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Heart rate variability measures poorly predict atrial fibrillation after off-pump coronary artery bypass grafting
Journal of Clinical Anesthesia (2011) 23, 451–455
Original contribution
Heart rate variability measures poorly predict atrial fibrillation after off-pump coronary artery bypass grafting☆ Dmitri Chamchad MD (Associate Professor)a,b , Jay C. Horrow MD, MS (Professor)b,⁎, Louis E. Samuels MD (Clinical Associate Professor) c , Lev Nakhamchik MSc (Research Associate)d a
Department of Anesthesia, Lankenau Hospital and Lankenau Institute of Medical Research, Wynnewood, PA 19096, USA Department of Anesthesiology, Drexel University College of Medicine, Philadelphia, PA 19102-1192, USA c Department of Cardiothoracic Surgery, Lankenau Hospital and Lankenau Institute of Medical Research, Wynnewood, PA 19096, USA d Independent Consultant, Toronto, Ontario, Canada b
Received 20 July 2009; revised 14 December 2010; accepted 15 December 2010
Keywords: Atrial fibrillation; Postoperative; Heart rate variability; Off-pump coronary artery bypass graft surgery
Abstract Study Objective: To investigate associations of heart rate variability (HRV) measurements with postoperative atrial fibrillation (AF) in patients undergoing off-pump coronary surgery. Design: Prospective, observational, exploratory study. Setting: Large university-affiliated community medical center. Patients: 50 patients undergoing off-pump coronary artery bypass grafting (CABG). Interventions: Preoperative recording of electrocardiograms (ECGs) with subsequent off-line HRV analysis. Monitored ECG telemetry for 5 days after operation. Measurements: Frequency and time domain analyses, and additional non-linear HRV determinations. Multivariate regression analysis of predictors of postoperative AF. Main Results: AF occurred in 23 (46%) patients. Only the low to high-frequency ratio was associated with AF (2.35 ± 1.8 v. 4.57 ± 5.0 for patients without AF, P b 0.05). Conclusions: The off-pump approach does not protect against AF, and nonlinear HRV analyses provide little value in predicting AF after off-pump CABG. © 2011 Elsevier Inc. All rights reserved.
1. Introduction Comparisons of off-pump coronary artery bypass grafting (CABG) with conventional CABG using cardiopulmonary ☆ Funding source: internal funding only. ⁎ Correspondence. Jay Horrow, MD, MS, Mail Stop 310, 245 N. 15th St., Philadelphia, PA 19102-1192, USA. Tel.: +1 215 762 3540; fax: +1 215 762 8656. E-mail address: [email protected] (J.C. Horrow).
0952-8180/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.jclinane.2010.12.016
bypass (CPB) focus on a variety of outcome measures, including the incidence of postoperative atrial fibrillation (AF). Reports variously indicate a decreased incidence [1,2], no change in incidence [3-7], or increased occurrence [8] of AF following off-pump CABG compared with that after CABG using CPB. CABG surgery is associated with increased autonomic instability of the heart, leading to unpredictable cardiac responses during and after surgery [9-11]. A balance between sympathetic and parasympathetic systems results
452 in the normal variation in heart rate (HR). The incidence of AF increases with age [12,13], with progressive disruption of autonomic balance being a likely mechanism. Early identification of patients who are likely to develop postoperative AF may allow targeted prophylaxis. For example, preemptive amiodarone administration starting at surgery may prevent postoperative AF in a susceptible group identified preoperatively. This investigation determined if measures of heart rate variability (HRV) were associated with postoperative AF in patients undergoing off-pump CABG.
2. Materials and methods With approval of the Office of Research Affairs Institutional Review Board of the Lankenau Institute of Medical Research, consecutive consenting patients between June, 2004 and August, 2006 provided written, informed consent. Anesthesia induction was achieved with propofol, fentanyl, and either vecuronium or rocuronium. Maintenance anesthesia consisted of isoflurane, desflurane, or sevoflurane. Patients resting supine before anesthesia induction provided baseline electrocardiographic (ECG) recordings via a 10-minute download from a conventional physiologic monitor. Real-time data acquisition software converted 800 to 1,500 stored analog signals at 1.0 kHz sampling (Windaq/ 200; Dataq Instruments, Akron, OH, USA). All patients underwent continuous ECG telemetry monitoring with automatic arrhythmia detection software for 5 days following surgery, recording all atrial and ventricular arrhythmias. Atrial fibrillation was defined as at least 24 hours’ duration, continuous or intermittent, of an irregularly-irregular pattern of QRS-waves on the displayed and printed ECG. Heart rate variability analyses proceeded as previously reported for a cohort undergoing CABG with CPB [12]. For time domain analysis, the mean and standard deviation of RR-intervals, root mean square of successive RR interval differences (rMSSD), number of RR intervals for which successive RR intervals differed by at least 50 ms (NN50), and number of times that successive RR intervals differed by N 50% from the index RR interval (pNN50) were calculated. The integral of the RR density distribution divided by the maximum of the density distribution (RR triangular), and the length of the base of a triangle approximating the NN interval distribution (TINN) also were determined. Frequency domain analyses included power spectral density using a recursive maximum entropy method (MEM-all poles); lowfrequency (LF; 0.04 to 0.15 Hz) and high-frequency (HF; 0.15 to 0.40 Hz) components using proprietary, nonparametric, fast Fourier transform software (Biomedical Signal Analysis Group, University of Kuopio, Kuopio, Finland); percentages of total power in the LF and HF ranges; and the ratio of LF to HF power. SAS software (SAS Institute Inc, Cary, NC, USA) was used to calculate Poincaré dispersions of the short-term
D. Chamchad et al. (SD1) and continuous long-term (SD2) intervals and their ratio. Point correlation dimension (PD2) reflects the complexity of information generated. Similar to a fractal, the PD2 is not restricted to a whole number, and represents the magnitude of independent sources of variability in a signal. Both mean and peak PD2 were calculated for each patient using Chaos Software (Bangor, PA, USA). Potential predictor variables with a univariate P-value b 0.40 by Wald statistics underwent stepwise multivariate logistic regression using SAS software version 9 (SAS Institute). The c-statistic, equivalent to the area under the ROC (receiver operating characteristic) curve, evaluated overall significance of the regression; a c-statistic of 0.5 represents poor predictive ability, values N 0.60 indicate good predictive ability, and perfect ability, a value of 1.0. Demographic, clinical, and HRV data are reported as means (SD), unless otherwise noted. A previous HRV analysis in patients undergoing CABG using CPB found significant differences in peak PD2 and the percentage of HF in power spectral density, with effect sizes of 0.73 and 0.80, respectively [12]. Based on a 40% expected incidence of AF [12] and two-sided type I error of 5%, a total of 50 patients provided 70% and 78% power, respectively, to detect effect sizes of 0.73 and 0.80, utilizing unpaired Student's t-tests. The analysis compared the post hoc groups of patients who did and who did not develop postoperative AF with respect to the demographic variables of age, gender, weight, body mass index, race (patient-defined), left ventricular function, concomitant diseases, baseline serum chemistry, and preoperative cardiac medications, as well as the intraoperative variables, number of diseased vessels, and completed surgical grafts. Chi-square contingency tables or the Fisher's exact test were used to analyze categorical data; continuous variables underwent Student's unequal variance unpaired t-tests or nonparametric tests, as appropriate. This exploratory work generates nominal significance levels without correction for multiple testing to identify potential predictors of AF for future research. One of the authors (JCH) conducted the statistical analyses.
3. Results Postoperative AF occurred in 23 (46%) patients overall. Patients who developed AF did not differ in demographic characteristics from those who did not (Table 1), except for more hypertensive patients among those with AF (P = 0.008) and fewer diabetics (P = 0.012). Of 21 demographic and preexisting disease variables, including site, and 26 calculated HRV variables analyzed, only hypertension and preexisting diabetes had univariate significance levels b 0.40. Hypertension alone independently predicted postoperative AF (P = 0.0124, c = 0.676).
Atrial fibrillation after OPCAB surgery Table 1
453
Characteristics of patients in groups
Variable
No AF = 27
AF = 23
P-value
Age (yrs) Gender (male/female) BMI (kg/m2) Ethnicity (Caucasian/other) Hypertension (yes/no) Diabetes mellitus (yes/no) Preoperative renal insufficiency ACE inhibitor (yes/no) β-blocker (yes/no) Ca channel blocker (yes/no) % grafted/diseased vessels
66.1 ± 10.7 20 / 7 28.9±8.42 24/ 3 14/13 22/ 5 3/24 11/16 20/7 19/8 107 ± 21
64.0 ± 9.7 15 / 8 29.2 ± 5.1 20/ 3 20/ 3 11/12 2/21 8/15 18/5 17/6 101 ± 21
0.134 0.496 0.898 0.834 0.008 ⁎ 0.012 ⁎ 0.777 0.665 0.730 0.781 0.284
Entries are means±SD or numbers of patients. P-values are nominal. AF = atrial fibrillation, BMI = body mass index, ACE = angiotensin converting enzyme, Ca = calcium. ⁎ Statistically significant difference.
Table 2 presents nominal significance levels for the explored HRV variables. No HRV index clearly separated patients with AF from those without it, with the exception of the ratio of LF to HF components in frequency domain analysis.
Table 2 Variable
4. Discussion These results show a 46% incidence of postoperative AF, confirming previous reports of the high incidence in patients undergoing off-pump CABG [14,15]. Previous work
Results of heart rate variability analyses Without AF (N = 27)
Frequency domain measures LF FFT% 39.91 ± 16.36 HF FFT% 21.38 ± 21.69 LF/HF 4.57 ± 4.97 Time domain linear measures Mean HR 69.09 ± 13.03 Mean NN 0.90 ± 0.16 SDNN 0.03 ± 0.01 RMSSD 19.09 ± 12.88 NN50 11.30 ± 20.26 pNN50 4.29 ± 8.19 HRV idx 0.05 ± 0.02 TINN 128.15 ± 60.32 Poincaré nonlinear measures SD1 13.72 ± 9.18 SD2 40.90 ± 20.31 SD1/SD2 0.36 ± 0.23 Point correlation dimension nonlinear measures pPD2 3.72 ± 0.91 mPD2 4.08 ± 0.83 Embedded spectral entropy analysis Entropy 1.11 ± 0.40
With AF (N = 23) 34.98 ± 13.04 24.03 ± 15.92 2.35 ± 1.79
P-value ⁎ 0.2504 0.6302 0.0485 ⁎
65.58 ± 10.50 0.94 ± 0.15 0.02 ± 0.01 19.00 ± 12.57 9.61 ± 15.08 3.92 ± 6.54 0.05 ± 0.02 123.04 ± 64.73
0.3050 0.3654 0.5397 0.9787 0.7435 0.8648 0.6503 0.7743
13.63 ± 8.98 37.91 ± 18.03 0.35 ± 0.15
0.9724 0.5884 0.9482
3.73 ± 0.77 4.17 ± 0.94
0.9606 0.6995
1.17 ± 0.34
0.5837
Columns are means ± SD for respective cohorts. Mean HR = average of all heart rate values in a subject, mean NN = average of all intervals in a subject, SDNN = standard deviation of all intervals in a subject, RMSSD = square root of the sum of the squares of successive interval differences, pNN50 = percentage of successive intervals that deviated by N 50% from the prior interval, HRV idx = HRV triangular index measurement, ie, the integral of the density distribution divided by the maximum of the density distribution, TINN = base of a triangle approximating the interval distribution, LF% = low frequency as a percentage of total power spectrum, HF % = high frequency as a percentage of total power spectrum, LF/HF = their ratio, pPD2 = peak point correlation dimension, mPD2 = mean point correlation dimension, SD1 = short-term intervals, SD2 = continuous long-term intervals, SD1/SD2 = their ratio. ⁎ Statistically significant difference (unpaired Student's t test).
454 exploring associations of various HRV parameters with postoperative AF after CABG using CPB identified two HRV associates: the high-frequency component of frequency domain transformed data, and peak PD2, a nonlinear measurement [12]. Another HRV parameter, embedded spectral entropy [16,17], is also associated with postoperative AF in that data set (D. Chamchad, L. Nakhamchik, unpublished data). Unlike previous results for patients undergoing CABG with CPB, current results in patients undergoing off-pump CABG provide few robust associations. Several possibilities may explain this difference. First, the current investigation possibly encompassed too few patients for some HRV parameters. However, other small cohorts have shown effects [9,10], and this cohort was sized to detect reasonable differences in HRV parameters identified as predictors of AF in CABG patients undergoing CPB [12]. Furthermore, the cohort split 46%-54%, near the optimum of 50%-50% for prediction analysis. Second, HRV analytic techniques frequently differ among HRV studies; however, investigators used identical sampling rates and analysis algorithms [12]. Third, CPB patients in the previous trial [12] originated at a different institution, Toronto General Hospital. Different perioperative protocols might have accounted for the difference. Fourth, off-pump CABG and CABG with CPB differ in degree and type of atrial manipulation, the use of atrial cannulation for CPB, temperature management, and other factors that possibly impacted the occurrence of postoperative atrial arrhythmias. Finally, the anesthetic technique for offpump CABG utilizes vasopressors more frequently, and more often as continuous infusions [18]. As in the previous study of patients having CABG with CPB [12], in patients undergoing off-pump CABG, the ratio of LF to HF components in the ECG (LF/HF ratio) predicts postoperative AF. The post hoc groups showed a large separation of means (2.35 in the AF group; 4.57 for those without AF); however, the accompanying high variability of these measures suggests that even if a larger cohort provided statistical separation, no discriminate will achieve sufficient sensitivity and specificity for reliable prediction in a given patient. Ksela et al also identified frequency measures, among other HRV parameters, as predictive of postoperative AF in patients undergoing off-pump CABG [19]. Other studies provide variable results. Hakala et al could not predict AF after elective CABG with CPB surgery using time-domain and spectral analysis [20]. They did not include nonlinear analyses. Bettoni and Zimmermann, also using time domain and spectral analyses, concluded that paroxysmal AF was preceded by increased adrenergic drive followed by a vagal predominance [21]. Hogue, Jr. et al identified increased HR and decreased approximate entropy as independent associates of AF [22]. Laitio et al identified nonlinear HRV measures as better predictors of complications after cardiac surgery than standard frequency analysis [23]. Jideus et al.
D. Chamchad et al. associated decreased HRV in the frequency domain with postoperative AF after CABG with CPB [24]. The current data suggest that HRV analyses provide little value in discriminating patients who develop AF after off-pump CABG from those who do not. These results contrast with those obtained in patients undergoing CABG using CPB. Future research should confirm these observations in patients undergoing off-pump CABG at a variety of institutions.
References [1] Murphy GS, Szokol JW, Marymont JH, et al. Recovery of neuromuscular function after cardiac surgery: pancuronium versus rocuronium. Anesth Analg 2003;96:1301-7. [2] Panesar SS, Athanasiou T, Nair S, et al. Early outcomes in the elderly: a meta-analysis of 4921 patients undergoing coronary artery bypass grafting–comparison between off-pump and on-pump techniques. Heart 2006;92:1808-16. [3] Siebert J, Anisimowicz L, Lango R, et al. Atrial fibrillation after coronary artery bypass grafting: does the type of procedure influence the early postoperative incidence? Eur J Cardiothorac Surg 2001;19:455-9. [4] Siebert J, Lewicki L, Mlodnicki M, et al. Atrial fibrillation after conventional and off-pump coronary artery bypass grafting: two opposite trends in timing of atrial fibrillation occurrence? Med Sci Monit 2003;9:CR137-41. [5] Hakala T, Pitkanen O, Hartikainen J. Cardioplegic arrest does not increase the risk of atrial fibrillation after coronary artery bypass surgery. Eur J Cardiothorac Surg 2004;25:415-8. [6] Place DG, Peragallo RA, Carroll J, Cusimano RJ, Cheng DC. Postoperative atrial fibrillation: a comparison of off-pump coronary artery bypass surgery and conventional coronary artery bypass graft surgery. J Cardiothorac Vasc Anesth 2002;16:144-8. [7] Marasco SF, Goldblatt J, McDonald M, Tatoulis J. No decrease in incidence of atrial fibrillation in patients undergoing off-pump coronary artery bypass grafting. Heart Lung Circ 2002;11:102-6. [8] Creswell LL, Alexander JC Jr, Ferguson TB Jr, Lisbon A, Fleisher LA; American College of Chest Physicians. Intraoperative interventions: American College of Chest Physicians guidelines for the prevention and management of postoperative atrial fibrillation after cardiac surgery. Chest 2005;128(2 Suppl):28S-35S. [9] Soares PP, Moreno AM, Cravo SL, Nóbrega AC. Coronary artery bypass surgery and longitudinal evaluation of the autonomic cardiovascular function. Crit Care 2005;9:R124-31. [10] Suda Y, Otsuka K, Niinami H, et al. Changes in ultra-low and very low frequency heart rate variability after coronary artery bypass grafting. Biomed Pharmacother 2001;55 Suppl 1:110s-114s. [11] Yavuz B, Duman U, Abali G, et al. Coronary artery bypass grafting is associated with a significant worsening of QT dynamicity and heart rate variability. Cardiology 2006;106:51-5. [12] Chamchad D, Djaiani G, Jung HJ, Nakhamchik L, Carroll J, Horrow JC. Nonlinear heart rate variability analysis may predict atrial fibrillation after coronary artery bypass grafting. Anesth Analg 2006;103:1109-12. [13] Leitch JW, Thomson D, Baird DK, Harris PJ. The importance of age as a predictor of atrial fibrillation and flutter after coronary artery bypass grafting. J Thorac Cardiovasc Surg 1990;100:338-42. [14] Creswell LL, Schuessler RB, Rosenbloom M, Cox JL. Hazards of postoperative atrial arrhythmias. Ann Thorac Surg 1993;56:539-49. [15] Maisel WH, Rawn JD, Stevenson WG. Atrial fibrillation after cardiac surgery. Ann Intern Med 2001;135:1061-73. [16] Rezek IA, Roberts SJ. Stochastic complexity measures for physiological signal analysis. IEEE Trans Biomed Eng 1998;45:1186-91.
Atrial fibrillation after OPCAB surgery [17] Sabatini AM. Analysis of postural sway using entropy measures of signal complexity. Med Biol Eng Comput 2000;38:617-24. [18] Schwann NM, Horrow JC, Strong MD 3rd, Chamchad D, Guerraty A, Wechsler AS. Does off-pump coronary artery bypass reduce the incidence of clinically evident renal dysfunction after multivessel myocardial revascularization? Anesth Analg 2004;99:959-64. [19] Ksela J, Suwalski P, Kalisnik JM, Avbelj V, Suwalski G, Gersak B. Assessment of nonlinear heart rate dynamics after beating-heart revascularization. Heart Surg Forum 2009;12:E10-6. [20] Hakala T, Pitkanen O, Hippelainen M. Feasibility of predicting the risk of atrial fibrillation after coronary artery bypass surgery with logistic regression model. Scand J Surg 2002;91:339-44.
455 [21] Bettoni M, Zimmermann M. Autonomic tone variations before the onset of paroxysmal atrial fibrillation. Circulation 2002;105:2753-9. [22] Hogue CW Jr, Domitrovich PP, Stein PK, et al. RR interval dynamics before atrial fibrillation in patients after coronary artery bypass graft surgery. Circulation 1998;98:429-34. [23] Laitio TT, Huikuri HV, Kentala ES, et al. Correlation properties and complexity of perioperative RR-interval dynamics in coronary artery bypass surgery patients. Anesthesiology 2000;93:69-80. [24] Jideus L, Ericson M, Stridsberg M, Nilsson L, Blomstrom P, Blomstrom-Lundqvist C. Diminished circadian variation in heart rate variability before surgery in patients developing postoperative atrial fibrillation. Scand Cardiovasc J 2001;35:238-44.
Original contribution
Heart rate variability measures poorly predict atrial fibrillation after off-pump coronary artery bypass grafting☆ Dmitri Chamchad MD (Associate Professor)a,b , Jay C. Horrow MD, MS (Professor)b,⁎, Louis E. Samuels MD (Clinical Associate Professor) c , Lev Nakhamchik MSc (Research Associate)d a
Department of Anesthesia, Lankenau Hospital and Lankenau Institute of Medical Research, Wynnewood, PA 19096, USA Department of Anesthesiology, Drexel University College of Medicine, Philadelphia, PA 19102-1192, USA c Department of Cardiothoracic Surgery, Lankenau Hospital and Lankenau Institute of Medical Research, Wynnewood, PA 19096, USA d Independent Consultant, Toronto, Ontario, Canada b
Received 20 July 2009; revised 14 December 2010; accepted 15 December 2010
Keywords: Atrial fibrillation; Postoperative; Heart rate variability; Off-pump coronary artery bypass graft surgery
Abstract Study Objective: To investigate associations of heart rate variability (HRV) measurements with postoperative atrial fibrillation (AF) in patients undergoing off-pump coronary surgery. Design: Prospective, observational, exploratory study. Setting: Large university-affiliated community medical center. Patients: 50 patients undergoing off-pump coronary artery bypass grafting (CABG). Interventions: Preoperative recording of electrocardiograms (ECGs) with subsequent off-line HRV analysis. Monitored ECG telemetry for 5 days after operation. Measurements: Frequency and time domain analyses, and additional non-linear HRV determinations. Multivariate regression analysis of predictors of postoperative AF. Main Results: AF occurred in 23 (46%) patients. Only the low to high-frequency ratio was associated with AF (2.35 ± 1.8 v. 4.57 ± 5.0 for patients without AF, P b 0.05). Conclusions: The off-pump approach does not protect against AF, and nonlinear HRV analyses provide little value in predicting AF after off-pump CABG. © 2011 Elsevier Inc. All rights reserved.
1. Introduction Comparisons of off-pump coronary artery bypass grafting (CABG) with conventional CABG using cardiopulmonary ☆ Funding source: internal funding only. ⁎ Correspondence. Jay Horrow, MD, MS, Mail Stop 310, 245 N. 15th St., Philadelphia, PA 19102-1192, USA. Tel.: +1 215 762 3540; fax: +1 215 762 8656. E-mail address: [email protected] (J.C. Horrow).
0952-8180/$ – see front matter © 2011 Elsevier Inc. All rights reserved. doi:10.1016/j.jclinane.2010.12.016
bypass (CPB) focus on a variety of outcome measures, including the incidence of postoperative atrial fibrillation (AF). Reports variously indicate a decreased incidence [1,2], no change in incidence [3-7], or increased occurrence [8] of AF following off-pump CABG compared with that after CABG using CPB. CABG surgery is associated with increased autonomic instability of the heart, leading to unpredictable cardiac responses during and after surgery [9-11]. A balance between sympathetic and parasympathetic systems results
452 in the normal variation in heart rate (HR). The incidence of AF increases with age [12,13], with progressive disruption of autonomic balance being a likely mechanism. Early identification of patients who are likely to develop postoperative AF may allow targeted prophylaxis. For example, preemptive amiodarone administration starting at surgery may prevent postoperative AF in a susceptible group identified preoperatively. This investigation determined if measures of heart rate variability (HRV) were associated with postoperative AF in patients undergoing off-pump CABG.
2. Materials and methods With approval of the Office of Research Affairs Institutional Review Board of the Lankenau Institute of Medical Research, consecutive consenting patients between June, 2004 and August, 2006 provided written, informed consent. Anesthesia induction was achieved with propofol, fentanyl, and either vecuronium or rocuronium. Maintenance anesthesia consisted of isoflurane, desflurane, or sevoflurane. Patients resting supine before anesthesia induction provided baseline electrocardiographic (ECG) recordings via a 10-minute download from a conventional physiologic monitor. Real-time data acquisition software converted 800 to 1,500 stored analog signals at 1.0 kHz sampling (Windaq/ 200; Dataq Instruments, Akron, OH, USA). All patients underwent continuous ECG telemetry monitoring with automatic arrhythmia detection software for 5 days following surgery, recording all atrial and ventricular arrhythmias. Atrial fibrillation was defined as at least 24 hours’ duration, continuous or intermittent, of an irregularly-irregular pattern of QRS-waves on the displayed and printed ECG. Heart rate variability analyses proceeded as previously reported for a cohort undergoing CABG with CPB [12]. For time domain analysis, the mean and standard deviation of RR-intervals, root mean square of successive RR interval differences (rMSSD), number of RR intervals for which successive RR intervals differed by at least 50 ms (NN50), and number of times that successive RR intervals differed by N 50% from the index RR interval (pNN50) were calculated. The integral of the RR density distribution divided by the maximum of the density distribution (RR triangular), and the length of the base of a triangle approximating the NN interval distribution (TINN) also were determined. Frequency domain analyses included power spectral density using a recursive maximum entropy method (MEM-all poles); lowfrequency (LF; 0.04 to 0.15 Hz) and high-frequency (HF; 0.15 to 0.40 Hz) components using proprietary, nonparametric, fast Fourier transform software (Biomedical Signal Analysis Group, University of Kuopio, Kuopio, Finland); percentages of total power in the LF and HF ranges; and the ratio of LF to HF power. SAS software (SAS Institute Inc, Cary, NC, USA) was used to calculate Poincaré dispersions of the short-term
D. Chamchad et al. (SD1) and continuous long-term (SD2) intervals and their ratio. Point correlation dimension (PD2) reflects the complexity of information generated. Similar to a fractal, the PD2 is not restricted to a whole number, and represents the magnitude of independent sources of variability in a signal. Both mean and peak PD2 were calculated for each patient using Chaos Software (Bangor, PA, USA). Potential predictor variables with a univariate P-value b 0.40 by Wald statistics underwent stepwise multivariate logistic regression using SAS software version 9 (SAS Institute). The c-statistic, equivalent to the area under the ROC (receiver operating characteristic) curve, evaluated overall significance of the regression; a c-statistic of 0.5 represents poor predictive ability, values N 0.60 indicate good predictive ability, and perfect ability, a value of 1.0. Demographic, clinical, and HRV data are reported as means (SD), unless otherwise noted. A previous HRV analysis in patients undergoing CABG using CPB found significant differences in peak PD2 and the percentage of HF in power spectral density, with effect sizes of 0.73 and 0.80, respectively [12]. Based on a 40% expected incidence of AF [12] and two-sided type I error of 5%, a total of 50 patients provided 70% and 78% power, respectively, to detect effect sizes of 0.73 and 0.80, utilizing unpaired Student's t-tests. The analysis compared the post hoc groups of patients who did and who did not develop postoperative AF with respect to the demographic variables of age, gender, weight, body mass index, race (patient-defined), left ventricular function, concomitant diseases, baseline serum chemistry, and preoperative cardiac medications, as well as the intraoperative variables, number of diseased vessels, and completed surgical grafts. Chi-square contingency tables or the Fisher's exact test were used to analyze categorical data; continuous variables underwent Student's unequal variance unpaired t-tests or nonparametric tests, as appropriate. This exploratory work generates nominal significance levels without correction for multiple testing to identify potential predictors of AF for future research. One of the authors (JCH) conducted the statistical analyses.
3. Results Postoperative AF occurred in 23 (46%) patients overall. Patients who developed AF did not differ in demographic characteristics from those who did not (Table 1), except for more hypertensive patients among those with AF (P = 0.008) and fewer diabetics (P = 0.012). Of 21 demographic and preexisting disease variables, including site, and 26 calculated HRV variables analyzed, only hypertension and preexisting diabetes had univariate significance levels b 0.40. Hypertension alone independently predicted postoperative AF (P = 0.0124, c = 0.676).
Atrial fibrillation after OPCAB surgery Table 1
453
Characteristics of patients in groups
Variable
No AF = 27
AF = 23
P-value
Age (yrs) Gender (male/female) BMI (kg/m2) Ethnicity (Caucasian/other) Hypertension (yes/no) Diabetes mellitus (yes/no) Preoperative renal insufficiency ACE inhibitor (yes/no) β-blocker (yes/no) Ca channel blocker (yes/no) % grafted/diseased vessels
66.1 ± 10.7 20 / 7 28.9±8.42 24/ 3 14/13 22/ 5 3/24 11/16 20/7 19/8 107 ± 21
64.0 ± 9.7 15 / 8 29.2 ± 5.1 20/ 3 20/ 3 11/12 2/21 8/15 18/5 17/6 101 ± 21
0.134 0.496 0.898 0.834 0.008 ⁎ 0.012 ⁎ 0.777 0.665 0.730 0.781 0.284
Entries are means±SD or numbers of patients. P-values are nominal. AF = atrial fibrillation, BMI = body mass index, ACE = angiotensin converting enzyme, Ca = calcium. ⁎ Statistically significant difference.
Table 2 presents nominal significance levels for the explored HRV variables. No HRV index clearly separated patients with AF from those without it, with the exception of the ratio of LF to HF components in frequency domain analysis.
Table 2 Variable
4. Discussion These results show a 46% incidence of postoperative AF, confirming previous reports of the high incidence in patients undergoing off-pump CABG [14,15]. Previous work
Results of heart rate variability analyses Without AF (N = 27)
Frequency domain measures LF FFT% 39.91 ± 16.36 HF FFT% 21.38 ± 21.69 LF/HF 4.57 ± 4.97 Time domain linear measures Mean HR 69.09 ± 13.03 Mean NN 0.90 ± 0.16 SDNN 0.03 ± 0.01 RMSSD 19.09 ± 12.88 NN50 11.30 ± 20.26 pNN50 4.29 ± 8.19 HRV idx 0.05 ± 0.02 TINN 128.15 ± 60.32 Poincaré nonlinear measures SD1 13.72 ± 9.18 SD2 40.90 ± 20.31 SD1/SD2 0.36 ± 0.23 Point correlation dimension nonlinear measures pPD2 3.72 ± 0.91 mPD2 4.08 ± 0.83 Embedded spectral entropy analysis Entropy 1.11 ± 0.40
With AF (N = 23) 34.98 ± 13.04 24.03 ± 15.92 2.35 ± 1.79
P-value ⁎ 0.2504 0.6302 0.0485 ⁎
65.58 ± 10.50 0.94 ± 0.15 0.02 ± 0.01 19.00 ± 12.57 9.61 ± 15.08 3.92 ± 6.54 0.05 ± 0.02 123.04 ± 64.73
0.3050 0.3654 0.5397 0.9787 0.7435 0.8648 0.6503 0.7743
13.63 ± 8.98 37.91 ± 18.03 0.35 ± 0.15
0.9724 0.5884 0.9482
3.73 ± 0.77 4.17 ± 0.94
0.9606 0.6995
1.17 ± 0.34
0.5837
Columns are means ± SD for respective cohorts. Mean HR = average of all heart rate values in a subject, mean NN = average of all intervals in a subject, SDNN = standard deviation of all intervals in a subject, RMSSD = square root of the sum of the squares of successive interval differences, pNN50 = percentage of successive intervals that deviated by N 50% from the prior interval, HRV idx = HRV triangular index measurement, ie, the integral of the density distribution divided by the maximum of the density distribution, TINN = base of a triangle approximating the interval distribution, LF% = low frequency as a percentage of total power spectrum, HF % = high frequency as a percentage of total power spectrum, LF/HF = their ratio, pPD2 = peak point correlation dimension, mPD2 = mean point correlation dimension, SD1 = short-term intervals, SD2 = continuous long-term intervals, SD1/SD2 = their ratio. ⁎ Statistically significant difference (unpaired Student's t test).
454 exploring associations of various HRV parameters with postoperative AF after CABG using CPB identified two HRV associates: the high-frequency component of frequency domain transformed data, and peak PD2, a nonlinear measurement [12]. Another HRV parameter, embedded spectral entropy [16,17], is also associated with postoperative AF in that data set (D. Chamchad, L. Nakhamchik, unpublished data). Unlike previous results for patients undergoing CABG with CPB, current results in patients undergoing off-pump CABG provide few robust associations. Several possibilities may explain this difference. First, the current investigation possibly encompassed too few patients for some HRV parameters. However, other small cohorts have shown effects [9,10], and this cohort was sized to detect reasonable differences in HRV parameters identified as predictors of AF in CABG patients undergoing CPB [12]. Furthermore, the cohort split 46%-54%, near the optimum of 50%-50% for prediction analysis. Second, HRV analytic techniques frequently differ among HRV studies; however, investigators used identical sampling rates and analysis algorithms [12]. Third, CPB patients in the previous trial [12] originated at a different institution, Toronto General Hospital. Different perioperative protocols might have accounted for the difference. Fourth, off-pump CABG and CABG with CPB differ in degree and type of atrial manipulation, the use of atrial cannulation for CPB, temperature management, and other factors that possibly impacted the occurrence of postoperative atrial arrhythmias. Finally, the anesthetic technique for offpump CABG utilizes vasopressors more frequently, and more often as continuous infusions [18]. As in the previous study of patients having CABG with CPB [12], in patients undergoing off-pump CABG, the ratio of LF to HF components in the ECG (LF/HF ratio) predicts postoperative AF. The post hoc groups showed a large separation of means (2.35 in the AF group; 4.57 for those without AF); however, the accompanying high variability of these measures suggests that even if a larger cohort provided statistical separation, no discriminate will achieve sufficient sensitivity and specificity for reliable prediction in a given patient. Ksela et al also identified frequency measures, among other HRV parameters, as predictive of postoperative AF in patients undergoing off-pump CABG [19]. Other studies provide variable results. Hakala et al could not predict AF after elective CABG with CPB surgery using time-domain and spectral analysis [20]. They did not include nonlinear analyses. Bettoni and Zimmermann, also using time domain and spectral analyses, concluded that paroxysmal AF was preceded by increased adrenergic drive followed by a vagal predominance [21]. Hogue, Jr. et al identified increased HR and decreased approximate entropy as independent associates of AF [22]. Laitio et al identified nonlinear HRV measures as better predictors of complications after cardiac surgery than standard frequency analysis [23]. Jideus et al.
D. Chamchad et al. associated decreased HRV in the frequency domain with postoperative AF after CABG with CPB [24]. The current data suggest that HRV analyses provide little value in discriminating patients who develop AF after off-pump CABG from those who do not. These results contrast with those obtained in patients undergoing CABG using CPB. Future research should confirm these observations in patients undergoing off-pump CABG at a variety of institutions.
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