Traumatic Spinal Cord Injury - Overview

Causes
  • 38% Motor vehicle accidents
  • 30% falls
  • 14% violence (primarily GSWs)
  • 9% sports
  • 5% medical/surgical
  • 4% other/unknown
Presentation
  • Spinal shock, loss of function/sensation below site of injury
  • Muscle spasticity
  • Difficulty breathing
  • Loss of bowel/bladder control
  • These symptoms vary depending on location and severity of injury
Demographics
  • More prevalent in males vs. females (4:1 ratio)
  • Average age of injury ~41 years
Treatment
  • Immobilization of spine, depending on location of injury
  • Steroid medications to control swelling
  • In some cases, immediate surgery is necessary to stabilize fractured vertebrae or relieve pressure
  • Neurons have a limited capacity to regenerate
    • Currently no way to stimulate or facilitate regeneration, although research is in early stages
  • Rehab for maintaining and restoring function

Spinal cord injury—incidence, prognosis, and outcome: an analysis of the TraumaRegister DGU - Stephan et al.

Research Overview
  • What is the prognosis of SCI patients based on injury severity?
  • Retrospective study over 4,285 polytrauma patients with SCI.
  • Inclusion criteria:
    • Blunt trauma patient
    • Age >16 years
    • Injury severity score (ISS) >16
  • Outcome measures: mortality and Glasgow Outcome Scale (GOS)

The subjects' injuries were classified by the Abbreviated Injury Scale (AIS), and these scores were assessed in the Injury Severity Score (ISS). ISS scores >16 were included in the study. The Glasgow Outcome Score was used to classify the subjects' recovery.

Abbreviated Injury Scale
  • AIS 3 - contusion with transient neurology
  • AIS 4 - incomplete paraplegia
  • AIS 5 - complete paraplegia
  • AIS 6 - cord contusion/laceration at C3 or above

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Top factors associated with poor outcomes:
  1. AIS 6
  2. AIS 5
  3. Age 80+
  4. Resuscitation
  5. AIS 4
  6. AIS 3
  7. RBC input
  8. Head injury
  9. Age 60-69
  10. Age 70-79
  11. Shock at scene of injury

Conclusion
  • AIS 6, AIS 5, >80 years old, and resuscitation were the top 4 factors related to poor outcomes in SCI patients.
  • AIS 6: 64.6% GOS 1
  • AIS 5: 85% GOS 1-3

These factors can be used to predict the extent of a patient’s functional recovery.

Clinical prediction model for acute inpatient complications after traumatic cervical spinal cord injury: a subanalysis from the Surgical Timing in Acute Spinal Cord Injury Study - Jefferson et al.

Methods
  • Question: How can we predict acute inpatient complications following SCI?
  • Prospective cohort study of 411 patients over a 7-year period
  • Inclusion criteria
    • C2-T1 SCI
    • Radiographic evidence of spinal cord compression
    • 16 years or older
    • AIS grade A-D
    • Underwent documented neuro exam within 24 hours of injury and follow-up information available at acute care discharge

Predictive factors
  • AIS (ASIA Impairment Scale)
    • A - complete; no muscle or sensory function preserved
    • B - incomplete; sensory but no motor function preserved below level of injury
    • C - incomplete; motor function preserved, but majority of key muscles below the injury level have a grade of < 3
    • D - incomplete; motor function preserved, but the majority of key muscles below the injury level have a grade of > 3
    • E - normal motor and sensory function
  • Age
  • Sex
  • Steroid administration at admission
  • Injury mechanism
    • High energy (MVCs including motorcycles) vs. Low-energy (falls from standing position, sports-related accidents, blunt violence)
  • Presence or absence of co-morbidities
    • Charlson co-morbidity index
      • Method of categorizing co-morbidities; each category of co-morbidities are given a specific weight. Weighted score indicates the predicted risk that those co-morbidities will result in mortality or higher resource use.
    • Presence of comorbidity = Charlson index score of ≥ 1

Outcome measures
  • Complications in the acute care setting
    • Cardiopulmonary
    • Surgical
    • Thrombotic
    • Infectious
    • Ulcer
  • Dichotomous outcome measure
    • Positive = one or more complications
    • Negative = no complications
Results
  • 160 patients (38.9%) developed complications
  • Multivariate logistic regression model revealed 5 predictors of complications with a p value of <0.10
  • Odds ratio (OR) tells us how likely each factor predicts development of at least one complication
Screen Shot 2016-04-19 at 3.29.54 PM.png

Conclusions
  • A more severe initial AIS grade, a high-energy injury mechanism, older age, the absence of steroid administration, and the presence of at least one comorbidity are associated with a greater risk of complication development during the acute hospital period
  • Acknowledging the other 4 factors in addition to the AIS grade vs. the AIS grade alone increases predictability of complication development
    • Area under the ROC curve = 0.75 vs 0.71, respectively
    • Screen Shot 2016-04-25 at 9.00.16 PM.png
  • From these analyses, we can develop a model to predict how susceptible someone is to developing complications before they occur
Screen Shot 2016-04-19 at 4.26.09 PM.png

GRASSP
Graded Redefined Assessment of Strength, Sensibility, and Prehension
• Impairment measure for upper limb use after tetraplegia
o Specifically cervical spinal cord injuries
• Assesses motor and sensory systems as well as changes in level of impairment and how these contribute to upper limb functional tasks.
• 3 domains: strength, sensibility, prehension
o 5 subtests
o Purpose of integrated domain is to provide assessment of how sensation and strength contribute to function (prehension)
 Important in understand recovery process

GRASSP, an Advanced Tool in Upper Limb Function
• Several outcome measures have been established for upper limb function, but very few in SCI
o Outcome measures are hard to define in SCI population due to high variability in upper limb function between patients
• Predictive validity of quantitative measures has not yet been established
o GRASSP was developed as a quantitative outcome measure specific to upper limb function in cervical SCI
o Covers different aspects of upper limb function to evaluate changes within the sensorimotor function and how changes in the level of impairment contribute to complex upper limb functional tasks in individuals with cervical SCI

Components of the GRASSP
MMT:
• All 10 muscles of the upper limb
o 3 arm and 7 hand
• 0-5 mmt scale for each limb
o Sum of scores = 0 to 100

Monofilament sensory testing:
• 3 palmar
• 3 dorsal
• Scale 0-4
o Sum of scores = 0 to 24

Qualitative Grasping: Test of 3 grips
• cylindrical grasp
• lateral key pinch
• tip to tip
o 0-4 score
o Sum of scores for both hands

Quantitative grasping:
• 6 special tests
• Score is based off of completion of test using expected grip.

Quantitative Grasping Score
Scoring (a maximum of 1 minute and 15 seconds is allowed for each task)
0- the task can not be conducted at all
1 – the task can not be completed, (less than 50% of the task) and the expected grasp is not used
2 – the task is not completed, (50% or more of the task) and the expected grasp is not used
3 – the task is conducted (completed) using tenodesis or an alternative grasp other than the expected grasp
4 – the task is conducted using the expected grasp with difficulty (lack of smooth movement or difficult slow movement)
5 – the task is conducted without difficulties using the expected grasping pattern and unaffected hand function

Prediction and Stratification of Upper Limb Function and Self-Care in Acute Cervical Spinal Cord Injury With the Graded Redefined Assessment of Strength, Sensibility, and Prehension (GRASSP)...Sage Journals: Neurorehabilitation and Neural Repair. Velstra et al.
• Evaluate the predictive value of GRASSP for upper limb function and self-care outcome at 6 and 12 months in patients with acute cervical SCI.
• Prospective longitudinal Multicenter study
• Inclusion Critera: traumatic or non-traumatic, acute tetraplegia with an ASIA impairment scale grade of A,B,C, or D; Between C3 and T1 for grade A and C1-T1 for incomplete injuries
• Exclusion critera: any accompanying severe neurological or medical disorders or age less than 16
• Used novel testing as part of GRASSP as predictive variables:
o Spinal Cord Independence Measure III (SCIM-III): self-care category
o International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI): neurological exam
o ASIA: Upper Extremeity Motor Score

Methods
• The subtests within GRASSP were assessed between days 16 and 40 after cervical SCI and selected as baseline predictor variables.
• QtG and the SCIM-SS were used as anchor outcome measures of upper limb function and self-care at 6 and 12 months after cervical SCI
• The SCIM-SS and the GRASSP subtest QtG were converted into 2 dichotomous outcome measures (“dependent” vs “independent” for self-care; “non-functional” vs “functional” for grasping), which represented a wide range of upper limb performance in all subgroups.
• Applied conditional inference tree (URP-CTREE) to predict upper limb function and self-care as outcome measures at 6 and 12 months based on different predictor variables assessed at 1 month after injury
Partitioning conditional inference tree (URP-CTREE) for self-care at 6 and 12 months

Results
MMT, as defined in the GRASSP, includes a greater number of muscles compared to the ISNCSCI protocol and probably contributes to the high outcome prediction seen in this study, lending further support to the continued development of the GRASSP as a standardized assessment tool of upper limb function.

URP-CTREE identified predictors based on favorable vs less favorable functional outcomes. UEMS proved better for less favorable while with more favorable functional outcomes MMT in combination with QlG and QtG demonstrated predictive utility. The combination of MMT strength and dexterity (QlG and QtG) interact to predict improved outcomes of upper limb function. The GRASSP permits the gathering of more comprehensive information and is capable of disentangling neurological and functional changes.

For self-care outcomes, tests of muscle strength (MMT and UEMS) were useful predictor variables while QIG and QtG were not.

URP-CTREE was able to show that predictors in the model demonstrated significantly differentiated predictive capacity when compared with the logistic and ROC models, including SWM and MMT as single predictors. The evidence suggests that the combination of MMT with other predictors, such as QlG and QtG, can improve outcome prediction.

Potential Effects of GRASSP
• Evaluate upper limb function early: treatment planning and goal setting

• Research: permit evaluation of novel interventions and patient classification

• Socioeconomically: early prediction of level of independence/independent living and required level of caregiving



A clinical prediction rule for ambulation outcomes after traumatic spinal cord injury: a longitudinal cohort study
Middendorp et al.
The Lancet (2011).

Longitudinal Cohort Study. 2011
Researchers were looking to create an accurate and simple clinical prediction rule for a pt to walk independently following a spinal cord injury.
They decided to perform this study because there is no clinical prediction rule for ambulation, following a traumatic spinal cord injury.

-492 pts from 19 European SCI centers, with baseline measures taken within 15 days of injury

Predictive measures for CPR
(1)Patient Age
(2)Motor score testing: for Quads and Gastroc
(3)Light touch sensory (LTS) and pinprick sensory (PPS) testing
(4)Sacral sparing scores, including voluntary anal contraction and anal sensation.
-Tests collected by trained and certified neurologists and rehabilitation physicians with at least 1 year of experience in examination of patients with spinal cord injury
-Outcome measure: Pt ability to walk independently at 1 year follow up. Cutoff Score: 0-3 non-independent, 4-8 considered independent.

Used a logistic regression model to choose the best predictive measures
(1)Age (≥65)
(2)Quadriceps femoris muscle grade (L3)
(3)Gastroc muscle grade (S1)
(4)LTS at L3
(5)LTS at S1
P <0.0001
Probability of walking independently 1 year after injury based on the prediction ...
Probability of walking independently 1 year after injury based on the prediction ...


All measures were taken by highly trained individuals
-Could be hard to carry this over to normal clinics
Compared the CI’s to those from the AIS measure
-Found the Dutch model to be have higher significance
The predictive measures that the researchers used are not invasive to the pt or time consuming for the clinician.
-This could allow this CPR to be highly relevant in the clinic

Validation of the Dutch clinical prediction rule for ambulation outcomes in an inpatient setting following traumatic spinal cord injury
Silfhout et al.
International Spinal Cord Society (2015).

Retrospective Study, 2015
The purpose of this study was to investigate the accuracy of the Dutch clinical prediction rule for ambulation outcomes, using routinely collected clinical data.

184 pts, with baseline measures within 15 days, and at least a 6 month follow up measure.
-Pts were excluded if the initial exam was beyond 15 days or there was no follow up data at least 6 months following the injury.
Took the predictive values concluded on in the first study
-(1) Age (≥65) (2) Quadriceps femoris muscle grade (L3) (3)Gastroc muscle grade (S1) (4) LTS at L3 (5) LTS at S1
Used these values to create the ROC curve

Used a ROC curve to test the predictive capabilities of the Dutch CPR
-The AUC and the CI were not significantly different from those in the original study.
Age related factor
-P=0.1889
Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, please contact help@nature.com or the author
Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, please contact help@nature.com or the author


Dutch CPR had a similar performance in a hospital spinal unit as routine care, as the initial study found.
-Appears that this rule can be applied readily to normal clinical settings

Only got pts from one hospital

-Could have a large cultural bias

This study found no significant difference for age related to return to walking
-Could be due to difference in inclusion factors
Neither study looked at comorbidities or previous medical history
-These could be very influential in return to walking, and future studies should incorporate these.

A Clinical Prediction Model for Long-Term Functional Outcome after Traumatic Spinal Cord Injury Based on Acute Clinical and Imaging Factors
Wilson et al.
Journal of Neurotrauma (2012).

-CPR relating acute clinical and imaging information to functional outcome at 1 year
-Only incorporates non-treatment predictors related to the natural history of recovery for SCI patients.
Predictor Variables: (1) acute AIS grade (see chart below), (2) acute ASIA motor score (dichotomized at a score of 50), (3) patient age at injury (continuous scale), and (4) intramedullary signal characteristics on the spinal MRI (signals consistent with spinal cord edema or hemorrhage).

Treatment: “All patients underwent an individualized rehabilitation protocol, tailored to specific patient needs and injury characteristics.”

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Results:
  • Better functional outcome: 1) Less severe ASIA Impairment Scale grade and 2) ASIA motor score > 50 at admission
  • Worse functional outcome: 1) Older age and 2) MRI signal characteristics consistent with spinal cord edema or hemorrhage
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Table 4 shows the applicability of the clinical prediction rule created with this study.

How good is it at predicting outcomes? Should we use it?
  • The linear model predicting FIM motor score demonstrated an R-square of 0.52 in the original dataset and an R square of .52 across a set of 200 bootstraps (random sampling).
  • For the logistic model, the AUC was 0.93 in the original dataset, and 0.92 (95% CI 0.92, 0.93) across the bootstraps, indicating excellent predictive discrimination.

Study Limitations
• Applying the pre-specified models to the bootstrap replicates confirms their validity within our own dataset, establishing true external validity of this prediction rule will require an evaluation of its performance within a separate group of SCI patients.
• Before such an evaluation takes place, the generalizability of this model, as well as its suitability for use in the clinical realm, remains unknown.
• A large number of patients failed to be included in the analysis due to inadequate follow-up.
• Treatment related factors not included in our model, such as time to decompressive surgery or steroid administration

Exercise Recommendations and Considerations with Spinal Cord Injury
• Obesity, cardiovascular disease, and diabetes are 2 to 4 times higher for people with SCI compared to the general population.
• Due, in part, to low levels of activity, limited access and opportunities to participate in exercise, as well as changes in muscle and heart function that are common after injury.
• Exercise is necessary to improve fitness and reduce long-term health complications after SCI

Cardiovascular Health with SCI
Clinical and research communities have focused more attention on cardiovascular fitness for people with SCI due to the high correlation of secondary health conditions.

Factors to consider:
• Level of injury
• Injury Severity
• Degree of physical conditioning
• Extent of autonomic NS impairment

Guidelines for Cardiovascular Health
• Frequency: Minimum of 2 days/week
• Intensity: Moderate to Vigorous
• Duration: 20-30 minutes/session
• Activity Examples: Wheeling, arm cycle, sports, recumbent stepper, aquatics, cycling, circuit training, functional electrical stimulation
• Any sustained physical activity will improve CV health if it meets requirements for time and intensity.


Muscle Strength and Endurance with SCI
Importance in maintaining:
• Bone mineral density
• Muscle mass
• Resting metabolic rate
• Force/power production
• Muscle/tendon health
• Glucose metabolism

Implications:
• Avoiding osteoporosis/fractures
• Higher level of functioning/improved usage of assistive devices
• Improved posture/pain
• Avoiding secondary diseases

Muscle Considerations for SCI Individuals
• Any muscle capable of being voluntarily activated can benefit from resistance training.
• Rely heavily on shoulder/arm muscles for mobility and ADL’s
• Emphasis on muscles supporting scapulae/posterior shoulders
• Specific exercises for common ADL’s:
o Bicep Curl, Triceps Press, Shoulder Press, Latissimus Pull-Down, Chest Fly, and Seated Row
• For diminished trunk or shoulder control external support may be used to reduce the risk of injury.
o Examples: Lumbar roll or chest strap.

Guidelines for Muscle Strength and Endurance
• Frequency: Minimum of 2 days/week
• Intensity: 8-10 Reps
• Duration: 3 sets; 1-2 minutes rest between sets (30-60 min total)
• Activity Examples: Free weights, elastic resistance bands, cable pulleys, weight machines, functional electrical stimulation
• Extra attention to chest, shoulders, and biceps as they are likely to be overused
• Caution with lower body stretching where impaired sensation exists
o Could lead to overstretching and damage to joint structures

Guidelines for Stretching and ROM with SCI
• Frequency: Daily
• Intensity: 30-60 sec/stretch; slow, gentle, pain free
• Duration: 2 sets 5-15 minutes
• Activity examples: Standing in standing frame (if medically cleared); passive and active static stretching
Exercise Participation Safety Considerations


References

Burns, A., and Ditunno, J. (2001). Establishing prognosis and maximizing functional outcomes after spinal cord injury. Spine 26, S137–S145.
Cifu, D., Seel, R., Kreutzer, J., and McKinley, W. (1999). A multicenter investigation of age-related differences in lengths of stay, hospitalization charges, and outcomes for a matched tetraplegic sample. Arch. Phys. Med. Rehab. 80, 733–740.
Stephan, K., Huber, S., Haberle, S., Kanz, K., Buhren, V., Griensven, M...Huber-Wagner, S. (2015). Spinal cord injury--incidence, prognosis, and outcomes: an analysis of the TraumaRegister DGU. The Spine Journal, 15(9), 1994-2001. doi:10.1016/j.spinee.2015.04.041
Wilson, J. R., Grossman, R. G., Frankowski, R. F., Kiss, A., Davis, A. M., Kulkarni, A. V., Fehlings, M. G. (2012). A clinical prediction model for long-term functional outcome after traumatic spinal cord injury based on acute clinical and imaging factors. Journal of Neurotrauma, 29(13), 2263-2271. doi:10.1089/neu.2012.2417
Joost J van Middendorp, Allard J F Hosman, A Rogier T Donders, Martin H Pouw, John F Ditunno Jr, Armin Curt, Alexander C H Geurts, Hendrik Van de Meent, (2011). A clinical prediction rule for ambulation outcomes after traumatic spinal cord injury: a longitudinal cohort study. Lancet 377: 1004-10. Pubmed. Web. 18 Apr. 2016.
L van Silfhout, AEJ Peters, M Graco, R Schembri, AK Nunnand DJ Berlowitz, (2015).Validation of the Dutch clinical prediction rule for ambulation outcomes in an inpatient setting following traumatic spinal cord injury. International Spinal Cord Institute 1-5. Pubmed.