Abstract

Key Points

• The incidence of POAF in this study was 32.2% (n = 517) of 1,606 patients who underwent cardiac surgery. Patients who developed POAF were 7 years older on average than patients without POAF and had traditional cardiovascular risk factors, higher STS risk scores, and a greater likelihood of undergoing valve surgery.

• Risk scores demonstrated poor performance in predicting POAF, with the highest AUROC of 0.65 coming from the POAF risk score, though it showed greater utility in risk stratification for patients with scores of 5 or more.

• Development of POAF was associated with significantly longer hospital LOS, increased ventilator time, and greater odds of being discharged to rehabilitation facilities, thereby emphasizing the need for improved risk-prediction tools and targeted interventions to prevent or mitigate POAF's impact on patient outcomes.

Abbreviations and Acronyms

AF

atrial fibrillation

AUROC

area under the receiver operator characteristic curve

CHA₂DS₂-VASc

congestive heart failure, hypertension, age ≥75 years, diabetes, stroke or transient ischemic attack, vascular disease, age 65-74 years, sex category

COPD

chronic obstructive pulmonary disease

HF

heart failure

LOS

length of stay

POAF

postoperative atrial fibrillation

STS

Society of Thoracic Surgeons

Introduction

Arrhythmias are common complications after cardiac surgery, with postoperative atrial fibrillation (POAF) being the most frequently observed arrhythmia.¹ The incidence rate of POAF ranges from 20% to 50%, depending on factors such as the type of surgery, patient population, and criteria used for diagnosis.^2,3 Notably, POAF has been associated with increased morbidity, mortality, and health care costs.⁴ Patients who develop POAF are at a higher risk of experiencing complications such as stroke, heart failure (HF), and prolonged hospital stays.⁵ Moreover, POAF has been linked to long-term adverse outcomes, including a greater risk of recurrence of AF/atrial flutter and increased mortality.⁶ The accurate prediction of POAF can help guide therapy and facilitate targeted interventions to prevent its occurrence.⁷ Several established risk scores, such as the POAF, HATCH, and COM-AF scores, have been developed to help identify patients at risk for developing POAF.^8-10 Early identification of patients at risk for POAF allows for the timely initiation of prophylactic measures, such as the use of β-blockers, amiodarone, or other antiarrhythmic drugs, which have been shown to reduce the incidence of POAF.^7,11 The comparative performance of each risk score, however—in addition to general AF risk scores, such as the Society of Thoracic Surgeons (STS) score and the congestive HF, hypertension, age ≥75 years, diabetes, stroke or transient ischemic attack, vascular disease, age 65-74 years, sex category (CHA₂DS₂-VASc) score—in predicting POAF is unknown.^12,13

The present study examined the incidence of POAF, the accuracy of POAF risk scores, and in-hospital adverse outcomes of POAF among patients who underwent cardiac surgery in a large tertiary referral hospital.

Patients and Methods

Results

Among the 2,208 candidate patients who underwent cardiac surgery between January 4, 2016, and December 12, 2022, 602 individuals were excluded based on the criteria of being on preoperative antiarrhythmics (n = 8), having a history of AF at baseline (n = 593), and having prior supraventricular arrhythmias (n = 1). Among the 1,606 participants in the analysis cohort, 517 (32.2%) developed POAF. Participants who developed POAF were older (mean age of 67 years for patients with POAF vs 60 years for patients without POAF; P < .001) and less likely to be smokers (11.6% vs 15.8%; P = .03) (Table I). Participants who developed POAF were more likely to have traditional cardiovascular risk factors, including hypertension (92.1% vs 87.6%; P = .009) and kidney disease (mean estimated glomerular filtration rate, 70 mL/min/1.73 m2 vs 74 mL/ min/1.73 m²; P = .02). Among surgical characteristics, patients who developed POAF were more likely to have undergone valve surgery (42.4% vs 32.0%; P < .001), had a higher STS risk score (median score, 1.76% vs 1.34%; P < .001), and were more likely to be in cardiogenic shock (5.4% vs 2.9%; P = .02) (Table I).

TABLE I. Baseline Characteristics of Patients With and Without POAF After Cardiac Surgery

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The median (IQR) values for the POAF, HATCH, COM-AF, CHA₂DS₂-VASc, and STS risk scores were 3 (2-3), 2 (1-3), 3 (2-4), 3 (2-4), and 1.47% (0.72%-2.47%), respectively. The overall performance of the risk scores was variable, ranging from an AUROC of 0.56 for the HATCH risk score to 0.65 for the POAF risk score (Fig. 1A). Notably, the POAF risk score more accurately stratified patients’ risk, with a POAF incidence of 51.5% among individuals with a score greater than or equal to 5 (Fig. 1B). The POAF risk score had the highest specificity, positive predictive value, and negative predictive value compared to the other 4 risk scores (Table II).

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TABLE II. Optimal Cut Points and Performance Metrics for Each Risk Score in Predicting POAF

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Finally, the association between POAF and clinical outcomes of interest was assessed. In unadjusted analysis, POAF was significantly associated with an increased log-LOS in days (β = .38 [95% CI, 0.30-0.46]; P < .001) and log-ventilator time in hours (β = .40 [95% CI, 0.26-0.53]; P < .001) as well as greater odds of being discharged to a rehabilitation facility (odds ratio, 2.67 [95% CI, 1.99-3.59]; P < .001) (Table III). All associations remained significant after propensity score matching, with a 1.5-day-longer LOS (log-LOS β = .33 [95% CI, 0.25-0.40]; P < .001), 1.4-hours longer on ventilator (log-ventilator β = .29 [95% CI, 0.18-0.40]; P < .001), and nearly 130% greater odds of being discharged to a rehabilitation facility (odds ratio, 2.30 [95% CI, 1.73-3.08]; P < .001) (Table III).

Discussion

The present study found that the incidence of POAF among the 1,606 patients who underwent cardiac surgery was 32.2% (n = 517). Substantial differences were observed between patients who developed POAF and patients who did not, with the POAF group being older; having traditional cardiovascular risk factors, such as a history of hypertension and kidney disease; having higher STS risk scores; and a having greater likelihood of undergoing valve surgery. Second, the overall performance of the risk scores was poor in predicting POAF, with the POAF risk score having the highest AUROC of 0.65; however, the POAF score demonstrated some utility in risk stratification, as patients with scores of 5 or greater had a 50% prevalence of POAF. The development of POAF was also associated with significantly longer hospital LOS and ventilator time as well as a greater likelihood of being discharged to a rehabilitation facility. These findings highlight the importance of identifying patients at risk for POAF because POAF is associated with increased patient morbidity and resource use.

TABLE III. Association Between POAF and Adverse Clinical Outcomes

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Despite advances in understanding the mechanisms and risk factors for POAF, it continues to be 1 of the most prevalent complications following cardiac procedures.^17,18 In this cohort, several risk factors were identified as being associated with the subsequent development of POAF, which is consistent with findings from previous epidemiological studies. Age, a well-established risk factor for both AF in general and POAF, was significantly higher among patients with POAF and the most critical risk factor for developing the complication.⁶ Notably, preoperative β-blocker therapy was less frequently prescribed in patients who developed POAF.

Though it is possible that these patients had more severe medical comorbidities that precluded the use of these agents, a protective effect in β-blockers against the development of POAF may explain in part the difference in β-blocker prescribing rates between the POAF and non-POAF groups.¹⁹ Several medical comorbidities were significantly associated with POAF, including hypertension, chronic kidney disease, and cardiogenic shock; however, there was no significant difference in POAF incidence among patients with HF, chronic obstructive pulmonary disease, and kidney failure, despite these conditions being identified as independent risk factors in the POAF score.⁷

Numerous tools have been developed for predicting either POAF, such as the POAF and COM-AF risk scores, or complications from AF, such as the CHA₂DS₂-VASc, HATCH, and STS risk scores.^8-10,12,13 In the present study, all risk scores were observed to have relatively poor performance, except for the POAF-specific POAF risk score (AUROC = 0.65). Although accurately predicting POAF remains challenging, its effects on adverse patient outcomes are well documented. Patients undergoing cardiac surgery complicated by POAF face an increased risk of stroke, thromboembolism, myocardial infarction, and kidney failure.⁵ In addition to short-term morbidity, POAF is associated with long-term adverse outcomes, including a 3-fold increase in AF recurrence and elevated rates of HF and overall mortality.⁶ In the present study cohort, POAF was associated with increased hospital LOS, prolonged total ventilator hours, and nearly 130% greater odds of being discharged to a rehabilitation facility.

These findings emphasize the importance of accurately identifying patients at risk for POAF and developing effective prevention and treatment strategies. Early identification of patients at high risk can facilitate the timely initiation of prophylactic measures, such as β-blockers, amiodarone, or other antiarrhythmic drugs, which have demonstrated effectiveness in reducing the incidence of POAF.^20,21 Personalized interventions targeting modifiable risk factors, such as optimizing blood pressure control and managing diabetes, could further potentially minimize a patient's risk of developing POAF. Despite the suboptimal performance of the current risk prediction tools, this study highlights the potential for refining and combining these models to enhance POAF prediction. Future research should focus on incorporating novel biomarkers—such as circulating inflammatory markers, genetic predisposition, and other clinical variables—to improve the accuracy of risk-stratification models.²² The application of advanced statistical and machine learning methods may also enable the development of more accurate and individualized risk prediction algorithms.²³

The present study was not without limitations. First, the data were derived from a single tertiary referral hospital, which may limit the generalizability of its findings to other populations and settings. Second, it did not assess the duration of POAF. The clinical significance and implications of POAF may depend on the duration of the arrhythmia, and this study's analysis did not differentiate between transient and persistent episodes of POAF. Third, analysis was limited to the data available in the STS registry. It did not include other potentially relevant biomarkers or clinical factors that may influence the development of POAF, such as inflammatory markers, genetic predispositions, or echocardiographic parameters. The inclusion of additional variables could improve the predictive ability of the risk scores and provide further insights into the pathophysiology of POAF.

Conclusion

The present study identified a high incidence of POAF following cardiac surgery and its association with increased patient morbidity and resource utilization. Despite the existence of several risk scores, accurately predicting POAF remains challenging, and there is a need to improve these models. Future research should focus on refining risk-prediction tools and exploring targeted interventions to prevent or mitigate the impact of POAF on patient outcomes.