ABSTRACT
OBJECTIVE: Restrictions to care access during the pandemic along with the increasing complexity of patients awaiting cardiac surgery provides unique challenges for care delivery. The University of Ottawa Heart Institute has developed a novel multidisciplinary digital platform, the Prehab Automated Follow-Up (AFU) Program, which delivers patient/caregiver teaching regarding risk factor mitigation, tracks patient symptoms, and screens for optimization using best practice guidelines. This study was conducted to quantify patient outcomes following initiation of the AFU Program. METHODS: Patients awaiting elective cardiac surgery are enrolled and screened via automated telephone conversation, according to best practice guidelines, and a Short Form-12 preoperative assessment. Following this screen, patients are referred for an in-person assessment by an appropriate multidisciplinary team member; namely, a diabetes specialist, physiotherapist, dietitian, smoking cessation counselor, social worker, vocational counselor, and/or psychologist. RESULTS: Since initiation in February 2021, the AFU Program has enrolled more than 1237 patients with 508 multidisciplinary team referrals prompted by the AFU screening platform. Before program initiation, there were no multidisciplinary team referrals for preoperative optimization. Compared with patients treated between February 2020 and February 2021, there was a 2.5% decrease in hospital readmission rate within 30 days of surgery, a 0.6-day shorter hospital stay, and a 2.5% decrease in surgical site infection. CONCLUSIONS: Our cardiac surgery AFU Program reduced adverse health outcomes for patients by identifying and optimizing risk factors that increased quality of patient care. The AFU Program provides patient/caregiver engagement through educational support and multidisciplinary team counseling.
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.
