Predictors of 1-Year Global Outcomes After Traumatic Brain Injury Among Older Adults: A NIDILRR Traumatic Brain Injury Model Systems Study

Distal and Proximal Predictors of Rehospitalization Over 10 Years Among Survivors of TBI: A National Institute on Disability, Independent Living, and Rehabilitation Research Traumatic Brain Injury Model Systems Study

OBJECTIVE

To describe the rates and causes of rehospitalization over a 10-year period following a moderate-severe traumatic brain injury (TBI) utilizing the Healthcare Cost and Utilization Project (HCUP) diagnostic coding scheme.

SETTING

TBI Model Systems centers.

PARTICIPANTS

Individuals 16 years and older with a primary diagnosis of TBI.

DESIGN

Prospective cohort study.

MAIN MEASURES

Rehospitalization (and reason for rehospitalization) as reported by participants or their proxies during follow-up telephone interviews at 1, 2, 5, and 10 years postinjury.

RESULTS

The greatest number of rehospitalizations occurred in the first year postinjury (23.4% of the sample), and the rates of rehospitalization remained stable (21.1%-20.9%) at 2 and 5 years postinjury and then decreased slightly (18.6%) at 10 years postinjury. Reasons for rehospitalization varied over time, but seizure was the most common reason at 1, 2, and 5 years postinjury. Other common reasons were related to need for procedures (eg, craniotomy or craniectomy) or medical comorbid conditions (eg, diseases of the heart, bacterial infections, or fractures). Multivariable logistic regression models showed that Functional Independence Measure (FIM) Motor score at time of discharge from inpatient rehabilitation was consistently associated with rehospitalization at all time points. Other factors associated with future rehospitalization over time included a history of rehospitalization, presence of seizures, need for craniotomy/craniectomy during acute hospitalization, as well as older age and greater physical and mental health comorbidities.

CONCLUSION

Using diagnostic codes to characterize reasons for rehospitalization may facilitate identification of baseline (eg, FIM Motor score or craniotomy/craniectomy) and proximal (eg, seizures or prior rehospitalization) factors that are associated with rehospitalization. Information about reasons for rehospitalization can aid healthcare system planning. By identifying those recovering from TBI at a higher risk for rehospitalization, providing closer monitoring may help decrease the healthcare burden by preventing rehospitalization.

Predictors of cognitive gains during inpatient rehabilitation for older adults with traumatic brain injury

INTRODUCTION

Traumatic brain injury (TBI) among older adults is increasing and can affect cognition. To effectively meet the rehabilitation needs of older adults, a clearer picture is needed of patient-, clinical-, and facility-level characteristics that affect cognitive recovery during inpatient rehabilitation facility (IRF) stays.

OBJECTIVE

To identify patient, clinical, and facility factors associated with cognitive recovery among older adults with TBI who received IRF care.

DESIGN

Secondary data analysis.

SETTING

Uniform Data System for Medical Rehabilitation-participating IRFs in the United States.

PATIENTS

Patients were 65 to 99 years of age at IRF admission for TBI. Participants received IRF care between 2002 and 2018 (N = 137,583); 56.3% were male; 84.2% were white; mean age was 78.7 years.

MAIN OUTCOME MEASURE

Change in Functional Independence Measure Cognitive Score (FIM-Cognitive) from IRF admission to discharge, categorized as favorable (FIM-cognitive score gains ≥3 points) or poor (FIM-cognitive score gains <3 points) cognitive outcomes.

INTERVENTIONS

Not applicable.

RESULTS

Patients had greater odds of favorable cognitive recovery if they were female (adjusted odds ratio [aOR] 1.05, 95% confidence interval [CI] 1.05-1.08), had higher motor functioning at IRF admission (aOR 1.03, 95% CI 1.03-1.04), longer length of stay (aOR 1.07, 95% CI 1.06-1.07), or received care at a freestanding IRF (vs. hospital rehab unit) (aOR 1.57, 95% CI 1.52-1.61). Patients who were older (aOR 0.99, 95% CI 0.98-0.99), Black (aOR 0.79, 95% CI 0.75-0.83), Hispanic or Latino (aOR 0.97, 95% CI 0.91-1.02), or were part of another racial or ethnic group (aOR 0.85, 95% CI 0.81-0.90) (vs. White), had high-cost comorbid conditions (aOR 0.71, 95% CI 0.65-0.76), or who had higher cognitive functioning at IRF admission (aOR 0.90, 95% CI 0.90-0.91) had lower odds of favorable cognitive recovery.

CONCLUSIONS

Patient (age, sex, race, ethnicity), clinical (level of functioning at IRF admission, length of stay) and facility (e.g., freestanding IRF) factors contributed to the cognitive recoveries of older adults during IRF stays.

Predictors of 10-year functional independence trajectories in older adults with traumatic brain injury: A Model Systems study

BACKGROUND

Older adults have the highest traumatic brain injury (TBI)-related morbidity and mortality, and rates in older adults are increasing, chiefly due to falls.

OBJECTIVE

This study used hierarchical linear modeling (HLM) to examine baseline predictors of functional independence trajectories across 1, 2, 5, and 10 years after TBI in older adults.

METHODS

Participants comprised 2,459 individuals aged 60 or older at the time of TBI, enrolled in the longitudinal TBI Model Systems database, and had Functional Independence Measure Motor and Cognitive subscale scores and Glasgow Outcome Scale-Extended scores during at least 1 time point.

RESULTS

Functional independence trajectories generally declined over the 10 years after TBI. Individuals who were older, male, underrepresented minorities, had lower education, were unemployed at time of injury, had no history of substance use disorder, or had difficulties with learning, dressing, and going out of the home prior to the TBI, or longer time in posttraumatic amnesia had lower functional independence trajectories across at least one of the functional independence outcomes.

CONCLUSION

These predictors of functional independence in older adults with TBI may heighten awareness of these factors in treatment planning and long-term health monitoring and ultimately as a way to decrease morbidity and mortality.

Comorbidity in traumatic brain injury and functional outcomes: a systematic review

INTRODUCTION

Comorbidities in people with traumatic brain injury (TBI) may negatively impact injury recovery course and result in long-term disability. Despite the high prevalence of several categories of comorbidities in TBI, little is known about their association with patients' functional outcomes. We aimed to systematically review the current evidence to identify comorbidities that affect functional outcomes in adults with TBI.

EVIDENCE ACQUISITION

A systematic search of Medline, Cochrane Central Register of Controlled Trials, Embase and PsycINFO was conducted from 1997 to 2020 for prospective and retrospective longitudinal studies published in English. Three researchers independently screened and assessed articles for fulfillment of the inclusion criteria. Quality assessment followed the Quality in Prognosis Studies tool and the Scottish Intercollegiate Guidelines Network methodology recommendations.

EVIDENCE SYNTHESIS

Twenty-two studies of moderate quality discussed effects of comorbidities on functional outcomes of patients with TBI. Cognitive and physical functioning were negatively affected by comorbidities, although the strength of association, even within the same categories of comorbidity and functional outcome, differed from study to study. Severity of TBI, sex/gender, and age were important factors in the relationship. Due to methodological heterogeneity between studies, meta-analyses were not performed.

CONCLUSIONS

Emerging evidence highlights the adverse effect of comorbidities on functional outcome in patients with TBI, so clinical attention to this topic is timely. Future research on the topic should emphasize time of comorbidity onset in relation to the TBI event, to support prevention, treatment, and rehabilitation. PROSPERO registration (CRD 42017070033).

The Traumatic Brain Injury Model Systems National Database: A Review of Published Research

The Traumatic Brain Injury Model Systems (TBIMS) is the largest longitudinal TBI data set in the world. Our study reviews the works using TBIMS data for analysis in the last 5 years. A search (2015–2020) was conducted across PubMed, EMBASE, and Google Scholar for studies that used the National Institute on Disability, Independent Living and Rehabilitation Research NIDILRR/VA-TBIMS data. Search terms were as follows: [“TBIMS” national database] within PubMed and Google Scholar, and [“TBIMS” AND national AND database] on EMBASE. Data sources, study foci (in terms of data processing and outcomes), study outcomes, and follow-up information usage were collected to categorize the studies included in this review. Variable usage in terms of TBIMS' form-based variable groups and limitations from each study were also noted. Assessment was made on how TBIMS' objectives were met by the studies. Of the 74 articles reviewed, 23 used TBIMS along with other data sets. Fifty-four studies focused on specific outcome measures only, 6 assessed data aspects as a major focus, and 13 explored both. Sample sizes of the included studies ranged from 11 to 15,835. Forty-two of the 60 longitudinal studies assessed follow-up from 1 to 5 years, and 15 studies used 10 to 25 years of the same. Prominent variable groups as outcome measures were “Employment,” “FIM,” “DRS,” “PART-O,” “Satisfaction with Life,” “PHQ-9,” and “GOS-E.” Limited numbers of studies were published regarding tobacco consumption, the Brief Test of Adult Cognition by Telephone (BTACT), the Supervision Rating Scale (SRS), general health, and comorbidities as variables of interest. Generalizability was the most significant limitation mentioned by the studies. The TBIMS is a rich resource for large-sample longitudinal analyses of various TBI outcomes. Future efforts should focus on under-utilized variables and improving generalizability by validation of results across large-scale TBI data sets to better understand the heterogeneity of TBI.