Forecasting recovery after traumatic brain injury using intelligent data analysis of admission variables and time series physiological data-a comparison with logistic regression

P. J. D. Andrews, Derek Henry Sleeman, P. F. X. Sathan, A. M. McQuatt, V. Corruble, P. A. Jones

    Research output: Contribution to journalArticle

    Abstract

    Object. 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. The aim of this prospective study was to compare results of logistic regression with those of decision tree analysis of an observational, head-injury data set, including a wide range of secondary insults and 12-month outcomes.

    Methods. One hundred twenty-four adult head-injured patients were studied during their stay in an intensive care unit by using a computerized data collection system. Verified values falling outside threshold limits were analyzed according to insult grade and duration with the aid of logistic regression. A decision tree was automatically produced from root node to target classes (Glasgow Outcome Scale [GOS] score).

    Among 69 patients, in whom eight insult categories could be assessed, outcome at 12 months was analyzed using logistic regression to determine the relative influence of patient age, admission Glasgow Coma Scale score, Injury Severity Score (ISS), papillary response on admission, and insult duration. The most significant predictors of mortality in this patient set were duration of hypotensive, pyrexic, and hypoxetnic insults. When good and poor outcomes were compared, hypotensive insults and pupillary response on admission were significant.

    Using decision tree analysis, the authors found that hypotension and low cerebral perfusion pressure (CPP) are the best predictors of death, with a 9.2% improvement in predictive accuracy (PA) over that obtained by simply predicting the largest outcome category as the outcome for each patient. Hypotension was a significant predictor of poor outcome (GOS Score 1-3). Low CPR patient age, hypocarbia, and pupillary response were also good predictors of outcome (good/poor), with a 5.1% improvement in PA. In certain subgroups of patients pyrexia was a predictor of good outcome.

    Conclusions. Decision tree analysis confirmed some of the results of logistic regression and challenged others. This investigation shows that there is knowledge to be gained from analyzing observational data with the aid of decision tree analysis.

    Original languageEnglish
    Pages (from-to)326-336
    Number of pages10
    JournalBritish Journal of Neurosurgery
    Volume97
    Publication statusPublished - 2002

    Keywords

    • head injury
    • secondary cerebral insult
    • decision tree analysis
    • intensive care unit
    • outcome
    • SEVERE HEAD-INJURY
    • CEREBRAL PERFUSION-PRESSURE
    • SECONDARY INSULTS
    • INTENSIVE-CARE
    • INTRAHOSPITAL TRANSPORT
    • INTRACRANIAL-PRESSURE
    • MORTALITY
    • DAMAGE
    • REGION
    • SCORE

    Cite this

    Forecasting recovery after traumatic brain injury using intelligent data analysis of admission variables and time series physiological data-a comparison with logistic regression. / Andrews, P. J. D.; Sleeman, Derek Henry; Sathan, P. F. X.; McQuatt, A. M.; Corruble, V.; Jones, P. A.

    In: British Journal of Neurosurgery, Vol. 97, 2002, p. 326-336.

    Research output: Contribution to journalArticle

    Andrews, P. J. D. ; Sleeman, Derek Henry ; Sathan, P. F. X. ; McQuatt, A. M. ; Corruble, V. ; Jones, P. A. / Forecasting recovery after traumatic brain injury using intelligent data analysis of admission variables and time series physiological data-a comparison with logistic regression. In: British Journal of Neurosurgery. 2002 ; Vol. 97. pp. 326-336.
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    abstract = "Object. 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. The aim of this prospective study was to compare results of logistic regression with those of decision tree analysis of an observational, head-injury data set, including a wide range of secondary insults and 12-month outcomes.Methods. One hundred twenty-four adult head-injured patients were studied during their stay in an intensive care unit by using a computerized data collection system. Verified values falling outside threshold limits were analyzed according to insult grade and duration with the aid of logistic regression. A decision tree was automatically produced from root node to target classes (Glasgow Outcome Scale [GOS] score).Among 69 patients, in whom eight insult categories could be assessed, outcome at 12 months was analyzed using logistic regression to determine the relative influence of patient age, admission Glasgow Coma Scale score, Injury Severity Score (ISS), papillary response on admission, and insult duration. The most significant predictors of mortality in this patient set were duration of hypotensive, pyrexic, and hypoxetnic insults. When good and poor outcomes were compared, hypotensive insults and pupillary response on admission were significant.Using decision tree analysis, the authors found that hypotension and low cerebral perfusion pressure (CPP) are the best predictors of death, with a 9.2{\%} improvement in predictive accuracy (PA) over that obtained by simply predicting the largest outcome category as the outcome for each patient. Hypotension was a significant predictor of poor outcome (GOS Score 1-3). Low CPR patient age, hypocarbia, and pupillary response were also good predictors of outcome (good/poor), with a 5.1{\%} improvement in PA. In certain subgroups of patients pyrexia was a predictor of good outcome.Conclusions. Decision tree analysis confirmed some of the results of logistic regression and challenged others. This investigation shows that there is knowledge to be gained from analyzing observational data with the aid of decision tree analysis.",
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    TY - JOUR

    T1 - Forecasting recovery after traumatic brain injury using intelligent data analysis of admission variables and time series physiological data-a comparison with logistic regression

    AU - Andrews, P. J. D.

    AU - Sleeman, Derek Henry

    AU - Sathan, P. F. X.

    AU - McQuatt, A. M.

    AU - Corruble, V.

    AU - Jones, P. A.

    PY - 2002

    Y1 - 2002

    N2 - Object. 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. The aim of this prospective study was to compare results of logistic regression with those of decision tree analysis of an observational, head-injury data set, including a wide range of secondary insults and 12-month outcomes.Methods. One hundred twenty-four adult head-injured patients were studied during their stay in an intensive care unit by using a computerized data collection system. Verified values falling outside threshold limits were analyzed according to insult grade and duration with the aid of logistic regression. A decision tree was automatically produced from root node to target classes (Glasgow Outcome Scale [GOS] score).Among 69 patients, in whom eight insult categories could be assessed, outcome at 12 months was analyzed using logistic regression to determine the relative influence of patient age, admission Glasgow Coma Scale score, Injury Severity Score (ISS), papillary response on admission, and insult duration. The most significant predictors of mortality in this patient set were duration of hypotensive, pyrexic, and hypoxetnic insults. When good and poor outcomes were compared, hypotensive insults and pupillary response on admission were significant.Using decision tree analysis, the authors found that hypotension and low cerebral perfusion pressure (CPP) are the best predictors of death, with a 9.2% improvement in predictive accuracy (PA) over that obtained by simply predicting the largest outcome category as the outcome for each patient. Hypotension was a significant predictor of poor outcome (GOS Score 1-3). Low CPR patient age, hypocarbia, and pupillary response were also good predictors of outcome (good/poor), with a 5.1% improvement in PA. In certain subgroups of patients pyrexia was a predictor of good outcome.Conclusions. Decision tree analysis confirmed some of the results of logistic regression and challenged others. This investigation shows that there is knowledge to be gained from analyzing observational data with the aid of decision tree analysis.

    AB - Object. 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. The aim of this prospective study was to compare results of logistic regression with those of decision tree analysis of an observational, head-injury data set, including a wide range of secondary insults and 12-month outcomes.Methods. One hundred twenty-four adult head-injured patients were studied during their stay in an intensive care unit by using a computerized data collection system. Verified values falling outside threshold limits were analyzed according to insult grade and duration with the aid of logistic regression. A decision tree was automatically produced from root node to target classes (Glasgow Outcome Scale [GOS] score).Among 69 patients, in whom eight insult categories could be assessed, outcome at 12 months was analyzed using logistic regression to determine the relative influence of patient age, admission Glasgow Coma Scale score, Injury Severity Score (ISS), papillary response on admission, and insult duration. The most significant predictors of mortality in this patient set were duration of hypotensive, pyrexic, and hypoxetnic insults. When good and poor outcomes were compared, hypotensive insults and pupillary response on admission were significant.Using decision tree analysis, the authors found that hypotension and low cerebral perfusion pressure (CPP) are the best predictors of death, with a 9.2% improvement in predictive accuracy (PA) over that obtained by simply predicting the largest outcome category as the outcome for each patient. Hypotension was a significant predictor of poor outcome (GOS Score 1-3). Low CPR patient age, hypocarbia, and pupillary response were also good predictors of outcome (good/poor), with a 5.1% improvement in PA. In certain subgroups of patients pyrexia was a predictor of good outcome.Conclusions. Decision tree analysis confirmed some of the results of logistic regression and challenged others. This investigation shows that there is knowledge to be gained from analyzing observational data with the aid of decision tree analysis.

    KW - head injury

    KW - secondary cerebral insult

    KW - decision tree analysis

    KW - intensive care unit

    KW - outcome

    KW - SEVERE HEAD-INJURY

    KW - CEREBRAL PERFUSION-PRESSURE

    KW - SECONDARY INSULTS

    KW - INTENSIVE-CARE

    KW - INTRAHOSPITAL TRANSPORT

    KW - INTRACRANIAL-PRESSURE

    KW - MORTALITY

    KW - DAMAGE

    KW - REGION

    KW - SCORE

    M3 - Article

    VL - 97

    SP - 326

    EP - 336

    JO - British Journal of Neurosurgery

    JF - British Journal of Neurosurgery

    SN - 0268-8697

    ER -