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
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
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DOI：
10.3171/jns.2002.97.2.0326
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发表时间：
2002-08-01
影响因子：
4.1
通讯作者：
Macmillan, CSA
中科院分区：
文献类型：
--
作者：
Andrews, PJD;Sleeman, DH;Macmillan, CSA
Object.决策树分析突出患者亚组和评估变量的临界值。重要的是，结果是视觉信息，并经常提出明确的临床解释的风险因素，患者面临的这些亚组。这项前瞻性研究的目的是比较逻辑回归的结果与决策树分析的观察，头部损伤的数据集，包括广泛的二次损伤和12个月的结局。124名成年颅脑损伤患者在重症监护病房住院期间使用计算机数据收集系统进行了研究。在逻辑回归的帮助下，根据损伤等级和持续时间分析超出阈值限制的验证值。从根节点到目标类别（格拉斯哥结局量表[GOS]评分）自动生成决策树。在69例患者中，可以评估8种损伤类别，使用Logistic回归分析12个月时的结局，以确定患者年龄、入院时格拉斯哥昏迷量表评分、损伤严重程度评分（ISS）、入院时乳头状反应和损伤持续时间的相对影响。该患者组中死亡率最显著的预测因素是水肿、发热和低氧性损伤的持续时间。当比较好的和差的结果时，入院时的过度损伤和瞳孔反应是显著的，使用决策树分析，作者发现低血压和低脑灌注压（CPP）是死亡的最佳预测因子，与简单预测每个患者的最大结局类别作为结局相比，预测准确性（PA）提高了9.2%。低血压是不良结局的重要预测因素（GOS评分1-3）。低CPR患者年龄、低碳酸血症和瞳孔反应也是预后良好的预测因素（好/差），PA改善5.1%。在某些亚组的患者中，发热是良好结局的预测因素。决策树分析证实了逻辑回归的一些结果，并对其他结果提出了挑战。这项调查表明，借助决策树分析来分析观测数据可以获得知识。
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.