbid IQ and PCS, adapting postconcussive interventions to meet the specific needs of individuals with varying levels of intellect may be important in minimizing ongoing symptomatology. Premorbid IQ, mTBI, Postconcussive syndrome, PTSD, Depression Introduction The conflicts in Iraq and Afghanistan spanning the past 15 years have given rise to an appreciation for the polytraumatic sequela of visible and non-visible combat related injuries (Bogdanova & Verfaellie, 2012). As such, there is a wealth of literature highlighting the constellation of somatic, emotional, and cognitive symptoms, as well as the symptom overlap in Operation Enduring Freedom (OEF), Operation Iraqi Freedom (OIF), and Operation New Dawn (OND) veterans (Hoge et al. 2008; Meares et al., 2008). As an area of public health interest, research on mild traumatic brain injury (mTBI), which is one of the hallmark injuries of the Iraq and Afghanistan conflicts (Cooper, Vanderploeg, Armistead-Jehle, Lewis, & Bowles, 2014), has significantly grown. This includes research on what defines mTBI, mechanisms of injury, expected length of recovery, treatments/interventions, complications (i.e., chronic postconcussive symptoms), and comorbidities (Burden of Adversity Hypothesis; Brenner, Vanderploeg, & Terrio, 2009). Deployment can be a disruptive experience after which returning veterans must adapt to the rigors of civilian life. The Burden of Adversity Hypothesis purports that successful recovery from polytrauma (e.g., combinations of war-related brain, spinal cord, eye, ear, musculoskeletal, amputation, and mental health injuries), as well as the long-term functioning of the veteran is often complicated by a cumulative disadvantage created by vocational, financial, and recreational challenges. Moreover, barriers to appropriate assessment and treatment pervade within the military culture (e.g., mindset that promotes underreporting, biases about injury and mental illness, and the fear of stigmatization). Subsequently, while a large majority of those who incur at least one mTBI experience a full cognitive and behavioral recovery within 3 months post-injury (consistent with; Belanger et al., 2005; Iverson & Lange, 2005; Schretlen & Shapiro, 2003), a select group of individuals continue to endorse distressing symptoms (e.g., headaches, dizziness, visual disturbance, memory difficulties, poor concentration, mental slowness, difficulty dividing attention, fatigue, irritability depression and anxiety) months and years post-injury (Ponsford, Cameron, Fitzgerald, Grant, & Mikocka-Walus, 2012). Such phenomenon has come to be termed postconcussive syndrome (PCS). Within the research and clinical community, there continues to be some debate regarding the etiology (psychogenic vs. neurogenic) of chronic postconcussive symptoms. Support for the physiological etiology of postconcussive symptoms is equivocal given mixed neuroimaging findings (i.e., increases and decreases in metabolism and perfusion levels in mTBI participants, absence of consistent or hallmark structural changes, and varied findings regarding axonal and gray matter changes in mTBI; Eierud, et al., 2014; Wintermark Sanelli, Anzai, Tsiouris, & Whitlow, 2015). Moreover, research based on DTI and MRI imaging techniques have examined biomarkers associated with mTBI samples; however, they have yet to establish clinical or neurocognitive correlations with statistically significant neuroimaging results (Bigler, 2013). Concurrent investigations regarding the strongest predictors of outcome in mTBI have consistently found that injury severity factors, which have proven useful in predicting moderate and severe TBI outcomes, have poorly generalized as predictors of ongoing sequelae following mTBI (Carroll, Cassidy, Holm, Kraus, & Coronado, 2004; Ponsford et al., 2000). Regarding cognitive outcomes, civilian and veteran research on the unique influence of mTBI on cognitive functioning has highlighted poor relationship between self-reported neurobehavioral symptoms and objective neuropsychological data, regardless of the mechanism of injury (i.e., blunt force, blast, motor vehicle accident, or falls) (Belanger et al. 2011; Cooper, Chau, Armistead-Jehle, Vanderploeg, & Bowels, 2012; French, Lange & Brickell, 2014; Rohling et al. 2011; Spencer, Drag, Walker, & Beiliauskas, 2010; Stulemeijer, Vos, Bleijenberg, & van der Werf, 2007; Verfaellie, Lafleche, Spiro, & Bousquet, 2014). As such, a great extent of the literature on mTBI has focused on psychological etiologies of PCS and clinical outcomes, for which there is a large amount of empirical support (Ryan & Warden, 2003). PCS following mTBI has been linked to non-specific and non-injury related factors among veterans with mTBI (i.e., unemployment, academic difficulties, social and interpersonal challenges, posttraumatic stress disorder (PTSD) and other mood disorders, sleep disturbance, and/or physiological factors such as pain). Within the neuropsychological community, the role of PTSD as factor influencing the cognitive, physiological, social, and psychological functioning in veterans with mTBI has also been well established. Researchers have found that the magnitude, and in some studies the presence, of an association between mTBI and PCS diminishes when emotional distress and PTSD severity are accounted for (Belanger, Spiegel & Vanderploeg, 2010; Vanderploeg, Belanger, & Curtiss, 2009). Furthermore, PTSD has been identified as a better predictor of PCS symptomology than mTBI (Schneiderman, Braver, & Kang, 2008). Studies using community, college, and veteran concussion samples have also linked exacerbations in subjective cognitive difficulties to the presence of chronic pain (McCracken & Iverson, 2001) and sleep disturbance (Combs et al. 2015; Martindale, Morrissette, Rowland, & Dolan, 2017; Orff, Drummond, Nowakowski, & Perlis, 2007; Ruff, Reichers, Wang, Piero, & Ruff, 2012). Moreover, a number of researchers have postulated that an accumulative or “mutually exacerbating” relationship exists for veterans’ with PCS in which medical/health, emotional, cognitive, and psychosocial (vocational, financial, and recreational) functioning include stressors that magnify premorbid factors and result in greater levels of disability (see Brenner et al., 2009). A number of veteran and civilian studies have also examined the contribution of premorbid factors to cognitive and functional outcomes following a mTBI (Carroll et al., 2004; Kashluba, Paniak, & Casey, 2008; McLean et al., 2009; Meares et al., 2008; Merritt & Arnett, 2014; Wood, 2004; Yeates et al. 2012). Stulemeijer and colleagues (2007) found that premorbid factors such as educational level, personality characteristics, and premorbid emotional functioning may be better indicators of self-reported wellbeing following concussion; whereas Ponsford, Cameron, Fitzgerald, Grant, and Mikocka-Walus (2012) found that premorbid physical or psychiatric problems were most predictive of PCS in a 3-month follow up of mTBI patients. Indeed, the common finding among these studies is that a significant relationship exists between premorbid factors and PCS, highlighting the importance of early identification and early provision of therapeutic and psychoeducational management to reduce level and severity of disability (2012). Investigations of premorbid cognitive ability as a relevant predictor of PCS in mTBI have also established a relationship between measures of estimated premorbid IQ, particularly the Wechsler Test of Adult Reading (WTAR; The Psychological Corporation, 2001), due to its resistance to injury and stability over time in adulthood. Meares and colleagues (2008) examined predictors of acute PCS, including full scale IQ, pre-injury anxiety, pre-injury substance use disorder, pain, and a dichotomous grouping variable comprised of (1) those who met the WHO criteria for mTBI and (2) a control group that did not. Among a mTBI population evaluated within 14 days of the injury, the study found that the strongest effect for PCS was previous affective or anxiety disorder. Pain, female gender, higher IQ, performance on the Symbol Digit Modalities Test, Oral Version (a measure of response speed), and acute posttraumatic stress were predictors of acute PCS; however, how these results generalize to persistent PCS symptoms is unclear, as the study focused on acute PCS. Larson, Zollman, Kondiles, and Starr (2013) examined the relationship between PTSD, memory, and PCS among veterans with adequate performance validity. They found that after controlling for premorbid IQ, as measured by the WTAR standard scores, severity of PTSD and severity of PCS complaints were not associated with cognitive functioning. While the study confirmed the influence of premorbid IQ in nullifying the relationship between PTSD and PCS severity, one of the factors suggested in the burden of adversity hypothesis, the authors note that further studies were needed to confirm the role of other relevant factors that are included in the burden of adversity hypothesis (e.g., polytraumatic sequelae, vocational, financial, and recreational challenges, and premorbid psychiatric history). Rabinowitz and Arnett (2013) also included premorbid IQ in their examination of cross-test neuropsychological intraindividual variability and level of performance in concussed, collegiate athletes, and found that while premorbid IQ was related to level of performance, it did not influence the relationship between intraindividual variability and post-concussion cognitive dysfunction. Combs and colleagues (2015) also examined baseline premorbid IQ group differences in PTSD only, mTBI only and mTBI with PTSD groups, and found significant difference between veteran control group and the mTBI with PTSD group, in that the veteran control group demonstrated consistently higher education levels and predicted FSIQ than the mTBI with PTSD group. However, the relationship between premorbid IQ and PCS has not yet been examined alongside other relevant factors that have been found to be related to PCS. The aim of this study was to examine the relationship between premorbid estimates of IQ and persistent PCS as endorsed on the Neurobehavioral Symptom Inventory (NSI; Cicerone & Kalmar, 1995). It was hypothesized, on the basis of previous studies examining post-injury factors, that emotional, sleep, pain factors, and educational level, but not objective cognitive measures, would significantly predict PCS severity. Second, we hypothesized, based on previous studies examining premorbid factors, that estimates of premorbid IQ would significantly predict postconcussive symptom severity above and beyond psychosocial, educational, and objective cognitive functioning factors. Estimated premorbid IQ was measured by the Wechsler Test of Adult Reading (WTAR; The Psychological Corporation, 2001), due to its resistance of injury and stability over time in adulthood. While poor outcomes in mTBI are often discussed as being associated with lower IQ (Stulemeijer et al. 2007), and it is traditionally believed that premorbid functioning is influential in postmorbid adjustment (McLean et al., 2009; Meares et al., 2008), the predictive significance of premorbid IQ, after accounting for common comorbidities and neuropsychological performance, has yet to be established in the literature. Certainly, for clinicians who perform post-injury evaluations in mTBI populations, the identification of salient factors that predict poor outcomes would foster more rapid identification of at risk veterans and timely directing of treatment, which may ultimately prevent future disability related to chronic PCS. Method Participants The present IRB approved, retrospective study examined a sub-sample of a larger archival database of U.S. veterans referred for outpatient neuropsychological evaluation at a VA Medical Center. Participants only included veterans, who were referred for neuropsychological consultation at least 6 months following a suspected or confirmed mTBI (n = 76). Of the 76 participants, individuals were excluded based on the following criteria: (1) failure of performance validity measures, which was measured by a Wechsler Adult Intelligence Scale, Fourth Edition, Reliable Digits score of less than six (n = 14, 18% of the original sample), (2) evidence of moderate or severe TBI, as defined by the American Congress of Rehabilitation Medicine (ACRM; Mild Traumatic Brain Injury Committee, 1993) guidelines for mTBI (n = 3, 4% of original sample), (3) absence of reported mTBI, (4) diagnosed serious mental illness such as a Bipolar or Psychotic Spectrum Disorder, (5) neurological illness/injury (n = 1, seizure disorder), or (6) significant medical diagnosis (i.e., cardiac, pulmonary, hepatic or renal disease). Additionally, missing data was excluded listwise as a function of the regression analysis (n = 27, 36% of the sample), rather than utilizing a permutation method, which has the potential to artificially reduce the variance within each variable. Thirty-one veterans met inclusion criteria for study participation. Demographic data are described in Table 1. Table 1 Profile of veteran participants Demographics Mean (SD) Age (years) 33.45 (9.76) Education (years) 12.9 (2.08) Time since injury (years) 9.64 (7) Gender Number (percentage) Male 28 (90.3%) Female 3 (9.7%) Ethnicity White 27 (87.1%) Latino/Hispanic 2 (6.5%) Black 2 (6.5%) Cause of injury Blast 10 (31.3%) MVA 8 (25%) Trauma/Impact 5 (15.7%) Fall 4 (12.5%) Assault 4 (12.5%) PTA < 24 h 7 (22.5%) LOC 21 (67.7%) # of Lifetime concussions 1 10 (32.3%) 2 8 (25.8%) 3 or more 13 (41.9%) Current PTSD Dx 14 (45.2%) Current Depression Dx 10 (32.3%) Pending VA disability claim Yes 4 (12.9%) No 25 (80.6%) Unknown 2 (6.5%) Service connection % 0 11 (35.4%) 10–50 4 (12.9%) 60–100 12 (38.8%) Unknown 4 (12.9%) Demographics Mean (SD) Age (years) 33.45 (9.76) Education (years) 12.9 (2.08) Time since injury (years) 9.64 (7) Gender Number (percentage) Male 28 (90.3%) Female 3 (9.7%) Ethnicity White 27 (87.1%) Latino/Hispanic 2 (6.5%) Black 2 (6.5%) Cause of injury Blast 10 (31.3%) MVA 8 (25%) Trauma/Impact 5 (15.7%) Fall 4 (12.5%) Assault 4 (12.5%) PTA < 24 h 7 (22.5%) LOC 21 (67.7%) # of Lifetime concussions 1 10 (32.3%) 2 8 (25.8%) 3 or more 13 (41.9%) Current PTSD Dx 14 (45.2%) Current Depression Dx 10 (32.3%) Pending VA disability claim Yes 4 (12.9%) No 25 (80.6%) Unknown 2 (6.5%) Service connection % 0 11 (35.4%) 10–50 4 (12.9%) 60–100 12 (38.8%) Unknown 4 (12.9%) Note: PTA = posttraumatic smnesia less than 24 h; LOC = loss of consciousness; number of lifetime concussions = number of reported mTBI’s prior to neuropsychological testing for most recent mTBI. Measures The dependent variable, PCS, was measured using the Neurobehavioral Symptom Inventory (NSI), which requires participants to rate the presence/severity of 22 items, which represents a unique symptom, on a 5-point scale (0 = none, and 4 = very severe). While factor analyses have demonstrated four (e.g., cognitive, affective, vestibular, and somatosensory) and six (e.g., cognitive, vestibular, affective, sensory, numbness, and anxiety) non-overlapping factor structures (Cicerone & Kalmar, 1995; Meterko et al., 2012), the present study used the NSI total score. The following measures were examined as potential predictors of PCS: Psychosocial Factors The Pittsburgh Sleep Quality Index (PSQI; Buysse, Reynolds, Monk, Berman & Kupfer, 1989), a 19 item self-rated questionnaire that assesses sleep quality and disturbances over a one-month interval, was used as a measure of subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, and sleep disturbance. While scoring methods have been established by the authors of the instrument, for the purposes of this study in the sleep quality global composite score was derived by summing all items rated on the 0–3 scale. The PTSD Checklist-Civilian Version (PCL-C) is a 17-item self-report survey of PTSD symptoms based on Diagnostic and Statistical Manual of Mental Disorders-Fourth Revision diagnostic criteria. Respondents are asked to rate the frequency of PTSD-related symptoms on a 5-point scale (1 = not at all and 5 = extremely) over the course of a 1-month period (Weathers, Huska, & Keane, 1994). Depression was measured using the Beck Depression Inventory, Second Edition (BDI-II; Beck, Steer & Brown, 1996), which consists of 21 groups of statements describing symptoms of depression. Total scores range from 0 to 63. The Brief Pain Inventory (BPI) is a self-report questionnaire that measures level of pain experience daily using a scale 11 point scale (0 = no pain and 10 = as bad as pain you can imagine). A pain severity index was calculated by summing items three through six of the BPI (Cleeland, 1991). Cognitive Factors The Attention, Immediate Memory, and Delayed Memory Indices from the Repeatable Battery for the Assessment of Neuropsychological Status – Update Form A (RBANS; Randolph, 1998) provided objective data regarding neuropsychological functioning across the cognitive domains that have been found among the literature to be most sensitive to mTBI (Karr, Areshenkoff, & Garcia-Barrera, 2014). Reliable Digit Span (RDS; Boone, 2007) captured performance validity. Premorbid Factors The Wechsler Test of Adult Reading (WTAR) standard score was used as a measure of premorbid IQ, due to its resistance to injury and stability over time in adulthood, compared to other cognitive variables that are affected by brain injury. Level of education, measured by the self-reported number of formally completed years of education, was also included as a predictor in the model, given its frequent use as a proxy for premorbid IQ (Griffin, Mindt, Rankin, Ritchie, & Scott, 2002). Education is also established to have a correlation with measures of PCS in that lower education has been found to be associated with higher endorsements of symptoms on measures of PCS (Sullivan, Edmed, Allan, Smith & Karlsson, 2015). Therefore, it was important to account for the influence of education on PCS, when examining the unique effect of empirically validated measure of premorbid IQ. Data Analyses Data were analyzed using IBM SPSS Statistics Version 19 (2010). Descriptive statistics were used to describe the demographic and mTBI injury characteristic of participants. A series of multivariable linear regressions were conducted once it was determined a priori that the assumptions for linear regression were met. Multicollinearity statistics were examined to ensure there were no intercorrelations that might inflate the regression results (Table 2). Table 2 Descriptive statistic for predictor and outcome variables Mean SD Range Skewness Kurtosis NSI 39.36 17.61 4–70 −.034 −.69 BDI-II 22.13 11.51 4–45 .51 −.57 PCL-C 52.77 14.92 23–79 .156 −.71 PSQI 14.12 4.76 4–28 .402 1.252 BPI 20.48 9.68 5–59 1.85 7.82 RBANS Attention 83.13 18.76 53–132 .228 .363 RBANS Immediate Memory 94.19 17.53 57–126 .159 −.756 RBANS Delayed Memory 88.84 19.61 48–122 −.709 −.116 Education level 12.90 2.119 10–19 1.30 1.08 WTAR SS 103.32 11.51 79–120 −.52 −.69 Mean SD Range Skewness Kurtosis NSI 39.36 17.61 4–70 −.034 −.69 BDI-II 22.13 11.51 4–45 .51 −.57 PCL-C 52.77 14.92 23–79 .156 −.71 PSQI 14.12 4.76 4–28 .402 1.252 BPI 20.48 9.68 5–59 1.85 7.82 RBANS Attention 83.13 18.76 53–132 .228 .363 RBANS Immediate Memory 94.19 17.53 57–126 .159 −.756 RBANS Delayed Memory 88.84 19.61 48–122 −.709 −.116 Education level 12.90 2.119 10–19 1.30 1.08 WTAR SS 103.32 11.51 79–120 −.52 −.69 Note: NSI = Neurobehavioral Symptom Inventory; BDI-II = Beck Depression Inventory, Second Edition; PCL-C = Posttraumatic Stress Disorder Checklist, Civilian Version; PSQI = Pittsburg Sleep Quality Index; RBANS = Repeatable Battery for the Assessment of Neuropsychological Status, Total Index Standard Score; RDS = Reliable Digits Span; WTAR SS = Wechsler Test of Adult Reading. Results Correlational analyses demonstrated significant associations between the outcome variable and all of the predictors included in the model except the RBANS Immediate and Delayed Memory Indices (see Table 3). Results were considered significant and indicative of collinearity if the variance inflation factor (VIF), which is the amount of inflation in the variance accounted for by a variable due to its linear dependence on other predictors, was greater than or equal to four. Multicollinearity (i.e., VIF) statistics did not indicate the presence of significant intercorrelations (see Table 4). Table 3 Correlations between the independent and outcome variables Composite variables 1 2 3 4 5 6 7 8 9 1. NSI — 2. BDI-II .723** — 3. PCL-C .718** −.692** — 4. PSQI .325* .360* .346* — 5. BPI .419** .169 .354* .277 — 6. RBANS Attention −.305* .012 −.240 .060 −.132 — 7. RBANS Imm Mem −.341* −.040 −.372* −.082 −.297 .388* — 8. RBANS Del Mem −.044 .170 .013 .215 .141 .457** .600** — 9. Education Level −.456** −.295* −.435** −.300* −.402* .293 −.098 −.020 — 10. WTAR SS −.631** −.378* −.321* −.263 −.512** .356* .013 −.186 .414** Composite variables 1 2 3 4 5 6 7 8 9 1. NSI — 2. BDI-II .723** — 3. PCL-C .718** −.692** — 4. PSQI .325* .360* .346* — 5. BPI .419** .169 .354* .277 — 6. RBANS Attention −.305* .012 −.240 .060 −.132 — 7. RBANS Imm Mem −.341* −.040 −.372* −.082 −.297 .388* — 8. RBANS Del Mem −.044 .170 .013 .215 .141 .457** .600** — 9. Education Level −.456** −.295* −.435** −.300* −.402* .293 −.098 −.020 — 10. WTAR SS −.631** −.378* −.321* −.263 −.512** .356* .013 −.186 .414** Note: *Correlation is significant at the .05 level (2-tailed). **Correlation is significant at the .01 level (2-tailed). NSI = Neurobehavioral Symptom Inventory; BDI-II = Beck Depression Inventory, Second Edition; PCL-C = Posttraumatic Stress Disorder Checklist, Civilian Version; PSQI = Pittsburg Sleep Quality Index; RBANS Imm Mem = Repeatable Battery for the Assessment of Neuropsychological Status, Immediate Memory; RBANS Del Mem = Repeatable Battery for the Assessment of Neuropsychological Status, Delayed Memory; Total Index Standard Score; RDS = Reliable Digits Span; WTAR SS = Wechsler Test of Adult Reading. Table 4 Two-step multivariable linear regression Model Adj R2 R2 (sig) B Std. error β t (sig) SR2 (sig) VIF Step 1 .615 .718 (.000) 10.924 BDI-II .773 .254 .505 3.038 (.006) .118 (.006) 2.153 PCL-C .223 .210 .189 1.061 (.300) .014 (.300) 2.467 PSQI .202 .502 .055 .403 (.691) .002 (.691) 1.435 BPI .259 .265 .142 .974 (.341) .012 (.341) 1.659 RBANS Attention −.198 .154 −.211 −1.282 (.213) .021 (.213) 2.109 RBANS Immediate Memory −.009 .148 −.009 −.064 (.949) .000 (.949) 1.684 RBANS Delayed Memory −.057 .144 −.064 −.398 (.695) .002 (.695) 2.003 Education Level −.751 1.120 −.090 −.671 (.509) .006 (.509) 1.416 Step 2 .693 .785 (.018) 9.764 BDI-II .600 .237 .392 2.529 (.020) .066 (.020) 2.344 PCL-C .317 .191 .269 1.660 (.112) .028 (.112) 2.563 PSQI .072 .452 .019 .159 (.875) .000 (.875) 1.453 BPI .086 .247 .047 .350 (.730) .001 (.730) 1.793 RBANS Attention −.085 .145 .015 −.583 (.566) .003 (.566) 2.327 RBANS Immediate Memory .015 .132 −.090 .114 (.910) .000 (.901) 1.693 RBANS Delayed Memory −.142 .133 −.159 −1.072 (.296) .012 (.296) 2.138 Education Level −.265 1.019 −.032 −.269 (.798) .001 (.798) 1.468 WTAR SS −.537 .201 −.351 −2.557 (.018) .067 (.018) 1.838 Model Adj R2 R2 (sig) B Std. error β t (sig) SR2 (sig) VIF Step 1 .615 .718 (.000) 10.924 BDI-II .773 .254 .505 3.038 (.006) .118 (.006) 2.153 PCL-C .223 .210 .189 1.061 (.300) .014 (.300) 2.467 PSQI .202 .502 .055 .403 (.691) .002 (.691) 1.435 BPI .259 .265 .142 .974 (.341) .012 (.341) 1.659 RBANS Attention −.198 .154 −.211 −1.282 (.213) .021 (.213) 2.109 RBANS Immediate Memory −.009 .148 −.009 −.064 (.949) .000 (.949) 1.684 RBANS Delayed Memory −.057 .144 −.064 −.398 (.695) .002 (.695) 2.003 Education Level −.751 1.120 −.090 −.671 (.509) .006 (.509) 1.416 Step 2 .693 .785 (.018) 9.764 BDI-II .600 .237 .392 2.529 (.020) .066 (.020) 2.344 PCL-C .317 .191 .269 1.660 (.112) .028 (.112) 2.563 PSQI .072 .452 .019 .159 (.875) .000 (.875) 1.453 BPI .086 .247 .047 .350 (.730) .001 (.730) 1.793 RBANS Attention −.085 .145 .015 −.583 (.566) .003 (.566) 2.327 RBANS Immediate Memory .015 .132 −.090 .114 (.910) .000 (.901) 1.693 RBANS Delayed Memory −.142 .133 −.159 −1.072 (.296) .012 (.296) 2.138 Education Level −.265 1.019 −.032 −.269 (.798) .001 (.798) 1.468 WTAR SS −.537 .201 −.351 −2.557 (.018) .067 (.018) 1.838 Note: B = unstandardized regression coefficient; Std. error = standard error and standard error of the estimate for Step 1 and Step 2; sig = significance or p-value; R2 = variance accounted for by the model; SR2 = squared semipartial change in R2 due the respective predictor variable; VIF = variance inflation factor; NSI = Neurobehavioral Symptom Inventory; BDI-II = Beck Depression Inventory, Second Edition; PCL-C = Posttraumatic Stress Disorder Checklist, Civilian Version; PSQI = Pittsburg Sleep Quality Index; RBANS Imm Mem = Repeatable Battery for the Assessment of Neuropsychological Status, Immediate Memory; RBANS Del Mem = Repeatable Battery for the Assessment of Neuropsychological Status, Delayed Memory; Total Index Standard Score; RDS = Reliable Digits Span; WTAR SS = Wechsler Test of Adult Reading The overall regression analysis that included the BDI-II, the PCL-C, PSQI, the BPI, RBANS Attention, Immediate Memory, and Delayed Memory Indices, and education level was statistically significant (R2 = .718, F(8, 22) = 6.993, p < .001). Addition of the WTAR in Step 2 significantly increased the amount of variance accounted for by the model beyond what was previously accounted for by psychosocial factors, current cognitive ability, and educational level alone (R2 = .785, F(1, 21) = 6.540, p = .018). Moreover, depression (B = .600; p < .05, partial R2 = .066) was a salient predictor of chronic PCS in that more self-reported depressive symptoms were related to more reported NSI symptoms. Premorbid IQ (B = −.537; p < .05, partial R2 = .067) also significantly predicted chronic PCS, in that higher WTAR standard scores were related to lower NSI endorsements. The PCL-C, PSQI, BPI, RBANS Indices, and education level did not significantly predict PCS (see Table 4). Discussion The results of the study demonstrated that, even after accounting for psychosocial factors, cognitive variables, and education, premorbid IQ was found to be a significant predictor of PCS in veterans with mTBI. Indeed, lower premorbid IQ predicted higher symptom endorsements on the NSI and added a statistically significant amount of predictive variance to the model proposed to explain persistent PCS in veterans. While the findings support premorbid functioning as an important predictor of chronic PCS, and provide empirical validation to conventional wisdom that premorbid functioning predicts postmorbid outcome; the results can be understood from a number of perspectives. First, the WTAR, which is an estimate of premorbid cognitive ability level, was found to predict chronic PCS while education did not. While the present study demonstrated a correlation between education and the NSI, which was consistent with previous research highlighting the educational effects inherent in the NSI (Sullivan et al., 2015), the relationship between education and PCS was attenuated when other salient cognitive, premorbid, and psychosocial variables were included in the model. This finding suggests that the WTAR, as an indicator of pre-concussion functioning, adds to our understanding of postconcussive outcome. It could also be suggested that the relationship is based on the level of intellectual resources an individual has to understand and appreciate their experience of postconcussive symptoms, as well as communicate those symptoms vis-à-vis the structured questions on a measure like the NSI. Individuals with higher IQ may simply be able to communicate those symptoms more concisely. One’s ability to parsimoniously describe, distinguish, and appropriately attribute their symptoms may certainly result in lower symptom endorsements. The therapeutic significance therefore would be adapting postconcussive interventions to better educate veterans about their symptoms, and to adapt reattribution strategies to meet the specific needs of individuals with varying levels of intellect. The findings further highlight premorbid IQ as a source of information regarding an individual’s previous level of functioning that is not attainable through education alone. As such, premorbid IQ may serve as a reference variable for understanding an individual’s ability to adapt and adjust to the stressors presented in the military, at war, and during injury and recovery. Particularly among veterans, the early identification of individuals who may be at risk for developing chronic PCS following mTBI may be important in minimizing ongoing symptomatology. Moreover, higher premorbid functioning, as a proxy for cognitive reserve (Stern, 2009; Sullivan et al., 2015) and resilience (Salmond, Menon, Chatfield, Pickard, & Sahakian, 2006) may serve as a protective factor against chronic PCS. In addition to premorbid IQ, higher levels of depression were related to greater postconcussive symptom endorsements above and beyond PTSD symptoms, sleep disturbance, and pain. Depression may be more heavily predictive of PCS, as measured by the NSI, particularly because the rates of depression within in three months following mTBI range from 12% to 44%t (see Iverson & Lange, 2011). Depression is often associated with cognitive, somatic, and affective symptoms, and the established factor structure of the NSI has an affective factor which is comprised of a high number (e.g., six) of the items (Cicerone & Kalmar, 1995; Meterko et al., 2012). Naturally there exist significant overlap between self-reported symptoms of depression and PCS, which creates challenges for disentangling presenting symptoms. On the other hand, the current results demonstrate depression as a predictor of PCS, providing further evidence of a potential target for treatments to reduce PCS following mTBI. This would be consistent with previous literature (King, Donnelly, Donnelly, Dunnam & Warner, 2012). CBT treatments and antidepressant medications, which have been discussed in the literature, may be beneficial to the extent that they account for the other data point emphasized in the present study, premorbid IQ (see Iverson & Lange, 2011). While PTSD has been heavily studied for its influence on post-injury outcomes, particularly in a sample of veteran’s with mTBI, its predictive influence was attenuated in the present regression model. Pain and sleep were also found to be statistically non-significant. These results were largely consistent with previous literature highlighting a complex interplay between pain severity, sleep quality, and mTBI (Carroll et al. 2004; Chaput, Giguere, Chauny, Denis, & Lavigne, 2009). Consistently in the literature, while greater pain severity and poor sleep have been found to be related to postconcussive symptoms, psychological distress as a construct has been found to be a more potent predictor (Lavigne, Khoury, Chauny, & Desautels, 2015; Towns, Silva, & Belanger, 2015). Though present results supported a statistical association between depression, sleep, and the subjective experience of pain, which essentially reflects the existence of these comorbidities in mTBI populations; the current investigation further demonstrated that depression plays a more significant or principal role as a factor predicting PCS, as it was a more salient predictor than reported levels of pain and sleep. Limitations of the present study must be discussed. Most notably, the sample size was small due to the number of participants who did not complete questionnaires in their entirety or correctly, resulting in missing data. As such, it is possible that the sample preferentially selected individuals who were more invested in the evaluation. As a result, this influences the extent to which these results can be generalized. Such analysis would benefit from further investigation that employs larger sample or uses a resampling technique such as bootstrapping or structural equation modeling. Second, the present study did not include a predictive variable for substance abuse. Because the literature has found substance abuse to be a predictor of post-injury outcomes among veterans with mTBI (Hanson, Schiehser, Clark, Sorg & Kim, 2016), future studies should include level of substance abuse in order to investigate its contribution, alongside premorbid factors, to post-injury outcomes. In addition, as the RBANS is considered a screening battery, findings that are more robust could be demonstrated with a more thorough neuropsychological battery. The present results only included valid neuropsychological profiles rendering performance validity statistically non-significant. The results may not necessarily generalize to mTBI populations for which performance validity is oftentimes an issue. In addition, the present study did not include data regarding compensation, pension, or service connection, which is a commonly considered factor among mTBI populations. Finally, there was not a non-treatment seeking comparison mTBI group, as such the results may not generalized to veteran not seeking an evaluation or treatment. Therefore, future studies should examine premorbid IQ as a predictor of PCS in non-treatment seeking samples, among individuals with both optimal and suboptimal cognitive performance validity, and consider factors associated with litigation or compensation seeking. Within the VA Healthcare System, discourse suggests that factors confounding post-military functioning include level of premorbid functioning (e.g., level of education, parental education, family SES), rank in the military, and pre-military history of trauma (Wilmoth, London, & Parker, 2010). Premorbid factors offer a most important piece of data about how an individual may adjust to civilian life, cope with unwanted stressors (e.g., resiliency), and ultimately function after the military. Clinicians can certainly appreciate that unique comorbidities within the mTBI veteran population can negatively influence post-military functioning, highlighting the importance of the current findings of premorbid IQ as a relevant factor to consider in PCS outcome, particularly regarding resilience and resources to adapt to such comorbidities. While the literature is equivocal regarding the effects of premorbid IQ on treatment outcomes (i.e., evidence for the presence and absence of IQ effects on CBT outcome; Haaga, DeRubeis, Stewart, & Beck, 1991), particularly with more structured CBT treatments, the present findings offer an additional data point in the identification of at risk veterans and appropriations of treatment. The results suggest that depressive symptomology is a particularly relevant target for interventions, and that such interventions may be most effective to the degree that they benefit individuals at varying levels of premorbid IQ. Conflict of Interest None declared. Disclaimer The contents of this publication do not represent the views of the U.S. Department of Veterans Affairs or the United States Government. Acknowledgment This material is the result of work supported with resources and the use of facilities at the Bay Pines VA Healthcare System in Bay Pines, Florida. References Beck, A. T., Steer, R. 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Archives of Clinical Neuropsychology – Oxford University Press
Published: Mar 1, 2018
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