TY  - JOUR
PY  - 2022//
TI  - Two symptoms to triage acute concussions: using decision tree modeling to predict prolonged recovery after a concussion
JO  - American journal of physical medicine and rehabilitation
A1  - Robinson, Michael
A1  - Johnson, Andrew M.
A1  - Fischer, Lisa K.
A1  - MacKenzie, Heather M.
SP  - 135
EP  - 138
VL  - 101
IS  - 2
N2  - OBJECTIVE: 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. <br><br>DESIGN: 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. <br><br>RESULTS: 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. <br><br>CONCLUSIONS: 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.<p />  <p>Language: en</p>
LA  - en
SN  - 0894-9115
UR  - http://dx.doi.org/10.1097/PHM.0000000000001754
ID  - ref1
ER  -