Prediction of outcome

Evaluation of effectiveness of health care delivery, stratification in clinical trials, and assessment of resource allocation requires an accurate estimator of severity of illness and probability of hospital outcome. Considerable debate exists surrounding the use of disease specific scoring systems or a one-for-all approach. The Glasgow Coma Scale (gCs) has been compared with SAPS II (Simplified Acute Physiology Score), MPM II0, MPM II24 (Mortality Prediction Model), and APACHE II (Acute Physiology And Chronic Health Evaluation). The GCS was not intended to be a predictor of outcome but was described as an assessment of depression of conscious level. Although the GCS can provide a quick guide to the assessment of severity of injury, only a comprehensive system that includes the admission variables, physiological derangement, and age will provide accurate discrimination and prediction of outcome.

McQuatt et al. compared logistic regression with decision tree analysis of an observational, head injury dataset, including a wide range of secondary insults and 12 month outcomes.73 Decision tree analysis highlights patient subgroups and critical values in variables assessed. Importantly, the results are visually informative and often present clear clinical interpretation about risk factors faced by patients in these subgroups. A decision tree was automatically produced from root node to target classes based on the Glasgow Outcome Scale (GOS) score (Table 2.4).74 The most significant predictors of mortality in this patient set were duration of hypotensive, pyrexic, and hypoxaemic insults. When good and poor outcomes were compared, hypotensive insults and pupillary response on admission were significant. In certain subgroups of patients pyrexia was a predictor of good outcome. Decision tree analysis confirmed some of the results of logistic regression and challenged others and notably identified that brain stem reflexes are important predictors of outcome.75 This was shown in the Glasgow-Liege Scale statistical analysis.76 Additionally the decision tree analysis showed that GCS 3 patients often had a better outcome than GCS 4 patients, demonstrating that the GCS is not a linear scale, with the GCS sum score being poor at discriminating between patient outcomes.

The outcome after TBI can be subdivided grossly into hospital survival or death. There are, however, many functional outcomes among survivors. Since this population of patients is largely made up of young males, the economic costs of survival of dependent patients is great. Up to half of all head-injured patients admitted to hospital remain disabled at one year. The combination of this factor and young age makes the economic burden greater than in, for example, stroke. Future models that predict outcome must focus upon prediction of functional outcome. Factors including genetic phenotype are known to be important and will require inclusion to achieve adequate calibration and discrimination.

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