Advertisement

Cognitive Profiles Among Individuals With Spinal Cord Injuries: Predictors and Relations With Psychological Well-being

Open AccessPublished:July 29, 2020DOI:https://doi.org/10.1016/j.apmr.2020.06.022

      Abstract

      Objectives

      To examine predictors of profiles of cognitive functioning among individuals receiving acute inpatient spinal cord injury (SCI) rehabilitation, as well as associations between their cognitive functioning and psychological well-being (life satisfaction and depression) 6 months after the baseline assessment.

      Design

      Prospective observational study design, with 2 assessments approximately 6 months apart.

      Setting

      A rehabilitation unit at a level 1 trauma hospital during acute SCI hospitalization and outpatient setting after discharge.

      Participants

      Individuals (N=89) with SCI.

      Intervention

      None.

      Main Outcome Measures

      Cognitive functioning (assessed by the Repeatable Battery for the Assessment of Neuropsychological Status), life satisfaction (measured by the Life Satisfaction Index A), and depressive symptoms (measured by the Patient Health Questionnaire-9).

      Results

      Latent profile analysis identified 3 classes of individuals with similar patterns of cognitive functioning: class1 (average levels of cognitive performance across all assessed domains; n=48), class 2 (average cognitive performance, except in recall and memory; n=23), and class 3 (low cognitive functioning across multiple domains of cognition; n=18). Fewer years of education, history of smoking, history of substance use other than alcohol, and greater postconcussion symptoms were associated with higher odds of classification in class 3 (P<.05). Six months post baseline, individuals in class 3 reported significantly lower levels of life satisfaction than individuals in class 1 (χ2(1)=5.86; P=.045) and marginally higher depressive symptoms than individuals in class 2 (χ2(1)=5.48; P=.057).

      Conclusions

      The impact of impaired cognition during acute rehabilitation may persist after discharge and influence the psychological well-being of individuals with SCI. Identifying individuals with cognitive dysfunction and attending to modifiable risk factors and may help ameliorate maladjustment after SCI.

      Keywords

      List of abbreviations:

      BLRT (bootstrapped likelihood ratio test), CI (confidence interval), LPA (latent profile analysis), LSIA (Life Satisfaction Index A), M (mean), OR (odds ratio), PCSS (postconcussion symptom scale), RBANS (Repeatable Battery for the Assessment of Neuropsychological Status), SCI (spinal cord injury), TBI (traumatic brain injury)
      Cognitive dysfunction among individuals with acute spinal cord injury (SCI) is common, with as many as 60% of individuals exhibiting impaired cognitive functioning.
      • Wilmot C.B.
      • Cope D.N.
      • Hall K.M.
      • Acker M.
      Occult head injury: its incidence in spinal cord injury.
      • Davidoff G.N.
      • Roth E.J.
      • Richards J.S.
      Cognitive deficits in spinal cord injury: epidemiology and outcome.
      • Sachdeva R.
      • Gao F.
      • Chan C.C.H.
      • Krassioukov A.V.
      Cognitive function after spinal cord injury: a systematic review.
      Recent estimates suggest that the relative risk of cognitive impairment may be 13 times greater among individuals with SCI than among the general population.
      • Craig A.
      • Guest R.
      • Tran Y.
      • Middleton J.
      cognitive impairment and mood states after spinal cord injury.
      Although impairments in attention, concentration, memory, problem solving, and reasoning tend to be most prevalent, there is considerable interindividual variability in cognitive functioning after SCI, with a wide range of performance in terms of severity and patterns of domains affected.
      • Davidoff G.N.
      • Roth E.J.
      • Richards J.S.
      Cognitive deficits in spinal cord injury: epidemiology and outcome.
      ,
      • Roth E.
      • Davidoff G.
      • Thomas P.
      • et al.
      A controlled study of neuropsychological deficits in acute spinal cord injury patients.
      ,
      • Strubreither W.
      • Hackbusch B.
      • Hermann-Gruber M.
      • Stahr G.
      • Jonas H.P.
      Neuropsychological aspects of the rehabilitation of patients with paralysis from a spinal injury who also have a brain injury.
      Furthermore, although cognitive impairment has been largely documented soon after injury and during rehabilitation, individuals with chronic SCI may also exhibit cognitive dysfunction long after injury.
      • Chiaravalloti N.D.
      • Weber E.
      • Wylie G.
      • Dyson-Hudson T.
      • Wecht J.M.
      Patterns of cognitive deficits in persons with spinal cord injury as compared with both age-matched and older individuals without spinal cord injury.
      • Jure I.
      • Labombarda F.
      Spinal cord injury drives chronic brain changes.
      • Lazzaro I.
      • Tran Y.
      • Wijesuriya N.
      • Craig A.
      Central correlates of impaired information processing in people with spinal cord injury.
      Thus, cognitive functioning among individuals with SCI shows significant heterogeneity.
      Concomitant traumatic brain injury (TBI) is often cited as a cause of cognitive impairment among individuals with SCI, as external forces causing trauma to the spinal cord sometimes result in TBI.
      • Davidoff G.N.
      • Roth E.J.
      • Richards J.S.
      Cognitive deficits in spinal cord injury: epidemiology and outcome.
      ,
      • Iida H.
      • Tachibana S.
      • Kitahara T.
      • Horiike S.
      • Ohwada T.
      • Fujii K.
      Association of head trauma with cervical spine injury, spinal cord injury, or both.
      ,
      • Michael D.B.
      • Guyot D.R.
      • Darmody W.R.
      Coincidence of head and cervical spine injury.
      However, research also suggests that a substantial number of individuals with traumatic and nontraumatic SCI, but without a history of TBI, present with neuropsychological dysfunction.
      • Kreutzer J.S.
      • Barth J.
      • Ellwood M.S.
      • et al.
      Occult Neuropsychological impairments in spinal-cord injured patients.
      • Macciocchi S.N.
      • Seel R.T.
      • Thompson N.
      The impact of mild traumatic brain injury on cognitive functioning following co-occurring spinal cord injury.
      • Comi G.
      • Filippi M.
      • Martinelli V.
      • et al.
      Brain MRI correlates of cognitive impairment in primary and secondary progressive multiple sclerosis.
      • Rahn K.
      • Slusher B.
      • Kaplin A.
      Cognitive impairment in multiple sclerosis: a forgotten disability remembered.
      Human and animal model studies suggest that cognitive impairment among individuals with SCI may also be a consequence of pathophysiological factors, including cortical reorganization and atrophy, neuroinflammation, hypoxia, vascular dysfunction, and accelerated aging.
      • Chiaravalloti N.D.
      • Weber E.
      • Wylie G.
      • Dyson-Hudson T.
      • Wecht J.M.
      Patterns of cognitive deficits in persons with spinal cord injury as compared with both age-matched and older individuals without spinal cord injury.
      ,
      • Wu J.
      • Stoica B.A.
      • Luo T.
      • et al.
      Isolated spinal cord contusion in rats induces chronic brain neuroinflammation, neurodegeneration, and cognitive impairment.
      • Wu J.
      • Zhao Z.
      • Sabirzhanov B.
      • et al.
      Spinal cord injury causes brain inflammation associated with cognitive and affective changes: role of cell cycle pathways.
      • Faden A.I.
      • Wu J.
      • Stoica B.A.
      • Loane D.J.
      Progressive inflammation-mediated neurodegeneration after traumatic brain or spinal cord injury.
      • Wecht J.M.
      • Rosado-Rivera D.
      • Jegede A.
      • et al.
      Systemic and cerebral hemodynamics during cognitive testing.
      • Jegede A.B.
      • Rosado-Rivera D.
      • Bauman W.A.
      • et al.
      Cognitive performance in hypotensive persons with spinal cord injury.
      Additionally, treatment-related factors (including medications prescribed for acute and chronic care), the sequelae of SCI (eg, impaired sleep and chronic pain), and premorbid conditions (such as psychiatric conditions and learning disabilities) may contribute to the development of cognitive impairment after SCI.
      • Davidoff G.N.
      • Roth E.J.
      • Richards J.S.
      Cognitive deficits in spinal cord injury: epidemiology and outcome.
      ,
      • Macciocchi S.N.
      • Seel R.T.
      • Thompson N.
      The impact of mild traumatic brain injury on cognitive functioning following co-occurring spinal cord injury.
      ,
      • Warren A.M.
      • Pullins J.
      • Elliott T.R.
      Concomitant cognitive impairment in persons with spinal cord injuries in rehabilitation settings.
      Impaired cognitive functioning may negatively influence health and well-being in the context of SCI. For instance, individuals with moderate to severe cognitive dysfunction have been noted to experience difficulties acquiring skills during rehabilitation, have greater disturbances to sleep and appetite, require higher levels of care, and have decreased functional independence.
      • Davidoff G.N.
      • Roth E.J.
      • Richards J.S.
      Cognitive deficits in spinal cord injury: epidemiology and outcome.
      ,
      • Avluk O.C.
      • Gurcay E.
      • Gurcay A.G.
      • Karaahmet O.Z.
      • Tamkan U.
      • Cakci A.
      Effects of chronic pain on function, depression, and sleep among patients with traumatic spinal cord injury.
      ,
      • Richards J.S.
      • Osuna F.J.
      • Jaworski T.M.
      • Novack T.A.
      • Leli D.A.
      • Boll T.J.
      The effectiveness of different methods of defining traumatic brain injury in predicting postdischarge adjustment in a spinal cord injury population.
      Although there is much evidence suggesting increased psychological distress and lowered quality of life after SCI, associations between impaired cognitive functioning and psychological well-being have been less well-examined.
      • Murray R.F.
      • Asghari A.
      • Egorov D.D.
      • et al.
      Impact of spinal cord injury on self-perceived pre- and postmorbid cognitive, emotional and physical functioning.
      ,
      • Whiteneck G.
      • Meade M.A.
      • Dijkers M.
      • Tate D.G.
      • Bushnik T.
      • Forchheimer M.B.
      Environmental factors and their role in participation and life satisfaction after spinal cord injury.
      Findings in this emerging research area suggest that, compared with individuals with intact cognition, those with impairment may experience more anxiety, stress, and depressed mood.
      • Craig A.
      • Guest R.
      • Tran Y.
      • Middleton J.
      cognitive impairment and mood states after spinal cord injury.
      ,
      • Nott M.T.
      • Baguley I.J.
      • Heriseanu R.
      • et al.
      Effects of concomitant spinal cord injury and brain injury on medical and functional outcomes and community participation.
      ,
      • Rapoport M.J.
      • McCullagh S.
      • Shammi P.
      • Feinstein A.
      Cognitive impairment associated with major depression following mild and moderate traumatic brain injury.
      However, the degree to which specific patterns of cognitive functioning relate to psychological functioning has not been examined in this population. Thus, in this study, we prospectively examined the association between profiles of cognitive functioning and indices of psychological wellbeing (specifically, depression and life satisfaction) in the context of SCI.
      Latent profile analysis (LPA) and other mixture modeling techniques have been used to identify subgroups of individuals with similar neurocognitive performance across a number of chronic conditions.
      • Brennan L.
      • Devlin K.M.
      • Xie S.X.
      • et al.
      Neuropsychological subgroups in non-demented Parkinson’s disease: a latent class analysis.
      • Pasipanodya E.C.
      • Montoya J.L.
      • Campbell L.M.
      • et al.
      Metabolic risk factors as differential predictors of profiles of neurocognitive impairment among older HIV+ and HIV− adults: an observational study.
      • Libon D.J.
      • Drabick D.A.
      • Giovannetti T.
      • et al.
      Neuropsychological syndromes associated with Alzheimer’s/vascular dementia: a latent class analysis.
      Like latent class analysis (for categorical observed variables), LPA identifies groups of individuals with similar unobserved (latent) characteristics from analyses of relations among several continuous observed variables. Although past work has used cluster analysis to model profiles of cognitive functioning among individuals with SCI, relations between profiles of cognitive functioning and later psychological outcomes have not been examined.
      • Dowler R.N.
      • Harrington D.
      • Haaland K.
      • Swanda R.
      • Fee F.
      • Fiedler K.
      Profiles of cognitive functioning in chronic spinal cord injury and the role of moderating variables.
      Thus, this exploratory project aimed to (1) determine profiles of cognitive functioning among individuals with SCI during acute inpatient rehabilitation using LPA, (2) examine the influence of covariates on latent classification, and (3) examine differences in depression and life satisfaction, after discharge, by specific profiles of cognitive functioning. We hypothesized that LPA would identify subgroups that differ in their profiles of cognitive functioning and that individuals in subgroups characterized by more extensive cognitive dysfunction would have lower life satisfaction and higher levels of depression after discharge.

      Methods

      Participants

      Ninety-two individuals were recruited from an acute SCI inpatient rehabilitation unit at a level 1 trauma center between September 2013 and October 2015. The study was approved by the Institutional Review Board, and all participants provided written informed consent. The aim of the parent study was to examine symptoms of TBI among individuals with SCI. Inclusion criteria were a diagnosis of SCI, age 18 years or older, English-speaking, and ability to provide informed consent. Potential candidates were excluded if they had a severe head injury precluding cognitive testing. To be included in these analyses, participants also had to have cognitive data obtained using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) (N=89). Participants were assessed in person during their inpatient hospitalization and approximately 6 months later as outpatients (n=55) by telephone or in-person visit.

      Measures

      Neurocognitive functioning

      Participants were assessed as inpatients using the RBANS, a validated cognitive screening instrument, by a trained speech and language pathologist.
      • Randolph C.
      • Tierney M.C.
      • Mohr E.
      • Chase T.N.
      The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): preliminary clinical validity.
      Assessments were conducted a median of 28 days (interquartile range, 17.00-46.50d) after injury.
      The RBANS consists of 12 subtests (List Learning, Story Memory, Figure Copy, Line Orientation, Picture Naming, Semantic Fluency, Digit Span, Coding, List Recall, List Recognition, Story Recall, and Figure Recall) that yield 5 index scores (Immediate Memory, Visuospatial/Constructional, Language, Attention, and Delayed Memory), and a total scale score. As many participants had impaired upper motor functioning, subtests requiring intact arm and hand functioning for valid inferences of cognitive ability (ie, Coding, Figure Copy, and Figure Recall) were excluded from analyses. Thus, scores for the remaining 9 subtests were interpreted individually and not grouped into their index scores. A number of studies indicate the interpretability and clinical utility of individual subtests on the RBANS, independent of index or total scores.
      • Duff K.
      • Patton D.
      • Schoenberg M.R.
      • Mold J.
      • Scott J.G.
      • Adams R.L.
      Age- and education-corrected independent normative data for the RBANS in a community dwelling elderly sample.
      • Duff K.
      • Clark H.J.D.
      • O’Bryant S.E.
      • Mold J.W.
      • Schiffer R.B.
      • Sutker P.B.
      Utility of the RBANS in detecting cognitive impairment associated with Alzheimer’s disease: sensitivity, specificity, and positive and negative predictive powers.
      • Gontkovsky S.T.
      • McSwan K.L.
      • Scott J.G.
      Sensitivity of the semantic fluency subtest of the Repeatable Battery for the Assessment of Neuropsychological Status.
      Subtests were administered and scored as outlined in the RBANS manual. Normative information provided in the appendices was used to calculate age-corrected scaled and cumulative percentage scores.
      • Randolph C.
      RBANS update: Repeatable Battery for the Assessment of Neuropsychological Status.

      Potential covariates of classification: sociodemographic and medical characteristics

      Baseline sociodemographic data (ie, age, sex, race, education, employment status, history of smoking, history of alcohol use, and history of other substance use) and relevant medical variables (ie, duration of injury at the time of cognitive assessment, level and completeness of injury, and traumatic vs nontraumatic etiology) were obtained by participant self-report and abstracted from participants’ medical records.

      Potential covariates of classification: diagnosis of TBI

      Diagnoses consistent with TBI and following the International Classification of Diseases–9th Revision–based surveillance definitions were obtained from participants’ medical records.
      • Faul M.
      • Xu L.
      • Wald M.
      • Coronado V.
      Traumatic brain injury in the United States: emergency department visits, hospitalizations and deaths 2002-2006.
      The following International Classification of Diseases–9th Revision codes were included: 800.0 to 801.9 (fracture of the vault or base of the skull), 803.0 to 804.9 (other and unqualified multiple fractures of the skull), 850.0 to 854.1 (intracranial injury, including concussion, contusion, laceration, and hemorrhage), 950.1 to 950.3 (injury to optic nerve and pathways), and 959.01 (head injury, unspecified).

      Potential covariates of classification: postconcussive symptoms

      Symptoms common after acquired brain injury were assessed at baseline using the 22-item Post-Concussion Symptom Scale (PCSS). Participants rated their symptoms on a 7-point Likert scale, ranging from 0 (no symptoms) to 6 (severe symptoms). Items on the PCSS were summed, yielding a total score ranging from 0 to 132.
      • Lovell M.R.
      • Iverson G.L.
      • Collins M.W.
      • et al.
      Measurement of symptoms following sports-related concussion: reliability and normative data for the Post-Concussion Scale.
      The reliability of the PCSS was good (Cronbach’s α=.87).

      Potential covariates of classification: functional independence

      Functional independence at admission was assessed using the FIM. The FIM is a well-validated 18-item scale often used in rehabilitation settings to quantify levels of disability.
      • Linacre J.M.
      • Heinemann A.W.
      • Wright B.D.
      • Granger C.V.
      • Hamilton B.B.
      The structure and stability of the Functional Independence Measure.
      ,
      • Graham J.E.
      • Granger C.V.
      • Karmarkar A.M.
      • et al.
      The Uniform Data System for Medical Rehabilitation: report of follow-up information on patients discharged from inpatient rehabilitation programs in 2002-2010.
      Scores on the FIM range from 1 (total assist) to 7 (complete independence) and total FIM scores range from 18 to 126, with lower scores corresponding to greater need for assistance to perform activities of daily living.

      Psychological well-being outcomes: depressive symptoms

      Depressive symptomatology 6 months after baseline was assessed using the Patient Health Questionnaire-9, a commonly-used and validated Diagnostic and Statistical Manual IV-based measure.
      • Kroenke K.
      • Spitzer R.L.
      • Williams J.B.W.
      The PHQ-9.
      • Spitzer R.L.
      • Kroenke K.
      • Williams J.B.W.
      and the Patient Health Questionnaire Primary Care Study Group. Validation and utility of a self-report version of PRIME-MDThe PHQ Primary Care Study.
      • Spitzer R.L.
      • Williams J.B.W.
      • Kroenke K.
      • Hornyak R.
      • McMurray J.
      Validity and utility of the PRIME-MD Patient Health Questionnaire in assessment of 3000 obstetric-gynecologic patients: the PRIME-MD Patient Health Questionnaire Obstetrics-Gynecology Study.
      Participants reported their mood on a Likert scale, with responses ranging from 0 (not at all) to 3 (nearly every day). Total scores on the Patient Health Questionnaire-9 range from 0 to 27. Reliability in this sample was acceptable (α=.76).

      Psychological well-being outcomes: life satisfaction

      Satisfaction with life was assessed using the Life Satisfaction Index A (LSIA) 6 months after baseline. The LSIA is a multidimensional 20-item measure of psychological well-being, with facets that include zest for life, fortitude, and congruence between desired and achieved goals.
      • Neugarten B.L.
      • Havighurst R.J.
      • Tobin S.S.
      The measurement of life satisfaction.
      Participants responded to questions on a 3-point scale ranging from 0 (disagree) to 2 (agree). Negatively worded items were reverse scored, yielding a range of scores from 0 to 40, with higher scores indicating greater levels of satisfaction. The reliability of the LSIA in this sample was moderate (α=.70).

      Data analysis

      LPA was used to characterize subgroups of individuals with similar patterns of neurocognitive performance. LPA is a statistical method for identifying underlying class membership when true class membership is unknown by inference from continuously-measured variables.
      To determine the number of subgroups of individuals with differential profiles of cognitive functioning, several models with increasing numbers of latent classes were estimated. Following established procedures, relative indices of model fit (ie, Bayesian Information Criteria, entropy, and the bootstrapped likelihood ratio test [BLRT]) were used to select a model with the number of classes best fitting the data.
      • Berlin K.S.
      • Williams N.A.
      • Parra G.R.
      an introduction to latent variable mixture modeling (part 1): overview and cross-sectional latent class and latent profile analyses.
      For each model with k number of classes, the best log-likelihood was replicated to avoid convergence at a local maximum. Moreover, in log-likelihood ratio tests to compare fit between models with k and k-1 classes, the log-likelihood for the null model with k-1 classes was confirmed to be equal to the best log-likelihood value of the previously-tested model with 1 fewer class.
      • Berlin K.S.
      • Williams N.A.
      • Parra G.R.
      an introduction to latent variable mixture modeling (part 1): overview and cross-sectional latent class and latent profile analyses.
      Once the number of classes was identified, the LPA model was adjusted for relevant covariates. Relevant covariates were any of the previously described potential covariates that were found to have some association with latent classification (P<.10) when examined in univariable multinomial regression models. Finally, between-class differences in the means of life satisfaction and depression were examined using Wald chi-square tests. Adjustments for multiple comparisons were made using a Bonferroni correction. In Wald tests, a 3-step approach was used, in which class membership was assigned using observed index scores and relevant covariates before the inclusion of outcome variables, to prevent outcomes from altering the structure of latent classes and influencing final class membership.
      • Asparouhov T.
      • Muthén B.
      Auxiliary variables in mixture modeling: three-step approaches using Mplus.
      ,

      Vermunt JK. Latent class modeling with covariates: two improved three-step approaches. Political Anal 18:450-469.

      LPAs were conducted using Mplus (version 8.11)a using maximum likelihood estimation that is robust to missing data and non-normal distributions of outcomes.
      • Muthén L.K.
      • Muthén B.
      Mplus user’s guide.
      Missing data were modeled using the expectation-maximization procedure under standard missing data assumptions (ie, missing at random with inferences of probable missing values made by a full maximum likelihood algorithm based on the relation between outcomes and predictors.
      • Little R.J.
      • Rubin D.B.
      Descriptive statistics were carried out in SPSS (version 24).b

      Results

      Descriptive statistics

      Participants were predominantly white (62.9%), with some college education (64.0%), an average age of 43.54±15.43 y, and employed before their hospital admission (64.0%). Most participants had traumatic SCI (70.8%) and tetraplegia (65.2%). Other descriptive statistics are presented in table 1.
      Table 1Descriptive statistics and characteristics of study participants
      CharacteristicsDescriptive StatisticTotal, n
      Demographic
      Male sex, n (%)59 (66.3)89
      Age, mean ± SD43.54±15.4389
      Race/ethnicity, n (%)89
       Non-Hispanic white56 (62.9)
       Black/African American4 (4.5)
       Asian/Asian American6 (6.7)
       Hispanic19 (21.3)
       Other4 (4.5)
      Employed, n (%)57 (64.0)88
      Married, n (%)33 (37.1)89
      Some college education (or higher), n (%)57 (64.0)89
      Behavioral
      History of smoking, n (%)13 (14.6)89
      Any alcohol use, n (%)54 (60.7)89
      Other substance use, n (%)23 (25.8)89
      Medical
      Traumatic spinal cord injury, n (%)63 (70.8)89
      Diagnosis of TBI, n (%)27 (30.3)89
      Degree of completeness of injury, n (%)89
       AIS A26 (29.2)
       AIS B10 (11.2)
       AIS C19 (21.3)
       AIS D33 (37.1)
       AIS E1 (1.1)
      Level of injury, n (%)89
       C1-C442 (47.2)
       C5-C816 (18.0)
       T1-T512 (13.5)
       T6-T1212 (13.5)
       L1-L57 (7.9)
      Etiology of injury, n (%)89
       Motor vehicle collision26 (29.2)
       Bicycle accident10 (11.2)
       Gunshot wound5 (5.6)
       Sports4 (4.5)
       Falls18 (20.2)
       Non-traumatic26 (29.2)
      FIM admission total, mean ± SD50.70±12.8989
      Psychosocial
      Post-Concussion Symptom Scale total, mean ± SD34.61±21.5489
      Patient Health Questionnaire-9, mean ± SD8.33±5.56155
      Life Satisfaction Index A, mean ± SD23.64±7.7955
      RBANS
      List Learning SS, mean ± SD7.92±3.4889
      Story Memory SS, mean ± SD8.98±3.1889
      Semantic Fluency SS, mean ± SD6.83±3.4189
      Digit Span SS, mean ± SD9.19±3.1088
      Story Recall SS, mean ± SD9.06±3.3388
      Line Orientation percentile band, median (IQR)
      Percentile bands translate to cumulative percentages as follows (percentile band → cumulative percentage): 1 → ≤2%; 2 → 3%-9%; 3 → 10%-16%; 4 → 17%-25%; 5 → 26%-50%; 6 → 51%-75%; 7 → >75%.
      6 (5-7)89
      Picture Naming percentile band, median (IQR)
      Percentile bands translate to cumulative percentages as follows (percentile band → cumulative percentage): 1 → ≤2%; 2 → 3%-9%; 3 → 10%-16%; 4 → 17%-25%; 5 → 26%-50%; 6 → 51%-75%; 7 → >75%.
      6 (4-6)89
      List Recall percentile band, median (IQR)
      Percentile bands translate to cumulative percentages as follows (percentile band → cumulative percentage): 1 → ≤2%; 2 → 3%-9%; 3 → 10%-16%; 4 → 17%-25%; 5 → 26%-50%; 6 → 51%-75%; 7 → >75%.
      5 (2-6)88
      List Recognition percentile band, median (IQR)
      Percentile bands translate to cumulative percentages as follows (percentile band → cumulative percentage): 1 → ≤2%; 2 → 3%-9%; 3 → 10%-16%; 4 → 17%-25%; 5 → 26%-50%; 6 → 51%-75%; 7 → >75%.
      6 (3-6)88
      Abbreviations: AIS, American Spinal Injury Association Impairment Scale; GED, general equivalency diploma; IQR, interquartile range; SS, scaled score.
      Percentile bands translate to cumulative percentages as follows (percentile band → cumulative percentage): 1 → ≤2%; 2 → 3%-9%; 3 → 10%-16%; 4 → 17%-25%; 5 → 26%-50%; 6 → 51%-75%; 7 → >75%.
      Baseline differences in demographic, behavioral, medical, and cognitive characteristics between individuals with completed follow-up study visits (n=55) and those lost to follow-up (n=34) were examined using t tests, chi-square tests, and Mann-Whitney tests, as appropriate. Study noncompleters were significantly more likely to have a history of substance use (χ2=4.41 (1); P=.036) and were marginally less likely to have received higher education (χ2=2.95 (1); P=.086).

      Identification of number of latent classes

      Table 2 reports the fit indices for LPA models with increasing numbers of classes. Through fit indices comparisons, a 3-class model was found to best fit the observed cognitive scores (log-likelihood, –1662.78; number of parameters, 38; entropy, 0.91; and BLRT, 55.89; P<.001). From the pattern of scores across domains, the 3 classes were describable as class 1 (highest cognitive performance, with cognitive performance in the average range across all subtests; n= 42; 47.19%), class 2 (intermediate, with cognitive performance in the low average to average range; n=27; 30.34%), and class 3 (low cognitive performance across most domains; n=20; 22.47%).
      Table 2Fit indices of latent profile analyses
      Number of ClassesLog-likelihood (Parameters)Sample Size-Adjusted Bayesian Information CriteriaClass Size, n (%)EntropyBLRT
      1–1791.28 (18)3606.5689 (100)1.000N/A
      2–1690.73 (28)3418.7822 (24.72)

      67 (75.82)
      0.964201.11; P<.001
      3–1662.78 (38)3376.2120 (22.47)

      27 (30.34)

      42 (47.19)
      0.91055.89; P<.001
      4–1647.73 (48)3359.4316 (17.98)

      5 (5.62)

      18 (20.23)

      50 (56.18)
      0.95430.12; P=.0789
      Abbreviation: N/A, not applicable.

      Predictors of classification

      Multinomial regressions were carried out to identify covariates affecting class formation (table 3). Age, sex, employment status, marital status, etiology of injury, duration of injury at the time of RBANS assessment, level and completeness of injury, TBI diagnosis, and alcohol use were not significantly associated with latent classification. Race, education, history of smoking, history of other substance use, FIM admission scores, and postconcussion symptoms had some association with class membership (P<.10). In particular, non-white individuals were marginally more likely to be classified as class 3 (odds ratio [OR], 3.28; 95% confidence interval [CI], 0.86-12.57; P=.082). Individuals with a high school education or less were more likely to be classified in class 3 (OR, 0.27; 95% CI, 0.08-0.94; P=.040) than in class 1. Additionally, individuals with a history of substance use (OR, 7.64; 95% CI, 1.82-32.00; P=.005), a history of smoking (OR, 6.88; 95% CI, 1.48-31.90; P=.014), and greater postconcussion symptoms (OR, 1.04; 95% CI, 1.01-1.07; P=.016) were significantly more likely to be classified in class 3 than class 1. Furthermore, individuals with higher FIM scores were marginally more likely to be classified as class 2 compared with class 1 (OR, 1.04; 95% CI, 0.99-1.08; P=.092). Thus, these factors were included as covariates affecting classification and a new 3-class LPA was estimated.
      Table 3Results of multinomial regression models examining baseline variables as potential covariates of latent classification
      CovariateClass 2Class 3
      Estimate (SE)OR95% CIEstimate (SE)OR95% CI
      OR LowerOR UpperOR LowerOR Upper
      Age–0.007 (0.03)0.990.931.06–0.01 (0.02)0.990.951.04
      Sex–0.476 (0.57)0.620.201.88–0.45 (0.60)0.640.192.08
      Race–0.49 (0.61)0.610.192.011.19 (0.69)
      .05<P<.10.
      3.280.8612.57
      College education–0.55 (0.63)0.580.172.00–1.31 (0.64)
      .01<P<.05.
      0.270.080.94
      Employed–0.23 (0.65)0.790.232.80–0.26 (0.74)0.770.183.29
      Married0.62 (0.56)1.860.625.611.08 (0.68)2.950.7811.22
      Traumatic SCI–0.35 (0.59)0.700.222.22–0.13 (0.63)0.870.253.03
      AIS Level
      Reference is AIS A.
      : B
      0.35 (0.43)1.420.613.301.03 (0.92)2.790.4616.85
       C1.40 (1.13)4.050.4437.031.74 (1.40)5.670.3788.06
       D/E0.97 (0.61)2.640.818.630.58 (0.43)1.780.764.14
      Tetraplegia0.19 (0.54)1.210.423.51–0.24 (0.61)0.780.242.60
      Diagnosis of TBI–0.05 (0.63)0.960.283.28–0.37 (0.64)0.690.202.41
      Postconcussion symptoms0.003 (0.01)1.000.981.030.04 (0.02)
      .01<P<.05.
      1.041.011.07
      Alcohol use0.09 (0.54)1.090.383.130.61 (0.61)1.830.565.99
      Other substance use0.92 (0.72)2.510.6110.342.03 (0.73)
      0.001<P<.01.
      7.641.8232.00
      Smoking0.45 (0.88)1.560.288.811.93 (0.78)
      .01<P<.05.
      6.881.4831.90
      FIM admission0.04 (0.02)
      .05<P<.10.
      1.040.991.08–0.03 (0.03)0.970.921.02
      NOTE. Reference group in all multinomial regressions is class 1.
      .05<P<.10.
      .01<P<.05.
      Reference is AIS A.
      § 0.001<P<.01.
      In a multivariable model accounting for covariates affecting classification, only substance use, FIM scores, and postconcussion symptoms had continued associations. Specifically, individuals with greater substance use (OR, 4.17; 95% CI, 0.87-20.06; P=.074) and greater postconcussion symptoms (OR, 1.04; 95% CI, 1.00-1.07; P=.057) were marginally more likely to be classified in class 3 than class 1. Individuals with higher FIM scores were marginally more likely to be in class 2, relative to class 1 (OR, 1.05; 95% CI, 1.00-1.11; P=.061). The covariate-adjusted model had the following fit statistics: log-likelihood of –1643.58, 50 parameters, entropy of 0.92, and BLRT of 55.89 (P<.001). The interpretation of classes remained the same, namely class 1 (highest cognitive performance; n=48; 53.93%), class 2 (intermediate cognitive performance; n=23; 25.84%), and class 3 (lowest cognitive performance; n= 18; 20.23%). Log-likelihood ratio comparisons, using a Satorra-Bentler chi-square test, suggested improved model fit with the inclusion of covariates (χ2(12)=33.59; P=.001.
      • Satorra A.
      • Bentler P.M.
      A scaled difference chi-square test statistic for moment structure analysis.
      Figure 1 (subtests with scaled scores) and table 4 (subtests with cumulative percentages) present differential patterns of cognitive functioning across the 3 groups. From the pattern of scaled and cumulative percentage scores across subtests, individuals in class 1 had performance in the average range across all subtests. Individuals in class 2 generally had cognitive functioning in the average range, except on List Recall and List Recognition where performance was in the borderline to low average range. Individuals in class 3 displayed cognitive performance in the borderline to low average range in all subtests.
      Figure thumbnail gr1
      Fig 1Differential cognitive functioning by latent class in a 3-class LPA among individuals with SCI.
      Table 4Differential cognitive functioning by latent class in a 3-class LPA among individuals with SCI
      SubtestClass 1Class 2Class 3
      Cumulative PercentageDescriptionCumulative PercentageDescriptionCumulative PercentageDescription
      List recall26-50Average3-9Borderline3-9Borderline
      List recognition26-50Average10-16Low Average10-16Low Average
      Line orientation26-50Average26-50Average17-25Low Average
      Picture naming26-50Average26-50Average10-16Low Average
      NOTE. RBANS subtests are those scored as cumulative percentages.

      Associations between cognition and psychological well-being

      Differences in quality of life outcomes among individuals in the 3 classes were examined 6 months after baseline. Omnibus chi-square tests indicated significant differences in life satisfaction (χ2(2)= 6.24; P=.044) and marginally significant differences in depressive symptoms (χ2(2)=5.52; P=.063) across the 3 classes. Follow-up tests with Bonferroni-adjusted alpha levels indicated significant differences in life satisfaction (χ2(1)=5.86; P=.045) between class 3 (mean [M]=19.34; SE=1.85) and class 1 (M=24.89; SE=1.34) and marginally significant depressive symptoms (χ2(1)=5.48; P=.057), between individuals in class 3 (M=10.98; SE=1.58) and individuals in class 2 (M=5.73; SE=1.56). Other paired comparisons were not significant.
      To examine the robustness of results, differences in quality of life outcomes across the 3 classes were examined among only study completers (n=55). Overall, the pattern of results was similar. Omnibus chi-square tests indicated significant differences in both life satisfaction (χ2(2)=6.12; P=.047) and depressive symptoms (χ2(2)=6.73; P=.035). Follow-up tests with Bonferroni-adjusted alpha levels indicated marginally significant differences in life satisfaction (χ2(1)=5.69; P=.051) between class 1 (M=25.38; SE=1.22) and class 3 (M=19.83; SE=1.98), as well as marginally significant depressive symptoms (χ2(1)=5.31; P=.063) between individuals in class 1 (M=7.60; SE=0.94) and class 3 (M=11.51; SE=1.41) and between individuals in class 2 (M=6.73; SE=1.58) and class 3 (χ2(1)=5.11; P=.072).

      Discussion

      Cognitive dysfunction among individuals with SCI is common. However, differential patterns of cognitive functioning during acute care and their relation to later psychological outcomes have not been examined. We examined prospective associations between profiles of cognitive functioning during inpatient hospitalization and depression and life satisfaction 6 months later among individuals with SCI. LPA suggested 3 groups of individuals, distinguishable from each other by their cognitive functioning. Most individuals (class 1 [54%]) displayed unimpaired cognition across all domains. However, consistent with other published works, a substantial percentage of participants had impairment. Analyses suggested a group of individuals with delayed memory impairment (class 2 [26%]), as well as a group of individuals with impaired cognition across multiple domains (class 3 [20%]). Education, smoking, substance use, and postconcussion symptoms were significantly associated with class membership and with cognitive impairment. Specifically, individuals with a high school education or less, a history of substance use, a history of smoking, and greater postconcussive complaints were more likely to be classified in class 3 than in class 1. When quality of life outcomes 6 months later were compared, individuals in class 3 had significantly lower life satisfaction than individuals in class 1 and marginally greater depressive symptoms than individuals in class 2, suggesting that impaired cognitive functioning during acute rehabilitation may be a risk factor for poorer psychosocial well-being after discharge.
      Our work reiterates the potential for cognitive impairment among individuals with SCI, regardless of a diagnosis of TBI or traumatic etiology of injury.
      • McKinley W.O.
      • Seel R.T.
      • Hardman J.T.
      Nontraumatic spinal cord injury: incidence, epidemiology, and functional outcome.
      Although it may not be feasible for all patients with SCI to be screened for cognitive impairment during acute hospitalization, clinicians may prioritize assessing individuals with a history of predisposing premorbid conditions and those experiencing symptoms akin to postconcussion syndrome. Although the construct of postconcussion syndrome is controversial (with some studies finding postconcussive symptoms to not be specific to TBI, present among clinical and nonclinical populations, and influenced by psychosocial factors), persistent symptoms circumscribed by the syndrome are clinically meaningful.
      • Donnell A.J.
      • Kim M.S.
      • Silva M.A.
      • Vanderploeg R.D.
      Incidence of postconcussion symptoms in psychiatric diagnostic groups, mild traumatic brain injury, and comorbid conditions.
      ,
      • Meares S.
      • Shores E.A.
      • Taylor A.J.
      • et al.
      Mild traumatic brain injury does not predict acute postconcussion syndrome.
      For instance, across multiple studies, individuals reporting more postconcussive symptoms have poorer clinical outcomes, poorer functional outcomes, and greater psychological distress.
      • Chamelian L.
      • Feinstein A.
      Outcome after mild to moderate traumatic brain injury: the role of dizziness.
      • van der Horn H.J.
      • Spikman J.M.
      • Jacobs B.
      • van der Naalt J.
      Postconcussive complaints, anxiety, and depression related to vocational outcome in minor to severe traumatic brain injury.
      • Landre N.
      • Poppe C.J.
      • Davis N.
      • Schmaus B.
      • Hobbs S.E.
      Cognitive functioning and postconcussive symptoms in trauma patients with and without mild TBI.
      Because postconcussive-like symptoms, which may be a broad indicator of generalized dysfunction and a risk factor for cognitive impairment, are easily assessed during rehabilitation and are amenable to intervention, postconcussive-like symptoms may be an identifiable and modifiable risk factor for cognitive impairment and later psychological wellbeing among individuals with SCI.
      • Lovell M.R.
      • Iverson G.L.
      • Collins M.W.
      • et al.
      Measurement of symptoms following sports-related concussion: reliability and normative data for the Post-Concussion Scale.
      ,
      • King N.S.
      • Crawford S.
      • Wenden F.J.
      • Moss N.E.G.
      • Wade D.T.
      The Rivermead Post Concussion Symptoms Questionnaire: a measure of symptoms commonly experienced after head injury and its reliability.
      • Al Sayegh A.
      • Sandford D.
      • Carson A.J.
      Psychological approaches to treatment of postconcussion syndrome: a systematic review.
      • Collins M.W.
      • Kontos A.P.
      • Okonkwo D.O.
      • et al.
      Statements of agreement from the Targeted Evaluation and Active Management (TEAM) Approaches to Treating Concussion Meeting held in Pittsburgh, October 15-16, 2015.
      • Mittenberg W.
      • Burton D.B.
      A survey of treatments for post-concussion syndrome.

      Study limitations

      Our study should be interpreted with consideration of its limitations. Although the RBANS is a well-validated measure of cognitive functioning, it contains several motor-dependent subtests that make it less suitable for cognitive assessment among individuals with SCI who have impaired upper motor functioning. Indeed, we excluded subtests with motor components, resulting in an inability to interpret some index scores and an RBANS total score. Future research is required to develop and validate neuropsychological test batteries for screening and comprehensive assessment among individuals with limited motor abilities.
      Second, although individuals in class 2 and class 1 were distinguishable by their recall and memory, they did not differ significantly from each other in examined predictors of cognitive functioning or outcomes after discharge. As our sample was moderately-sized, the study may have been underpowered to adequately examine predictors of cognition and differences in outcomes among these 2 classes. As there are known significant implications to impaired recall and memory, future work should use larger sample sizes to identify risk factors for delayed recall impairment and outcomes among individuals with SCI.
      • Parikh P.K.
      • Troyer A.K.
      • Maione A.M.
      • Murphy K.J.
      The impact of memory change on daily life in normal aging and mild cognitive impairment.
      ,
      • Farias S.T.
      • Mungas D.
      • Reed B.R.
      • Harvey D.
      • Cahn-Weiner D.
      • Decarli C.
      MCI is associated with deficits in everyday functioning.
      Although the prospective nature of this study lends stronger support for directionality of the effects (ie, from lower cognitive functioning to poorer psychological well-being) than cross-sectional research provides, mechanisms by which cognition during acute rehabilitation exert effects on wellbeing after discharge were not examined. The pathways of influence, from lower cognitive functioning to poorer psychological wellbeing, are currently not elucidated and may involve a combination of factors (eg, lower ability to learn skills, disruption of learning efforts, and unfolding inflammatory processes).
      • Wu J.
      • Zhao Z.
      • Sabirzhanov B.
      • et al.
      Spinal cord injury causes brain inflammation associated with cognitive and affective changes: role of cell cycle pathways.
      ,
      • Whyte E.
      • Skidmore E.
      • Aizenstein H.
      • Ricker J.
      • Butters M.
      Cognitive impairment in acquired brain injury: a predictor of rehabilitation outcomes and an opportunity for novel interventions.
      Future research should examine the longitudinal course of adaptation among individuals with SCI to identify mediators of the association between cognition during acute hospitalization and later psychological well-being. Furthermore, given the potential for attrition in longitudinal studies, future work should also incorporate study designs that facilitate exploring alternative missing data mechanisms (eg, missing not at random using selection and pattern-mixture models), as assumptions of missing data as missing at random may not always be accurate, particularly when missingness is related to unmeasured variables or to the outcome.
      • Little R.J.A.
      Pattern-mixture models for multivariate incomplete data.
      • Dziura J.D.
      • Post L.A.
      • Zhao Q.
      • Fu Z.
      • Peduzzi P.
      Strategies for dealing with missing data in clinical trials: from design to analysis.
      • Enders C.K.
      Analyzing longitudinal data with missing values.
      Latent profile analysis is a probabilistic data analytical method with findings that may be sample-dependent and influenced by the specific variables assessed. For instance, some identified covariates of cognitive functioning may be proxies of other contextual factors (eg, lower educational attainment may be a proxy of premorbid intellectual functioning or of preexisting learning disorders), whereas other variables may be unassessed in this study. Given this, the latent profiles identified may not represent natural types present within a population.
      • Lanza S.T.
      • Rhoades B.L.
      Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment.
      However, the findings of this study may still provide a useful heuristic for understanding differential patterns cognition among individuals with SCI. Future work should attempt to replicate findings, include higher-quality indicators of cognitive functioning, and examine a diverse range of correlates that may influence cognition.

      Conclusions

      Prospective associations between profiles of cognitive functioning during acute rehabilitation hospitalization and psychological well-being after discharge suggest that lower cognitive functioning is a risk factor for poorer adjustment after SCI. Identifying individuals with cognitive dysfunction and attending to modifiable risk factors may help clinicians direct resources to those with the greatest need and ameliorate the psychological sequelae of SCI. Additional longitudinal observational studies may help to inform and target interventional approaches.

      Acknowledgments

      We thank the research coordinators and assistants who made data collection possible. We also thank Alessandra Barlas, BS, posthumously, for her assistance with data collection and data entry, and for her overall dedication to the participants in this research study.

      Suppliers

      • a.
        Mplus.
      • b.
        IBM Corp.

      References

        • Wilmot C.B.
        • Cope D.N.
        • Hall K.M.
        • Acker M.
        Occult head injury: its incidence in spinal cord injury.
        Arch Phys Med Rehabil. 1985; 66: 227-231
        • Davidoff G.N.
        • Roth E.J.
        • Richards J.S.
        Cognitive deficits in spinal cord injury: epidemiology and outcome.
        Arch Phys Med Rehabil. 1992; 73: 275-284
        • Sachdeva R.
        • Gao F.
        • Chan C.C.H.
        • Krassioukov A.V.
        Cognitive function after spinal cord injury: a systematic review.
        Neurology. 2018; 91: 611-621
        • Craig A.
        • Guest R.
        • Tran Y.
        • Middleton J.
        cognitive impairment and mood states after spinal cord injury.
        J Neurotrauma. 2016; 34: 1156-1163
        • Roth E.
        • Davidoff G.
        • Thomas P.
        • et al.
        A controlled study of neuropsychological deficits in acute spinal cord injury patients.
        Spinal Cord. 1989; 27: 480-489
        • Strubreither W.
        • Hackbusch B.
        • Hermann-Gruber M.
        • Stahr G.
        • Jonas H.P.
        Neuropsychological aspects of the rehabilitation of patients with paralysis from a spinal injury who also have a brain injury.
        Spinal Cord. 1997; 35: 487-492
        • Chiaravalloti N.D.
        • Weber E.
        • Wylie G.
        • Dyson-Hudson T.
        • Wecht J.M.
        Patterns of cognitive deficits in persons with spinal cord injury as compared with both age-matched and older individuals without spinal cord injury.
        J Spinal Cord Med. 2020; 43: 88-97
        • Jure I.
        • Labombarda F.
        Spinal cord injury drives chronic brain changes.
        Neural Regen Res. 2017; 12: 1044-1047
        • Lazzaro I.
        • Tran Y.
        • Wijesuriya N.
        • Craig A.
        Central correlates of impaired information processing in people with spinal cord injury.
        J Clin Neurophysiol. 2013; 30: 59-65
        • Iida H.
        • Tachibana S.
        • Kitahara T.
        • Horiike S.
        • Ohwada T.
        • Fujii K.
        Association of head trauma with cervical spine injury, spinal cord injury, or both.
        J Trauma. 1999; 46: 450-452
        • Michael D.B.
        • Guyot D.R.
        • Darmody W.R.
        Coincidence of head and cervical spine injury.
        J Neurotrauma. 1989; 6: 177-189
        • Kreutzer J.S.
        • Barth J.
        • Ellwood M.S.
        • et al.
        Occult Neuropsychological impairments in spinal-cord injured patients.
        Arch Phys Med Rehabil. 1988; 69: 764-765
        • Macciocchi S.N.
        • Seel R.T.
        • Thompson N.
        The impact of mild traumatic brain injury on cognitive functioning following co-occurring spinal cord injury.
        Arch Clin Neuropsychol. 2013; 28: 684-691
        • Comi G.
        • Filippi M.
        • Martinelli V.
        • et al.
        Brain MRI correlates of cognitive impairment in primary and secondary progressive multiple sclerosis.
        J Neurol Sci. 1995; 132: 222-227
        • Rahn K.
        • Slusher B.
        • Kaplin A.
        Cognitive impairment in multiple sclerosis: a forgotten disability remembered.
        Cerebrum. 2012; 2012: 14
        • Wu J.
        • Stoica B.A.
        • Luo T.
        • et al.
        Isolated spinal cord contusion in rats induces chronic brain neuroinflammation, neurodegeneration, and cognitive impairment.
        Cell Cycle. 2014; 13: 2446-2458
        • Wu J.
        • Zhao Z.
        • Sabirzhanov B.
        • et al.
        Spinal cord injury causes brain inflammation associated with cognitive and affective changes: role of cell cycle pathways.
        J Neurosci. 2014; 34: 10989-11006
        • Faden A.I.
        • Wu J.
        • Stoica B.A.
        • Loane D.J.
        Progressive inflammation-mediated neurodegeneration after traumatic brain or spinal cord injury.
        Br J Pharmacol. 2016; 173: 681-691
        • Wecht J.M.
        • Rosado-Rivera D.
        • Jegede A.
        • et al.
        Systemic and cerebral hemodynamics during cognitive testing.
        Clin Auton Res. 2012; 22: 25-33
        • Jegede A.B.
        • Rosado-Rivera D.
        • Bauman W.A.
        • et al.
        Cognitive performance in hypotensive persons with spinal cord injury.
        Clin Auton Res. 2010; 20: 3-9
        • Warren A.M.
        • Pullins J.
        • Elliott T.R.
        Concomitant cognitive impairment in persons with spinal cord injuries in rehabilitation settings.
        in: Gontovsky S.T. Golden C.J. Neuropsychology within the inpatient rehabilitation environment. Nova Science Publishers, Hauppauge, NY2008: 79-98
        • Avluk O.C.
        • Gurcay E.
        • Gurcay A.G.
        • Karaahmet O.Z.
        • Tamkan U.
        • Cakci A.
        Effects of chronic pain on function, depression, and sleep among patients with traumatic spinal cord injury.
        Ann Saudi Med. 2014; 34: 211-216
        • Richards J.S.
        • Osuna F.J.
        • Jaworski T.M.
        • Novack T.A.
        • Leli D.A.
        • Boll T.J.
        The effectiveness of different methods of defining traumatic brain injury in predicting postdischarge adjustment in a spinal cord injury population.
        Arch Phys Med Rehabil. 1991; 72: 275-279
        • Murray R.F.
        • Asghari A.
        • Egorov D.D.
        • et al.
        Impact of spinal cord injury on self-perceived pre- and postmorbid cognitive, emotional and physical functioning.
        Spinal Cord. 2007; 45: 429-436
        • Whiteneck G.
        • Meade M.A.
        • Dijkers M.
        • Tate D.G.
        • Bushnik T.
        • Forchheimer M.B.
        Environmental factors and their role in participation and life satisfaction after spinal cord injury.
        Arch Phys Med Rehabil. 2004; 85: 1793-1803
        • Nott M.T.
        • Baguley I.J.
        • Heriseanu R.
        • et al.
        Effects of concomitant spinal cord injury and brain injury on medical and functional outcomes and community participation.
        Top Spinal Cord Inj Rehabil. 2014; 20: 225-235
        • Rapoport M.J.
        • McCullagh S.
        • Shammi P.
        • Feinstein A.
        Cognitive impairment associated with major depression following mild and moderate traumatic brain injury.
        J Neuropsychiatry Clin Neurosci. 2005; 17: 61-65
        • Brennan L.
        • Devlin K.M.
        • Xie S.X.
        • et al.
        Neuropsychological subgroups in non-demented Parkinson’s disease: a latent class analysis.
        J Parkinsons Dis. 2017; 7: 385-395
        • Pasipanodya E.C.
        • Montoya J.L.
        • Campbell L.M.
        • et al.
        Metabolic risk factors as differential predictors of profiles of neurocognitive impairment among older HIV+ and HIV− adults: an observational study.
        Arch Clin Neuropsychol. 2019 Nov 5; ([Epub ahead of print])
        • Libon D.J.
        • Drabick D.A.
        • Giovannetti T.
        • et al.
        Neuropsychological syndromes associated with Alzheimer’s/vascular dementia: a latent class analysis.
        J Alzheimers Dis. 2014; 42: 999-1014
        • Dowler R.N.
        • Harrington D.
        • Haaland K.
        • Swanda R.
        • Fee F.
        • Fiedler K.
        Profiles of cognitive functioning in chronic spinal cord injury and the role of moderating variables.
        J Int Neuropsychol Soc. 1997; 3: 464-472
        • Randolph C.
        • Tierney M.C.
        • Mohr E.
        • Chase T.N.
        The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS): preliminary clinical validity.
        J Clin Exp Neuropsychol. 1998; 20: 310-319
        • Duff K.
        • Patton D.
        • Schoenberg M.R.
        • Mold J.
        • Scott J.G.
        • Adams R.L.
        Age- and education-corrected independent normative data for the RBANS in a community dwelling elderly sample.
        Clin Neuropsychol. 2003; 17: 351-366
        • Duff K.
        • Clark H.J.D.
        • O’Bryant S.E.
        • Mold J.W.
        • Schiffer R.B.
        • Sutker P.B.
        Utility of the RBANS in detecting cognitive impairment associated with Alzheimer’s disease: sensitivity, specificity, and positive and negative predictive powers.
        Arch Clin Neuropsychol. 2008; 23: 603-612
        • Gontkovsky S.T.
        • McSwan K.L.
        • Scott J.G.
        Sensitivity of the semantic fluency subtest of the Repeatable Battery for the Assessment of Neuropsychological Status.
        Psychol Rep. 2002; 90: 858-860
        • Randolph C.
        RBANS update: Repeatable Battery for the Assessment of Neuropsychological Status.
        Pearson Education, White Plains, NY2012
        • Faul M.
        • Xu L.
        • Wald M.
        • Coronado V.
        Traumatic brain injury in the United States: emergency department visits, hospitalizations and deaths 2002-2006.
        Centers for Disease Control and Prevention, Atlanta2010
        • Lovell M.R.
        • Iverson G.L.
        • Collins M.W.
        • et al.
        Measurement of symptoms following sports-related concussion: reliability and normative data for the Post-Concussion Scale.
        Appl Neuropsychol. 2006; 13: 166-174
        • Linacre J.M.
        • Heinemann A.W.
        • Wright B.D.
        • Granger C.V.
        • Hamilton B.B.
        The structure and stability of the Functional Independence Measure.
        Arch Phys Med Rehabil. 1994; 75: 127-132
        • Graham J.E.
        • Granger C.V.
        • Karmarkar A.M.
        • et al.
        The Uniform Data System for Medical Rehabilitation: report of follow-up information on patients discharged from inpatient rehabilitation programs in 2002-2010.
        Am J Phys Med Rehabil. 2014; 93: 231-244
        • Kroenke K.
        • Spitzer R.L.
        • Williams J.B.W.
        The PHQ-9.
        J Gen Intern Med. 2001; 16: 606-613
        • Spitzer R.L.
        • Kroenke K.
        • Williams J.B.W.
        and the Patient Health Questionnaire Primary Care Study Group. Validation and utility of a self-report version of PRIME-MDThe PHQ Primary Care Study.
        JAMA. 1999; 282: 1737-1744
        • Spitzer R.L.
        • Williams J.B.W.
        • Kroenke K.
        • Hornyak R.
        • McMurray J.
        Validity and utility of the PRIME-MD Patient Health Questionnaire in assessment of 3000 obstetric-gynecologic patients: the PRIME-MD Patient Health Questionnaire Obstetrics-Gynecology Study.
        Am J Obstet Gynecol. 2000; 183: 759-769
        • Neugarten B.L.
        • Havighurst R.J.
        • Tobin S.S.
        The measurement of life satisfaction.
        J Gerontol. 1961; 16: 134-143
      1. Hagenaars J.A. McCutcheon A.L. Applied latent class analysis. Cambridge University Press, New York2002
        • Berlin K.S.
        • Williams N.A.
        • Parra G.R.
        an introduction to latent variable mixture modeling (part 1): overview and cross-sectional latent class and latent profile analyses.
        J Pediatr Psychol. 2013; 39: 174-187
        • Asparouhov T.
        • Muthén B.
        Auxiliary variables in mixture modeling: three-step approaches using Mplus.
        Struct Equ Modeling. 2014; 21: 329-341
      2. Vermunt JK. Latent class modeling with covariates: two improved three-step approaches. Political Anal 18:450-469.

        • Muthén L.K.
        • Muthén B.
        Mplus user’s guide.
        8th ed. Muthén & Muthén, Los Angeles1998
        • Little R.J.
        • Rubin D.B.
        Statistical analysis with missing data. 793. John Wiley & Sons, Hoboken, NJ2019
        • Satorra A.
        • Bentler P.M.
        A scaled difference chi-square test statistic for moment structure analysis.
        Psychometrika. 2001; 66: 507-514
        • McKinley W.O.
        • Seel R.T.
        • Hardman J.T.
        Nontraumatic spinal cord injury: incidence, epidemiology, and functional outcome.
        Arch Phys Med Rehabil. 1999; 80: 619-623
        • Donnell A.J.
        • Kim M.S.
        • Silva M.A.
        • Vanderploeg R.D.
        Incidence of postconcussion symptoms in psychiatric diagnostic groups, mild traumatic brain injury, and comorbid conditions.
        Clin Neuropsychol. 2012; 26: 1092-1101
        • Meares S.
        • Shores E.A.
        • Taylor A.J.
        • et al.
        Mild traumatic brain injury does not predict acute postconcussion syndrome.
        J Neurol Neurosurg Amp Psychiatry. 2008; 79: 300
        • Chamelian L.
        • Feinstein A.
        Outcome after mild to moderate traumatic brain injury: the role of dizziness.
        Arch Phys Med Rehabil. 2004; 85: 1662-1666
        • van der Horn H.J.
        • Spikman J.M.
        • Jacobs B.
        • van der Naalt J.
        Postconcussive complaints, anxiety, and depression related to vocational outcome in minor to severe traumatic brain injury.
        Arch Phys Med Rehabil. 2013; 94: 867-874
        • Landre N.
        • Poppe C.J.
        • Davis N.
        • Schmaus B.
        • Hobbs S.E.
        Cognitive functioning and postconcussive symptoms in trauma patients with and without mild TBI.
        Arch Clin Neuropsychol. 2006; 21: 255-273
        • King N.S.
        • Crawford S.
        • Wenden F.J.
        • Moss N.E.G.
        • Wade D.T.
        The Rivermead Post Concussion Symptoms Questionnaire: a measure of symptoms commonly experienced after head injury and its reliability.
        J Neurol. 1995; 242: 587-592
        • Al Sayegh A.
        • Sandford D.
        • Carson A.J.
        Psychological approaches to treatment of postconcussion syndrome: a systematic review.
        J Neurol Neurosurg Amp Psychiatry. 2010; 81: 1128
        • Collins M.W.
        • Kontos A.P.
        • Okonkwo D.O.
        • et al.
        Statements of agreement from the Targeted Evaluation and Active Management (TEAM) Approaches to Treating Concussion Meeting held in Pittsburgh, October 15-16, 2015.
        Neurosurgery. 2016; 79: 912-929
        • Mittenberg W.
        • Burton D.B.
        A survey of treatments for post-concussion syndrome.
        Brain Inj. 1994; 8: 429-437
        • Parikh P.K.
        • Troyer A.K.
        • Maione A.M.
        • Murphy K.J.
        The impact of memory change on daily life in normal aging and mild cognitive impairment.
        Gerontologist. 2015; 56: 877-885
        • Farias S.T.
        • Mungas D.
        • Reed B.R.
        • Harvey D.
        • Cahn-Weiner D.
        • Decarli C.
        MCI is associated with deficits in everyday functioning.
        Alzheimer Dis Assoc Disord. 2006; 20: 217-223
        • Whyte E.
        • Skidmore E.
        • Aizenstein H.
        • Ricker J.
        • Butters M.
        Cognitive impairment in acquired brain injury: a predictor of rehabilitation outcomes and an opportunity for novel interventions.
        PM R. 2011; 3: S45-51
        • Little R.J.A.
        Pattern-mixture models for multivariate incomplete data.
        J Am Stat Assoc. 1993; 88: 125-134
        • Dziura J.D.
        • Post L.A.
        • Zhao Q.
        • Fu Z.
        • Peduzzi P.
        Strategies for dealing with missing data in clinical trials: from design to analysis.
        Yale J Biol Med. 2013; 86: 343-358
        • Enders C.K.
        Analyzing longitudinal data with missing values.
        Rehabil Psychol. 2011; 56: 267-288
        • Lanza S.T.
        • Rhoades B.L.
        Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment.
        Prev Sci. 2013; 14: 157-168