BACKGROUND: The need for permanent cardiac pacing after cardiac operations is infrequent but associated with increased morbidity and resource utilization. We identified patient risk factors for pacemaker insertion to enable development of a predictive model. METHODS: Data were collected prospectively for 10,421 consecutive patients who had cardiac operations between January 1990 and December 1995. Two hundred fifty-five patients (2.4%) were identified as having received a permanent pacemaker during the same hospitalization. Logistic regression analysis was performed to determine the independent, multivariate predictors of permanent pacing. The predictive accuracy and precision of the logistic regression model was evaluated in the 1996 database of 2,236 consecutive patients by the calculation of Brier scores. RESULTS: Eight independent predictors of permanent pacemaker requirement were identified. The factor-adjusted odds ratios (OR) with 95% confidence interval (CI) associated with each predictor are as follows: (1) valve replacement surgery (aortic: OR 5.8, CI 3.9-8.7; mitral: OR 4.9, CI 3.1-7.8; tricuspid: OR 8.0, CI 5.5-11.9; double: OR 8.9, CI 5.5-14.6; and triple: OR 7.5, CI 2.9-19.3); (2) repeat operation: OR 2.4, CI 1.8-3.3; (3) age 75 years or older: OR 3.0, CI 2.0-4.4; (4) ablative arrhythmia operation: OR 4.2, CI 1.9-9.5; (5) mitral valve annular reconstruction: OR 2.4, CI 1.4-4.2; (6) use of cold blood cardioplegia: OR 2.0, CI 1.2-3.6; (7) preoperative renal failure: OR 1.6, CI 1.0-2.6; and (8) active endocarditis: OR 1.7, CI 0.9-3.0. A model for postoperative permanent pacemaker requirement using the eight predictors was formulated and tested (Brier score = 0.017+/-0.003; Z = 0.18). CONCLUSIONS: The proposed predictive model correlated highly with actual pacemaker use, which suggests that the requirement for pacing results from either operative trauma or increased ischemic burden. Preoperative identification of patients at increased risk of conduction disturbances may allow for earlier detection and improved treatment. Patients requiring postoperative pacing had increased morbidity and length of stay.
BACKGROUND: The need for permanent cardiac pacing after cardiac operations is infrequent but associated with increased morbidity and resource utilization. We identified patient risk factors for pacemaker insertion to enable development of a predictive model. METHODS: Data were collected prospectively for 10,421 consecutive patients who had cardiac operations between January 1990 and December 1995. Two hundred fifty-five patients (2.4%) were identified as having received a permanent pacemaker during the same hospitalization. Logistic regression analysis was performed to determine the independent, multivariate predictors of permanent pacing. The predictive accuracy and precision of the logistic regression model was evaluated in the 1996 database of 2,236 consecutive patients by the calculation of Brier scores. RESULTS: Eight independent predictors of permanent pacemaker requirement were identified. The factor-adjusted odds ratios (OR) with 95% confidence interval (CI) associated with each predictor are as follows: (1) valve replacement surgery (aortic: OR 5.8, CI 3.9-8.7; mitral: OR 4.9, CI 3.1-7.8; tricuspid: OR 8.0, CI 5.5-11.9; double: OR 8.9, CI 5.5-14.6; and triple: OR 7.5, CI 2.9-19.3); (2) repeat operation: OR 2.4, CI 1.8-3.3; (3) age 75 years or older: OR 3.0, CI 2.0-4.4; (4) ablative arrhythmia operation: OR 4.2, CI 1.9-9.5; (5) mitral valve annular reconstruction: OR 2.4, CI 1.4-4.2; (6) use of cold blood cardioplegia: OR 2.0, CI 1.2-3.6; (7) preoperative renal failure: OR 1.6, CI 1.0-2.6; and (8) active endocarditis: OR 1.7, CI 0.9-3.0. A model for postoperative permanent pacemaker requirement using the eight predictors was formulated and tested (Brier score = 0.017+/-0.003; Z = 0.18). CONCLUSIONS: The proposed predictive model correlated highly with actual pacemaker use, which suggests that the requirement for pacing results from either operative trauma or increased ischemic burden. Preoperative identification of patients at increased risk of conduction disturbances may allow for earlier detection and improved treatment. Patients requiring postoperative pacing had increased morbidity and length of stay.
Authors: Chin C Lee; Khuyen Do; Sati Patel; Steven K Carlson; Tomas Konecny; Philip M Chang; Rahul N Doshi Journal: J Interv Card Electrophysiol Date: 2019-08-20 Impact factor: 1.900
Authors: J Hunter Mehaffey; Nathan S Haywood; Robert B Hawkins; John A Kern; Nicholas R Teman; Irving L Kron; Leora T Yarboro; Gorav Ailawadi Journal: Ann Thorac Surg Date: 2018-03-22 Impact factor: 4.330
Authors: Alexander D Simms; Andrew J Hogarth; Elizabeth A Hudson; Victoria L Worsnop; Daniel J Blackman; David J O'Regan; Muzahir H Tayebjee Journal: Interact Cardiovasc Thorac Surg Date: 2013-04-25
Authors: Robert M van der Boon; Rutger-Jan Nuis; Nicolas M Van Mieghem; Luc Jordaens; Josep Rodés-Cabau; Ron T van Domburg; Patrick W Serruys; Robert H Anderson; Peter P T de Jaegere Journal: Nat Rev Cardiol Date: 2012-05-01 Impact factor: 32.419
Authors: G Limongelli; V Ducceschi; A D'Andrea; A Renzulli; B Sarubbi; M De Feo; F Cerasuolo; R Calabrò; M Cotrufo Journal: Heart Date: 2003-08 Impact factor: 5.994
Authors: Zahra Emkanjoo; Mansour Mirza-Ali; Abollfath Alizadeh; Saied Hosseini; Mohammad Vahid Jorat; Mohammad Hossein Nikoo; Mohammad Ali Sadr-Ameli Journal: Indian Pacing Electrophysiol J Date: 2008-02-01