Bedside Tool for Predicting the Risk of Postoperative Atrial Fibrillation After Cardiac Surgery: The POAF Score

Atrial fibrillation (AF) remarkably remains the most common complication after cardiac surgery.^{1, 2} Postoperative AF (POAF) carries severe clinical and economic implications, increasing early and late morbidity and mortality, and having a great impact on resource utilization.^{1, 2, 3, 4, 5} In addition, the incidence of this complication has been reported to steadily increase, owing mostly to an increasing number of elderly patients with severe comorbidities.¹

In light of the importance of POAF for patient outcomes, there has been great interest in preventing this arrhythmia.^{1, 2, 3, 4, 5, 6, 7, 8, 9, 10} Several studies have sought to identify patients at highest risk for the development of AF, but their number is testament to the failure to effectively prevent AF through prophylactic measures in unselected patients.^{6, 7, 8, 9, 10} Routine pharmacological AF prophylaxes could expose as many as 60% to 80% of patients to the side effects of antiarrhythmic drugs for which there is no indication, with unnecessary drug‐related costs.^{4, 11, 12}

As a result, it is of utmost importance to identify patients whose risk of developing AF is very low and who should not receive routine antiarrhythmic therapy. Accordingly, efforts have been made to develop risk prediction models to identify patients most likely to develop POAF.^{4, 5, 6, 7, 8, 9, 10}

However, these models often are statistically underpowered and, because of this, inconclusive.^{6, 7} In addition, they are limited by the exclusion of high‐risk patients with renal and cardiac failure, as well as those undergoing complex procedures, despite the fact that these patients are those most affected by POAF.^{6, 7, 8, 9, 10} These risk models also imply complex calculations without rapid estimation for targeting patients, hampering a prompt prophylactic approach, and confounding the clinical decision making and patient counseling.^{6, 7, 8, 9, 10} The present study aimed to derive an efficient and reliable bedside tool to predict POAF and its related complications.

AF after cardiac surgery remains a vexing complication, resulting in prolonged hospital stay, and causes additional morbidity and mortality in a substantial number of patients.^{1, 2, 3, 4, 5, 6, 7, 8, 9, 10} Consequently, considerable efforts have been directed toward reduction of the risk and POAF management, mainly focusing on pharmacological agents.^{1, 2, 3, 4, 5, 6, 7, 8, 9, 10} However, a caveat with the use of the antiarrhythmic approaches is that most patients undergoing cardiac surgery do not develop POAF, and 60% to 80% of them are exposed to the costs and potential side effects of unnecessary prophylaxes.^{1, 2, 3, 4, 5, 6, 7, 8, 9, 10} On the one hand, it is critical that before such pharmacological managements are implemented in all cardiac surgery patients, the safety and effectiveness of these strategies in improving patient outcomes must be proved. On the other hand, a rapid, accurate estimation of individual patients' risk for POAF may facilitate correct identification of patients who are at the lowest risk of the development of AF and should not be treated with preventive strategies. In this setting, our study demonstrated that the POAF score is a simple, accurate bedside risk tool, allowing for the identification of high‐risk AF patients in whom preventive antiarrhythmic therapies could be justified. The POAF score was also found to be of value in developing an easy‐to‐use risk scoring method for AF‐related or accompanying complications, suggesting possible simultaneous preventive approaches.

Other risk prediction models have been previously proposed to identify patients most likely to develop POAF.^{4, 5, 6, 7, 8, 9, 10} Zaman et al⁶ first enrolled 326 elective isolated CABG patients and created a model for preoperative risk stratification in patients affected by AF, demonstrating that P‐wave duration >155 ms, age, and male sex were able to predict POAF in 59% of their patient population. Amar et al⁷ reported in 1851 patients undergoing isolated CABG that 4 preoperative and postoperative variables were independently associated with AF development. Using their prediction model, 3 risk categories for AF were identified, suggesting increased AF occurrence (from 14% to 60%) with category worsening.⁷ Mathew et al⁸ performed a prospective multicenter observational study of 4657 CABG patients, obtaining a final model with 3 AF risk classes (low, medium, and high risk) based on 17 preoperative, intraoperative, and postoperative variables. More recently, Magee et al⁹ extracted 19 083 patients undergoing isolated CABG from the Society of Thoracic Surgeons Database, using perioperative risk factors to develop a predictive risk algorithm of AF development. The final model included 14 preoperative, intraoperative, and postoperative variables, which provided statistically significant coefficients and ORs.⁹

However, all of these prediction models provided controversial results, revealing important limitations, and limiting their widespread adoption in the clinical practice of AF preventive strategies.^{6, 7, 8, 9, 10} Some of these studies were computationally complex due to the relevant number of variables considered and underpowered due to their small sample size.^{4, 6, 7, 8, 10} In addition, considered risk factors of POAF such as P‐wave duration, male sex, or angiotensin‐converting enzyme inhibitor treatment have been demonstrated to be poorly associated with the arrhythmia, while other well‐known AF predictors were surprisingly not included.^{6, 7, 8, 9, 10} Patients with deteriorated left ventricular function, compromised renal function, or complex operations including valve surgery were often excluded from these AF prediction models. Nevertheless, these subjects are the ones mostly affected by AF, as demonstrated in several studies, and would benefit the most from antiarrhythmic measures.^{4, 6, 7, 9, 10} On the other hand, the same risk prediction models included postoperative variables such as low cardiac output, postoperative drug treatment, and prolonged ventilation, hampering prompt prophylactic treatment, especially in the case of urgent or emergent operations.^{7, 8, 9} In fact, Magee et al⁹ included prolonged ventilation use as one of the most relevant AF predictors, limiting the possibility to adopt preventive measures before such a condition develops and postponing the usefulness of their model to the postoperative period only. However, it is well known that POAF most commonly occurs within the first 48 hours after surgery, and it remains distinctly possible that AF occurred as a consequence of the observed prolonged ventilation rather than being a cause of it, confounding the predictive value of this variable and consequently invalidating the final model. Furthermore, patients with preexistent AF were inopportunely included in the earlier mentioned prediction models, ignoring the fact that these subjects represent a specific surgical subgroup, being at higher risk for mortality and morbidity.^{7, 8, 10, 20, 21, 22, 23} Preexisting AF is often encountered in older patients with significant mitral valve disease and left atrial enlargement, and such patients are certainly more prone to develop AF after surgery than are those without this preexisting condition.^{19, 20} In fact, both Amar et al⁷ and Mathew et al⁸ included the history of AF as a relevant risk factor in their prediction models, although patients with preexisting arrhythmia were largely affected by POAF (68% and 53%, respectively). Consonant data are obtained in the prediction model of Magee et al,⁹ with inclusion of the preoperative arrhythmia variable in the final prediction model, although patients affected by it experienced POAF in 70% of cases. In our study, patients with a preoperative history of supraventricular arrhythmias were excluded to avoid confusing the incidence of POAF with recurrence of a preexisting arrhythmic condition and because these patients have been demonstrated to benefit from extensive antiarrhythmic prophylaxis or a contemporary surgical ablation.^{22, 23, 24}

The present study has important indirect clinical implications with reference to possible AF preventive strategies, although it was not designed to specifically address the complex AF preoperative prophylaxis issue. Our aim was to identify a patient population more likely to develop AF and its related complications who may most benefit from of any preventive strategy.

The present prediction model includes variables such as COPD, valve surgery, emergency status, preoperative IABP need, advanced renal failure, and reduce LVEF, which have normally constituted exclusion criteria in several observational and randomized studies testing different AF prophylactic regimens, because the adverse drug effects registered in such patient classes.^{13, 21, 25, 26} Low‐risk patients (POAF score <3) may benefit from an extensive preoperative AF prophylaxis based on β‐blockers and statins.^{13, 21, 25} However, high‐risk patients (POAF score ≥3) are likely to be the most appropriate candidates for amiodarone prophylaxis, also for subjects already receiving β‐blockers.^{21, 25, 26} Amiodarone has demonstrated a large compliance in the routine antiarrhythmic care of cardiac surgery patients, because of its low incidence of acute complications and a large effectiveness independently from dose (high or low) or administration time (preoperative or intraoperative).^{4, 21, 25, 26}

In addition, high‐risk patients (POAF score ≥3) may benefit from a prolonged postoperative surveillance because of the possible occurrence of late AF episodes.^{3, 27, 28}

Finally, in comparison with previous prediction models, the POAF score for the first time was also tested in predicting related or accompanying complications of POAF.^{4, 6, 7, 8, 9, 10} Having the highest AF risk as identified by POAF score ≥3 was strongly associated with higher hospital mortality, postoperative CVA, AKI, and RRT, suggesting a wider preventive approach based on both antiarrhythmic prophylaxes and other protective measures for AF‐related morbidities. In particular, patients with a POAF score ≥3 and affected by POAF experienced a 2‐ to 4‐fold increased risk of postoperative CVA compared with those in other POAF score classes. Patients with a POAF score ≥3 may largely benefit from antiarrhythmic prophylaxis accompanied by routine postoperative use of low‐molecular‐weight heparin, starting in the immediate period after surgery, especially in CABG patients as previously anticipated.²² The use of oral anticoagulation only at the onset of AF has been demonstrated to be inadequate to prevent stroke.²² Interestingly, oral anticoagulation in patients affected by POAF has been demonstrated to reduce early and late mortality associated with thromboembolic AF sequelae.^{3, 5, 28, 29, 30} Therefore, it should be considered in high‐risk group patients (POAF score ≥3) immediately after cardiac surgery. In this patient class, the ligation of the let atrial appendage should also be considered.³¹

The POAF score based on routinely available preoperative data is a simple, accurate bedside tool, allowing for identification of patients at high risk for POAF and in whom preventive antiarrhythmic therapies seem justified. Permitting identification of patients at high risk for AF and related or accompanying complications, the POAF score may also help to develop and update clinical and therapeutic strategies that not only would minimize the occurrence of POAF but also would result in an improvement in the otherwise dismal morbidity and mortality rates observed among patients affected by this arrhythmia.