Objective: To identify factors that can predict which emergency department (ED) patients with mTBI are likely to develop persistent post-concussion symptoms (PPCS).
Design: A matched case-control study was conducted at a Level 1 trauma centre between June 2006 and July 2009. Patients diagnosed with mTBI in the ED and diagnosed at a concussion management programme with at least one PPCS (85 cases) were compared to patients diagnosed with mTBI in the ED (340 controls) to determine if factors assessed at the time of ED presentation could predict patients likely to develop persistent symptoms.
Results: Multivariable hierarchical logistic regression with variables indicating increased risk for PPCS (prior mTBI, history of depression, history of anxiety, multiple injury, forgetfulness/poor memory, noise sensitivity, or light sensitivity) resulted in a final predictive model including prior mTBI, history of anxiety, forgetfulness/poor memory and light sensitivity. The final model had a specificity of 87.9% and a sensitivity of 69.9%.
Conclusions: A strong prediction model to identify those ED patients with mTBI at risk for PPCS was developed and could be easily implemented in the ED; therefore, helping to target those patients who would potentially benefit from close follow-up.
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http://dx.doi.org/10.3109/02699052.2014.884241 | DOI Listing |
Int J Surg
January 2025
Department of Trauma and Emergency Surgery, Chang Gung Memorial Hospital, Linkou; Chang Gung University, Taoyuan, Taiwan.
Background: Detecting kidney trauma on CT scans can be challenging and is sometimes overlooked. While deep learning (DL) has shown promise in medical imaging, its application to kidney injuries remains underexplored. This study aims to develop and validate a DL algorithm for detecting kidney trauma, using institutional trauma data and the Radiological Society of North America (RSNA) dataset for external validation.
View Article and Find Full Text PDFJAMA
January 2025
Division of Hematology, Brigham and Women's Hospital, Boston, Massachusetts.
JAMA Intern Med
January 2025
Harvard Medical School, Boston, Massachusetts.
JAMA Intern Med
January 2025
Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Importance: There are no validated decision rules for terminating resuscitation during in-hospital cardiac arrest. Decision rules may guide termination and prevent inappropriate early termination of resuscitation.
Objective: To develop and validate termination of resuscitation rules for in-hospital cardiac arrest.
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