@inproceedings{e2ab6e35a43642f28f490c2ea82cc49e,
title = "Incorporating intra-operative medication information for prediction of post-operative atrial fibrillation",
abstract = "This study aimed to construct and evaluate a novel prediction model for postoperative atrial fibrillation (PoAF) with the addition of intraoperative medications. The study patient population included 4731 patients who underwent CABG surgery, of which 1363 developed PoAF, and the prediction methods included three logistic regression models. Multivariate logistic regression was performed with traditional clinical variables only for the first model, intraoperative medications added in the second model, and a subset of all variables chosen by the Least Absolute Shrinkage and Selection Operator (LASSO) in the third model. Age and prior AF diagnosis were consistently the strongest predictors for PoAF across all three models. The specific intra-operative medications moderately improved predictive accuracy as compared to the clinical feature-only model.",
keywords = "Atrial fibrillation, Cardiac artery bypass graft surgery, Postoperative complication arrhythmia, Prediction model",
author = "Johnson, {Ethan M.I.} and Jingzhi Yu and Yu Deng and Melnick, {David S.} and Sandhu, {Sukhveer S.} and Farhad Ghamsari and Mozziyar Etemadi and Kho, {Abel N.}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 7th IEEE International Conference on Healthcare Informatics, ICHI 2019 ; Conference date: 10-06-2019 Through 13-06-2019",
year = "2019",
month = jun,
doi = "10.1109/ICHI.2019.8904493",
language = "English (US)",
series = "2019 IEEE International Conference on Healthcare Informatics, ICHI 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 IEEE International Conference on Healthcare Informatics, ICHI 2019",
address = "United States",
}