ID

24255

Descrizione

Traumatic brain injury (TBI) is a leading cause of death and disability. A reliable prediction of outcome on admission is of great clinical relevance. Using several large patient series for model development as available in the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) project, three prognostic models of increasing complexity (Core, Core + CT, Core + CT + Lab) were developed. They provide adequate discrimination between patients with good and poor 6 month outcomes after TBI, especially if CT and laboratory findings are considered in addition to traditional predictors. The model predictions may support clinical practice and research, including the design and analysis of randomized controlled trials. Reference: Steyerberg, E. W., Mushkudiani, N., Perel, P., Butcher, I., Lu, J., McHugh, G. S., ... & Maas, A. I. (2008). Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS medicine, 5(8), e165. http://www.tbi-impact.org/?p=impact/calc

collegamento

http://www.tbi-impact.org/?p=impact/calc

Keywords

  1. 28/07/17 28/07/17 -
  2. 25/01/18 25/01/18 -
Titolare del copyright

Ewout Steyerberg

Caricato su

28 luglio 2017

DOI

Per favore, per richiedere un accesso.

Licenza

Creative Commons BY-NC 3.0

Commenti del modello :

Puoi commentare il modello dati qui. Tramite i fumetti nei gruppi di articoli e articoli è possibile aggiungere commenti a quelli in modo specifico.

Commenti del gruppo di articoli per :

Commenti dell'articolo per :

Per scaricare i modelli di dati devi essere registrato. Per favore accesso o registrati GRATIS.

IMPACT model for 6 month outcome after TBI

IMPACT model for 6 month outcome after TBI

Core data
Descrizione

Core data

Age
Descrizione

Age

Tipo di dati

integer

Unità di misura
  • years
Alias
UMLS CUI [1]
C0001779
Motor score
Descrizione

Motor score

Tipo di dati

integer

Pupillary reactivity
Descrizione

Pupillary reactivity

Tipo di dati

integer

CT data
Descrizione

CT data

Hypoxia
Descrizione

Hypoxia

Tipo di dati

integer

Hypotension
Descrizione

Hypotension

Tipo di dati

integer

CT classification
Descrizione

CT classification

Tipo di dati

integer

Traumatic subarachnoid hemorrhage (CT)
Descrizione

Traumatic subarachnoid hemorrhage

Tipo di dati

integer

Epidural hematoma (CT)
Descrizione

Epidural hematoma

Tipo di dati

integer

Sub score CT
Descrizione

Sub score CT = hypoxia + hypotension + CT characteristics

Tipo di dati

integer

Lab data
Descrizione

Lab data

Glucose
Descrizione

Glucose

Tipo di dati

integer

Unità di misura
  • mmol/L
Hb
Descrizione

Hemoglobin

Tipo di dati

integer

Unità di misura
  • g/dL
Sub score lab
Descrizione

glucose + Hb

Tipo di dati

integer

Sum scores
Descrizione

Sum scores

Sum score: core model
Descrizione

Sum score core model = age + motor score + pupillary reactivity. The probability of 6 mo outcome is defined as 1 / (1+e^-LP), where LP refers to the linear predictor in a logistic regression model. LP core, mortality = -2.55 + 0.275 x sum score core. LP core, unfavorable outcome = -1.62 + 0.299 x sum score core.

Tipo di dati

integer

Sum score: extended model
Descrizione

Sum score extended model = core + hypoxia + hypotension + CT characteristics. The probability of 6 mo outcome is defined as 1 / (1+e^-LP), where LP refers to the linear predictor in a logistic regression model. LP extended, mortality = -2.98 + 0.256 x (sum score core + subscore CT). LP extended, unfavorable outcome = -2.10 + 0.276 x (sum score core + subscore CT).

Tipo di dati

integer

Sum score: lab model
Descrizione

Sum score lab model = core + hypoxia + hypotension + CT + glucose + Hb. The probability of 6 mo outcome is defined as 1 / (1+e^-LP), where LP refers to the linear predictor in a logistic regression model. LP lab, mortality = -3.42 + 0.216 x (sum score core + subscore CT + subscore lab). LP lab, unfavorable outcome = -2.82 + 0.257 x (sum score core + subscore CT + subscore lab).

Tipo di dati

integer

Similar models

IMPACT model for 6 month outcome after TBI

Name
genere
Description | Question | Decode (Coded Value)
Tipo di dati
Alias
Item Group
Core data
Item
Age
integer
C0001779 (UMLS CUI [1])
Code List
Age
CL Item
≤30 (0)
CL Item
30-39 (1)
CL Item
40-49 (2)
CL Item
50-59 (3)
CL Item
60-69 (4)
CL Item
70+ (5)
Item
Motor score
integer
Code List
Motor score
CL Item
None/extension (6)
CL Item
Abnormal flexion (4)
CL Item
Normal flexion (2)
CL Item
Localizes/obeys (0)
CL Item
Untestable/missing (3)
Item
Pupillary reactivity
integer
Code List
Pupillary reactivity
CL Item
Both pupils reacted (0)
CL Item
One pupil reacted (2)
CL Item
No pupil reacted (4)
Item Group
CT data
Item
Hypoxia
integer
Code List
Hypoxia
CL Item
Yes or suspected (1)
CL Item
No (0)
Item
Hypotension
integer
Code List
Hypotension
CL Item
Yes or suspected (2)
CL Item
No (0)
Item
CT classification
integer
Code List
CT classification
CL Item
I (-2)
CL Item
II (0)
CL Item
III/IV/V/VI (2)
Item
Traumatic subarachnoid hemorrhage (CT)
integer
Code List
Traumatic subarachnoid hemorrhage (CT)
CL Item
Yes (2)
CL Item
No (0)
Item
Epidural hematoma (CT)
integer
Code List
Epidural hematoma (CT)
CL Item
Yes (-2)
CL Item
No (0)
Sub score CT
Item
Sub score CT
integer
Item Group
Lab data
Item
Glucose
integer
Code List
Glucose
CL Item
<6 (0)
CL Item
6-8.9 (1)
CL Item
9-11.9 (2)
CL Item
12-14.9 (3)
CL Item
15+ (4)
Item
Hb
integer
Code List
Hb
CL Item
<9 (3)
CL Item
9-11.9 (2)
CL Item
12-14.9 (1)
CL Item
15+ (0)
Sub score lab
Item
Sub score lab
integer
Item Group
Sum scores
Sum score core model
Item
Sum score: core model
integer
Sum score extended model
Item
Sum score: extended model
integer
Sum score lab model
Item
Sum score: lab model
integer

Si prega di utilizzare questo modulo per feedback, domande e suggerimenti per miglioramenti.

I campi contrassegnati da * sono obbligatori.

Do you need help on how to use the search function? Please watch the corresponding tutorial video for more details and learn how to use the search function most efficiently.

Watch Tutorial