Spreadsheet: faa61288-8812-492e-a4c0-4ee53156108e
Sheet: Sheet1

Unnamed: 0 | IMPACT prediction rule spreadsheet
 | This indexsheet provides the aim, description, data, formulas and references for this spreadsheet. The spreadsheet is intended for information to physicians and (relatives of) patients with severe or moderate traumatic brain injury.
Aim: | To estimate the probability of poor 6 month outcome (mortality / unfavorable outcome according to GOS)
Description: | Patient characteristics are filled in in the PtData sheet, where the probabilities of 6 month outcomes are directly calculated, together with the 95% prediction interval. We consider mortality and unfavorable outcome as outcomes.
Data: | The probability estimates are obtained from regression equations derived from the IMPACT study.  1. The [NAME] model includes age , GCS admission Motor score, and pupillary reactivity.  Coding: age: continuous, linear variable in years; Motor 1 - 6, 9 for untestable; pupils: both reacting 1, 1 reacting 2, non reacting 3. Estimated with n=8509. 2. The [NAME] model extends the [NAME] model with secondary insults (hypoxia, hypotension) and CT characteristics. Hypoxia, hypotension, presence of traumatic subarachnoidal blood (tSAH), and presence of an epidural hematoma (EDH) are all scored 1 for present and 0 for absent). Estimated with n=6999. 3. The Core+CT+Lab model extends the model further with 2 laboratory parameters: glucose (mmol / l, range truncated to 6 - 20), and hemoglobin (Hb, g / dl, range truncated to 6 - 17). Estimated with n=3554.
Uncertainty: | The estimates of the probability are uncertain for several reasons. 1. The number of events was large, resulting in narrow 95% prediction intervals, but  the model makes assumptions of the relationship between predictors (no interactions, and linearity of age effect). 2. The predictions do not have to be valid for other populations than that used for the development of the prediction rule. For example, the measurement of some predictors may differ according to center, and the referral pattern may differ. Relevant differences are especially those on risk factors that were not considered in the prediction rule.
[NAME]: | A proportional odds logistic regression model was used to calculate multivariable regression coefficients for risk factors for 6 month outcome. A linear predictor (lp) is calculated by multiplication of covariable values and regression coefficients. Probabilities are calculated as P=1/(1+exp(-lp)).  Standard errors around the lp are calculated using the covariance matrix from the regression model. The following matrix multiplication is performed ('calculations' sheet): MMULT(TRANSPOSE(vector of covariate values);MMULT(covariance matrix;vector of covariate values)).
References: | A series of papers is published in J Neurotrauma describing results from the IMPACT data base.  The paper describing the prediction models is in preparation.
File: | F:\Proj\TBI\Predict\[Impact.prediction rule.2008.xls]PtData
Author: | [NAME], PhD, Dept of Public Health, Erasmus University [NAME], The Netherlands. http://www2.eur.nl/fgg/mgz/ ; [EMAIL]
Version: | April 29, 2006
Disclaimer: | No responsibility for any errors can be taken, nor for any consequences from using this spreadsheet.