Does history of substance use disorder predict acute traumatic brain injury rehabilitation outcomes?

Abstract

BACKGROUND/OBJECTIVE: The study explored whether premorbid substance use disorder (SUD) predicts acute traumatic brain injury (TBI) outcomes.

METHODS: 143 participants with moderate (34.2%) and severe (65.8%) TBI were enrolled at two Level 1 trauma center inpatient brain injury rehabilitation units. Acute outcomes were measured with the Disability Rating Scale (DRS), the FIM^TM; self and informant ratings of the Patient Competency Rating Scale (PCRS); self and family rating of the Frontal Systems Behavioral Scale (FrSBe), and the Neurobehavioral Rating Scale-Revised (NRS-R).

RESULTS: Hierarchical linear modeling revealed that SUD history significantly predicted trajectories of PCRS clinician ratings, PCRS self-family and PCRS self-clinician discrepancy scores, and more negative FrSBE family ratings. These findings indicate comparatively greater post-injury executive functions (EF) impairments, particularly self-awareness (SA) of injury-related deficits, for those with SUD history. No significant SUD^*time interaction effect was found for FIM or NRS-R scores.

CONCLUSIONS: SUD history and TBI are associated with impaired SA and EF but their co-occurrence is not a consistent predictor of acute post-injury functional outcomes. Pre-morbid patient characteristics and rater expectations and biases may moderate associations between SA and recovery after TBI.

Keywords

Traumatic brain injury substance use disorder rehabilitation

1 Introduction

Substance use disorder (SUD) and time-of-injury intoxication are frequent co-morbid challenges for the approximately 1.7 million persons in the U.S who sustain a traumatic brain injury (TBI) each year and their providers (Andelic et al., 2010; Faul, Xu, Wald, & Coronado, 2010). Available national and international TBI literatures report similar associations between injury and SUD, particularly alcohol use disorder (AUD). as well as the largely negative impact of intoxication at time of injury (Bombardier, Rimmele, & Zintel, 2002; Corrigan, Bogner, & Holloman, 2012; Corrigan & Deutschle, 2008; Corrigan, Lamb-Hart, & Rust, 1995; Corrigan, Smith-Knapp, & Granger, 1998; De Guise et al., 2009; Engberg, 1995; Ingebrigtsen, Mortensen, & Romner, 1998; O’Brien & Phillips, 1996; Parry-Jones, Vaughan, & Miles Cox, 2006; Pickelsimer, Selassie, Sample, Gu, & Veldheer, 2007; Ponsford, Tweedly, & Taffe, 2013; Ponsford, Whelan-Goodinson, & Bahar-Fuchs, 2007; Taylor, Kreutzer, Demm, & Meade, 2003; Vazquez-Barquero et al., 1992; Vickery et al., 2008). Up to 79% of persons with TBI have co-occurring SUD (Taylor, Kreutzer, Dem & Mease, 2003). Up to 50% of persons with TBI presenting for treatment at hospital emergency departments in the United States are intoxicated (Andelic et al., 2010; De Guise et al., 2009; Vickery et al., 2008). International statistics show that 29% of Danish, 51% of Spanish, 31% of Irish, and 24% of Norwegian persons diagnosed with TBI are recorded as being intoxicated at time of injury (Engberg, 1995; Ingebrigtsen, Mortensen, & Romner, 1998; O’Brien & Phillips, 1996; Vazquez-Barquero et al., 1992). Intoxication at time of TBI is known to impact the emergent care and acute phases of recovery (Andelic et al., 2010; De Guise et al., 2009; Vickery et al., 2008). When patients with TBI are identified as intoxicated at time of admission, acute clinical providers observe an impact on presentation as well as hospital course for those patients (Melvan et al., 2013; Ranzer, Chen, & DiPietro, 2011; Rendon, Li, Akhtar, & Choudhry, 2013; Wiener et al., 2010; Zehtabchi, Sinert, Baron, Paladino, & Yadav, 2005). For example, depending on patient age, the trauma surgery literature documents an association between acute alcohol intoxication (Blood alcohol level [BAL] >0.08) and post-traumatic complications such as greater resuscitation and operative requirements, more vascular complications, increased length of stay (Faul et al., 2010; Melvan et al., 2013; Ranzer et al., 2011; Rendon et al., 2013; Wiener et al., 2010; Zehtabchi, Sinert, Baron, Paladin, & Yadav, 2005), increased infection due to enhanced gut permeability (Rendon et al., 2013), increased acidosis (Zehtabchi et al., 2005), and poorer wound healing (Ranzer et al., 2011). Additionally, the combination of alcohol and cocaine results in an increased need for ICU admission, independent of TBI severity (Wiener et al., 2010).

Despite the statistics related to frequency and impact of co-occurrence of intoxication, pre-injury SUD history, and TBI, research findings related to the impact of history of SUD on acute and long-term outcomes are mixed (Parry-Jones, Vaughn, Miles-Cox, 2006). In some cases researchers have found that SUD is a significant and negative predictor of especially long-term outcomes such as employment patterns, particularly for patients 50 years and younger (Cuthbert et al., 2015; Cuthbert, Pretz et al., 2015; Olson-Madden, Brenner, Corrigan, Emrick, & Britton, 2012). In other studies findings have not supported strong evidence of influence of SUD history on acute or post-injury outcomes (Barnfield & Leathem, 1998; De Guise et al., 2009; Vickery et al., 2008).

The TBI literature does provide evidence of a close association between SUD and TBI. High percentages of persons with TBI have a history of alcohol abuse (up to 79%) or of illicit drug use (up to 33%) (Taylor et al., 2003). Researchers have also studied persons who sustain TBI when under the influence of alcohol. For example, Vaaramo and colleagues (Vaaramo, Puljula, Tetri, Juvela, & Hillbom, 2014) found that intoxicated persons who sustain a TBI are at increased risk of subsequent TBIs. After TBI, individuals with premorbid substance abuse are at increased risk for relapse, but a proportion without a premorbid history also go on to develop substance abuse (Corrigan, 1995; Taylor et al., 2003). Therefore, TBI and SUD may be risk factors for each other. Winqvist and colleagues, (Winqvist et al., 2008) reported that alcohol-related TBI risk remains for several years after the first injury.

The brain injury literature chronicles several factors that may contribute to inconsistency of findings related to effect on outcomes of preinjury SUD. First, methodologies of assessing for and identifying substance use/abuse history vary from one investigator and facility to the next (Ashman, Schwartz, Cantor, Hibbard, & Gordon, 2004). Differences in assessor choice of measures as well as timing of assessments further muddy the water in terms of interpretation of impact on recovery after TBI. As well, researchers have found that reliability, validity, and applicability of existing assessment tools vary depending on SUD subpopulation or setting of persons being studied. For example, The CAGE questionnaire has been widely used for detecting alcohol abuse and dependence but, in at least one study, did not screen well for women or college students or for heavy drinkers (Dhalla & Kopec, 2007). In addition, with acute and polytrauma TBI patients the standard Michigan Alcohol Screening Test (MAST) is long and complex for consistent clinical use (Storgaard, Nielsen, & Gluud, 1994). The Alcohol Use Disorders Identification Test (AUDIT) is primarily recommended for use with general and primary care medical patient populations (Berner, Kriston, Bentele, & Harter, 2007). Few studies have been conducted comparing the feasibility, appropriateness, sensitivity and specificity of the multiple existing measures with an acute TBI sample. An exception was a 2004 comparison between the CAGE, Brief MAST, and the Substance Abuse Subtle Screening Inventory (SASSI) that found evidence for superior sensitivity of the CAGE with post-acute persons with TBI (Ashman et al., 2004). The range of existing instruments and varying applicability depending on population characteristics may lead to problems with sensitivity and specificity, especially given potential unreliability of self-report by acute TBI patients.

The clinically challenging heterogeneity of the TBI population creates barriers to parsing out the effects of preinjury SUD vs. TBI-related symptoms and/or premorbid characteristics on acute and long-term outcomes (Corrigan & Hammond, 2013). As noted by Corrigan in 1995, subgroups of persons with more severe SUD who have not been identified within TBI study samples may skew existing findings that suggest pre-injury history of SUD predicts post-TBI outcomes (Corrigan, 1995). Table 1 below provides a number of variables related to associations between SUD and TBI that may moderate recovery and community readjustment trajectories, as well as implications of the mixed research findings about the strengths of these associations.

There is a lack of consistency of measurement and recording of BALs at time of acute hospital admission for TBI (Andelic et al., 2010). If intoxication and type of substance ingested are not identified initially, efforts to determine variables that are associated with comorbidities and most predictive of recovery trajectories after TBI are complicated. Finally, the complexity of relationships between a range of TBI and SUD variables and outcomes or risks for comorbidities further points to the need for ongoing systematic research into the impact of preinjury SUD on TBI recovery and outcomes.

In summary, the TBI literature is mixed related to impact of preinjury SUD on outcomes (Andelic et al., 2010; Corrigan, 1995; De Guise et al., 2009). Further research is needed to clarify the underlying complexities in the inconsistencies (Parry-Jones et al., 2006; Pickelsimer et al., 2007; Ponsford, Tweedly, & Taffe, 2013; Ponsford, Whelan-Goodinson, & Bahar-Fuchs, 2007; Taylor et al., 2003; Vickery et al., 2008). The current project aimed to address the above need by exploring the potential impact of a history of premorbid SUD or AUD on inpatient rehabilitation outcomes through a secondary analysis of prospectively collected data from a randomized controlled trial (RCT). The acute sample of patients with moderate and severe TBI in this RCT was thought to allow study of history of SUD and its possible effect on inpatient outcomes with somewhat greater control of variability. The inclusion and exclusion criteria for enrollment in the RCT reduced within group and sample variability related to injury severity, severity of co-morbid psychiatric disorders, and medical stability. The current study examined potential effects of pre-injury SUD on early functional, neurobehavioral and cognitive status following TBI. Based on the above review of relevant literature our hypothesis was: Compared to persons with a TBI and no history of SUD/AUD, persons undergoing acute brain injury rehabilitation with pre-injury history of SUD will have poorer functional, neurobehavioral, and cognitive status from admission to discharge measurement occasions.

2 Method

2.1 Subjects

One hundred forty-three persons with moderate (34.2%) and severe (65.8%) TBI were consented and enrolled in order of admission for a randomized controlled trial (RCT) of effectiveness of an acute neurobehavioral and cognitive intervention (Niemeier, Kreutzer, Marwitz, Gary, & Ketchum, 2011). The RCT took place at two level one trauma centers, in acute brain injury rehabilitation units. The study enrollment occurred from 2009 to 2012 at the first site. The study was transferred to the second site where enrolment continued until January 1, 2014. Causes of injury were vehicular crash (62.5%), fall (20.4%), assault (6.6%), hit as pedestrian (4.6%), and other unspecified (5.9%). During their intensive care stays, prior to transfer to inpatient rehabilitation, 41.4% of study participants had neurosurgical intervention and 58.6% did not. These procedures included placement of intracranial pressure monitors, and occasionally craniotomy. The mean Glasgow Coma Scale (GCS) (Edwards, 2001; Teasdale & Jennett, 1976) score for participants was 8.15 (standard deviation [SD] = 4.55) with a minimum score of 3 and a maximum of 15. Over the course of the study, at both sites, a total of 19 patients in the sample were admitted to the emergency departments with initial GCS scores of 15. However, following their admissions these persons were reclassified as “moderately” vs. “mildly” injured by the brain injury inpatient attending physicians at both sites when 1) diagnostic imaging uncovered presence of a skull fracture or small intracerebral bleed, 2) the patient’s cognitive status significantly deteriorated after admission to the Emergency Department (ED) or acute rehabilitation, or 3) both. This reclassification to complicated mild or moderate is supported by the scientific literature showing differences between persons with “high risk” or complicated mild TBI and uncomplicated mild TBI in both symptoms, length of symptom duration, and outcomes (Hsiang, Yeung, Yu, & Poon, 1997; Iverson, 2006; Kashluba, Hanks, Casey, & Millis, 2008; Williams, Levin, & Eisenberg, 1990).

Length of stay mean for the entire sample was 16.82 days (SD = 11.04) with a maximum stay of 56.00 days. Since the participants were originally enrolled in a prospective RCT of a neurobehavioral and cognitive intervention, data about history of SUD or AUD was collected prospectively during the trial from subjects’ medical records, specifically from their History and Physical forms in their medical charts. Attending physicians at both RCT study sites followed National Institute on Drug Abuse (NIDA), Centers for Disease Control (CDC), and Diagnostic and Statistical Manuals – IV and V (DSM IV and V) guidelines (National Institute on Alcohol Abuse and Alcoholism, 2015; American Psychiatric Association, 1994, 2013; Centers for Disease Control and Prevention [CDC], 2015) related to amount of consumption when determining presence or absence of history of SUD at admission to acute rehabilitation. At both sites, physicians entered “yes” to presence of pre-injury SUD and AUD if the medical history and current interview factors listed in Table 2 were present.

The investigators also collected data on level of intoxication when recorded at time of injury. Information about intoxication was inconsistently recorded with regard to type of substance or substances the patients were intoxicated by and BALs. However, 37.5% of participants were characterized by emergency department provider chart notes as “intoxicated” on alcohol or polysubstances at time of injury. Given the inconsistency of recording of BAL and lack of evidence that intoxication was correlated with history of substance abuse, we did not include the variable “intoxication” in our analyses.

2.2 Measures

Measures given at baseline, at discharge, and at six-month post-discharge follow-up included The Disability Rating Scale (Gouvier, Blanton, LaPorte, & Nepomuceno, 1987; Rappaport, Hall, Hopkins, Belleza, & Cope, 1982), the FIM^TM (Corrigan, Smith-Knapp, & Granger, 1997; Hall, Bushnik, Lakisic-Kazazic, Wright, & Cantagallo, 2001; Hall, Hamilton, Gordon, & Zasler, 1993), self, family, and clinician ratings of the Patient Competency Rating Scale (Kolakowsky-Hayner, Wright, & Bellon, 2012; Sveen, Mongs, Roe, Sandvik, & Bautz-Holter, 2008); self and family rating of the Frontal Systems Behavior Scale (Grace & Malloy, 2001; Hicks et al., 2013; Lane-Brown & Tate, 2009; Stout, Ready, Grace, Malloy, & Paulsen, 2003), and the Neurobehavioral Rating Scale (McCauley et al., 2001; Rapoport, McCauley, Levin, Song, & Feinstein, 2002; Soury et al., 2005; Vanier, Mazaux, Lambert, Dassa, & Levin, 2000). The scores of these measures were later analysed to determine if there were associations between history of substance abuse and patient functional and neurobehavioral status at these three time points.

2.3 Disability Rating Scale (DRS)

The DRS was developed to measure functional status over time with persons undergoing rehabilitation for traumatic brain injury. It is a validated rehabilitation clinical tool for acute as well as post-acute settings for persons who have sustained moderate and severe TBI (Gouvier et al., 1987; Rappaport et al., 1982). The Scale contains 8 items which describe patient status in eye opening, communication, motor response; awareness of how to perform grooming, feeding, and toileting, as well as more general activities of daily living and employability skills (Gouvier et al., 1987; Rappaport et al., 1982). The DRS has been shown to reliably measure a range of severity of disability as well as accurately measure incremental acute patient improvement (Gouvier et al., 1987; Rappaport et al., 1982).

2.4 Functional Independence Measure (FIM™)

The FIM^™ is an 18-item measure that is widely used in inpatient rehabilitation settings to quantify brain injury outcome and treatment response (Corrigan, Smith-Knapp, & Granger, 1997; Hall et al., 1993, 2001). The FIM™ yields a total score and separate scores for motor and cognitive functioning, with higher values denoting greater degrees of independence. Scores on the measure reflect burden of care in that it asks how much assistance a person needs to complete a range of daily living tasks. FIM™-certified interdisciplinary team members rated levels of patient functional ability and burden of care at Time 1 and Time 2 (baseline and discharge) using the 7-point scale in the areas of self-care, continence, mobility, communication and cognition (Corrigan et al., 1997; Hall et al., 1993, 2001). Reliability of the FIM™ total score as well as the subscales and individual items is acceptable, ranging from 0.92– 0.95 for the total score and from 0.61– 0.95 for subscales (Corrigan et al., 1997; Hall et al., 1993). The FIM™ was chosen to measure acute outcomes in this study because of its reliability as a clinician-rated measure of acute functional status during and at the conclusion of rehabilitation (Hall et al., 1993).

2.5 Patient Competency Rating Scale (PCRS)

The PCRS is a 30-item scale which was developed to measure the executive functions attribute, SA, in persons with brain injury. Awareness of one’s limitations and injury-related deficits is often impairedafter TBI (Kolakowsky-Hayner et al., 2012; Sveen et al., 2008). The items on the PCRS ask persons with TBI to rate their ability to perform several daily and independent living tasks. A parallel form for informants (family members, caregivers, or clinicians) asks for observers’ perceptions of the patient’s competency on the same tasks. The self-report of competencies is then compared to the family and clinician ratings. The amount of discrepancy between self- and informant-rating scores is used as a measure of the patient’s awareness (Kolakowsky-Hayner et al., 2012; Sveen et al., 2008). The patient and informants are asked to rate from 1, “I can’t do it,” to 5, “I can do it easily.” Examples of rated competencies include scheduling of daily tasks, keeping appointments, and participating in group activities. The reliability and validity of the PCRS is well established (Kolakowsky-Hayner et al., 2012). Factor analysis has identified several domains of activities within the PCRS including instrumental activities of daily living (IADLs), cognitive abilities, and social and emotional competencies (Kolakowsky-Hayner et al., 2012). The current study used discrepancies between clinician and patient, and between patient and family scores on the PCRS as measures of SA at Time 1 and Time 2.

2.6 Frontal Systems Behavior Scale (FrSBe)

The FrSBe (Grace & Malloy, 2001; Hicks et al., 2013; Lane-Brown & Tate, 2009; Stout et al., 2003) is a 46-item self-report measure with a foundational history in neurology, neuropsychiatry, and brain-behavior literature. In particular, developers were interested in tracing emotional and neurobehavioral deficits after brain injury, especially of the frontal lobes. Common post brain injury neurobehavioral and emotional disorders are reflected in the 46 Likert-type items and three subscales of the FrSBe (Grace & Malloy, 2001; Lane-Brown & Tate, 2009; Stout et al., 2003). The FrSBe has primarily been used in post-acute settings. The patient and an informant are thus asked to rate the patient’s competency in daily life activities following brain injury with more of a general neurobehavioral than task-specific focus, such as in the PCRS (Grace & Malloy, 2001). In addition, in contrast to the PCRS, the FrSBe asks for a conceptually more difficult comparison between how someone was before and then after their injury on each item. For example, the FrSBe self- and informant-forms ask how true behavioral descriptions like “I laugh or cry too easily” or “I do things impulsively” are of the person before as compared to after their injury. Similar to the PCRS discrepancy score between the informant and patient scores, the FrSBe discrepancy scores are used as a measure of patient SA. Patients and family members, but not clinician informants, completed the FrSBe in the current study at Time 1 and Time 2. The FrSBe is used to measure neurobehavioral symptoms and to track improvement in these symptoms over time for a wide range of neurological and medical patient populations. Internal consistency and test- retest reliability have ranged from 0.78 to 0.95 for both acute and post-acute patients. Factor analysis of the family ratings yielded three factors represented by three subscales (Apathy, Disinhibition, and Executive Dysfunction (Grace & Malloy, 2001; Lane-Brown & Tate, 2009; Stout et al., 2003). The FrSBe was recommended for inclusion in the common data elements to more consistently measure neurobehavioral deficits and outcomes in recovery from TBI by the Interagency Traumatic Brain Injury Outcomes Workgroup (Grace & Malloy, 2001).

2.7 Neurobehavioral Rating Scale-Revised (NRS-R)

The NRS-R is a 29-item scale used to measure neurobehavioral disturbances resulting from acquired brain injury (McCauley et al., 2001; Rapoport et al., 2002; Soury et al., 2005; Vanier et al., 2000). Items address orientation, memory, attention, abstract thinking, planning, emotional status, and behavior. Assessors rate each item on a 4-point Likert-type scale ranging from 1 (absent) to 4, indicating severe impairment. Ratings for each item are summed to yield a total score. In addition, a factor analytic investigation revealed three independent factors comprising three scales: Cognitive, Emotional and Hyperarousal (Soury et al., 2005). Research has indicated acceptable inter-rater reliability and criterion related validity (McCauley et al., 2001; Rapoport et al., 2002; Soury et al., 2005; Vanier et al., 2000).

2.8 Data analyses

To determine whether patients with a history of SUD differed in demographic characteristics from those without, analyses of variance (ANOVAs) were run for continuous demographic variables and chi-square tests for categorical variables. All demographic variables that differed between patients with and without a history of SUD were entered as covariates into the following analyses. Twelve hierarchical linear models (HLMs) (Kwok et al., 2008) were performed to examine whether linear trajectories of TBI outcomes across the three time points (baseline, discharge, and follow up) could be predicted by history of SUD. These outcomes included the DRS; FIM motor and cognitive; self, family, and clinician ratings of the PCRS; self and family ratings of the FrSBe; NRS; PCRS self-family and self-clinician discrepancies; and the FrSBe self-family discrepancy. History of SUD (0 = no SUD, 1 = SUD), time, and the SUD^*time interaction were entered as fixed effects into each HLM, in addition to the covariates. TBI outcome scores at each of the three time points (except the PCRS which was collected at baseline and discharge) were entered as the dependent variables. Note that for simplicity, only the effects of SUD and the SUD^*time interaction term are reported.

Statistically significant fixed effects on outcome trajectories were then graphed across each of the time points. Main effects would indicate that outcome scores over time vary as a function of participants’ history of SUD. Significant time^*SUD interaction terms would indicate the effect of SUD on outcome trajectories changed differentially over time (i.e., differed in slope). Missing data ranged from 2.6% (PCRS – Self) to 13.8% (FrSBe – Self) at Time 1, 5.3% (FIM – Cognitive) to 21.1% (FrSBe Self-Family Discrepancy) at Time 2, and 32.9% (FrSBe – Self and FIM – Cognitive) to 41.4% (DRS) at Time 3. Full information maximum likelihood (FIML) estimation was used to account for missing data. The alpha level of was set at α= 0.05, and all analyses were performed using SPSS v. 22 with the raw scores of each variable.

3 Results

3.1 Demographic differences in SUD history

Patients with a history of SUD were younger [35.90 years (SD = 13.32) vs. 44.68 (SD = 20.87), p = 0.003], were more likely to be male (56% vs. 44%, p < 0.001), had a lower level of education (p = 0.042), and had a longer time to commands [11.40 days (SD = 14.49) vs. 5.73 (SD = 9.75), p = 0.006]. There were no differences, however, in racial/ethnic minority status (p = 0.162), injury as a result of violence (p = 0.299), GCS score at admission (p = 0.390), or length of posttraumatic amnesia (p = 0.108). Based on these significant differences, the following HLMs were run with the age, gender, education, and time to commands as covariates.

In the first five HLMs with the DRS, FIM motor, FIM cognitive, PCRS self, and PCRS family as the dependent variables, there were no effects of history of SUD (all ps≥0.064) or of the SUD^*time interaction term (all ps≥0.143) on TBI outcome trajectories. In the sixth HLM with PCRS clinician as the dependent variable, the effect of history of SUD was significant (b = – 8.12, p = 0.018), suggesting that clinicians’ PCRS ratings of individuals with TBI were lower over time if the patient had a prior history of SUD (i.e., lower abilities; Fig. 1). However, the SUD^*time interaction term was not (p = 0.864), suggesting that the slopes of these trajectories were not significantly different.

In the seventh HLM with FrSBe self as the dependent variable, the effect of history of SUD was not statistically significant (p = 0.988), and neither was the SUD^*time interaction term (p = 0.751). The eighth HLM with FrSBe family as the dependent did not yield a significant main effect of SUD (p = 0.960), and but the SUD^*time interaction term was significant (b = 6.63, p = 0.047), with family members’ FrSBe ratings of patients increasing (more executive dysfunction) among those with a history of SUD, but decreasing (less executive dysfunction) among those without a history of SUD (Fig. 2).

In the ninth and tenth HLMs with NRS and FrSBe self-family discrepancy scores as the dependent variables, the effects of history of SUD were not statistically significant (both ps≥0.052), and neither were the interaction terms (both ps≥0.117). In the eleventh HLM with PCRS self-family discrepancy scores as the dependent variable, the effect of history of SUD was statistically significant (b = 0.013, p = 0.013), such that patients had more positive PCRS self-family discrepancy scores over time when there was a prior history of SUD (Fig. 3). However, the SUD^*time interaction term was not significant (p = 0.562)

In the twelfth HLM with PCRS self-clinician discrepancy scores, the effect of history of SUD was statistically significant (b = 13.66, p = 0.003), such that patients had higher PCRS self-clinician discrepancy scores over time when there was a prior history of SUD (Fig. 4), although the interaction term was not (p = 0.247).

4 Discussion

The purpose of this study was to examine whether trajectories of TBI outcomes varied as a function of whether patients had a history of SUD. After controlling for demographics, SUD history significantly predicted trajectories of PCRS clinician ratings, PCRS self-family discrepancy scores, and PCRS self-clinician discrepancy scores, revealing comparatively greater post-injury impairment of SA for those with SUD. Also, there was a significant SUD*time interaction effect on family FrSBe ratings, showing family FrSBe ratings of patients increasing (more executive dysfunction) among those with a history of SUD, but decreasing (less executive dysfunction) among those without a history of SUD. These results suggested that individuals with TBI who have a SUD history tend to be more at risk for poor awareness (impaired executive functions) of their injury-related deficits over time on a number of measures. However, there was not a significant association between history of SUD and acute functional outcome after TBI, as measured by the FIM. The current findings thus mirror prior research showing mixed findings between SUD and post-injury functional recovery trajectories (Barnfield & Leathem, 1998; Cuthbert, Harrison-Felix, et al., 2015; Cuthbert, Pretz, et al., 2015; Olson-Madden et al., 2012; De Guise et al., 2009; Parry-Jones et al., 2006; Vickery et al., 2008). The results do suggest an indirect outcomes-SUD history association given the relationship between impaired SA after TBI and long-range outcomes (Kelley et al., 2014).

The current significant SUD findings triangulated on impaired SA, a widely recognized major, and common post-TBI deficit, especially early in recovery. Impaired SA makes it difficult for the person to return to the community in a productive and satisfying role and creates burden for caregivers as it involves both a cognitive impairment component and, at times, psychological denial (Bombardier & Turner, 2009). The current study findings suggest that persons with a history of SUD may be at greater risk for impaired awareness of their injury-related cognitive and neurobehavioral challenges than persons who sustain a TBI who do not have a history of SUD, particularly if individuals have longer PTA duration, are young, male, and have limited levels of education. Thus, compared to persons with TBI and no history of SUD, persons with co-occurring TBI and SUD may face greater challenges in successful community reintegration. However, persons with impaired SA may be more at risk for SUD whether they have a TBI or not. Identifying deficits in SA after brain injury (Bombardier & Turner, 2009) relies on self-report findings. When participants have moderate and severe TBI, and significant cognitive deficits, self-report will not necessarily lead to consistently interpretable results. In the current study, only measures of SA were significantly associated with history of SUD. In addition, prior research has shown that social behavioral ratings can reflect a negative bias toward individuals in terms of perceived competence or culpability. Biases have been associated with both age and gender (Corrigan & Hart-Lamb, 2004; Glendon, Dorn, Davies, Matthews, & Taylor, 1996; Hart, Seignourel, & Sherer, 2009; Linden, Hanna, & Redpath, 2007). In the case of car crashes, for example, raters perceive younger males as significantly less competent than older drivers (Bombardier & Turner, 2009). In addition, when the patient is viewed as having been at least partially responsible for their injury, such as being intoxicated at time of injury, medical personnel and rehabilitation therapists rate them significantly lower on scales of functional and neurobehavioral status than they rate patients who are seen as innocent bystanders (Glendon et al., 1996; Hart et al., 2009; Linden et al., 2007). Future studies should include a bias questionnaire related to social desirability, gender, or persons with disabilities, as the current findings may reflect a bias toward individuals with a SUD history.

While heavy alcohol consumption may initially decrease following TBI, many investigators have found that it often increases several years later (Bombardier, Temkin, Machamer, & Dikmen, 2003; Ponsford et al., 2007). Risk factors for heavier post-injury drinking include male gender, younger age, premorbid history of SUD, diagnosis of depression and better physical functioning (Beasley et al., 2014; Bombardier et al., 2003; Corrigan, Bogner, Hungerford, & Schomer, 2010; Graham & Cardon, 2008; McCarthy et al., 2006). In a subset of combat-exposed veterans with mild TBI, studies have shown an increased risk for chronic SUD, especially when co-occurring post-traumatic stress disorder (PTSD) is present (Beasley et al., 2014). Thus, longitudinal data beyond 1 year following discharge could aid in determining factors associated with return to the community (e.g., employment) and sobriety, as well as the improving effectiveness of community-based models that have been established and tailored for treating SUD in individuals with TBI (Corrigan & Hart-Lamb, 2004; Hart et al., 2009). Indeed this tailoring is critical, because although many individuals with TBI may lack SA of their injury-related deficits and behavioral health issues during inpatient brain injury rehabilitation, the current study suggested that this may be especially so in those with a SUD history. Given this combination of neurobehavioral and cognitive deficits, community-based SUD providers must address these potential barriers to SUD treatment for this population. Provider education related to persons with TBI would be useful in identifying and fully including consumers with TBI in tailored recovery programs. Having family members involved and educated would also be useful, especially for altering the environment to reduce access to substances.

The present study has several limitations. First, the researchers relied upon self-report, prior provider report, or family report, and physiatrists’ History and Physical forms rather than one of the standardized assessments for determining current or history of SUD. While many family members, usually parents, spouses, or significant others were present on the study site units on a daily basis, went to therapies with the patient, or came as often as they could after work, there were other patients with caregivers who were friends or other relatives who only visited occasionally. This may have contributed to some unevenness in the level of awareness of the raters related to the patient’s TBI and SUD symptoms or use patterns. There may have been cases where caregivers or patients denied history, or the patient was not intoxicated at time of injury, but did have history of SUD that was unknown to the rater. While clinicians were likely capable assessors, limited familiarity with the patients or personal biases could have been reflected in their scores.

Second, blood alcohol levels (BAL), the most accurate measure of intoxication, were inconsistently recorded by emergency department or traumamedical staff at study sites. Policies for standardization of obtaining BAL vary between states and hospitals (e.g., trauma level), and screening may be omitted due to urgency to get to the operating room, rapid decline in medical status, or early discharge. In addition, when the term “intoxication” was recorded in the emergency department admission chart notes, patients were often also described as having combinations of substances including marijuana, alcohol, and cocaine, in their system. There was not sufficient power in this study for stratifying according to substance of choice or combinations of these in order to add this variable to our statistical model.

Future studies could track acute TBI rehabilitation outcomes after prospective assessment with standardized screening measures for SUD. Consistent screening with such measures as the CAGE (Dhalla, & Kopec, 2007) or the Brief Michigan Alcoholism Screening Test (MAST) (Storgaard et al., 1994) at time of admission to acute brain injury rehabilitation could provide more comprehensive information about the associations between history of SUD and outcomes found in the current study. In addition, consistent screening for history of SUD and assessment in the ED for presence of alcohol and illicit substances could help identify those at risk following TBI. This guideline, known as SBI or Screening and Brief Alcohol Intervention (CDC, 2015), is now endorsed by the CDC and adopted as a guideline by the American College of Trauma Surgeons.

There is frequent concern that individuals underreport SUD after TBI. Several authors have called for increased efforts to more consistently screen for SUD in this population (Arenth, Bogner, Corrigan, & Schmidt, 2001; Ashman et al., 2004; Corrigan et al., 1995). Future research could add to validated screening tools with more indirect methods for gathering data about SUD. Given the common post-TBI symptom of impaired SA, SUD researchers should consider that self-report inventories of SUD validated for use in the general population may not be as accurate in a TBI population (Berner et al., 2007). Substance use varies by culture in terms of norms, and future literature could address whether history of polysubstance use has a differing impact on TBI recovery based on country of origin. Considering the present study’s findings and existing limitations in prior literature on substance use/abuse and TBI, there is a need for continued research that promotes methodological and screening consistency, attention to the potentially confounding variables of age, gender, injury severity, and rater biases; and gathering information on past history of SUD, beyond just alcohol. The current study findings also highlight the importance of early interventions and education for at-risk populations following TBI.

Conflict of interest

Dr. Niemeier was awarded an RO1 by the National Center for Medical Rehabilitation Research (NCMRR), Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), 1R01HD052922-01A2, that supported this research. In addition the research was supported, in part, by NIH CTSA Award UL1 TR00058 awarded to Virginia Commonwealth University. All other authors report no conflicts.

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