TY - GEN
T1 - Simulation-Optimization to Distinguish Optimal Symptom Free Waiting Period for Return-to-Play Decisions in Sport-Related Concussion
AU - Garcia, Gian Gabriel P.
AU - Czerniak, Lauren L.
AU - Lavieri, Mariel S.
AU - Liebel, Spencer W.
AU - McCrea, Michael A.
AU - McAllister, Thomas W.
AU - Pasquina, Paul F.
AU - Broglio, Steven P.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Approximately 1.6-3.8 million sport and recreation concussions occur annually. Yet, there is currently no universal agreement on when an athlete should be permitted to unrestricted play after being diagnosed with a concussion. Simulation-optimization provides a tractable method to optimize the length of the symptom-free waiting period (SFWP), i.e., the number of consecutive days after starting the return-to-play protocol that an athlete must be symptom-free before they are permitted to unrestricted play. We develop a two-part treatment initiation/cessation simulation model consisting of a (1) Controlled Hidden Markov Model [pre-return-to-play] and (2) Uncontrolled Markov Chain [post-return-to-play] and apply four simulation-optimization methods (Crude Monte Carlo, 2-Stage Decomposition, NSGS, KN) to optimize the SFWP. For collegiate men's football and women's soccer, we find an optimal SFWP of approximately 2 and 3.5 weeks, respectively. This research provides clinical decision-support for return-to-play decisions.
AB - Approximately 1.6-3.8 million sport and recreation concussions occur annually. Yet, there is currently no universal agreement on when an athlete should be permitted to unrestricted play after being diagnosed with a concussion. Simulation-optimization provides a tractable method to optimize the length of the symptom-free waiting period (SFWP), i.e., the number of consecutive days after starting the return-to-play protocol that an athlete must be symptom-free before they are permitted to unrestricted play. We develop a two-part treatment initiation/cessation simulation model consisting of a (1) Controlled Hidden Markov Model [pre-return-to-play] and (2) Uncontrolled Markov Chain [post-return-to-play] and apply four simulation-optimization methods (Crude Monte Carlo, 2-Stage Decomposition, NSGS, KN) to optimize the SFWP. For collegiate men's football and women's soccer, we find an optimal SFWP of approximately 2 and 3.5 weeks, respectively. This research provides clinical decision-support for return-to-play decisions.
UR - http://www.scopus.com/inward/record.url?scp=85147448350&partnerID=8YFLogxK
U2 - 10.1109/WSC57314.2022.10015285
DO - 10.1109/WSC57314.2022.10015285
M3 - Conference contribution
AN - SCOPUS:85147448350
T3 - Proceedings - Winter Simulation Conference
SP - 1021
EP - 1032
BT - Proceedings of the 2022 Winter Simulation Conference, WSC 2022
A2 - Feng, B.
A2 - Pedrielli, G.
A2 - Peng, Y.
A2 - Shashaani, S.
A2 - Song, E.
A2 - Corlu, C.G.
A2 - Lee, L.H.
A2 - Chew, E.P.
A2 - Roeder, T.
A2 - Lendermann, P.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 Winter Simulation Conference, WSC 2022
Y2 - 11 December 2022 through 14 December 2022
ER -