AZ ÉLETTUDOMÁNYI- KLINIKAI FELSŐOKTATÁS GYAKORLATORIENTÁLT ÉS HALLGATÓBARÁT

KORSZERŰSÍTÉSE A VIDÉKI KÉPZŐHELYEK NEMZETKÖZI VERSENYKÉPESSÉGÉNEK ERŐSÍTÉSÉRE

TÁMOP-4.1.1.C-13/1/KONV-2014-0001

Andras Buki M.D., Ph.D.,D.Sc.Department of Neurosurgery, Medical Faculty of Pecs University, Pecs, Hungary, H-7624

Clinical Evaluation and Prognosis

of Traumatic Brain Injury

• Clinical evaluation is still the most important independent predictor of outcome

• Focused neurological examination

• Other prognostic factors

Focused neurological

examination

Post-resuscitation GCS

• Systolic blood pressure is over 90mmHg

• SatO2 is over 90%

• Mind the conditions with unreliable pulse

oxymetry reading!

Standardisation of pain stimuli

Best response of best arm

Courtesy of Prof. Andrew Maas

59/451 (13%) non surgical cases:

mistakenly severe

Courtesy of Prof. Andrew Maas

Trade-off

Clinical observation

Deep sedation for

ICP control

Ventilation, etc.

Sedation and myorelaxants for

airways and ventilation

Courtesy of Prof. Andrew Maas

PRIMARY BRAIN INJURY

SECONDARY BRAIN INJURY

ASSOCIATED CNS INJURYassociated C-spine (CO-II) injurytandem injury

ASSOCIATED INJURYmultiple/polytraumaassociated multiorgan injury/failure (MOF)

General classification

Advanced Trauma Life Support®-ATLS ®

The gold standard upon

radiological evaluation …• (Forget skull Xrays!!!!!!!!!!!!!!!!!!!!!!!!)

• …is CT

15

• NOOOO SKULL X RAYSSS!!!

Outcome prediction: why do we bother with?

• support early clinical decision-making

• inform the relatives

• facilitate comparison of outcomes (patient series, results over time)

• audit of care

• provide endpoint and facilitate the selection of target population in RCTs

Building Blocks for prognosisCharacteristics of the individual

Admission Clinical Course Early Endpoints

Outcome

Biological constitution- genotype

________________Demographic factors- age- race

________________Socioeconomic status and education

________________Medical History

Injury details

- type (closed, penetrating etc.)- cause

_________________Clinical severity

- intracranial (GCS/pupils)

- extracranial(AIS/ISS)

_________________Second insults

- systemic (hypoxia, hypotension, hypothermia)

- intracranial (neuroworsening, seizures)

_________________CT characteristics

_________________Biomarkers/lab values

Biological response to injury- metabolomics

_______________Change in adm.parameters- clin. severity- change in CT

- biomarkers, lab values

_______________‘New’ predictorsSecond insult

Clinical Monitoring (ICP, brain tissue PO2, evoked potentials)

Early mortality(day 14)

_______________Neuroworsening

_______________ICP control

_______________Neuro-imaging

Mortality

______________GOS (E)

______________HRQoL

______________Neuro-imaging

______________Neuro-psychological assessment

Factors contributing to outcome -AGE

• studies identified age as the strongest independent predictive factor

• Cut off for survival: 50y

• Cut off for good outcome 30y

Factors contributing to outcome -GCS

• clinical utility of the GCS is limited by the application of therapeutic guidelines based on sedation

Saatman KE, Duhaime AC, Bullock R, Maas AI, Valadka A, Manley GT; Workshop Scientific Team and Advisory Panel Members. Classification of traumatic brain injury for targeted therapies. J Neurotrauma. 2008;25(7):719-38.

The gold standard upon

radiological evaluation …• (Forget skull Xrays!!!!!!!!!!!!!!!!!!!!!!!!)

• …is CT

23

Marshall CT classification

Category Definition

Diffuse Injury I./No visible pathology/

No visible pathology seen on CT scan

Diffuse Injury II. Cisterns are present with midline shift 0-5 mm

and/or:

lesion densities present

no high- or mixed-density lesion > 25 cc may

include bone fragments and foreign bodies

Diffuse Injury III./Swelling/

Cisterns compressed or absent with midline shift

0-5 mm, no high- or mixed-density lesion > 25 cc

Diffuse Injury IV./Shift/

Midline shift > 5 mm, no high- or mixed-density

lesion > 25 cc

Evacuated mass lesion Any lesion surgically evacuated

Nonevacuated mass lesion High- or mixed-density lesion > 25 cc, not

surgically evacuated

Based on: J Neurosurg. 1991 Nov;75(5S):S14 – S20

Rotterdam CT Score Basal cysterns

• Normal 0

• Compressed 1

• Absent 2

Midline shift

• No shift or shift ≤ 5mm 0

• Shift > 5mm 1

Epidural mass lesion

• Present 0

• Absent 1

Intraventricular blood or tSAH

• Absent 0

• Present 1

• Add plus 1 to make the grading numerically consistent with the grading ofthe motor score of the GCS and with the Marshall CT classification.

• Based on: Maas et al.: Neurosurgery 57:1173-1182, 2005

Factors contributing to outcome –CT, MR

• CT misses diffuse lesions,

• predictive value of diffuse lesions identified on CT is relatively low

• MR: problems with:– Availability

– Cost efficiency

• The significance of MRI-only lesions is not

yet established

Factors contributing to outcome – Monitoring

• Multiparametric ICU monitoring primarily reflects

secondary insults;

• initial results do not necessarily harbour

predicitve value

• Conflicting data on the significance of Pbr02-

monitoring

Classic models of risk prediction at critical care

IMPACT (International Mission for Prognosis

And Clinical Trial Design)

Outcome calculator

• core prognostic model: based on three

clinical predictors: age, motor component

of Glasgow coma score (GCS), and

pupillary reactivity

• extended model: core + secondary insults

and CT characteristics

• laboratory model: also includes

haemoglobin and glucose

CRASH

• Corticosteroid Randomisation After

Significant Head Injury trial

MRC CRASH Trial Collaborators, Perel P, Arango M,

Clayton T, Edwards P, et al. (2008) Predicting outcome

after traumatic brain injury: Practical prognostic models

based on large cohort of international patients. BMJ 336:

425-429.

0%

20%

40%

60%

80%

100%

0% 20% 40% 60% 80% 100%

probability of unfavorable outcome, IMPACT CORE model

observ

ed m

ort

alit

y

Probability of unfavorable outcome: IMPACT-calculator

• Concerns: – Statistics: decision tree analysis vs.

Logistic regression analysis– Content: limitations due to initial data-

collection: • Lack of :

– data on coagulopathy– Rotterdam score– detailed data on surgery– some physiological parameters

• Work is needed to establish the accuracy of these models prospectively in patients not enrolled in clinical trials.

What measures should help?

• Coagulopathy

• Rotterdam score

• Biomarkers

2,289

0,930 0,850 0,946

6.955*

2,173

0,01,02,03,04,05,06,07,08,0

OR

Patients with SDH above 60

4,1051.160* 0,788 0,435

55.513*

23.846*

0,05,0

10,015,020,025,030,035,040,045,050,055,060,0

OR

Patients with SDH below 60

BIOMARKERS

0,171 0,188

0,2640,289

0,3100,355

0,429 0,428

0,501

0

0,1

0,2

0,3

0,4

0,5

0,6R2

Take home message…

• Outcome prediction is important:– Continuous validation and revision of

institutional protocols

• Outcome calculators provide useful information, but fine tuning by expert opinion is important–and vica versa.

• Outcome calculators should take into consideration additional issues primarily biomarkers

Thank You!