Wednesday, September 13, 2017

Predicting Recovery in Patients suffering from Traumatic Brain Injury by using admission variables and physiological data

Predicting Recovery in Patients Suffering from Traumatic Brain Injury By Using Admission Variables and Physiological Data: A Comparison between Decision Tree Analysis and Logistic Regression

Authors: Peter J.D. Andrews, M.D., F.R.C.A., Derek H. Sleeman, Ph.D., F.R.S. (ED), Patrick F.X. Statham F.R.C.S (Sn), Andrew McQuatt M. Sc., Vincent Corruble Ph.D., Patricia A. Jones, M. App. Sci., Timothy P. Howells, Ph.D., and Carol S. A. Macmillan, M.R.C.P., F.R.C.A

Summary:

This study compared results given by logistic regression and decision tree analysis on patients suffering from Traumatic Brain Injury. The researchers studied 124 patients in an intensive care unit over a 12 month period by using a data collection system. They input the values into a logistic regression, then created a decision tree from root nodes to target classes. This is called the Glasgow Outcome Score.

The researchers then assessed the outcome after 12 months of 69 patients with 8 insult or head injury categories. They input the data into a logistic regression to assess how patient age, Glasgow Coma Scale Score, Injury Severity Score, pupilary response upon admission, and insult duration. After they input the data, they found hypotensive, pyrexic, and hypoxemic insults to be the most significant predictors of mortality. 

After they used decision tree analysis, they found that hypotension and low cerebral perfusion pressure to be the most significant predictors of death. The analysis proved to 9.2 percent more accurate than just using the largest outcome category as a predictor of mortality.

Critique:

The article gave a good explanation for why decision tree makes for more accurate analysis. It also did a decent job of listing the variables used for the analysis. However, this is one study done on the decision tree analysis in the medical field and more specifically dealing with brain injuries. The study will need to be replicated using different numbers of patients to give an complete assessment of how accurate decision tree analysis is in that field.

Citation:

http://thejns.org/doi/pdf/10.3171/jns.2002.97.2.0326. 

4 comments:

  1. The result from this statistical analysis were found better than previous research and shown that improved monitoring techniques offer a more realistic estimate of the nature, frequency, and duration of secondary pathophysiological insults. Yet this study needs to be replicated in different clinical fields to get a full assessment of how accurate this decision tree model is. In addition, I found it interesting that one of the shortcomings of the study was the results for certain subgroups were difficult to explain.

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  2. I agree with Pouch. Medicine is an iffy subject to be testing something like decision trees simply because symptoms are not always visible and diagnoses can be very wrong. A longer duration and more types of disorders and diseases should be tested.

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  3. While I agree that the article helped explain how accurate decision trees can be and it did show what the most significant predictors would be, there still need to be more studies in the medical field especially with brain injuries.

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  4. With the limited data collected and algorithm like logit and decision tree being used. The scope of finding does find universal applicability. I feel that more data must be collected across emergency department and further subjected to statistical analysis to get a better predictor.

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