Diem T T Tran1, Jeffery J Perry2, Jean-Yves Dupuis3, Elsayed Elmestekawy4, George A Wells5. 1. Division of Cardiac Anesthesiology, Department of Anesthesiology. Electronic address: dtran@ottawaheart.ca. 2. Ottawa Hospital Research Institute, Department of Emergency Medicine, The Ottawa Hospital, Ottawa, Ontario. 3. Division of Cardiac Anesthesiology, Department of Anesthesiology. 4. Division of Cardiac Surgery, Department of Surgery. 5. Cardiovascular Research Methods Center, Department of Epidemiology and Community Medicine, The University of Ottawa Heart Institute, Ottawa, Ontario.
Abstract
OBJECTIVE: To derive a simple clinical prediction rule identifying patients at high risk of developing new-onset postoperative atrial fibrillation (POAF) after cardiac surgery. DESIGN: Retrospective analysis on prospectively collected observational data. SETTING: A university-affiliated cardiac hospital. PARTICIPANTS: Adult patients undergoing coronary artery bypass grafting and/or valve surgery. INTERVENTIONS: Observation for the occurrence of new-onset postoperative atrial fibrillation. MEASUREMENTS AND MAIN RESULTS: Details on 28 preoperative variables from 999 patients were collected and significant predictors (p<0.2) were inserted into multivariable logistic regression and reconfirmed with recursive partitioning. A total of 305 (30.5%) patients developed new-onset POAF. Eleven variables were associated significantly with atrial fibrillation. A multivariable logistic regression model included left atrial dilatation, mitral valve disease, and age. Coefficients from the model were converted into a simple 7-point predictive score. The risk of POAF per score is: 15.0%, if 0; 20%, if 1; 27%, if 2; 35%, if 3; 44%, if 4; 53%, if 5; 62%, if 6; and 70%, if 7. A score of 4 has a sensitivity of 44% and a specificity of 82% for POAF. A score of 6 has a sensitivity of 11% and a specificity of 97%. Bootstrapping with 5,000 samples confirmed the final model provided consistent predictions. CONCLUSIONS: This study proposed a simple predictive score incorporating three risk variables to identify cardiac surgical patients at high risk of developing new-onset POAF. Preventive treatment should target patients ≥ 65 years with left atrial dilatation and mitral valve disease.
OBJECTIVE: To derive a simple clinical prediction rule identifying patients at high risk of developing new-onset postoperative atrial fibrillation (POAF) after cardiac surgery. DESIGN: Retrospective analysis on prospectively collected observational data. SETTING: A university-affiliated cardiac hospital. PARTICIPANTS: Adult patients undergoing coronary artery bypass grafting and/or valve surgery. INTERVENTIONS: Observation for the occurrence of new-onset postoperative atrial fibrillation. MEASUREMENTS AND MAIN RESULTS: Details on 28 preoperative variables from 999 patients were collected and significant predictors (p<0.2) were inserted into multivariable logistic regression and reconfirmed with recursive partitioning. A total of 305 (30.5%) patients developed new-onset POAF. Eleven variables were associated significantly with atrial fibrillation. A multivariable logistic regression model included left atrial dilatation, mitral valve disease, and age. Coefficients from the model were converted into a simple 7-point predictive score. The risk of POAF per score is: 15.0%, if 0; 20%, if 1; 27%, if 2; 35%, if 3; 44%, if 4; 53%, if 5; 62%, if 6; and 70%, if 7. A score of 4 has a sensitivity of 44% and a specificity of 82% for POAF. A score of 6 has a sensitivity of 11% and a specificity of 97%. Bootstrapping with 5,000 samples confirmed the final model provided consistent predictions. CONCLUSIONS: This study proposed a simple predictive score incorporating three risk variables to identify cardiac surgical patients at high risk of developing new-onset POAF. Preventive treatment should target patients ≥ 65 years with left atrial dilatation and mitral valve disease.
Authors: Mina F Mirhoseini; Susan E Hamblin; W Paul Moore; Jonathan Pouliot; Judith M Jenkins; Wei Wang; Rameela Chandrasekhar; Bryan R Collier; Mayur B Patel Journal: J Surg Res Date: 2017-10-31 Impact factor: 2.192
Authors: Jimmy T Efird; Andy C Kiser; Patricia B Crane; Hope Landrine; Linda C Kindell; Margaret-Ann Nelson; Charulata Jindal; Daniel F Sarpong; William F Griffin; T Bruce Ferguson; W Randolph Chitwood; Stephen W Davies; Alan P Kypson; Preeti Gudimella; Ethan J Anderson Journal: Pharmacotherapy Date: 2017-02-03 Impact factor: 4.705
Authors: Sheng-Chia Chung; Benjamin O'Brien; Gregory Y H Lip; Kara G Fields; Jochen D Muehlschlegel; Anshul Thakur; David Clifton; Gary S Collins; Peter Watkinson; Rui Providencia Journal: Clin Res Cardiol Date: 2022-08-05 Impact factor: 6.138
Authors: Lucrecia María Burgos; Andreina Gil Ramírez; Victoria Galizia Brito; Leonardo Seoane; Juan Francisco Furmento; Juan Espinoza; Mirta Diez; Mariano Benzadon; Daniel Navia Journal: J Atr Fibrillation Date: 2020-08-31
Authors: Michael R Mathis; Neal M Duggal; Allison M Janda; Jordan L Fennema; Bo Yang; Francis D Pagani; Michael D Maile; Ryan E Hofer; Elizabeth S Jewell; Milo C Engoren Journal: J Cardiothorac Vasc Anesth Date: 2021-01-27 Impact factor: 2.894
Authors: Jimmy T Efird; Charulata Jindal; Andy C Kiser; Shahab A Akhter; Patricia B Crane; Alan P Kypson; Aaron L Sverdlov; Stephen W Davies; Linda C Kindell; Ethan J Anderson Journal: J Int Med Res Date: 2018-05-29 Impact factor: 1.671