Discussion
The prevalence of new-onset POAF following CABG was 25.3% at Townsville University Hospital between 2016 and 2017, which compares with other international studies.1 4 10 Independent risk factors for the development of POAF included age, p-wave duration in lead II, HATCH score and CPB time.
Patient age was the most significant risk factor and is a well-documented association.1 10 11 Mathew et al estimated that for every 10 years of age, the risk of POAF increased by 75%.1 Our model assessed the OR at 15-year increments from 50 years old and found a similar positive association with the development of POAF. It should be noted however, that Matthew et al’s study included patients undertaking valve replacement, which itself is associated with higher rates of POAF, therefore, making direct comparison difficult.1 11 Physiologically, it has been postulated that age-associated anatomical remodelling and fibrosis of the conduction pathways may predispose to the development of POAF.12
Furthermore, it has been hypothesised that preoperative ECGs may detect underlying atrial conductive abnormalities, particularly those associated with left atrial dilatation.13 14 Increased p-wave amplitude and duration in lead 2, and the p-wave negative deflection characteristics of terminal p-wave duration (ms), depth (mV) and force (ms×mV) in precordial lead V1 has previously been correlated with increased left atrial size.9 A 2018 metanalysis and prospective cohort study by Wu et al found similar results to our study with pre-operative p-wave duration >105 ms being strongly associated with increased risk of POAF, OR 4.63, 95% CI 2.66 to 8.03 p<0.001.14 Moreover, a majority of the literature surrounding pre-operative p-wave duration is more than 15 years old, and while their results support our study’s findings, more up-to-date literature is needed to reflect the change in clinical and post-operative practice.15 16 With an intraclass correlation value of 0.725, measuring p-wave duration in lead II is moderately reliable.17
The surgical factors of CPB time and cross clamp time were both associated with increased rates of POAF. The ischaemic reperfusion injury associated with cross clamp release, and the proinflammatory state triggered by complement system activation associated with CPB use have both been proposed as possible triggers for the development of POAF.18 19 Importantly, when CPB time exceeded 100 min the patients’ risk of developing POAF doubled. Similarly, the POAF group had significantly longer CPB times, p=0.041, in Tsai et al’s 2015 study on isolated CABG patients.11 However, Hashemzadeh et al in a multivariate analysis found increased CPB time was associated with lower rates of POAF (OR 0.984 (95% CI 0.976 to 0.992) p<0.001).20
The presence of aortic regurgitation was associated with POAF on univariate analysis, but failed to demonstrate an independent association on multivariate analysis. However, since patients undergoing concurrent valve replacement were excluded from the study, severe valvular disease was unlikely to be seen, hence only the presence of valvular pathology was included rather than disease severity. Aksu et al and Abdel-Salam and Nammas supported these findings, finding no association between valvular pathology or LVEF and POAF following CABG.21 22 Furthermore, no studies could be found assessing tricuspid regurgitation and POAF, making our study potentially the first to assess this variable despite its non-significant finding.
The study population had well-preserved left ventricular function with only 10.3% of the total population having an ejection fraction of <40%, while the median LVEF were 59% (IQR 46–61) in the POAF group and 55% (50–60) in the SR group. A meta-analysis by Yanashita et al found both reduced LVEF and history of heart failure (LVEF<40%) to be associated with the development of POAF across 7 and 4 studies, respectively. Yamashita et al did highlight significant heterogeneity between data sets especially with respect to LVEF which had an I2 of 0.79.23
The studies investigating the relationship between the HATCH score and POAF are limited.10 24 25 Selvi et al and Emren et al had a similar study design being retrospective studies assessing POAF following CABG. Burgos et al have produced two papers the first from 2019 which focused on all cardiac surgeries of which only 2% of patients had an isolated CABG; compared with 31% having isolated CABG in their 2021 paper, making comparison difficult.10 24–26 In our study for every 1 point increase in the HATCH score, the risk of POAF increased by 1.312 times. This was comparable to Selvi et al who found an adjusted OR of 1.334 (95% CI 1.022 to 1.741) p=0.034.10 Similarly, Burgos et al’s 2021 paper who found an adjusted OR of 1.18 (95% CI 1.018 to 1.36) p=0.04, while Emren et al found a statistically significant relationship in univariate analysis, but failed to perform a multivariate analysis.24 26
Both Selvi et al and Emren used receiver operator curves to validate the potential use of the HATCH score as a predictive test for POAF. Both studies also used a HATCH score of ≥2. Selvi et al found similar results with a sensitivity of 42% and specificity of 70%. These are comparable to our findings of 34.7% sensitivity and a 72.8% specificity. Emren et al on the other hand, found a sensitivity of 77% and specificity of 62%. Furthermore, Emren et al found a statistically significant relationship between POAF and COPD and lower ejection fraction (<40%), a relationship not supported by either Selvi et al, or the findings of our study.10 24 Our results suggest that by including CPB time ≥100 min and p-wave duration (≥100 ms) in addition to the HATCH score, proved to be more sensitive screening test than HATCH alone. While difficult to directly compare; the HATCH-PC model, which exclusively assesses patients for risk of POAF following CABG, demonstrated higher sensitivity but lower specificity compared with the COM-AF model, a recent model proposed in Burgos et al’s 2021 paper for predicting POAF in all postoperative cardiac patients.26
Clinical implications
The financial costs associated with POAF are estimated at US$10 000–US$11 500 per patient, with increased ICU length of stay being a contributor.3 In our study the development of POAF increased median ICU length of stay by 16 hours, costing approximately $A3276 (US$2286) per patient with POAF, based on the 2019 Independent Hospital Pricing Authority estimate of average ICU cost in Australia, and a $1AUD to $0.70USD exchange rate.27 28 While this is a crude measure of the economic burden, it clearly demonstrates the financial implications of POAF and value in findings measures to reduce its prevalence.
Several prophylactic interventions for POAF have been investigated, however, inconsistent results have limited their adoption. Treatments such as amiodarone and beta-blockers in the early postoperative period have been trialled, but limited by inconsistent evidence and fears that such interventions may cause more harm than good.29 30 Offering therapy only to high-risk individuals has the potential to reduce adverse effects on a population level with less patients unnecessarily exposed, while providing an intervention with a potential benefit to those most in need. To date, no studies have combined prophylactic therapies in conjunction with a risk stratification model—that is, providing targeted prophylactic intervention. Hence, the utility of the HATCH or HATCH-PC model in combination with prophylactic therapy is an area where future research is required in the form of prospective clinical trials.
Limitations
This study relied on well-documented and accurate medical records, with a major limitation being the availability and reliability of the data. To overcome this challenge, data were assessed from various sections of the medical charts including preoperative surgical notes, ECG data records and discharge letters. An example of information not found was left atrial size, a known risk factor for POAF, instead indirect measures such as p-wave length and amplitude were used. Furthermore, continuity and standards of data collection were maintained by only having one team member performing the data collection. An example where observer measurement error may have taken place includes the measurements of p-waves, where the small measurement values on a standardly calibrated ECG, may have predisposed to error. Furthermore, the wide CI and low number of participants with stroke suggest that this study is likely too underpowered to draw meaningful conclusions regarding this risk factor. Two additional limitations of this study are the single-centre experience and retrospective design meaning that the findings of the study only show an association between the HATCH-PC model and predicting POAF in patients post-CABG. Furthermore this study does not demonstrate that the HATCH-PC model improves outcomes, it only serves to highlight where this model could be used to potentially improve clinical outcomes, formal prospective trials would be required to confirm its clinical utility.
Strengths
This is the largest study to date that has evaluated the HATCH scoring method in relation to POAF in an isolated CABG population, and the first that has combined the HATCH scoring method with additional risk factors to help improve its sensitivity and clinical utility. The addition of p-wave duration and CPB time to the HATCH score provides a scoring system that is easy to calculate and does not require invasive laboratory tests to improve sensitivity, and identify people who are at increased risk of developing POAF following CABG. This information could be used to improve monitoring or commence prophylactic interventions on patients who are identified as being at higher risk.