The document compares functional outcomes between pediatric and adult patients with traumatic brain injury (TBI) who underwent inpatient rehabilitation. It finds: 1) Increasing age was associated with improved outcomes in children but poorer outcomes in adults, as measured by Functional Independence Measure (FIM) scores. 2) Several factors like gender, Glasgow Coma Scale scores, and presence of midline shift differed between pediatric and adult groups and impacted functional outcomes. 3) The relationship between age and functional outcome after TBI differs between pediatric and adult populations, with moderating variables also having different effects between the two age groups.

1. J Head Trauma Rehabil
Vol. 23, No. 4, pp. 209–219
Copyright c⃝ 2008 Wolters Kluwer Health | Lippincott Williams & Wilkins
Traumatic Brain Injury: A Comparison
of Inpatient Functional Outcomes
Between Children and Adults
Christian M. Niedzwecki, DO; Jennifer H. Marwitz, MA; Jessica M. Ketchum, PhD;
David X. Cifu, MD; Charles M. Dillard, MD; Eugenio A. Monasterio, MD
Objectives: To examine age-related differences in functional outcomes following traumatic brain injury. Participants
and procedure: Seventy-six patients admitted to a pediatric acute rehabilitation hospital were compared with 2548
adult patients in the National Institute on Disability and Rehabilitation Research–funded traumatic brain injury
model systems national database. Main outcome measures: Functional Independence Measure totals during inpa-
tient rehabilitation. Results: Increasing age was significantly associated with improved outcome in children and with
poorer outcome in adults. Conclusion: The relationship between age and functional outcome is different within
different age groups (pediatric vs adult), and the effect of moderating variables differs by age group. Keywords: age,
functional independence measure, functional outcomes, pediatric rehabilitation, predictive model, traumatic brain injury
TRAUMATIC BRAIN INJURY (TBI) is not just a
lifelong healthcare problem for the adult or child
survivor and their families; it also has short- and long-
term costs to society. Acutely, across all ages there are 1.4
million TBI cases per year in the United States, 50,000
are fatal, 235,000 require hospitalization, and 1.1 mil-
lion are treated and discharged from emergency depart-
ments. Of these, children account for 475,000 TBI cases
per year with 2685 deaths and 37,000 hospitalizations.1
Chronically, it is estimated that more than 2% of the
US population, has a long-term or lifelong disability as
a result of TBI.2
The direct and indirect costs of TBI
are approximately $60 billion.3
For children, estimates
of residual neurological disabilities after sustaining a se-
vere TBI range from 30% to 50%.4,5
In response to the significant effect of TBI on soci-
ety, the US National Institute on Disability and Reha-
bilitation Research established the TBI model system
(TBIMS) national database, collecting large amounts of
From the Departments of Physical Medicine and Rehabilitation (Ms
Marwitz and Drs Niedzwecki, Cifu, Dillard, and Monasterio) and
Biostatistics (Dr Ketchum), Virginia Commonwealth University, and
Children’s Hospital of Richmond (Dr Monasterio), Richmond, Virginia.
Nocommercialpartyhavingdirectfinancialinterestintheresultsoftheresearch
supporting this article has or will confer a benefit upon the authors or upon any
organization with which the authors are associated. Supported in part by the
National Institute on Disability and Rehabilitation Research, Office of Special
Education and Rehabilitative Services, and US Department of Education
(grant no. H133A020516).
Corresponding author: Christian M. Niedzwecki, DO, Department of Phys-
ical Medicine and Rehabilitation, Virginia Commonwealth University, Box
980677, Richmond, VA 23298 (e-mail: cniedzwecki@mcvh-vcu.edu).
detailed short-term and long-term data on individuals
older than 15 and with TBI.6
The research derived from
the TBIMS has significantly contributed to the under-
standing of TBI in adults through identification of out-
come predictors, providing longitudinal perspectives on
the complexity of functional outcomes, and informing
rehabilitation strategy changes by identifying adverse
medical outcomes.7–10
It has even allowed for the iden-
tification of subpopulations within the model system’s
participants.11,12
There is no similar system for individ-
uals with TBI and younger than 16, posing a significant
obstacle to understanding basic outcome predictors, de-
veloping appropriate functional outcome measures, and
identifying the long-term effects of childhood TBI.
Because of this limited federal funding, obtaining ap-
propriate sample size has been one of many challenges
in the study of pediatric TBI. To overcome this issue, re-
searchers have primarily used 2 sample-gathering strate-
gies: existing databases and collaborative efforts to build
sample populations. The main databases used have little
longitudinal data, but larger numbers of affected chil-
dren. They include the Uniform Data System of Med-
ical Rehabilitation database (sample sizes ranged from
n = 814 to n = 3815)13–15
and the National Pediatric
Trauma Registry database (sample sizes ranged from n =
598 to n = 16 586).16–20
The samples of children with
TBI obtained from independent regional medical cen-
ters generally have lower numbers but increased longitu-
dinal data. The most notable efforts have been in Seattle,
Wash (sample sizes ranged from n = 33 to n = 98),21–26
and Melbourne, Australia (sample sizes ranged from
n = 16 to n = 122).27–29
These studies have shown that,
209

2. 210 JOURNAL OF HEAD TRAUMA REHABILITATION/JULY–AUGUST 2008
similar to adults, TBI recovery is a complex process that
can be related to premorbid, cognitive, behavioral, func-
tional, and support (ie, family) factors.30–32
The research
has shown that increasing severity of injury generally
leads to increasing disability in the cognitive, behavioral,
and functional domains.23,26,29,33,34
These studies have
also suggested that during recovery from pediatric TBI
some gains may not be solely due to an improvement
of condition, but possibly due to obtaining appropriate
developmental milestones.14,29
The unique challenge of studying children with TBI
has led to the development of numerous scales attempt-
ing to measure functional outcomes in the setting of
a developing child. The Wee Functional Independence
Measure (WeeFIM), adapted from the adult FIM in-
strument to address children aged from 6 months to 7
years, has allowed for valid, efficient, and reproducible,
comparisons among children with disabilities, includ-
ing TBI.14,27,35–40
The WeeFIM has been shown to have
good correlation with the FIM in children (2–12 years)
with cerebral palsy.41
Both the WeeFIM and FIM have
since been used as equivalent measures to study chil-
dren with spinal cord injury42
and TBI.26,43
Interest-
ingly, age-related trends in the WeeFIM have been shown
to be similar in normative35,44,45
and disabled pediatric
populations,26,36,43
lending more support to using the
WeeFIM as a tool to measure functional outcome in the
developing child.
The authors believe that a greater appreciation for dif-
ferences between adult and pediatric TBI will be gained
by viewing TBI across the entire age spectrum, and that
this better understanding will be important in realisti-
cally predicting outcomes. The objectives of this study
were to (1) identify and quantify differences in functional
outcome with respect to age following TBI, (2) examine
moderating variables that may impact functional out-
come across ages, (3) and examine how these variables
relate specifically to a pediatric rehabilitation sample.
METHODS
Participants
After institutional review board approval, 85 charts
from consecutive pediatric inpatient rehabilitation ad-
missions with TBI were identified and retrospectively re-
viewed. Seventy-six charts had complete data and were
then used in this study. Data were collected via chart
review and entered into a database. The National Insti-
tute on Disability and Rehabilitation Research TBIMS
database6
was used to obtain an adult sample (aged
20–60 years) for comparison. Only TBIMS participants
with complete data for the covariates of interest were
included in the study (N = 2548). The adult and pedi-
atric databases were then combined into 1 data set for
analysis (N = 2624).
Instruments
The FIM and WeeFIM were used to determine pa-
tients’ functional progress during their rehabilitation
stay. The FIM and WeeFIM are 18-item measures of
function with higher scores indicating greater levels of in-
dependence. The items describe levels of functional abil-
ity in the areas of daily living, continence, mobility, com-
munication, and cognition. Items are rated on a 7-point
scale with values denoting degrees of dependence and
independence. The measure is typically administered at
the time of admission and discharge and at 1- to 2-week
intervals in between. The instrument has been widely
used to measure brain injury outcome and treatment
response.36,46–48
Both the FIM and WeeFIM have been
validated and have good interrater reliability.35,38,49
In
addition, research has demonstrated the interchangeabil-
ity and high correlation of FIM and WeeFIM scores.40–42
Procedure
All patients, whether pediatric or adult, underwent
a comprehensive inpatient rehabilitation program that
included nursing, occupational therapy, physical ther-
apy, psychology and neuropsychology, physiatry, social
work services, speech-language pathology, recreational
therapy, and other medical services. All admission and
discharge decisions were made using Commission on Ac-
creditation of Rehabilitation Facilities standards applied
to patient’s needs.
Data were collected by clinicians and research as-
sistants and included age at injury, gender, ethnicity,
cause of injury, emergency department admission Glas-
gow Coma Score (GCS), and acute care and inpatient
rehabilitation length of stay (LOS). Per TBIMS proto-
col, presence of intracranial compression, defined as mid-
line shift or cistern compression greater than 5 mm, was
documented by a physiatrist on the basis of a combina-
tion of reports taken from radiographic CT scan results
within 7 days of injury. Functional Independence Mea-
sure and WeeFIM scores were assigned by the interdis-
ciplinary rehabilitation team. Functional Independence
Measure efficiency was calculated by dividing net change
in FIM/WeeFIM score (discharge FIM score − admis-
sion FIM score) by the number of days in inpatient re-
habilitation. Thus, higher efficiencies are associated with
shorter LOS for a given change in FIM scores.50
Data analysis
Summary statistics of the demographic and injury
characteristics (means and standard deviations for con-
tinuous variables and counts and percentages for cat-
egorical variables) were calculated for each group and
compared using t tests (continuous variables) and chi-
square tests (categorical variables). Summary statistics for

3. Traumatic Brain Injury 211
TABLE 1 Summary statistics for demographic and injury characteristics∗
Pediatric (N = 76) Adult (N = 2548)
Mean (SD) Mean (SD) T (df); P
Age 12.58 (5.71) 36.90 (11.29)
Acute LOS 17.53 (14.21) 19.69 (15.15) 1.23 (2622); .2184
Rehabilitation LOS 21.47 (21.36) 26.15 (22.76) 1.77 (2527); .0772
Count (%) Count (%) χ2
(df); P
Ethnicity 1.0 (1); .3069
White 34 (44.7) 1556 (61.1)
Other 42 (55.3) 992 (38.9)
Gender 21.2 (1); <.0001
Female 36 (47.4) 617 (24.2)
Male 40 (52.6) 1931 (75.8)
Cause of injury 14.3 (5); .0139
Fall 6 (7.9) 409 (16.1)
MVA 46 (60.5) 1068 (41.9)
Other vehicle 6 (7.9) 329 (12.9)
Pedestrian 7 (9.2) 240 (9.4)
Sports/other 4 (5.3) 77 (3.0)
Violence 7 (9.2) 425 (16.7)
GCS 27.6 (2); <.0001
Severe (3–8) 61 (80.3) 1291 (50.7)
Moderate (9–12) 9 (11.8) 435 (17.1)
Mild (13–15) 6 (7.9) 822 (32.3)
Acute care LOS 3.07 (3); .3807
0–1 week 17 (22.4) 476 (18.7)
1–2 weeks 25 (32.9) 674 (26.4)
2–3 weeks 13 (17.1) 537 (21.1)
>3 weeks 21 (27.6) 861 (33.8)
Midline shift 14.8 (1); .0001
≤ 5 mm 57 (75.0) 2272 (89.2)
>5 mm 19 (25.0) 276 (10.8)
∗LOS indicates length of stay; MVA, motor vehicle accident; GCS, Glasgow Coma Score.
the FIM (admission, discharge, and efficiency) were also
calculated. The t tests were used as a preliminary step to
compare the mean FIM scores across 2 age groups.
For each FIM score (admission, discharge, and effi-
ciency) an analysis of covariance (ANCOVA) model was
used to determine the form of the relationship between
age (examined as a continuous variable) and FIM, after
adjusting for covariates (linear, quadratic, and cubic rela-
tionship forms were considered). Because the manner in
which the FIM was measured could depend on the group
(WeeFIM for pediatric and FIM for adult cases), a FIM-
group effect was always included in the ANCOVA mod-
els to adjust for possible mean differences in the FIM
scores between the 2 groups. In addition, the ANCOVA
models included effects for selected covariates (ethnic-
ity, gender, cause of injury, acute care LOS, GCS, and
midline shift) and the 2-way interaction effects between
WeeFIM and FIM group and each of the covariates.
Regardless of the significance of the interaction effect,
tests of each covariate were performed for pediatric and
adult subjects separately. This allowed authors to draw
inferences for both pediatric and adult subjects. In an
effort to control the overall type I error rate, differences
among levels of covariates within each group were ex-
amined only if the overall F tests for the main effects
or interaction effects were significant. In these cases,
Bonferroni methods for multiple comparisons were
utilized.
RESULTS
The demographic and injury characteristics of the
sample are summarized in Table 1 by group (pediatric
vs adult). Compared with adults, pediatric subjects were
more likely to be female, have a midline shift greater
than 5 mm, and have different distributions of cause of
injury and GCS (all P values ≤ .0139). No significant
differences were noted between age groups in ethnicity,
acute care LOS, or rehabilitation LOS (all P values ≥
.0772).
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4. 212 JOURNAL OF HEAD TRAUMA REHABILITATION/JULY–AUGUST 2008
TABLE 2 Summary of functional independence measures by group
Admission Discharge Efficiency
Age group, y N Mean (SD) N Mean (SD) N Mean (SD)
1–3 9 29.67 (29.73) 9 47.89 (29.01) 9 1.28 (1.24)
4–6 7 38.71 (14.49) 7 80.29 (10.58) 7 3.33 (2.11)
7–9 10 42.30 (21.85) 10 80.60 (23.38) 10 2.34 (1.75)
10–12 5 41.00 (29.89) 5 86.20 (31.85) 5 3.46 (2.09)
13–15 13 50.85 (26.98) 13 98.15 (10.03) 13 2.80 (1.79)
16–19 32 58.44 (25.81) 32 94.13 (13.73) 32 2.44 (2.10)
20–24 517 53.83 (26.09) 521 96.12 (22.69) 488 2.32 (1.61)
25–29 320 55.22 (26.68) 323 96.55 (22.03) 302 2.25 (1.43)
30–34 279 57.43 (25.46) 279 98.54 (21.27) 266 2.34 (1.38)
35–39 313 57.50 (25.94) 315 96.90 (21.50) 296 2.11 (1.38)
40–44 333 56.68 (25.19) 339 96.78 (20.37) 320 2.20 (1.23)
45–49 276 55.02 (24.60) 288 96.53 (19.13) 260 2.09 (1.08)
50–54 225 53.42 (23.23) 226 93.96 (21.14) 213 2.14 (1.35)
56–60 164 56.61 (25.05) 169 93.22 (24.88) 155 2.09 (1.39)
Breakdowns of FIM admission, discharge, and effi-
ciency scores by age are displayed in Table 2. Without
adjusting for covariates, the trend was for FIM admission
and discharge scores to increase and then steady out as
age increased (see Fig 1). More substantial increases were
observed in the younger subjects. Figure 2 displays FIM
efficiency scores across ages. As expected from review
of the literature,35,44,45
very young children (ages 1–3)
showed less efficiency in making functional gains (mean
Figure 1. Mean Functional Independence Measure (FIM) scores and predicted FIM scores.
= 1.00). For children older than the age of 3, FIM ef-
ficiency scores were highly variable but more efficient
than the adult sample (overall mean for adults was 2.21
vs 2.58 for pediatric sample aged 3–19).
Admission and discharge FIM scores
The relationship between age and admission FIM
was examined using an ANCOVA model and included

5. Traumatic Brain Injury 213
Figure 2. Mean efficiency and predicted efficiency.
effects for age, WeeFIM versus FIM group, ethnicity,
gender, cause of injury, GCS, acute care LOS, and mid-
line shift, as well as interaction effects between WeeFIM
versus FIM group and each covariate. This model ac-
counted for 29% of the variations in admission FIM
scores (F30,2 472 = 32.90, P < .0001). There was evidence
of a significant nonlinear (cubic) relationship between
age and admission FIM (F1,2 472 = 4.58, P = .0324). As
shown in Figure 1, as age increased, there were greater
increases in FIM admission scores for younger subjects
than for older subjects.
A similar ANCOVA model was used to examine the
relationship between age and discharge FIM and in-
cluded an additional effect for admission FIM. This
model accounted for 48% of the variations in FIM dis-
charge scores (F32,2 438 = 70.05, P < .0001). There was
evidence of a significant nonlinear (cubic) relationship
between age and discharge FIM (F1,2 438 = 6.64, P =
.0100). In general, there was a trend for discharge FIM
scores to increase with age until middle age. As age in-
creased in adults, there was a slight decrease in discharge
scores.
Next, the selected covariates included in the adjusted
ANCOVA models were examined individually. In ev-
ery analysis completed, there were no significant dif-
ferences found between the mean WeeFIM and FIM
admission or discharge scores (admission, P = .5697;
and discharge, P = .8385). Further analyses of covariates
were completed and no significant interactions between
group (WeeFIM vs FIM) and the covariates were identi-
fied (all P values ≥ .0885); thus, there was no evidence
that the effect of the covariates on WeeFIM and FIM
scores (admission or discharge) was significantly differ-
ent. The estimated differences among the levels of the
gender, ethnicity, midline shift, GCS, and acute LOS
for each group (pediatric and adult) are summarized in
Table 3.
Covariates
Admission FIM: When predicting discharge FIM,
there was evidence of a nonlinear (quadratic) rela-
tionship between FIM admission and discharge scores
(F1,2 438 = 81.70, P < .0001). In general, individuals
with admission FIM scores of 100 or lower had larger
increases in discharge FIM scores than those with ad-
mission FIM scores greater than 100. The greatest rate
of increase was among individuals with admission FIM
scores lower than 37.
Cause of injury: For the pediatric subjects, there was no
evidence that FIM admission (F5,2 472 = 1.14, P = .3395)
or discharge scores (F5,2 438 = 1.82, P = .1056) were sig-
nificantly different among the cause of injury groups. For
the adult subjects, admission FIM scores were not sig-
nificantly different among the groups (F5,2 472] = 2.17,
P = .0545); however, discharge FIM scores were (F52 438
= 3.04, P = .0096). After adjusting for multiple compar-
isons (Bonferroni α = .0036), the other vehicle group
showed significantly greater discharge FIM scores than
the motor vehicle accident group, by 3.48 (SE = 1.04,
P = .0008).
Glasgow Coma Score: As shown in Table 3, for pe-
diatric subjects, there was no evidence that admission
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6. 214 JOURNAL OF HEAD TRAUMA REHABILITATION/JULY–AUGUST 2008
TABLE 3 Estimated differences between pediatric and adult samples∗
Pediatrics Adults
Difference (SE) P Difference (SE) P
Ethnicity
White to other
Admission −3.16 (6.18) .6092 1.52 (0.97) .1170
Discharge −2.93 (4.48) .5124 1.31 (0.71) .0633
Efficiency −0.70 (0.37) .0614 0.03 (0.06) .5645
Gender
Male to female
Admission 1.64 (5.78) .7765 1.47 (1.07) .1676
Discharge 2.15 (4.19) .6072 1.94 (0.78) .0132†
Efficiency −0.01 (0.35) .9793 0.12 (0.07) .0682
GCS
Mild to moderate
Admission 18.91 (13.02) .1467 4.92 (1.34) .0002†
Discharge 8.52 (9.44) .3671 −1.48 (0.98) .1308
Efficiency 1.76 (0.79) .0256 0.14 (0.08) .0883
Mild to severe
Admission 13.63 (10.25) .1837 14.80 (1.09) <.0001†
Discharge 8.50 (7.43) .2527 −1.32 (0.83) .1118
Efficiency 1.60 (0.62) .0102†
0.17 (0.07) .0130†
Moderate to severe
Admission −5.27 (8.52) .5358 9.87 (1.27) .0001†
Discharge −0.01 (6.17) .9983 0.16 (0.94) .8661
Efficiency −0.16 (0.52) .7554 0.03 (0.08) .7331
Midline shift
<5 mm to >5 mm
Admission 6.33 (6.61) .3385 5.56 (1.44) .0001†
Discharge −2.86 (4.79) .5502 3.29 (1.06) .0020†
Efficiency −0.13 (0.40) .7424 0.06 (0.09) .5143
LOS (acute)
0–1 week to 1–2 weeks
Admission 17.03 (7.45) .0224 7.03 (1.34) <.0001†
Discharge −8.45 (5.41) .1183 −0.10 (0.98) .9825
Efficiency −0.87 (0.45) .0527 0.25 (0.08) <.0001†
0–1 week to 2–3 weeks
Admission 30.01 (8.87) .0007†
13.61 (1.44) <.0001†
Discharge −0.77 (6.45) .9050 0.84 (1.07) .4307
Efficiency 0.11 (0.52) .0788 0.63 (0.09) <.0001†
0–1 week to >3 weeks
Admission 29.59 (7.56) .0001†
24.18 (1.35) <.0001†
Discharge −2.37 (5.50) .6671 7.11 (1.05) <.0001†
Efficiency 0.63 (0.46) .1672 1.19 (0.08) <.0001†
1–2 weeks to 2–3 weeks
Admission 12.99 (8.29) .1172 6.58 (1.30) <.0001†
Discharge 7.68 (6.01) .2015 0.94 (0.95) .3248
Efficiency 0.98 (0.50) .0502 0.28 (0.08) .0006†
1–2 weeks to >3 weeks
Admission 12.57 (7.00) .0729 17.15 (1.18) <.0001†
Discharge 6.08 (5.08) .2313 7.20 (0.91) <.0001†
Efficiency 1.50 (0.43) .0004†
0.84 (0.07) <.0001†
2–3 weeks to >3 weeks
Admission −0.42 (8.56) .9609 10.57 (1.24) <.0001†
Discharge −1.60 (6.21) .7970 6.27 (0.92) <.0001†
Efficiency 0.52 (0.52) .3141 0.56 (0.08) <.0001†
∗GCS indicates Glasgow Coma Score; LOS, length of stay.
† Indicates statistical significance at Bonferroni adjusted significance levels.

7. Traumatic Brain Injury 215
FIM (F2,2 472 = 1.12, P = .3277) or discharge FIM
scores (F2, 2 438] = 0.67, P = .5189) were signifi-
cantly different among the GCS groups. For adults,
there was no evidence that discharge FIM scores were
different (F2,2 438 = 1.64, P = .1945), but there was
evidence that admission FIM scores were (F2,2 472 =
96.35, P < .0001). After adjusting for multiple compar-
isons (Bonferroni α = .0167), all 3 GCS groups showed
significantly different admission FIM scores from each
other with lower admission FIM associated with severe
GCS and higher admission FIM associated with mild
GCS.
Acute care LOS: For pediatric subjects, there was no ev-
idence that discharge FIM scores were significantly dif-
ferent among the acute care LOS groups (F3,2 438 = 1.05,
P = .3694), but there was evidence that admission FIM
scores were (F3,2 472 = 6.22, P = .0003). After adjusting
for multiple comparisons (Bonferroni α = .0083), the
0- to 1-week group had significantly higher FIM scores
than the 2- to 3-week group and the greater-than-3-week
group. For adult subjects, there was evidence of mean
differences in both admission FIM (F3,2 472 = 124.68,
P < .0001) and discharge FIM (F3,2 438 = 27.26, P <
.0001) among the groups. After adjusting for multi-
ple comparisons (Bonferroni α = .0083), all 4 groups
showed significantly different admission FIM scores
with lower FIM scores associated with greater LOS. With
regard to discharge FIM scores, after adjusting for mul-
tiple comparisons (Bonferroni α = .0083), the 0- to
1-week, 1- to 2-week, and 2- to 3-week groups all had sig-
nificantly higher discharge FIM scores than the greater-
than-3-week group.
Midline shift, ethnicity, and gender: For pediatric sub-
jects, there was no evidence of significant differences
among the midline shift, ethnicity, or gender groups for
either admission FIM or discharge FIM. For adult sub-
jects, there was no evidence of significant differences
between the Caucasians and other ethnicities for admis-
sion or discharge FIM. Admission FIM scores for adult
subjects were not significantly different between men
and women, but men showed significantly greater dis-
charge FIM scores than women. Furthermore, for adult
subjects, those with 5 mm or less shift had significantly
greater admission and discharge FIM scores than those
with greater than 5 mm shift.
FIM Efficiency
The relationship between age and FIM efficiency was
examined using an ANCOVA model similar to the ones
for admission and discharge. This model accounted for
13.9% of the variations in FIM efficiency (F28,2 347 =
13.49, P < .0001). After adjusting for covariates, there
was evidence of a negative linear relationship between
age and FIM efficiency (F [12 347] = 13.62, P = .0002).
Each year increase in age was associated with a 0.010 unit
decrease in FIM efficiency (SE = 0.003).
After adjusting for age and other covariates, no sig-
nificant differences were found between mean WeeFIM
and FIM efficiency scores (P = .4096). Further anal-
yses of covariates were completed and no significant
differences were found between WeeFIM and FIM ef-
ficiency scores for any variables (P > .0525) with the
exception of cause of injury (P = .0330). The esti-
mated differences among the levels of the covariates
for each group (pediatric and adult) are summarized in
Table 3.
Covariates
Cause of injury: The differences in scores among the
cause of injury groups were significantly different for
WeeFIM and FIM efficiency (F5,2 347 = 2.43, P = .0330);
thus, the differences among the injury groups were dif-
ferent for children and adults. There was evidence that
efficiency scores were significantly different among the
cause of injury groups for both pediatric (F5,2 347 = 2.86,
P = .0141) and adult (F5,2 347 = 2.86, P = .0141) subjects.
After adjusting for multiple comparisons (Bonferroni
α = .0033), the other vehicle group showed significantly
higher WeeFIM efficiency than the violence group in
both groups (pediatric difference = 2.71, P = .0009;
adult difference = 0.32, P = .0025), and within the adults
the other vehicle group showed significantly higher FIM
efficiency than the pedestrian group (difference = 0.41,
P = .0007).
Glasgow Coma Scale: There was evidence that FIM ef-
ficiencies were significantly different among the GCS
groups for both the pediatric (F22 347 = 3.40, P = .0335)
and adult (F2,2 347 = 3.27, P = .0379) subjects. After ad-
justing for multiple comparisons (Bonferroni α = .0167),
the mild GCS group showed significantly greater FIM
efficiency scores than the severe GCS group in both
children and adults.
Acute care of LOS: There was evidence that FIM ef-
ficiency scores were significantly different among the
acute care LOS groups for both pediatric (F [32 347]
= 4.29, P < .005) and adult (F [32 347] = 77.66, P <
.0001) subjects. After adjusting for multiple comparisons
(Bonferroni α = .0083), the 1- to 2-week group had sig-
nificantly higher FIM efficiency than the greater than
3-week group within the pediatric subjects. For adults,
all pairwise comparisons of acute care LOS groups were
significant (see Table 3). Specifically within the adults,
the 0- to 1-week group had significantly greater FIM ef-
ficiency than the 1- to 2-week group; the 1- to 2-week
group had significantly greater FIM efficiency than the
2- to 3-week group; and the 2- to 3-week group had sig-
nificantly greater FIM efficiency than the greater than
3-week group.
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8. 216 JOURNAL OF HEAD TRAUMA REHABILITATION/JULY–AUGUST 2008
Midline shift, ethnicity, and gender: FIM efficiency scores
were not significantly different among the midline shift,
ethnicity, or gender groups for either pediatric or adult
subjects.
DISCUSSION
The primary goal of this study was to assess and
quantify the impact of age on functional improve-
ment and acute outcomes in TBI using similar measures
(WeeFIM/FIM). Our study represents the first evalua-
tion of the TBI age spectrum. It utilizes a large, standard-
ized, and multicenter database of adults for comparison
to a pediatric sample, and takes the field of research from
an observational level to an empirical level. Previous re-
searchers have made observations regarding age-related
differences but have not examined these differences sta-
tistically. The high level of agreement with earlier stud-
ies suggests that a bridge between 2 previously separate
volumes of literature can be built, and that previously
assumed differences between these 2 populations can be
shown empirically.
Foremost, after adjusting for injury severity and other
covariates, the mean differences between WeeFIM and
FIM scores were not statistically significant, suggesting
that our comparisons are valid within our sample. This is
in accordance with previous researchers’ work.26,36,42,43
Interestingly, FIM efficiency scores were highest for chil-
dren and lowest for older adults. The negative relation-
ship of age and FIM efficiency may suggest that children
respond more quickly to rehabilitation than adults.
Within our pediatric sample, WeeFIM scores were
lowest for very young children but increased with age.
Similar trends have been reported in both pediatric
normative35,44,45
and TBI26,51,52
samples. Physiologic
reasons have been postulated to account for these trends,
such as different fulcrums of injury (increased head-to-
body proportion),15
relative vulnerability of the imma-
ture brain to injury due to incomplete myelinization,51
or region-specific changes in gray matter with age.52
In
general though, the mainstay of reasoning has been ei-
ther a greater willingness of rehabilitation teams to al-
low younger children trials of rehabilitation care despite
their deficits, or a lack of younger children’s attainment
of specific developmental milestones.26,35,36
Recent re-
search into the discordance of the psychometric prop-
erties of the WeeFIM has begun to bear this out.14
Despite these limitations, the authors feel that the ex-
tensive validation,27,39,40
good interrater reliability,38
and
widespread use of the WeeFIM41–43
make it the best tool
currently available.
Another objective of this study was to quantify trends
in discharge FIM scores for the pediatric sample. No-
tably, with each year of increased age, pediatric patients
gained approximately 4 points on discharge FIM score.
The clinical significance of point changes in WeeFIM
have not been defined in the literature; however, in
adults, studies have clearly shown increased FIM points
are associated with decreased minutes of assistance53
and decreased expected costs of inpatient rehabilitation
stays.54
To better appreciate the role of moderating variables
on functional outcome and age, we examined relation-
ships between a number of injury characteristics (cause of
injury, GCS, acute care LOS, presence of midline shift,
and admission FIM) and patient demographics (ethnic-
ity and gender). In general, across the age spectrum, we
found that individuals with lower admission FIM scores
had lower discharge FIM scores, as expected.
Because of the small variety of the cause of injury
in our pediatric sample, decisions were made to com-
bine some categories. The other vehicle category in-
cluded bicycle, all-terrain vehicle, and motorcycle ac-
cidents. In the pediatric sample, the only significant
difference noted was that the other vehicle group had
higher FIM efficiency scores than the violence group.
Among adults, the other vehicle group had higher FIM
discharge scores than the motor vehicle accident group
and higher FIM efficiency scores than the pedestrian or
violence groups. We expected to see more differences;
however, after reviewing the literature, we found con-
flicting views on what constitutes an injury group. Some
researchers have shown that inflicted injuries have worse
outcomes than noninflicted injuries,55,56
whereas oth-
ers have shown that traffic-related injuries have more
impairments than nontraffic related injuries,16
and still
others have shown that penetrating head injuries have
worse outcomes than nonpenetrating head injuries.57,58
On the basis of our findings and review of the literature,
we feel that injury cause may be 1 piece in the puzzle of
describing the severity of a TBI.
The pediatric literature has shown there to be very lit-
tle correlation of initial GCS to functional outcome,24,26
which is contrary to the adult literature, showing a mod-
erately high correlation between initial GCS and func-
tional outcome.59–61
Aspects of the findings in this study
are in agreement with both pools of literature. For ex-
ample, there was no evidence that the pediatric sample’s
FIM admission or discharge scores differed on the basis
of admission GCS, whereas the adult sample’s discharge
and efficiency FIM scores were lower with lower GCS.
Interestingly, our results show that pediatric FIM effi-
ciency was higher for subjects with mild GCS and lower
for subjects with severe GCS. Although the authors feel
this finding is logical, there are currently no published
studies examining GCS and FIM efficiency in children.
The relationship between acute care LOS and all
FIM scores was clear in the adult group; longer acute
LOS was associated with reduced FIM scores. This
result was in agreement with the current adult and

9. Traumatic Brain Injury 217
pediatric literature.11,12,15
Our study’s pediatric findings
were congruent with admission FIM but not discharge
FIM scores. Hence, in our sample after adjusting for se-
lected covariates, all children reached similar discharge
scores during their inpatient rehabilitation stay despite
their acute care LOS.
Furthermore, a greater than 5-mm intracranial mid-
line shift has been clearly associated with decreased func-
tional outcomes in both adult59,62
and pediatric60,63
pop-
ulations. Again, our findings were in agreement with
prior adult research but in contrast to the pediatric re-
search. The authors are concerned with overstating the
importance of this finding because of its contradiction of
both pediatric literature and logic. Possible explanations
for the lack of midline-shift effect could be paucity of
sample power that midline shift might affect acute out-
comes more than acute rehabilitation outcomes through
selection for inpatient rehabilitation, or that medical
advances in midline shift management (neuroimaging
and neurosurgical intervention) have significantly im-
proved since the reference literature was published. Fur-
ther study is needed to better understand the effects of
midline shift across age.
Although the present study showed no differences
with regard to ethnicity and functional outcome, dif-
ferences were found in regard to gender. Our findings
indicated that female adult were an average of almost 2
points worse on discharge FIM scores. This was not the
case for our pediatric sample. The clinical or research
implications of these findings are unclear, but further
controlled investigation examining a potential care or
genetic bias is warranted.
The present investigation has a number of limitations
that should be considered. First, in any study involv-
ing inpatient rehabilitation, there is an inherent bias
toward those patients who will have significant gains
in functional outcomes due to the selection process
for admission. Generalizations to populations not re-
ceiving inpatient rehabilitation must be made with cau-
tion. In addition, due to sample size, there is statistical
power to detect very small differences within the adult
group. However, the ability to detect differences within
the pediatric group is limited with only 76 children in-
cluded in the study. Although we are confident about
the differences that we did find in the pediatric sam-
ple, there may or may not be more differences we were
unable to detect. Finally, data were collected for the pe-
diatric sample at a single children’s rehabilitation cen-
ter. A multicenter investigation on pediatric TBI would
provide a better understanding of acute functional
outcome.
CONCLUSION
The goal of this study was to examine the effects of
TBI across the age spectrum by looking at acute func-
tional outcomes and several accepted adult modifying
variables. Overall, our analysis showed that children re-
cover more completely and efficiently than adults, and
that within the pediatric age group, older children re-
cover more completely and efficiently than younger chil-
dren. Our findings suggest that the effects of accepted
adult modifying variables cannot be extrapolated to the
pediatric TBI population without careful consideration
of the individual.
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