Background Atrial fibrillation increases the postoperative morbidity and mortality after cardiac surgery. This study aimed to estimate the incidence of postoperative atrial fibrillation and to identify the risk factors in patients undergoing cardiac surgery. Methods This was a prospective cohort study carried out over a period of one year among 280 patients undergoing cardiac surgery. An independent t test was used to compare quantitative parameters between categories. The chi-square test was used to find association between categorical variables. ROC (Receiver Operating Characteristic) graphs were plotted and the area under the curve was calculated to assess diagnostic accuracy of CPB time and ACC time in detecting AF (Atrial Fibrillation) and to assess the optimal cut‑off scores. Multiple logistic regressions was used to predict independent risk factors of AF. For all statistical interpretations, p<0.05 was considered the threshold for statistical significance. Results The incidence of new-onset atrial fibrillation is 31.8%. The most common risk factors were RCA Stenosis 70%, lactate level > 4 mmol/L, use of CPB, CPB time > 116.5 mins, and aortic cross-clamp time > 64.5 mins.Incidence was higher in valve surgeries. Use of preoperative beta blockers reduced the incidence of new-onset AF. Abnormal serum electrolytes, pain, and hypoxia were also risk factors for the development of AF. Development of AF prolonged the time spent in ICU, time spent on a ventilator, and also time spent on inotropic supports. Subjects developing new-onset postop AF required antiarrhythmic medications at discharge. Conclusion There is a high incidence for developing atrial fibrillation after cardiac surgery. Significant RCA stenosis, long CPB time, and elevated lactate levels were the significant risk factors. POAF adversely affected the postoperative outcome
Atrial fibrillation is one of the most common postoperative complications following cardiac surgery. Atrial fibrillation incurs considerable clinical and economic burden and increases the postoperative morbidity and mortality. Even though various drugs and surgical modalities are available to treat atrial fibrillation, these will add on to the already existing morbidity of the patient. Hence, identifying the exact risk factors for the development of new-onset atrial fibrillation after cardiac surgeries is very important. It is only by identifying these risk factors that we can prevent it from happening.
A multitude of risk factors for this condition have been described in the literature. However, in spite of being recognized as a common complication, an exact etiology or specific risk factors contributing to this condition have not been definitely identified. This significantly affects the intra operative and postoperative management and outcome of patients. Hence, this study aims to elucidate the incidence and risk factors for new-onset atrial fibrillation occurring during and after cardiac surgeries.
This was a prospective cohort study carried out over a period of one year among 280 patients undergoing cardiac surgery. Patients who were having any arrhythmias preoperatively were excluded from the study.
The variables considered for the study were as depicted in Table 1.
Pre-Op Factors |
Post-Op Risk Factors |
Post-Op Outcome |
|
Age |
Lactate level |
Ventilation(hours) |
|
<50 |
Abnormal S.electrolytes |
ICU time(days) |
|
50 – 60 |
Hypoxia |
Inotropic support (Duratiion in hours) |
|
60-70 |
Pain |
ALI/ARDS |
|
70-80 |
|
CVA/Embolic episodes |
|
>80 |
|
AKI |
|
Male |
|
Blood Transfusion |
|
BMI |
|
Need for antiarrthmics at discharge |
|
>40 |
|
|
|
Emergency Surgery |
Preop drugs |
||
Current smoker |
B Blockers |
||
IABP |
ACEI |
||
Prev.Cardiac surgery |
CCBs |
||
Prior MI |
CPB time(min) |
||
LVEF <40 |
ACC time(min) |
||
Hypertension |
Use of CPB |
||
Diabetes |
W/o CPB |
||
COPD |
Valve surgery |
||
PVD |
Left Main Stenosis ≥ 50% |
||
CVD |
RCA Stenosis ≥ 70% |
||
CKD |
Blood transfusion > 2 in OPCAB and > 4 in CPB |
||
Table 1: Study Variables |
|||
Categorical and quantitative variables were expressed as frequency (percentage) and mean ± SD respectively. An independent t-test was used to compare quantitative parameters between categories. Chi-square test was used to find the association between categorical variables. ROC graphs were plotted and the area under the curve was calculated to assess diagnostic accuracy of CPB time and ACC time in detecting AF and to assess the optimal cut‑off scores. Sensitivity, specificity, PPV (Positive Predictive Value), NPV (Negative Predictive Value) and accuracy have been calculated for the diagnostic accuracy of PB time and ACC time in prediction of AF. Multiple logistic regressions was used to predict independent risk factors of AF. For all statistical interpretations, p<0.05 was considered the threshold for statistical significance. Statistical analyses were performed by using a statistical software package SPSS, version 20.0.
The mean age was 64.5 ± 9.4, with a majority belonging to the age group of 61-70 years. The distribution of the sample according to gender showed a slight female preponderance: 58.6% versus 41.4%.
Age |
Count |
Percent |
<50 |
21 |
7.5 |
50 - 60 |
52 |
18.6 |
61 - 70 |
109 |
38.9 |
71 - 80 |
96 |
34.3 |
>80 |
2 |
0.7 |
Mean ± SD |
64.5 ± 9.4 |
|
Table 2: Percentage Distribution of the Sample According to Age |
The incidence of smoking, previous cardiac surgery, and prior MI was 9.3%, zero, and 51.1%, respectively.
Percentage distribution of the sample according to comorbidities is as shown in Figure 1. A majority suffered from hypertension and diabetes, followed by COPD.
Figure 1: Percentage Distribution of the Sample According to Co-Morbidities |
The incidence of AF among the study population was 31.8%. The incidence with respect to various risk factors is as shown in tables 3 and 4.
Risk factors |
AF |
c2 |
p |
95% CI |
||||
No |
Yes |
|||||||
Count |
Percent |
Count |
Percent |
|||||
Age |
<=60 |
58 |
79.5 |
15 |
20.5 |
10.09** |
0.006 |
1 |
61 - 70 |
77 |
70.6 |
32 |
29.4 |
1.61 (0.80 – 3.24) |
|||
>70 |
56 |
57.1 |
42 |
42.9 |
2.60 (1.45 – 0.80) |
|||
Emergency Surgery |
Yes |
2 |
28.6 |
5 |
71.4 |
5.2* |
0.023 |
5.63 (1.07 – 29.58) |
No |
189 |
69.2 |
84 |
30.8 |
1 |
|||
IABP |
Yes |
0 |
0.0 |
2 |
100.0 |
- |
- |
- |
No |
191 |
68.7 |
87 |
31.3 |
- |
|||
Use of CPB |
Yes |
60 |
52.2 |
55 |
47.8 |
23.16 |
p<0.01 |
3.53 (2.08 – 5.97) |
No |
131 |
79.4 |
34 |
20.6 |
1 |
|||
RCA Stenosis 70% |
Yes |
1 |
1.7 |
57 |
98.3 |
149.16 |
p<0.01 |
337.54 ( 45.23 – 25181.49) |
No |
190 |
85.6 |
32 |
14.4 |
1 |
|||
Lactate level > 4 mmol/ |
Yes |
1 |
1.5 |
65 |
98.5 |
177.18 |
p<0.01 |
512.13 (68.23 – 3843.78) |
No |
190 |
88.8 |
24 |
11.2 |
1 |
|||
Abnormal S.electrolyte |
Yes |
0 |
0.0 |
72 |
100.0 |
208 |
p<0.01 |
- |
No |
191 |
91.8 |
17 |
8.2 |
- |
|||
Hypoxi |
Yes |
0 |
0.0 |
68 |
100.0 |
192.74 |
p<0.01 |
- |
No |
191 |
90.1 |
21 |
9.9 |
- |
|||
Pain |
Yes |
0 |
0.0 |
77 |
100.0 |
227.93 |
p<0.01 |
- |
No |
191 |
94.1 |
12 |
5.9 |
- |
|||
CPB time |
>=116.5 |
8 |
9.0 |
81 |
91.0 |
211.07 |
p<0.01 |
231.61 (84.0 – 638.64) |
<116.5 |
183 |
95.8 |
8 |
4.2 |
1 |
|||
ACC time |
>=64.5 |
68 |
43.3 |
89 |
56.7 |
102.22 |
p<0.01 |
- |
<64.5 |
123 |
100.0 |
0 |
0.0 |
- |
|||
Table 3: Comparison of Risk Factors |
||||||||
**: - Significant at 0.01 level, *: - Significant at 0.05 level |
Among various outcome variables, ventilation, ICU time, and the requirement of inotropic
Supports were significantly higher with AF (p<0.01). The correlation with other variables is as shown in Table 7.
Risk Factors |
AF |
c2 |
p |
||||
No |
Yes |
||||||
Count |
Percent |
Count |
Percent |
||||
Gender |
Male |
75 |
64.7 |
41 |
35.3 |
1.16 |
0.282 |
Female |
116 |
70.7 |
48 |
29.3 |
|||
Valve surgery |
Yes |
43 |
54.4 |
36 |
45.5 |
9.6 |
<0.01 |
No |
148 |
73.6 |
53 |
26.3 |
|||
Current smoker |
Yes |
17 |
65.4 |
9 |
34.6 |
0.11 |
0.745 |
No |
174 |
68.5 |
80 |
31.5 |
|||
Previous cardiac surgery |
Yes |
0 |
0.0 |
0 |
0.0 |
- |
- |
No |
191 |
68.2 |
89 |
31.8 |
|||
Prior MI |
Yes |
94 |
65.7 |
49 |
34.3 |
0.83 |
0.363 |
No |
97 |
70.8 |
40 |
29.2 |
|||
LVEF |
<=40 |
33 |
61.1 |
21 |
38.9 |
1.56 |
0.212 |
>40 |
158 |
69.9 |
68 |
30.1 |
|||
Hypertension |
Yes |
93 |
66.9 |
46 |
33.1 |
0.22 |
0.641 |
No |
98 |
69.5 |
43 |
30.5 |
|||
Diabetes |
Yes |
114 |
65.9 |
59 |
34.1 |
1.12 |
0.289 |
No |
77 |
72.0 |
30 |
28.0 |
|||
COPD |
Yes |
38 |
67.9 |
18 |
32.1 |
0 |
0.949 |
No |
153 |
68.3 |
71 |
31.7 |
|||
PVD |
Yes |
10 |
66.7 |
5 |
33.3 |
0.02 |
0.895 |
No |
181 |
68.3 |
84 |
31.7 |
|||
CVD |
Yes |
4 |
66.7 |
2 |
33.3 |
0.01 |
0.934 |
No |
187 |
68.2 |
87 |
31.8 |
|||
CKD |
Yes |
5 |
55.6 |
4 |
44.4 |
0.69 |
0.407 |
No |
186 |
68.6 |
85 |
31.4 |
|||
B Blockers |
Yes |
160 |
83.3 |
32 |
16.6 |
64.4 |
<0.01 |
No |
31 |
35.2 |
57 |
64.7 |
|||
ACEI |
Yes |
117 |
68.4 |
54 |
31.6 |
0.01 |
0.926 |
No |
74 |
67.9 |
35 |
32.1 |
|||
CCBs |
Yes |
104 |
68.0 |
49 |
32.0 |
0.01 |
0.924 |
No |
87 |
68.5 |
40 |
31.5 |
|||
Left Main Stenosis 50% |
Yes |
61 |
67.0 |
30 |
33.0 |
0.09 |
0.768 |
No |
130 |
68.8 |
59 |
31.2 |
|||
Blood transfusion> 2 in OPCAB and > 4in CPB |
Yes |
45 |
68.2 |
21 |
31.8 |
0 |
0.995 |
No |
146 |
68.2 |
68 |
31.8 |
|||
Table 4: Comparison of Risk Factors |
The cutoff for CPB time of 116.5 minutes (AUC 0.97) and the cutoff for aortic cross clamp time of 64.5 minutes (AUC 0.91) were significantly associated with AF (p<0.01) as depicted in the ROC curve below.
Figure 2: ROC Curve for CBP Time ACC Time in Predicting AF among Patients Undergoing Cardiac Surgery |
Sensitivity and specificity for CBP time and ACC time in predicting AF among patients undergoing cardiac surgery.
|
CPB Time |
ACC Time |
Sensitivity |
91.0 |
100.0 |
Specificity |
95.8 |
64.4 |
False Negative |
9.0 |
0.0 |
False positive |
4.2 |
35.6 |
Positive Predictive value |
91.0 |
56.7 |
Negative Predictive value |
95.8 |
100.0 |
Positive Likelihood ratio |
21.7 |
2.8 |
Negative Likelihood ratio |
0.1 |
0.0 |
Accuracy |
94.3 |
75.7 |
Table 5: Sensitivity and Specificity for CBP Time ACC Time in Predicting AF among Patients Undergoing Cardiac Surgery |
Among those undergoing coronary surgeries, RCA stenosis was an important predictor.
|
|
B |
S.E. |
p |
95% CI) |
RCA Stenosis 70% (No ®) |
Yes |
5.60 |
1.55 |
p<0.01 |
269.25 (12.83 - 5648.14) |
Lactate Level > 4 (No ®) |
Yes |
6.21 |
1.49 |
p<0.01 |
496.05 (26.65 - 9232.72) |
CPB Time (<116.5 ®) |
>=116.5 |
4.98 |
1.12 |
p<0.01 |
144.81 (16.14 - 1299.59) |
Table 6: Independent predictors of AF among Patients Undergoing Cardiac Surgery (Multiple Logistic Regression) |
Risk Factors |
AF |
c2 |
p |
||||
No |
Yes |
||||||
Count |
Percent |
Count |
Percent |
||||
ALI/ARDS |
Yes |
58 |
30.4 |
27 |
30.3 |
0 |
0.996 |
No |
133 |
69.6 |
62 |
69.7 |
|||
CVA/Embolic episodes |
Yes |
1 |
0.5 |
1 |
1.1 |
0.31 |
0.579 |
No |
190 |
99.5 |
88 |
98.9 |
|||
AKI |
Yes |
0 |
0.0 |
0 |
0.0 |
- |
- |
No |
191 |
100.0 |
89 |
100.0 |
|||
Post OP Blood Transfusion |
Yes |
59 |
30.9 |
26 |
29.2 |
0.08 |
0.776 |
No |
132 |
69.1 |
63 |
70.8 |
|||
Need for antiarrthmics at discharge |
Yes |
5 |
2.6 |
69 |
77.5 |
175.21 |
p<0.01 |
Table 7: Comparison of Post-Operative Variables (2) |
Atrial fibrillation is one of the most common complications that can occur after a cardiac surgery. Its incidence after CABG has been reported to be between 20% and 40%.[1] Atrial fibrillation is the most common arrhythmia occurring after cardiac surgery, with new onset POAF (Post-Operative Atrial Fibrillation) affecting a significant proportion of patients. POAF typically presents within the first few days after surgery and can contribute to increased morbidity, prolonged hospital stays, healthcare costs, and a higher risk of stroke and mortality. Understanding the risk factors associated with POAF during the early post-operative period is vital to identify high-risk patients and develop preventive strategies. The present study aimed at calculating the incidence, identifying the risk factors for the development of AF and determining the potential implications that new-onset atrial fibrillation has on the post-operative outcome.
Out of 325 patients who underwent cardiac surgery during the study period, 280 subjects who satisfied the inclusion and exclusion criteria were included in the study. The study population consisted of 41.4% (116) males and 58.6% (164) females. The average age group was 64.5 ± 9.4 years. Advanced age is the most consistent independent risk factor for POAF. The risk of developing POAF increases markedly with each decade beyond 60 years. Aging is associated with structural and electrophysiological changes in the atria, including fibrosis, atrial dilation, and conduction abnormalities that create a substrate favorable for AF initiation and maintenance. Age-related reduction in left atrial compliance and altered autonomic tone further predispose elderly patients to POAF. Contrary to our observation, male sex has been associated with a slightly higher incidence of POAF in several studies, though this is less consistently reported. Obesity, smoking, and systemic inflammation are also implicated as contributory risk factors, potentially by exacerbating oxidative stress and autonomic imbalance.[2]
All patients were followed up until discharge, and out of 280, 31.8% developed new-onset atrial fibrillation during and after surgery. The incidence of POAF ranges widely from 20% to 50% in patients undergoing CABG and other cardiac surgeries, depending on patient populations and surgical techniques.[3]
61.8% of subjects had diabetes mellitus, 49.6% of subjects were hypertensive, and 20% had COPD. Though no statistically significant relation between the incidence of AF and these co-morbidities could be demonstrated in this study, the absolute number of subjects developing AF was slightly higher in those having the comorbidities, especially diabetes mellitus and hypertension. Many previous studies have shown a statistically significant relation between diabetes, hypertension, and COPD and the development of atrial fibrillation. Hypertension contributes by causing atrial remodeling and increased myocardial stiffness. A prior history of myocardial infarction and the presence of coronary artery disease further add to the risk due to myocardial ischemia and scarring that affect atrial conduction.[4]
A higher incidence of atrial fibrillation was found in those patients who underwent surgery using a CPB machine (Cardio-Pulmonary Bypass). This finding is contrary to the results of many well-established trials that state that atrial fibrillation is more common in off-pump CABGs. This may be explained by the facts that valve surgeries have a higher incidence of atrial fibrillation and that more complicated CADs are chosen for on-pump CABG. In this study, we were able to identify a cutoff value for CPB and ACC time that predicted an increased chance for developing AF. A CPB time of more than 116.5 mins and an aortic cross clamp time of more than 64.6 mins were associated with an increase in the incidence of postoperative atrial fibrillation. Use of CPB has been strongly linked to increased POAF incidence, likely due to the systemic inflammatory response it induces, myocardial ischemia-reperfusion injury, and oxidative stress. Off-pump surgery has been associated with significantly lower rates of POAF, underscoring the role of CPB-related factors. Cross-clamp time increases atrial ischemia and myocardial injury, amplifying the risk of early AF onset. These factors correlate with sustained inflammatory cytokine release and atrial electrical instability.[5]
Postoperative hypoxia due to COPD, ARDS, pulmonary hypertension, pulmonary edema etc., are risk factors for AF. Adequate ventilatory support, preload control with judicious fluid administration and diuretics, preoperative and postoperative chest physiotherapy, postural drainage, etc., can prevent the occurrence of AF. Lactate levels more than 4mmol/L, that are persistently elevated or rising are shown to be an independent risk factor for AF. Lactate occurs predominantly as a result of anaerobic glycolysis. Elevated lactate levels signify tissue hypoxia and inadequate tissue perfusion. A thorough investigation as to the exact causes leading to decreased tissue perfusion and hypoxia need to be done and the causes needs to be remedied.
The need for adequate analgesia cannot be emphasized enough. Pain and discomfort will cause the patient to struggle to fight the ventilator, which will increase the metabolic demand and increase cardiac workload. This along with the subsequent metabolic derangements will precipitate the development of AF. Abnormal serum electrolytes, hypoxia and pain are established risk factors for atrial fibrillation. Hence, rigorous monitoring of serum electrolytes and timely correction of any abnormalities are very important in preventing AF. Elevated catecholamines and sympathetic activation post-surgery shorten the atrial refractory period and promote arrhythmogenic foci. Electrolyte shifts, volume overload, and ischemia further aggravate conduction abnormalities and atrial stretch.[6]
Valve surgeries tend to have a higher risk of developing AF. Mitral stenosis and patients with dilated LA tend to develop AF preoperatively itself. But patients with preoperative AF were excluded in this study. Hence, this finding merits attention. The reasons as to the exact cause and mechanism for postop AF in valve surgeries need to be evaluated by a separate study. As already mentioned, Dilated LA and RA, increased intraoperative handling of the heart during retractions, and the nature of incisions and sutures all could have contributed to this. Patients undergoing combined procedures, such as valve replacement combined with CABG, exhibit higher rates of POAF compared to isolated CABG surgery. Complex surgeries involve longer operative times and increased myocardial manipulation, which lead to greater inflammatory responses and perioperative stress.[7]
Stenosis of the right coronary artery is a powerful and independent risk factor for the development of postoperative AF.[8] Right coronary artery supplies the right atrium, right ventricle, and SA node. In the present study the mechanism by which RCA stenosis predisposes to atrial fibrillation remains unknown. Previous studies have suggested that intraoperative ischemia in the right coronary distribution may be important in the pathogenesis of this arrhythmia.[9] In studies unrelated to cardiac surgery, atrial fibrillation has been associated with right atrial and right ventricular infarctions accompanying an acute myocardial infarction.[10,11] Several investigators have reported that supraventricular arrhythmias, especially atrial fibrillation, are the most frequent ECG abnormality associated with atrial infarctions. Similarly, ischemia resulting in right ventricular dysfunction may also play a role in the pathogenesis of atrial fibrillation.
The protective effect of beta-blocker therapy after cardiac surgery has been demonstrated by several studies. Rubin et al., demonstrated that of 123 patients undergoing CABG, 16.2% of patients treated with propranolol developed postoperative atrial fibrillation compared with 37.5% of control patients (p < 0.03). In the present study we were able to further strengthen this finding and demonstrate that beta blocker use reduced the postoperative occurrence of atrial fibrillation. 68.3% of subjects who did not have preoperative beta blockers developed post-operative atrial fibrillation while only 12% who used beta blockers preoperatively developed postoperative atrial fibrillation ( p <0.01).
The importance of all these results lies in the fact that a new-onset of post operative atrial fibrillation adversely affects the post operative outcome. Some of the previous studies that evaluated the outcomes of postoperative atrial fibrillation found an increased incidence of stroke and mortality. However, in the present study, the incidence of stroke was very low among both the groups and could not be compared. Stroke incidence in POAF patients was reported at 9.4% compared to 1.1% in non-POAF patients, with hospital mortality significantly higher in the POAF group (9.4% vs. 1.1%). POAF can precipitate or worsen heart failure through tachycardia-induced cardiomyopathy and loss of atrioventricular synchrony.[7,12]
The significant findings of the present study were that post-operative atrial fibrillation significantly increased the time the patient spent on ventilator support (mean 19.7 hrs vs. 12 hrs, p< 0.01), increased the time the patient had to stay in ICU (7.7 days vs. 4.9 days, p<0.01) and increased the duration the patient needed inotropic supports (18.3 hrs vs. 11.9 hrs, p<0.01). One similar study reported a median ICU stay of 27.0 hours in POAF patients versus 24.0 hours in non-POAF patients (p=0.036). Another large regional analysis found an average increase of 9 hours in ICU length of stay (58 hours vs. 49 hours, p < 0.0001) for patients with POAF, along with a longer overall postoperative hospital stay of approximately 2 days.[7,12]
Even though atrial fibrillation after cardiac surgery is self-limiting in most scenarios, once the underlying causative factors are corrected, a statistically significant number of patients in this study required antiarrhythmics at the time of discharge. 77.5% of patients who developed post operative atrial fibrillation required antiarrhythmics at the time of discharge(p <0.01). Even when AF is uncomplicated, its treatment requires additional medical and nursing time and a prolonged hospital stay. Consequently, AF after cardiac surgery leads to increased use of resources and causes increased morbidity for patients.
New-onset atrial fibrillation during the early postoperative period after cardiac surgery is a common and clinically significant complication. RCA stenosis > 70%, elevated lactate levels, increased CPB and ACC time, abnormal electrolytes, hypoxia, pain, and valve surgery are the major risk factors for postoperative AF. Development of postoperative AF adversely affects the outcome by increasing ventilation time, ICU stay, and inotropic support time and also makes the patients need to have antiarrhythmic drugs during discharge. Identification of high-risk patients through clinical assessment and echocardiography, along with careful perioperative management including beta-blockers and anti-inflammatory strategies, could help mitigate POAF incidence and its associated morbidity.