predicting outcome after traumatic brain injury

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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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