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Ann Thorac Surg 2005;79:1698-1703
© 2005 The Society of Thoracic Surgeons

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Original articles: General thoracic

Prediction Rule for Atrial Fibrillation After Major Noncardiac Thoracic Surgery

^a Northwestern University Feinberg School of Medicine and the Feinberg Cardiovascular Institute, Chicago, Illinois
^b Memorial Sloan-Kettering Cancer Center, New York, New York

Accepted for publication October 28, 2004.

^_* Address reprint requests to Dr Passman, Cardiac Electrophysiology Section, Northwestern Memorial Hospital, 201 East Huron, Suite 10–240, Chicago, IL 60611 (E-mail: r-passman{at}northwestern.edu).

Presented at the Poster Session of the Fortieth Annual Meeting of The Society of Thoracic Surgeons, San Antonio, TX, Jan 26–28, 2003.

	Abstract

Top Abstract Introduction Patients and Methods Results Comment Acknowledgments References

 
BACKGROUND: Atrial fibrillation (AF) is a common complication after major noncardiac thoracic surgery and increases the cost and morbidity of these operations. We sought to derive and validate a clinical prediction rule to risk-stratify patients for postoperative AF.

METHODS: For a cohort of cancer patients who underwent noncardiac thoracic surgery, we examined the association of preoperative clinical variables with development of postoperative AF. Logistic regression identified multivariable predictors of AF and a clinical risk score was developed by assigning weighted point scores for the presence of each significant covariate. An independent data set was used for validation purposes.

RESULTS: Of the 856 patients, 147 (17.2%) developed postoperative AF. Male gender (odds ratio [OR] 1.7, 95% confidence interval [CI] 1.1 to 2.4), advanced age (55 to 74 years OR 4.4, 95% CI 2.0 to 9.8; 75 years OR 9.2, 95% CI 3.9 to 21.5), and preoperative heart rate greater than or equal to 72 beats per minute (OR 1.7, 95% CI 1.2 to 2.5) were independent predictors of postoperative AF. A risk score was assigned with male gender and heart rate greater than or equal to 72 beats per minute each receiving 1 point, and age 55 to 74 and greater than or equal to 75 years receiving 3 and 4 points, respectively. The risk of postoperative AF ranged from 0% (0 points) to 54.6% (6 points) (p < 0.001). The score-based risk in both derivation and validation sets was similar (p = 0.66).

CONCLUSIONS: A prediction rule using clinical variables can be used to predict the risk of postoperative AF after noncardiac thoracic surgery. This information can be used to guide prophylactic therapy.

	Introduction

Top Abstract Introduction Patients and Methods Results Comment Acknowledgments References

 
Atrial fibrillation (AF) is the most common sustained arrhythmia after major noncardiac thoracic surgery and is associated with increases in the morbidity and length of hospital stay associated with these procedures [1–4]. Risk factors for the development of postoperative AF after noncardiac thoracic surgery have been evaluated in prior studies [4–6], with advanced age consistently demonstrated as the single greatest risk factor. Whereas prophylactic therapies such as ß-blockers and other antiarrhythmics have been shown to reduce the occurrence of postoperative AF, widespread use has been limited by concerns over potential side effects [7–9].

In order to optimize patient care, those individuals at highest risk of developing postoperative AF should be targeted for prophylaxis while those at low risk should be spared the potential untoward effects of prophylactic therapy from which they are unlikely to derive benefit. The purpose of this study was to identify the incidence and clinical correlates for the development of postoperative AF and to develop and validate a clinical prediction rule for postoperative AF for cancer patients undergoing noncardiac thoracic surgery.

	Patients and Methods

Top Abstract Introduction Patients and Methods Results Comment Acknowledgments References

 
The study was approved by the Institutional Review Board of Memorial Sloan-Kettering Cancer Center before the initiation of the study. Using a prospective database, we studied 856 cancer patients from a single large comprehensive cancer center who were in sinus rhythm before surgery and had elective lobectomy, pneumonectomy, esophagectomy, pleurectomy, or extrapleural pneumonectomy between the years 1991 and 2003. Excluded were patients with any history of AF (paroxysmal, persistent, or chronic), patients receiving class I or III antiarrhythmic agents, and patients with a history of congestive heart failure. Preoperative clinical variables, pulmonary function data, and standard 12-lead electrocardiograms (ECG) were analyzed. Routine anesthetic care was used perioperatively and there were no changes in anesthetic management during the study period. The primary endpoint was postoperative AF, defined as an irregularly irregular atrial rhythm without clear P waves on any tracing and lasting greater than or equal to 5 minutes or associated with symptoms or hemodynamic compromise, from the time of arrival in the postanesthesia care unit until postoperative day 30. Patients who were discharged and readmitted within 30 days of their operation were analyzed as a single admission. Patients were monitored continuously for 72 to 96 hours postoperatively with either Holter recordings or telemetry. Longer monitoring periods were used as clinically indicated. Documentation of the primary endpoint was obtained from physicians' and nurses' daily notes, outpatient records, telemetry and Holter recording strips, and ECGs, and verified by a board-certified cardiologist. P-wave duration (lead II), PR interval (interval between onset of P wave and onset of QRS complex on electrocardiogram), and heart rate (HR) were derived from the preoperative standard 12-lead ECG performed within one week of the operative date. Ten-second samples of simultaneously acquired twelve leads were analyzed using software provided by General Electrical Medical Systems (Milwaukee, WI) that measured mean PR interval and P-wave duration in lead II.

All statistical analyses were performed using STATA/SE version 8.0 (STATA Corporation, College Station, TX) and SPSS version 11.5 (SPSS, Chicago, IL). Continuous variables are expressed as mean ± standard deviation. To assess for differences between patients with and without postoperative AF, univariate analyses were performed using t tests for normally distributed continuous variables and Wilcoxon rank-sum tests for variables that were not normally distributed. The ² and Fisher's exact tests were used, as appropriate, for categorical variables. The study sample was divided into derivation (n = 444) and validation sets (n = 412) using randomly generated numbers aiming to include approximately 50% of the study sample in each set. In order to improve risk prediction, continuous variables were evaluated for linear versus threshold effects on the primary endpoint. In the case of the significant multivariable predictors of age and heart rate, there were clear risk thresholds. Therefore, patients were stratified by age into three categories: less than 55, 55 to 74, and greater than or equal to 75 years (yrs). Similarly, patients were stratified according to resting preoperative HR of less than 72 beats per minute (bpm) and greater than or equal to 72 bpm. Stepwise logistic regression was used to select significant multivariable predictors of postoperative AF. All variables with a univariate p value less than or equal to 0.2 were eligible for selection in the final multivariable model, with a p value of less than or equal to 0.10 for retention in the model. Using the variable estimates (coefficients) for each of the multivariable predictors of postoperative AF, we created a clinical prediction rule by assigning weighted point scores for the presence of each covariate. The sum of all points was calculated for each patient. Patients were then stratified by total point scores and the risk of postoperative AF was examined for each point score in the derivation and validation sets. The results of the multivariable model were evaluated for calibration and goodness-of-fit in the derivation and validation sets. To test for differing performance of the prediction rule in the derivation and validation sets, an interaction term was entered into the multivariable model and assessed for significance. Durations of hospitalization and intensive care unit (ICU) stays were compared using the Mann-Whitney or Kruskal-Wallis tests and rates of ICU use were compared for patients with and without AF using the ² statistic.

	Results

Top Abstract Introduction Patients and Methods Results Comment Acknowledgments References

 
The study included 856 patients, 147 (17.2%) of whom developed postoperative AF. Clinical characteristics of the patient population are described in Table 1. Table 2 depicts the univariate comparisons for the primary endpoint. In the stepwise model, using all variables with p less than or equal to 0.20 (gender, age, heart rate [HR], PR interval, hypertension, diabetes, coronary artery disease), there were three multivariable predictors of postoperative AF. There was no statistically significant difference in the incidence of postoperative AF across the varying types of operations (esophagectomy, 22 out of 116 [19.0%]; extrapleural pneumonectomy, 12 out of 51 [23.5%]; pneumonectomy, 17 out of 128 [13.3%]; lobectomy, 94 out of 554 [17.0%]; pleurectomy 2 out of 7 [28.6%]; Fisher's exact p = 0.36). Multivariable analysis (Table 3) demonstrated that male gender (odds ratio [OR] 1.95, 95% confidence interval [CI] 1.16 to 3.30), advanced age (55 to 74 years OR 4.88, 95% CI 1.69 to 14.13; 75 years OR 9.31, 95% CI 3.01 to 29.50), and preoperative HR greater than or equal to 72 bpm on preoperative ECG (OR 1.89, 95% CI 1.15 to 3.13) were independent predictors of postoperative AF (area under the receiver operating characteristic (ROC) curve 0.67). Points were assigned based on the logistic regression coefficient for each variable (Table 4). As shown in Figure 1A, in the derivation set, the incidence of postoperative AF increased markedly from 0% for patients with 0 points to 54.6% for patients with 6 points (test for trend p < 0.001). Figure 1B shows the rates of AF by point score in the validation set. The application of the prediction rule to the validation set demonstrated a range of risk from 0% to patients with 0 points to 35.3% for patients with 6 points (test for trend p < 0.001). A comparison of the score-based risk of postoperative AF in both the derivation and validation populations found no significant differences between the two groups (p for interaction = 0.66).

View larger version (43K): [in this window] [in a new window]   Fig 1. (A) Risk points versus incidence of postoperative atrial fibrillation (AF) in derivation set. (B) Risk points versus incidence of postoperative AF in validation set.

Length of stay was significantly longer in patients who developed postoperative AF (13.5 ± 13.6 days vs 9.2 ± 7.4 days, p < 0.001). Intensive care unit admission was required in 25 patients (17%) with AF and 22 patients (3.1%) without AF (p < 0.001). Of the 25 patients admitted with AF, 5 were admitted solely for AF and the remainder admitted for coexisting conditions. The mean length of ICU stay was not significantly different between the two groups (9.7 ± 8.7 days with AF vs 12.3 ± 14.6 days without AF, p = 0.46). Extended length of stay, defined as greater than or equal to 10 days, occurred more frequently in patients with greater than or equal to 5 points compared with those with 4 or less points (43% vs 32%; p < 0.001). In addition, there was a statistically nonsignificant trend toward increasing incidence of ICU admission for patients with increasing risk scores (p = 0.10).

	Comment

Top Abstract Introduction Patients and Methods Results Comment Acknowledgments References

 
Using only three easily obtainable clinical variables, we derived and validated a clinical prediction rule that clearly stratified subgroups of cancer patients who have very low, modest, and elevated risk for developing postoperative AF. The arrhythmia developed in nearly one-fifth of patients who were in normal sinus rhythm before surgery and the clinical implications were significant. As in prior studies, the mean duration of hospitalization was longer and the rates of ICU use were greater among persons who developed this complication. In interpreting our findings, several factors should be considered.

The etiology of postoperative AF is unclear. In general, AF is the result of multiple microreentrant circuits existing simultaneously within the atrium, often triggered or driven from a focal source within the pulmonary veins [10, 11]. The formation of these wavelets requires a variable combination of a critical mass of atrial tissue, an abbreviated refractory period, a dispersion of atrial refractoriness, and a slowing of impulse conduction. The maintenance of sinus rhythm in the majority of patients exposed to the same conditions suggests that this postoperative arrhythmia is a product of a preexisting electrophysiologic substrate and superimposed trigger manifesting as AF. Purported inciting factors include trauma to the atria, sterile pericarditis, myocardial ischemia, atrial distention, high catecholamine states, and systemic inflammation, but these theories [12–19] have been poorly corroborated with clinical data. Recently [20], analyses of heart rate variability before the onset of AF have demonstrated significant increases in the heart rate and time-domain and frequency-domain variables, implying that vagal activation in the setting of sympathetic predominance serves as the trigger for postoperative AF in this patient population.

Prior studies have also evaluated clinical correlates of postoperative AF for various surgical procedures. From these, older age is the dominant risk factor for AF both inside and outside the surgical setting. The aging process is associated with decreases in cell-to-cell communication because of atrial myocardial apoptosis and fibrosis [21]. Resultant intraatrial conduction delay may serve as a substrate for the multiple reentrant wavefronts responsible for AF [22]. The association between resting heart rate and the development of postoperative atrial fibrillation is likely related to the relationship between heart rate and vagal tone, with lower sympathetic tone at baseline providing a protective effect for postoperative arrhythmias [23]. Male gender is associated with an increased prevalence of AF in the community as it is in this study, possibly due to the linear relationship between patient height and atrial size, the latter of which is associated with a higher propensity for AF [24–28]. In a multivariate predictive model developed by Polancyzk and colleagues [29], based on data from 1,285 cancer patients and 2,386 noncancer patients, significant predictors of postoperative supraventricular arrhythmias included male gender, age more than 70 years, asthma, significant valvular heart disease, a history of congestive heart failure or supraventricular arrhythmia, ECG findings of premature atrial complexes, and American Society of Anesthesiologists Class III or IV, and type of procedure. Recently, Vaporciyan and colleagues [30] analyzed data on 2,588 patients undergoing thoracic surgery at a single institution and concluded that advanced age, male gender, history of congestive heart failure, history of arrhythmias, history of peripheral vascular disease, intraoperative transfusions, and the type of procedure performed were all multivariate predictors of postoperative AF. Though large in number, neither of these studies attempted to develop a clinical prediction rule based on the findings. As the majority of risk factors for postoperative AF are associated with low odds ratios, no single variable can be used to appropriately risk stratify patients. In contrast, our stratification system uses only three variables, is simple to use in the clinical setting, and provides good sensitivity and specificity for the primary outcome. Assuming that these findings hold up across attempted replications, clinical use of this staging system to identify patients at relatively high risk for experiencing AF would be quite efficient. For example, if a cancer patient has a cumulative score greater than 4, a score associated with 28.9% of the cohort in this study, then the postoperative risk of AF is predicted to be 41.8%. Individuals with 0, 3, and 6 points would have a risk of AF of 0%, 12.5%, and 54.5% and constitute 3.6%, 14.4%, and 2.5% of the derivation set, respectively.

Our findings have important clinical implications. In view of the adverse events associated with postthoracotomy AF, efforts have been focused on primary prevention. Historically, digitalis has been used to prevent postoperative AF though it has not been shown to be efficacious and its use may be associated with unwanted side effects [31–33]. Calcium-channel-blockers, ß-blockers, and flecainide have, however, been used with success [7–9]. The use of flecainide has not gained widespread acceptance presumably due to concerns over proarrhythmia and other adverse effects [34]. Once AF develops in the postoperative population, there is no consensus on how to manage the arrhythmia. Typically, the decision is whether or not to anticoagulate and/or attempt to restore and maintain normal sinus rhythm with antiarrhythmic agents. Anticoagulation has been shown to decrease risk of stroke by 68% in the nonsurgical population, and the restoration of normal sinus rhythm may improve symptoms and potentially reduce the risk of thromboembolic events [35]. Although the management of patients with nonoperative AF has been extensively studied, the same cannot be said for those with postoperative AF. Anticoagulation may decrease the risk of stroke, but concerns about bleeding in the postoperative state have limited the routine use of these agents. Attempts at restoring sinus rhythm with direct current energy or antiarrhythmic agents or maintaining sinus rhythm with the latter may be employed, but no data supports that these interventions decrease the complication rate from AF [36]. Lastly, some may advocate no intervention beyond ventricular rate control as the arrhythmia is usually self-limited in duration; this approach still exposes patients to the possible deleterious effects of rate controlling agents and fails to account for the increased morbidity associated with AF itself.

The use of a single tertiary care center may limit the generalizability of these results, and the conclusions would be strengthened by prospective validation in a different clinical care setting. Furthermore, although the predictive ability of the risk assessment score might be improved by the inclusion of additional diagnostic or clinical factors such as nonchronic AF, this would also result in a more complex staging system that would be less likely to be routinely used at the bedside. In addition, the low prevalence of many additional risk factors limits their clinical utility.

In conclusion, we have developed a clinical prediction rule for postoperative AF based on data from 856 patients all of whom were prospectively enrolled and carefully studied for this complication. This staging system is based on three easily obtainable clinical variables (age, gender, preoperative heart rate) and had an overall classification accuracy of 67%. The primary application of this easy to use prediction rule is for the targeted use of prophylactic therapy, thereby minimizing the expense and risk of such interventions to those patients unlikely to derive benefit. Although most would agree that high-risk patients should receive prophylaxis and that low-risk patients should not, cost-to-benefit analyses of preventive strategies would provide evidence-based data to better delineate which interventions in which patients best achieve the balance between limiting drug-related adverse events, controlling costs, and improving patient outcomes.

	Acknowledgments

Top Abstract Introduction Patients and Methods Results Comment Acknowledgments References

 
Dr Passman is funded by National Institutes of Health Grant No. 1 K23 HL0688.

	References

Top Abstract Introduction Patients and Methods Results Comment Acknowledgments References

 

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This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to Personal Folders Download to citation manager Author home page(s): Hao Zhang Valerie W. Rusch Permission Requests Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Passman, R. S. Articles by Rusch, V. W. Search for Related Content PubMed PubMed Citation Articles by Passman, R. S. Articles by Rusch, V. W. Related Collections Lung - other