Hierarchical log linear analysis of admission blood parameters and clinical outcome following traumatic brain injury

作者：Helmy Adel^*; Timofeev Ivan; Palmer Christopher R; Gore Alison; Menon David K; Hutchinson Peter J

来源：Acta Neurochirurgica, 2010, 152(6): 953-957.

DOI：10.1007/s00701-009-0584-y

• Traumatic brain injury
• Statistical models
• Hematologic tests
• Hierarchical log linear model

Numerous statistical methods have been utilised to generate predictive models that identify clinical and biochemical parameters of prognostic value following traumatic brain injury. While these methods provide an accurate statistical description between these variables and outcome, they are difficult to interpret intuitively. Hierarchical log linear analysis can be utilised to present the complex interactions between these variables and outcome visually.
We compiled a database of 327 traumatic brain injury patients, their admission blood parameters, clinical admission parameters, and 6-month Glasgow Outcome Score. Seven variables (age, injury severity, Glasgow Coma Score, glucose, albumin, haemoglobin, white cell count) that correlated with outcome in a univariate analysis and two further variables, included on the basis of biological plausibility, (abnormal clotting and magnesium) were used to derive and present a hierarchical log linear model.
Seventeen (out of an original forty-five possible) inter-relationships between the chosen variables were identified as remaining in the hierarchical log linear model. This data is presented pictorially in a hierarchy demonstrating the directness of the statistical association between each of the variables and dichotomised outcome. Four variables within the hierarchical log linear model (age, raised serum glucose, low haemoglobin, Glasgow Coma Score) had a direct independent statistical relationship with outcome. The remaining five variables only had a statistical relationship with outcome via at least one other variable.
Hierarchical log linear analysis allows the presentation of multivariate, categorical data sets in a pictorial and more easily interpretable fashion.

• 出版日期2010-6