Two Symptoms to Triage Acute Concussions: Using Decision Tree Modeling to Predict Prolonged Recovery After a Concussion. (February 2022)
- Record Type:
- Journal Article
- Title:
- Two Symptoms to Triage Acute Concussions: Using Decision Tree Modeling to Predict Prolonged Recovery After a Concussion. (February 2022)
- Main Title:
- Two Symptoms to Triage Acute Concussions
- Authors:
- Robinson, Michael
Johnson, Andrew M.
Fischer, Lisa K.
MacKenzie, Heather M. - Abstract:
- Abstract : 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. 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. 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. 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 symptomsAbstract : 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. 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. 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. 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. … (more)
- Is Part Of:
- American journal of physical medicine & rehabilitation. Volume 101:Number 2(2022)
- Journal:
- American journal of physical medicine & rehabilitation
- Issue:
- Volume 101:Number 2(2022)
- Issue Display:
- Volume 101, Issue 2 (2022)
- Year:
- 2022
- Volume:
- 101
- Issue:
- 2
- Issue Sort Value:
- 2022-0101-0002-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-02
- Subjects:
- Decision Trees -- Brain Concussion -- Brain Injuries -- Physical and Rehabilitation Medicine
Rehabilitation -- Periodicals
Medicine, Physical -- Periodicals
617.062 - Journal URLs:
- http://journals.lww.com/ajpmr/pages/default.aspx ↗
http://journals.lww.com ↗ - DOI:
- 10.1097/PHM.0000000000001754 ↗
- Languages:
- English
- ISSNs:
- 0894-9115
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 0832.160000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 26304.xml