Browsing by Author "Sun, Haoqi"

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    Improved tracking of sevoflurane anesthetic states with drug-specific machine learning models
    (2020) Kashkooli, Kimia; Polk, Sam L.; Hahm, Eunice Y.; Murphy, James; Ethridge, Breanna R.; Gitlin, Jacob; Ibala, Reine; Mekonnen, Jennifer; Pedemonte, Juan C.; Sun, Haoqi; Westover, M. Brandon; Barbieri, Riccardo; Akeju, Oluwaseun; Chamadia, Shubham
    Objective.The ability to monitor anesthetic states using automated approaches is expected to reduce inaccurate drug dosing and side-effects. Commercially available anesthetic state monitors perform poorly when ketamine is administered as an anesthetic-analgesic adjunct. Poor performance is likely because the models underlying these monitors are not optimized for the electroencephalogram (EEG) oscillations that are unique to the co-administration of ketamine.Approach.In this work, we designed twok-nearest neighbors algorithms for anesthetic state prediction.Main results.The first algorithm was trained only on sevoflurane EEG data, making it sevoflurane-specific. This algorithm enabled discrimination of the sevoflurane general anesthesia (GA) state from sedated and awake states (true positive rate = 0.87, [95% CI, 0.76, 0.97]). However, it did not enable discrimination of the sevoflurane-plus-ketamine GA state from sedated and awake states (true positive rate = 0.43, [0.19, 0.67]). In our second algorithm, we implemented a cross drug training paradigm by including both sevoflurane and sevoflurane-plus-ketamine EEG data in our training set. This algorithm enabled discrimination of the sevoflurane-plus-ketamine GA state from sedated and awake states (true positive rate = 0.91, [0.84, 0.98]).Significance.Instead of a one-algorithm-fits-all-drugs approach to anesthetic state monitoring, our results suggest that drug-specific models are necessary to improve the performance of automated anesthetic state monitors.
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    Intraoperative electroencephalographic marker of preoperative frailty: a prospective cohort study
    (2023) Boncompte, Gonzalo; Sun, Haoqi; Elgueta Le-Beuffe, María Francisca; Benavides, Javiera; Carrasco, Marcela; Morales, María I.; Calderón, Natalia; Contreras, Victor; Westover, M. Brandon; Cortínez, Luis I.; Akeju, Oluwaseun; Pedemonte Trewhela, Juan Cristóbal
    Frailty was common in elderly patients undergoing non cardiac surgery. Electroencephalogram alpha-band power does not predict preoperative frailty above patients' age. Frailty predictions by machine learning algorithms, were not improved by the addition of electroencephalogram features. Frailty might be different to the concept of brain vulnerability, accounting for a possible brain-body dissociation.
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    Postoperative delirium mediates 180-day mortality in orthopaedic trauma patients
    (2021) Pedemonte, Juan C.; Sun, Haoqi; Franco-Garcia, Esteban; Zhou, Carmen; Heng, Marilyn; Quraishi, Sadeq A.; Westover, Brandon; Akeju, Oluwaseun
    Background: Frailty has been associated with increased incidence of postoperative delirium and mortality. We hypothesised that postoperative delirium mediates a clinically significant (>= 1%) percentage of the effect of frailty on mortality in older orthopaedic trauma patients.