医学
脑震荡
急诊分诊台
急诊科
决策树
决策树模型
预测值
物理疗法
毒物控制
急诊医学
内科学
伤害预防
机器学习
精神科
计算机科学
作者
Michael Robinson,Andrew M. Johnson,Lisa Fischer,Heather M. MacKenzie
标识
DOI:10.1097/phm.0000000000001754
摘要
The objective was to examine the 22 variables from the Sport Concussion Assessment Tool's 5th Edition Symptom Evaluation using a decision tree analysis to identify those most likely to predict prolonged recovery after a sport-related concussion.A cross-sectional design was used in this study. A total of 273 patients (52% men; mean age, 21 ± 7.6 yrs) initially assessed by either an emergency medicine or sport medicine physician within 14 days of concussion (mean, 6 ± 4 days) were included. The 22 symptoms from the Sport Concussion Assessment Tool's 5th Edition were included in a decision tree analysis performed using RStudio and the R package rpart. The decision tree was generated using a complexity parameter of 0.045, post hoc pruning was conducted with rpart, and the package carat was used to assess the final decision tree's accuracy, sensitivity and specificity.Of the 22 variables, only 2 contributed toward the predictive splits: Feeling like "in a fog" and Sadness. The confusion matrix yielded a statistically significant accuracy of 0.7636 (P [accuracy > no information rate] = 0.00009678), sensitivity of 0.6429, specificity of 0.8889, positive predictive value of 0.8571, and negative predictive value of 0.7059.Decision tree analysis yielded a statistically significant decision tree model that can be used clinically to identify patients at initial presentation who are at a higher risk of having prolonged symptoms lasting 28 days or more postconcussion.
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