PT  - JOURNAL ARTICLE
AU  - Jared M Bruce
AU  - Ruben J Echemendia
AU  - Willem Meeuwisse
AU  - Michael G Hutchison
AU  - Mark Aubry
AU  - Paul Comper
TI  - Development of a risk prediction model among professional hockey players with visible signs of concussion
AID  - 10.1136/bjsports-2016-097091
DP  - 2018 Sep 01
TA  - British Journal of Sports Medicine
PG  - 1143--1148
VI  - 52
IP  - 17
4099  - http://bjsm.bmj.com/content/52/17/1143.short
4100  - http://bjsm.bmj.com/content/52/17/1143.full
SO  - Br J Sports Med2018 Sep 01; 52
AB  - Background Little research examines how to best identify concussed athletes. The purpose of the present study was to develop a preliminary risk decision model that uses visible signs (VS) and mechanisms of injury (MOI) to predict the likelihood of subsequent concussion diagnosis.Methods Coders viewed and documented VS and associated MOI for all NHL games over the course of the 2013–2014 and 2014–2015 regular seasons. After coding was completed, player concussions were identified from the NHL injury surveillance system and it was determined whether players exhibiting VS were subsequently diagnosed with concussions by club medical staff as a result of the coded event.Results Among athletes exhibiting VS, suspected loss of consciousness, motor incoordination or balance problems, being in a fight, having an initial hit from another player’s shoulder and having a secondary hit on the ice were all associated with increased risk of subsequent concussion diagnosis. In contrast, having an initial hit with a stick was associated with decreased risk of subsequent concussion diagnosis. A risk prediction model using a combination of the above VS and MOI was superior to approaches that relied on individual VS and associated MOI (sensitivity=81%, specificity=72%, positive predictive value=26%).Conclusions Combined use of VS and MOI significantly improves a clinician’s ability to identify players who need to be evaluated for possible concussion. A preliminary concussion prediction log has been developed from these data. Pending prospective validation, the use of these methods may improve early concussion detection and evaluation.