Predicting Outcome After Traumatic Brain Injury
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Transcript of Predicting Outcome After Traumatic Brain Injury
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From:British Medical Journals (BMJ)
Predicting outcome after traumatic brain injury:
practical prognostic models based on large
cohort of international patients
Medical Research Council (MRC) CRASH Trial Collabolators
Editorial by Menon and Harrison
BMJ/ February 23rd2008/ Vol. 336/ page: 425-429
Presented by:Zulhijrian Noor
Irana Priska
Advisor: Ahmad Zuhro Maruf, dr., SpBS
JOURNAL READING
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INTRODUCTION
Traumatic brain injury is a leading cause of death
and disability worldwide1.5 million / year
Most of the burden (90%) low and middle
income countries
Clinicians treating by assesment of prognosis,
80% believed the accurate assesment of
prognosis was important decisions to specifictreatment such as hyperventilation,barbiturates
and mannitol ( by survey, 2005 )
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Prognostic models
statistic models that combine data from
patients to predict outcame more accurate
then simple clinical predictions
Computers based prediction of outcome
Increase certain therapeutic interventions in
predicted good outcome, reduces it in poor
outcome
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Many prognostic model have been reported
but non are widely used
No were developed in populations from low
and middle income countries
MRC CRASH trial, the cohort study
prospectively included patient within 8
hours of the injury and achieved almost
complete follow up at 6 month
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Develoved and validated prognostic models
for death at 14 dayy and death and disabilityat
6 month in patient with traumatic brain injury
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METHODS
Patients 10.008 adult patients withtraumatic brain injury ( GCS 14 ), within 8hours of injury
Outcomesdeath of a patient was recordedon a early outcome form that was completedat hospital discharge,death, or 14 days afterrandomisation. Unfavourable outcome ( deathor severe disability ) at 6 months was definedwith Glasgow outcome scale
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Glasgow outcome scale
5 categories
1. Good recovery: able to return to work or school
2. Moderate disability: able to live indipendently;
unable to return to work or school
3. Severe disability : able to follow
commands/unable to live independently
4. Persistent vegetative state : unable to interact
with environment; unresponsive
5. dead
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prognostic variables age,sex,cause of injury,timefrom injury to randomisation,Glasgow coma score atrandomisation,pupil reactivity,result of CT,levelincome in country.
analysis included all of variables in a firstmultivariable logistic regression analysis. Exploredliniearity between age and mortality at 14 days
prognosticmodels
developed different models foreach of the two outcomes: a basic models (only
clinical and demographic variables ),CT model ( resultCT ).
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Performance of models assessed
performance of the models in term of
calibration with the Hosmer-Lemeshow and
discrimination was assessed with the C
statistic.
Internal validation the internal validity of
the final models was assessed by thebootstrap re-sampling technique
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External validationexternally validated the
model in an external cohort of 8509 patients
with moderate and severe traumatic brain
injury from 11 studies conduted in high
income countries.
Score development a clinical score base on
regression coefficient
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General characteristics
more patients were men (81%), more come fromlow-middle income countries (75%)
58% of participants were included within threehours of injury.
Road traffic crashes were the most commoncause of injury (65%)
79% underwent computed tomography
1948 patient (19%) died in 2 weeks,2323 (24%)dead at 6 month,3556 (37%) were dead orseverly dependent at 6 month
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Low middle vs high income countries
in comparison from low-middle incomecountries were younger,more men,were recruitedlater, had less severeTBI ( as defined by GCS and
pupil reactivity ), abnormal result on CT.Older age was a stronger predictor of 14 day
mortality in high income countries, alsoobliteration 3th ventricle and non-evacuated
haematoma.Lower GCS was a stronger predictor in low-
middle countries
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Multivariable predictive models
o Basic models 4 predictors : age,GCS, pupil
reactivity and the presence of major
extracranial injury.
o CT models characteristics on CT were
strongly assosiated with the outcomes.
Petechial haemorrhages,obliteration of the
third ventricle or basal cystern,SAH,Midlineshift,and non-evacuated haematoma.
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o obliteration of the third ventricle and midline
shift strongest predictor of mortality at 14
days
o non-evacuated haematoma strongest
predictor of mortality at 14 unfavourable
outcome at 6 months
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o Performance of modelsgood calibration when
evaluated with the Hosmer-Lemesshow test.
o Clinical score for example : a 26 year old
patient froom low-middle income countries withGCS 11,one pupil reactive,and absen of a major
extracranial injury, according to basic models :
probably dead at 14 days of 10% and 23.9% riskof death or severe disability at 6 month.
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DISCUSSION
There are differences in outcomes and on thestrenght of predictors of outcomes on patient
from high and middle-low income countries.
Older age, Low GCS, absent pupil reactivity,absent major extracranial injurypoor
prognosis
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GCS showed a clear linier relation with
mortality
GCS 3 was lower than in patient with a score
of 4 may be because scores of sedated
patients are reported as 3
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Increasing age was associated with worse
outcome but this association was apparent
only after age 40
Plausible explanation extracranial
comorbidities, changes in brain plastisity,
differences in clinical management associated
with increasing age
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Present of obliteration of 3rdventricle or basal
cistern as on Ct Scanassociated with the
worse prognosis at 14thdays
Recent findingsabsnce of basal cistern is a
strongets predictors of sixth month mortality
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Patient from low-middle income countries hadworse early prognosis than those from highincome countries
The strength of association between somepredictors and outcomes differed by region:
Low GCS (poorer in low-middle income countries)quality of care and greater use of sedation
Incresing age (poorer in high income countries) CT-Scantechnology and accurate diagnosis
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STRENGTHS AND WEAKNESS
Strength
The use of a well described cohort of patients
Prospective and standadised collection of data on
prognostic factor
Low loss to follow up
The use of a validated outcome measure at a fixed
time after the injury The large sample size
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STRENGTHS AND WEAKNESS
Weakness
Data from wich models were developed come
from a clinicl trial and this could therefore limit
external validity
For the validation they were forced to exclude the
variabels major extracranial injury and petechial
haemorrages because they were not available in
the IMPACT sample
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IMPLICATIONS
They have developed a methodology valid,
simple, accurate model that may help
decisions about health care for individual
patients
Help in the design and analysis of clinical
trials, through prognostic stratification.
Can be used in clinical audit by allowing
adjustment for case mix
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FUTURE RESEARCHE
Future research could also evaluate
different ways, or formats, for presenting the
models to physicians; their use in clinical
practice; and whether ultimately they haveany impact on the management and
outcomes of patients with traumatic brain
injury.
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SUMMARY
Traumatic brain injury is a leading cause of death
and disability worldwide with most cases
occurring in lowmiddle income countries
Prognostic models may improve predictions ofoutcome and help in clinical research
Many prognostic models have been published but
methodological quality is generally poor, samplesizes small, and only a few models have included
patients from low-middle income countries
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