Lucrecia María Burgos1, Andreina Gil Ramírez2, Victoria Galizia Brito3, Leonardo Seoane4, Juan Francisco Furmento4, Juan Espinoza5, Mirta Diez1, Mariano Benzadon4, Daniel Navia5. 1. Heart failure, pulmonary hypertension and transplant department, Instituto Cardiovascular de Buenos Aires (ICBA), Buenos Aires, Argentina. 2. Clinical cardiology department, Instituto Cardiovascular de Buenos Aires (ICBA), Buenos Aires, Argentina. 3. Electrophysiology and Arrhythmias Unit. Hospital Universitario Son Espases. Palma de Mallorca. Spain. 4. Critical care cardiology department, Instituto Cardiovascular de Buenos Aires (ICBA), Buenos Aires, Argentina. 5. Cardiac Surgery. Instituto Cardiovascular de Buenos Aires (ICBA), Buenos Aires, Argentina.
Abstract
INTRODUCTION: Postoperative atrial fibrillation (POAFib) occurs in 20 to 40% of patients following cardiac surgery, and is associated with an increased perioperative morbidity and mortality. We aimed to develop and validate a simple clinical risk model for the prediction of POAFib after cardiac surgery. METHODS: An analytical single center retrospective cohort study was conducted, including consecutive patients undergoing cardiac surgery between 2004 and 2017 with POAFib. To create the predictive risk score, a logistic regression model was performed using a random sample of 75% of the population. Coefficients of the model were then converted to a numerical risk score, and three groups were defined: low risk (≤1 point), intermediate risk (2-5 points) and high risk (≥6 points). The score was validated using the remaining 25% of the patients. Discrimination was evaluated through the area under the curve (AUC) ROC, and calibration using the Hosmer-Lemeshow (HL) test, calibration plots, and ratio of expected and observed events (E/O). RESULTS: Six thousand five hundred nine patients underwent cardiac surgery: 52% coronary artery bypass grafting (CABG), 20% valve surgery, 14% combined (CABG and valve surgery) and 12% other. New-onset AF occurred in 1222 patients (18.77%). In the multivariate analysis, age, use of cardiopulmonary bypass pump, severe reduction in left ventricular ejection fraction (LVEF), chronic renal disease and heart failure were independent risk factors for POAFib, while the use of statins was a protective factor. The NOPAF score was calculated by adding points for each independent risk predictor. In the derivation cohort, the AUC was 0.71 (CI95% 0.69-0.72), and in the validation cohort the model also showed good discrimination (AUC 0.67 IC 0.64-0.70) and excellent calibration (HL P = 0.24). The E/O ratio was 1 (CI 95%: 0.89-1.12). According to the risk category, POAFib occurred in 5% of low; 11% of intermediate and 27.7% of high risk patients in the derivation cohort (P <0.001), and 5.7%; 12.6%; and 23.6% in the validation cohort respectively (P <0.001). CONCLUSION: From a large hospitalized population, we developed and validated a simple risk score named NOPAF, based on clinical variables that accurately stratifies the risk of POAFib. This score may help to identify high-risk patients prior to cardiac surgery, in order to strengthen postoperative atrial fibrillation prophylaxis.
INTRODUCTION: Postoperative atrial fibrillation (POAFib) occurs in 20 to 40% of patients following cardiac surgery, and is associated with an increased perioperative morbidity and mortality. We aimed to develop and validate a simple clinical risk model for the prediction of POAFib after cardiac surgery. METHODS: An analytical single center retrospective cohort study was conducted, including consecutive patients undergoing cardiac surgery between 2004 and 2017 with POAFib. To create the predictive risk score, a logistic regression model was performed using a random sample of 75% of the population. Coefficients of the model were then converted to a numerical risk score, and three groups were defined: low risk (≤1 point), intermediate risk (2-5 points) and high risk (≥6 points). The score was validated using the remaining 25% of the patients. Discrimination was evaluated through the area under the curve (AUC) ROC, and calibration using the Hosmer-Lemeshow (HL) test, calibration plots, and ratio of expected and observed events (E/O). RESULTS: Six thousand five hundred nine patients underwent cardiac surgery: 52% coronary artery bypass grafting (CABG), 20% valve surgery, 14% combined (CABG and valve surgery) and 12% other. New-onset AF occurred in 1222 patients (18.77%). In the multivariate analysis, age, use of cardiopulmonary bypass pump, severe reduction in left ventricular ejection fraction (LVEF), chronic renal disease and heart failure were independent risk factors for POAFib, while the use of statins was a protective factor. The NOPAF score was calculated by adding points for each independent risk predictor. In the derivation cohort, the AUC was 0.71 (CI95% 0.69-0.72), and in the validation cohort the model also showed good discrimination (AUC 0.67 IC 0.64-0.70) and excellent calibration (HL P = 0.24). The E/O ratio was 1 (CI 95%: 0.89-1.12). According to the risk category, POAFib occurred in 5% of low; 11% of intermediate and 27.7% of high risk patients in the derivation cohort (P <0.001), and 5.7%; 12.6%; and 23.6% in the validation cohort respectively (P <0.001). CONCLUSION: From a large hospitalized population, we developed and validated a simple risk score named NOPAF, based on clinical variables that accurately stratifies the risk of POAFib. This score may help to identify high-risk patients prior to cardiac surgery, in order to strengthen postoperative atrial fibrillation prophylaxis.
Entities:
Keywords:
Atrial fibrillation; Cardiac arrhythmia; Cardiac surgical procedures; Thoracic surgery
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