Association of Mitral Regurgitation with Postoperative Atrial Fibrillation in Critically Ill Noncardiac Surgery Patients: A Prospective Cohort Study

Introduction

Atrial fibrillation (AF) is the most common complication in intensive care units (ICUs).1,2 Observational studies have suggested that the occurrence of AF is 5–26% in noncardiac ICUs3,4 and up to 10% in surgical ICUs.5 The mechanisms that promote the development of postoperative AF (POAF) in critically ill patients are complex and multi-factorial. Many potential mechanisms have been implicated, including atrial ischemia, increased catecholamines, inflammation, and increased atrial pressure.6,7 According to recent studies, patients with left atrial (LA) or left ventricular (LV) dysfunction were vulnerable to AF under the impact of surgery or inflammation.8–10 Experimental and clinical observations have demonstrated that increased atrial stretch induced by increased atrial pressure shortens the atrial refractory period and may play an important role in AF development.11 Mitral regurgitation (MR) has been reported as a marker of increased LA filling pressure.12 On the other hand, LV diseases cause global dilatation or regional remodeling of the LV, which can also result in secondary MR.13,14 In the canine model of MR, the LA size increases with a corresponding decrease in LV systolic function, and elevated atrial activation lowers the effective refractory period and increases the inducibility of AF.5,15 These findings demonstrate that the influence of MR on AF cannot be ignored. Based on previous studies, we speculate that MR is a risk factor for POAF and has great prediction value. Recent studies have shown that MR was associated with AF recurrence after ablation.16,17 Bahouth et al have also found that there was a graded independent association between MR severity and new-onset AF in patients with acute myocardial infarction.12 Similarly, after analysis of acute decompensated heart failure hospitalization, the prevalence of AF increased with increased MR severity.18 Although previous studies have clarified the relationship between MR and AF, few studies have been performed in postoperative critically ill patients. Additionally, no prior study has established the prediction model using MR in POAF patients.

At present, it has been reported that advanced age, sex, previous cardiac arrhythmias, pre-existing cardiorespiratory disease, myocardial ischemia, and perioperative factors, as well as MR, are significant risk factors for developing new-onset POAF in patients.19 Currently, there has been a non-validated model to predict POAF in critically ill patients. Recent studies have created and validated several models to predict POAF after cardiac surgery, such as HATCH20 and COM-AF.21 HART has been used to predict POAF after noncardiac elective surgery.22 C2HEST has been detected with the potential to be utilized as a risk stratification tool for decision-making regarding a screening approach for AF in stroke.23 Moreover, the CHA2DS2-VASc score, initially created to predict the risk of thromboembolism in patients with AF, has been validated for the prediction of AF.24 However, there is no appropriate model to predict critically ill patients after noncardiac surgery. We hypothesized that MR plays a vital role in POAF prediction in thoracic and general surgery postoperative critically ill patients.

To test this hypothesis, in this study, we demonstrated that MR is an independent risk factor and a strong predictor of POAF. Multiple scoring systems have been proposed to predict POAF occurrence, with modest predicting power. We further created an MR-centered nomogram to predict POAF and demonstrated the superiority of this nomogram to the existing scoring system in a prospective cohort of thoracic and general surgery postoperative critically ill patients.

Materials and Methods

The present study was approved by the Human Ethics Committee of Beijing Chao-Yang Hospital, Capital Medical University (Beijing, China) (approval no. 2020-ke-236), and written informed consent was obtained from each individual or their representative before enrolling in the study.

Study Design and Patients’ Population

The study was designed, performed, and reported following the STROBE reporting guidelines and in accordance with the Helsinki Declaration.25 It was performed in a 20-bed surgical ICU at Beijing Chaoyang Hospital from January 1, 2018, to December 31, 2021. We continuously screened postoperative adult patients who underwent general and thoracic surgery. Patients had multiple ICU admissions or were included only once. The exclusion criteria were age < 18 years; AF or flutter detected in preoperative examination or electrical monitoring during surgery; received amiodarone before operation; the presence of a cardiac implantable electronic device with a functioning atrial lead (pacemaker, implantable cardioverter defibrillator, or cardiac resynchronization device); underwent transplantation surgery; or none critical data (missing data of electrocardiogram [ECG], ultrasonic cardiogram, and baseline data before admission).

Definitions and Clinical Endpoints

POAF was defined as any AF episode lasting > 30 seconds on 12-lead surface ECG or telemetry monitoring or when the patient referred symptoms during a hospital stay.26 All patients had continuous bedside electronic monitoring at least during the first 24 hours after ICU admission. All arrhythmic events were adjusted by cardiologists.

The primary endpoint was the development of POAF from the time of arrival in the ICU admission to postoperative day 7. The secondary endpoints included postoperative complications such as infection, fistula, postoperative bleeding, myocardial infarction, acute kidney injury (AKI), duration of mechanical ventilation and ICU and hospital stays, and hospital mortality.

Data Collection

Previous studies have suggested that demographic information,27 clinical data, medications,28 and complications29 were associated with the incidence of POAF. Preoperative cardiac function was assessed by ECG (including sinus tachycardia, sinus bradycardia, arrhythmia, myocardial ischemia, conduction block, P-wave, PR interval, and QTc interval) and ultrasonic cardiogram (UCG) (including MR, tricuspid regurgitation [TR], LV mass, segmental wall motion abnormality, left atrium [LA] volume, LV ejection fraction [LVEF], rheumatic heart disease, pulmonary hypertension, aortic sinus inner diameter, and E/A]). Acute Physiology and Chronic Health Evaluation (APACHE II) and Sequential Organ Failure Assessment (SOFA) scores were used to estimate the severity of the patient’s illness on the day of ICU admission. Clinical variables containing prior health history, thoracic surgery, surgery procedure, laboratory blood tests, postoperative complications, duration of mechanical ventilation, ICU stay, and hospital stays were collected from the electronic medical record system.

Echocardiography was performed by an experienced sonographer who had received advanced training and certification in echocardiographic imaging, according to the guidelines of the American Society of Echocardiography (ASE). M-mode echocardiography was used to measure LA dimensions, and the LVEF was calculated with Simpson’s method. Doppler echocardiography assessed early (E) and late (A) diastolic mitral inflow velocities and the E/A ratio.30,31

Risk Scoring System

The scores were calculated as follows. The CHA2DS2‐VASc score comprised a history of coronary heart failure (CHF): 1 point; hypertension (HT): 1 point, diabetes: 1 point; age 65–74 years: 1 point, age ≥ 75 years: 2 points; female sex: 1 point; peripheral vascular disease: 1 point; stroke/transient ischemic attack (TIA): 2 points.24,32 The C2HEST score comprised coronary artery disease (CAD): 1 point; chronic obstructive pulmonary disease (COPD): 1 point; HT: 1 point; age ≥ 75 years: 2 points; systolic HF: 2 points; thyroid disease: 1 point.33 The COM-AF score comprised age 65–74 years: 1 point, aged ≥ 75 years: 2 points; CHF: 2 points; female sex: 1 point; HT: 1 point; diabetes: 1 point; previous stroke: 2 points.21 The HATCH score comprised stroke or TIA: 2 points; HT 1 point; CHF: 2 points; age ≥ 75 years: 1 point; COPD: 1 point.34 The HART score comprised HT 1 point; age 65–74 years: 1 point, age ≥ 75 years: 2 points; intermediate risk surgery: 3 points, high-risk surgery: 3 points; thyroid dysfunction: 1 point.22

Covariates Identified by Directed Acyclic Graphs (DAGs)

Recently, it has been reported that directed acyclic graphs (DAGs) can identify confounding variables and mediators in exposure-outcome relationships, reduce confounding bias, and avoid over-adjustment.35–37 By reviewing possible causal mechanisms reported by previously published studies, we constructed a DAG framework to evaluate the effects of MR on the occurrence of POAF. After selecting the variables, directed paths were created according to standard procedures and analyzed with DAGitty 3.0 software (http://www.dagitty.net) (Figure 1).

Figure 1 Directed acyclic graph of mitral regurgitation and postoperative atrial fibrillation.

Abbreviations: MR, mitral regurgitation; CHF, congestive heart failure; POAF, postoperative atrial fibrillation; CHD, coronary heart disease.

Statistical Analysis

SPSS v25 (SPSS Inc, Chicago, IL, USA), MedCalc v.16.4.3 (MedCalc, Ostend, Belgium), and R 4.0.3 (R Project for Statistical Computing, Vienna, Austria) were used for statistical analysis. Categorical variables were presented as percentiles, and continuous variables were presented as a median with the 25th and 75th percentiles (interquartile range [IQR]). Mann–Whitney U-test was used to compare continuous data between groups, and the chi-squared test or Fisher’s exact test was used to compare categorical data. Univariate logistic regression was used to assess the correlation between variables and POAF. Due to DAGs, we selected minimal sufficient adjustment sets to evaluate the effects of MR on POAF. Excluding the mediator variables of MR and POAF, clinical parameters with p < 0.05 in the univariate analysis were added to the multivariate logistic regression model. A nomogram evaluating POAF was established based on the multivariate analysis using the rms package in R. The predictive accuracy of the nomogram was assessed by calibration. Receiver operator characteristic (ROC) curve analysis was used to assess the predictive value of the MR-nomogram and other scoring systems for POAF. The net contribution of the nomogram was assessed using the Hosmer-Lemeshow test. Bootstrapping with repeated sampling was performed to confirm the stability of the nomogram. The area under the ROC curve (AUC), sensitivity, specificity, and their corresponding 95% confidence intervals (CIs),38 defined as follows: 0.90–1.0, excellent; 0.80–0.89, good; 0.70–0.79, useful; 0.60–0.69, poor; and 0.50–0.59, not useful, were determined.39 The cutoff point was the value with the highest specificity and sensitivity. Improvement in the predictive accuracy of the nomogram was evaluated by calculating the relative integrated discrimination improvement (IDI) and net reclassification improvement (NRI).40 We also estimated the clinical utility and net benefit of the new prediction nomogram by decision curve analysis (DCA), which identifies patients at risk of POAF based on the new prediction nomogram and other scoring systems. For all analyses, statistical significance was taken as a two-sided p-value < 0.05.

Results Overall Patient Characteristics

A total of 6885 subjects who were admitted to the ICU postoperatively were screened. Among them, 3268 adult patients underwent general or thoracic surgery. A total of 794 patients were excluded for the following reasons: AF or atrial flutter in admission ECG (n = 37); receiving amiodarone prior to surgery (n = 45); the presence of a cardiac implantable electronic device with a functioning atrial lead (pacemaker, implantable cardioverter defibrillator, or cardiac resynchronization device) (n = 23); undergoing transplantation surgery (n = 593); noncritical data before surgery (n = 96). Thus, 2474 patients were ultimately enrolled in the prospective cohort. Their baseline characteristics are shown in Table 1. The flow diagram is shown in Figure 2.

Table 1 Baseline Characteristics of Patients Stratified by Postoperative Atrial Fibrillation

Figure 2 Patient selection flow.

Abbreviations: SICU, surgical intensive care unit; AF, atrial fibrillation; ECG, electrocardiogram; POAF, postoperative atrial fibrillation.

In total, 213 patients (8.6%) met the primary endpoint of POAF. There was a statistically significant difference in the postoperative comorbidities, including infection (27.2% vs 10.0%, p < 0.001), fistula (15.0% vs 6.8%, p < 0.001), postoperative bleeding (7.5% vs 3.1%, p = 0.001), myocardial infarction (6.6% vs 1.9%, p < 0.001), and AKI (8.5% vs 1.2%, p < 0.001). The duration of mechanical ventilation (15.4 [6.2–24.1] vs 9.42 [3.32–16.18], p < 0.001), ICU stay (94.9 [46.9–165.5] vs 46.2 [24.0–83.2], p < 0.001), and hospital stay (554.0 [405.0–865.0] vs 463.0 [342.0–673.0], p < 0.001) was longer in patients with POAF than that in patients without POAF (Table 2).

Table 2 Outcomes Between Patients with and without Postoperative Atrial Fibrillation

Identification of MR as a Predictive Factor for POAF

We identified MR on UCG related to POAF by using univariable analysis. Additionally, the presence of POAF was also associated with age, hypertension, coronary heart disease, CHF, history of paroxysmal AF, anticoagulant use, sinus bradycardia on ECG, TR on UCG, segmental wall motion abnormality on UCG, pulmonary hypertension on UCG, thoracic surgery, blood loss during surgery, red cell infusion volume during surgery, plasma infusion volume during surgery, liquid balance during surgery, cedilanid during surgery, APACHE II score at ICU admission, SOFA score at ICU admission, heart rate at ICU admission, mean arterial pressure (MAP) at ICU admission, and BNP at ICU admission (see Table S1). The DAG showed confounding factors for MR in POAF, including age, HT, CHF, history of paroxysmal AF, CHD, and segmental wall motion abnormality (Figure 1). Thus, of these variables, MR on UCG, age, history of paroxysmal AF, CHF, thoracic surgery, blood loss, heart rate at ICU admission, and MAP at ICU admission were independent predictors of POAF, which were identified by multivariable analysis (see Table S2). Hosmer-Lemeshow goodness-of-fit test (p > 0.05) was used to confirm the calibration of the MR nomogram. This MR-nomogram predicted POAF with an AUC of 0.824 (95% CI: 0.805–0.842, p < 0.001).

The predictive nomogram combining all significant independent predictive factors for POAF is shown in Figure 3. A sum score could be calculated as the total scores of related predictors and referred to the probability of POAF in the basal axis. For example, in an 80-year-old MR patient without a history of paroxysmal AF or CHF undergoing thoracic surgery with intraoperative 5000-mL blood loss on admission to ICU with an HR of 100 bpm and MAP of 60 mm Hg, the total score would be 210 and POAF probability was approximately 85%. The calibration plot for the probability of POAF showed optimal agreement between the prediction by the MR-nomogram and actual observation. Remarkably, the calibration plot for the probability of POAF showed good consistency between the MR-nomogram prediction and actual observation (Figure 4).

Figure 3 Nomogram for POAF risk and its predictive performance. Each variable is assigned a point on the top axis by drawing a line upward. The sum of these numbers is located on the Total Points axis, and a line is drawn downwards to the Probability axis to identify the likelihood of POAF in postoperative critically ill patients.

Abbreviations: POAF, postoperative atrial fibrillation, ICU intensive care unit, MAP, mean arterial pressure; AF, atrial fibrillation.

Figure 4 MR-nomogram calibration curves for predicting postoperative atrial fibrillation among critically ill patients. MR-Nomograms-predicted probability of POAF is plotted on the x-axis, and actual probability is plotted on the y-axis.

Abbreviation: MR, mitral regurgitation.

Predictive Performance of the MR Nomogram Compared to Other Scoring Systems

The scores of CHA2DS2‐VASc, HATCH, COM-AF, HART, and C2HEST were significantly higher in patients with POAF compared with patients without POAF (Table 3). The AUCs for the MR nomogram and other scoring systems are shown in Figure 5. The MR nomogram had an AUC of 0.824 (95% CI: 0.805–0.842, P < 0.001) for predicting POAF. In contrast, CHA2DS-VASc, HATCH, COM-AF, C2HEST, and HART showed lower performance in predicting POAF, with AUCs of 0.668 (95% CI: 0.646–0.691, P < 0.001), 0.671 (95% CI: 0.648–0.693, P < 0.001), 0.687 (95% CI: 0.664–0.708, P < 0.001), 0.702 (95% CI: 0.680–0.724, P < 0.001), and 0.669 (95% CI: 0.646–0.691, P < 0.001), respectively. The statistical significance of the difference between AUCs of the MR-nomogram and other scoring systems was supported by the DeLong method, IDI, and NRI (P < 0.001 for each scoring system and new nomogram) (Table 4).

Table 3 Other Scoring Systems in POAF and Non POAF Patients

Table 4 Comparison of the ROC Curves, NRI and IDI of Model vs Other Score Systems in Predicting POAF

Figure 5 Predictive value of MR-nomogram and other score system for POAF.

Abbreviations: ROC, receiver operator characteristic; AUC, area under the ROC curve.

Predictive Superiority of the MR Nomogram Over Other Scoring Systems

We conducted DCA to further investigate the clinical utility of the MR nomogram and other scoring systems in predicting POAF. DCA revealed that the new MR nomogram had the highest net benefit at 10–50% of the probability threshold (Figure 6); that is, if a patient with a risk of POAF between 10% and 50% warranted further therapy (such as preventive interventions or hemodynamic monitoring), POAF screening using the MR nomogram showed a better benefit with a wide range of threshold probabilities and better performances than CHA2DS-VASc, HATCH, COM-AF, C2HEST, and HART. Although the net benefits of the six models increased similarly with increasing probability thresholds, they deviated significantly at low probability thresholds.

Figure 6 Decision curve for prediction of POAF using different prediction models. The x axis shows threshold values for POAF while the y axis represents the net benefit for the different threshold values of POAF; a higher net benefit is provided by new prediction nomograms that are farthest away from the slanted dashed gray line (assuming all adverse events) and horizontal black line (assuming no adverse event).

Abbreviation: MR, mitral regurgitation.

Sensitivity Analysis

As the history of paroxysmal AF may affect MR, we concluded that MR was an independent risk factor (odds ratio [OR] = 1.750, 95% CI: 1.085–2.821, p = 0.022) after excluding MR patients with a history of paroxysmal AF (n = 12) and repeating the risk prediction analysis. Moreover, the MR nomogram also showed a useful value for predicting POAF (AUC = 0.823 [0.790–0.857], p < 0.001).

Discussion

AF is the most common sustained dysrhythmia independently associated with poor prognosis in critically ill patients. A total of 8.6% of critically ill adult patients experienced AF within 7 days after surgery in our study, similar to a previous report.7 AF is the risk factor for in-hospital mortality and long-term mortality in elderly patients undergoing hip fracture surgery.41,42 In our study, although POAF did not show an influence on mortality, it was associated with perioperative myocardial infarction, acute kidney injury, and complications, such as infection, fistula, and bleeding. Patients with AF had prolonged mechanical ventilation time, ICU, and hospital length of stay. New-onset POAF can be a marker of increased illness severity.43,44 However, early identification of high-risk patients can lead to initiating preventive measures (eg, utilizing drugs such as beta-blockers, statins, oral anticoagulants, antiarrhythmics, and electrolyte supplementation45) before adverse cardiac events to reduce mortality and improve clinical outcomes. Prophylactic amiodarone used in high-risk individuals effectively reduced the incidence of POAF, improved outcomes, and reduced the associated health resource utilization and costs.46 We first constructed an accurate nomogram combining MR based on DAGs and multivariate logistic regression to predict POAF following thoracic and general surgery in critically ill patients.

MR involves the retrograde blood flow from the LV into the LA,47 leading to volume overload in the LV and LA, which may serve as a predictor of LA enlargement and remodeling.48,49 LA remodeling predicts adverse cardiovascular outcomes and thus can be utilized as a marker to monitor diseased states.50 LA enlargement, remodeling, and dysfunction promote a milieu conducive to AF.51 A previous study has shown that patients with MR had a substantial risk (up to 28%) of AF post-ablation recurrence.49 Patients with MR have a spherical atrium, which is associated with a higher rate of AF.52 In our large cohort of postoperative critically ill patients, we demonstrated MR as an independent risk factor for POAF. This was the first study to investigate the association between MR and POAF in postoperative critically ill patients.

Besides, we also identified the following independent risk factors associated with POAF: age, thoracic surgery, history of paroxysmal AF, CHF, blood loss in surgery, HR, and MAP at ICU admission. As previous studies have identified, advanced age was associated with changes in ion channel conduction, contributing to intra-atrial conduction disorder.53 Additionally, CHF may act synergistically with advanced age to increase the risk for POAF development.53 Similarly, thoracic surgery,54 HR,46 atrial pressure,55 and a history of paroxysmal AF56 have been implicated in POAF development. Blood loss during surgery may increase oxidative stress and cause sympathetic/parasympathetic activation, resulting in POAF. Actually, ECG examination plays a vital role in AF detection.57 Interatrial block and P wave parameters are other confirmed risk factors for AF,58 which was different from our study. Previous research has focused on patients with an acute ischemic stroke rather than postoperative patients, who might have a different trigger of AF and experience fewer cardiovascular events before surgery.

Our study was novel because we combined MR to establish a new nomogram for predicting POAF in critically ill patients following thoracic and general surgery. The predictive power of the new MR nomogram was compared to CHA2DS2‐VASc, HATCH, COM-AF, HART, and C2HEST scoring systems. The CHA2DS2-VASc score is widely used to predict stroke risk in patients with AF. Traditionally, persistent anticoagulation is indicated for patients with AF and a CHA2DS2-VASc score of 2. Nevertheless, recent studies have extended the CHA2DS2-VASc score to predict the incidence of POAF following cardiac surgery.59–62 Patients with higher CHA2DS2-VASc scores are more likely to have AF.24 The C2HEST score can predict AF in patients with previous ischemic stroke and stratify poststroke patients into different risk groups for incident AF.23 However, for predicting AF among patients with end-stage renal disease, both score systems showed poor predictive value, with an AUC of approximately 0.6. However, this has not been extended to more populations.63 Considering the effect of long-term LA enlargement on AF, De Vos et al have developed the HATCH score for AF prediction.34 Tischer T. has shown a significant increase in the prevalence of AF with an increasing HATCH score.64 Emren et al have found that the HATCH score presented a higher predictive ability with an AUC of 0.77 vs 0.71 for the CHA2DS2‐VASc score in patients undergoing CABG surgery65 but with a poor discriminative ability to predict POAF after cardiac surgery with an AUC of 0.57.66 From the combination of variables with higher predictive value, including CHA2DS2‐VASc and HATCH, a new risk system COM-AF involved a large cohort to predict AF, which presented an AUC of 0.78 vs CHA2DS2‐VASc AUC of 0.76 and HATCH AUC of 0.70.21 However, diverse screening score systems may have different capabilities in detecting unrevealed AF of various populations. These scoring systems have been mostly used to predict the occurrence of AF after cardiac surgery rather than noncardiac surgery. Stronati et al have found 4 independent predictors and established HART scores (including age, hypertension, thyroid dysfunction, and intermediate or high-risk surgery) for all types of noncardiac elective surgery.22 They have recorded different surgical procedures and classified them as low, intermediate, or high-risk according to European guidelines.67 That study differs from ours in that it focused on all candidates instead of critically ill patients. According to European guidelines, the majority of the population in our study experienced intermediate- or high-risk surgery, which was the reason why the HART scoring system showed limited predictive value for POAF in ICU.67 Our study filled the gap in predicting AF in the ICU population. We demonstrated that our new MR nomogram had better predictive capacity for POAF in critically ill patients than CHA2DS2‐VASc, HATCH, COM-AF, HART, and C2HEST scoring systems, as supported by IDI, NRI, and DCA. Studies on POAF based on predictive analytics have rarely mentioned critically ill patients and explored the association of MR. Critically ill individual risk evaluation for developing incidents of POAF is important for decision-making in early primary prevention and detection of AF, which might be associated with better outcomes.28 The novelty of our study is not only demonstrating a promising nomogram but contributing to the pathophysiologic mechanisms of POAF in postoperative critically ill patients.

There were some limitations in this study. First, it was a single-center study susceptible to bias from practices. The predictive value of the MR nomogram needs to be further assessed in a multicenter study. Second, although it has been reported that the morbidity of thoracic and general noncardiac surgery is high, our findings might limit the external validity, which should be addressed in future studies with an extended patient population. In addition, the quantitative data of MR were not recorded, which might have limited our exploration of the relationship between MR and POAF. Our future step will evaluate the effect of the quantitative assessment of MR in this cohort. Finally, we did not have a validation cohort to confirm the predictive ability of the MR nomogram; thus, the possibility of overfitting could not be excluded.

Conclusion

In summary, MR was an independent risk factor for POAF in postoperative critically ill patients. Additionally, the MR nomogram was a better predictive model of POAF in our cohort than established scoring systems such as CHA2DS2‐VASc, HATCH, COM-AF, HART, and C2HEST. These findings provide a basis for further investigations into the role of MR in the pathogenesis of POAF in critically ill patients.

Abbreviations

POAF, Postoperative atrial fibrillation; ICU, Intensive care unit; MR, Mitral regurgitation; LV, Left ventricular; ECG, Electrocardiogram; AKI, Acute kidney injury; UCG, Ultrasonic cardiogram; TR, Tricuspid regurgitation; LA, Left atrium; LVEF, LV ejection fraction; APACHE II, Acute physiology and chronic health evaluation; SOFA, Sequential organ failure assessment; CHF, Congestive heart failure; HT, Hypertension; TIA, Transient ischemic attack; CHD, Coronary heart disease; COPD, Chronic obstructive pulmonary disease; DAGs, Directed acyclic graphs; IQR, Interquartile range; ROC, Receiver operator characteristic; AUC, Area under the ROC curve; CIs, Confidence intervals; IDI, Integrated discrimination improvement; NRI, Net reclassification improvement; DCA, Decision curve analysis; MAP, Mean arterial pressure.

Data Sharing Statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Ethic Approval

The original study as well as secondary analysis was approved by the Human Ethics Committee of Beijing Chaoyang Hospital, Capital Medical University (Beijing, China) (approval no. 2020-ke-236).

Consent for Publication

Written informed consent was obtained from patients or their next of kin before patients participated in this study. Consent requirements were waived for patients who died at the scene and never reached the hospital and for participants without known legal representatives.

Acknowledgments

We thank the R project for providing high-quality data processing. And we thank Capital Medical University provide us SPSS v25 for statistical analysis. We thank Dr. Dong Wang and Pro. Lirong Liang in Beijing Chaoyang Hospital for statistical analysis. Jin Zhang and Jingyi Wang are co-first authors for this study. Wenxiong Li and Hui Li are co-correspondence authors for this study.

Funding

No funding was received for this study.

Disclosure

The authors report no conflicts of interest in this work.

References

1. Bedford JP, Ferrando-Vivas P, Redfern O, et al. New-onset atrial fibrillation in intensive care: epidemiology and outcomes. Eur Heart J Acute Cardiovasc Care. 2022;11(8):620–628. doi:10.1093/ehjacc/zuac080

2. Chean CS, McAuley D, Gordon A, Welters ID. Current practice in the management of new-onset atrial fibrillation in critically ill patients: a UK-wide survey. PeerJ. 2017;5:e3716. doi:10.7717/peerj.3716

3. Carrera P, Thongprayoon C, Cheungpasitporn W, Iyer VN, Moua T. Epidemiology and outcome of new-onset atrial fibrillation in the medical intensive care unit. J Crit Care. 2016;36:102–106. doi:10.1016/j.jcrc.2016.06.032

4. Chen AY, Sokol SS, Kress JP, Lat I. New-onset atrial fibrillation is an independent predictor of mortality in medical intensive care unit patients. Ann Pharmacother. 2015;49(5):523–527. doi:10.1177/1060028015574726

5. Knotzer H, Mayr A, Ulmer H, et al. Tachyarrhythmias in a surgical intensive care unit: a case-controlled epidemiologic study. Inten Care Medi. 2000;26(7):908–914. doi:10.1007/s001340051280

6. Schmitt J, Duray G, Gersh BJ, Hohnloser SH. Atrial fibrillation in acute myocardial infarction: a systematic review of the incidence, clinical features and prognostic implications. Eur Heart J. 2009;30(9):1038–1045. doi:10.1093/eurheartj/ehn579

7. Aronson D, Boulos M, Suleiman A, et al. Relation of C-reactive protein and new-onset atrial fibrillation in patients with acute myocardial infarction. Am J Cardiol. 2007;100(5):753–757. doi:10.1016/j.amjcard.2007.04.014

8. Maesen B, Nijs J, Maessen J, et al. Post-operative atrial fibrillation: a maze of mechanisms. Europace. 2012;14(2):159–174. doi:10.1093/europace/eur208

9. Nardi F, Diena M, Caimmi PP, et al. Relationship between left atrial volume and atrial fibrillation following coronary artery bypass grafting. J Card Surg. 2012;27(1):128–135. doi:10.1111/j.1540-8191.2011.01373.x

10. Polanczyk CA, Goldman L, Marcantonio ER, et al. Supraventricular arrhythmia in patients having noncardiac surgery: clinical correlates and effect on length of stay. Ann Intern Med. 1998;129(4):279–285. doi:10.7326/0003-4819-129-4-199808150-00003

11. Ravelli F, Allessie M. Effects of atrial dilatation on refractory period and vulnerability to atrial fibrillation in the isolated Langendorff-perfused rabbit heart. Circulation. 1997;96(5):1686–1695. doi:10.1161/01.cir.96.5.1686

12. Bahouth F, Mutlak D, Furman M, et al. Relationship of functional mitral regurgitation to new-onset atrial fibrillation in acute myocardial infarction. Heart. 2010;96(9):683–688. doi:10.1136/hrt.2009.183822

13. Fan Y, Wan S, Wong RH-L, Lee AP-W. Atrial functional mitral regurgitation: mechanisms and surgical implications. Asian Cardiovasc Thorac Ann. 2020;28(7):421–426. doi:10.1177/0218492320941388

14. Levine RA, Schwammenthal E. Ischemic mitral regurgitation on the threshold of a solution, from paradoxes to unifying concepts. Circulation. 2005;112(5):745–758. doi:10.1161/CIRCULATIONAHA.104.486720

15. Ruaengsri C, Schill MR, Lancaster TS, et al. The hemodynamic and atrial electrophysiologic consequences of chronic left atrial volume overload in a controllable canine model. J Thorac Cardiovasc Surg. 2018;156(5):1871–1879. doi:10.1016/j.jtcvs.2018.05.078

16. Nakamura K, Takagi T, Kogame N, et al. Impact of atrial mitral and tricuspid regurgitation on atrial fibrillation recurrence after ablation. J Electrocardiol. 2021;66:114–121. doi:10.1016/j.jelectrocard.2021.04.005

17. Gertz ZM, Raina A, Mountantonakis SE, et al. The impact of mitral regurgitation on patients undergoing catheter ablation of atrial fibrillation. Europace. 2011;13(8):1127–1132. doi:10.1093/europace/eur098

18. Arora S, Brown ZD, Sivaraj K, et al. The relationship between atrial fibrillation, mitral regurgitation, and heart failure subtype: the ARIC study. J Card Fail. 2022;28(6):883–892. doi:10.1016/j.cardfail.2021.10.015

19. Almassi GH, Schowalter T, Nicolosi AC, et al. Atrial fibrillation after cardiac surgery, a major morbid event? Ann Surg. 1997;226(4):501–513. doi:10.1097/00000658-199710000-00011

20. Hu W-S, Lin C-L. Prediction of new-onset atrial fibrillation for general population in Asia: a comparison of C2HEST and HATCH scores. Int J Cardiol. 2020;313:60–63. doi:10.1016/j.ijcard.2020.03.036

21. Burgos LM, Ramirez AG, Seoane L, et al. New combined risk score to predict atrial fibrillation after cardiac surgery: COM-AF. Ann Cardiac Anaesth. 2021;24(4):458–463. doi:10.4103/aca.ACA_34_20

22. Stronati G, Mondelli C, Urbinati A, et al. Derivation and validation of a clinical score for predicting postoperative atrial fibrillation in noncardiac elective surgery (the HART score). Am J Cardiol. 2022;170:56–62. doi:10.1016/j.amjcard.2022.01.020

23. Li Y-G, Bisson A, Bodin A, et al. C 2 HEST score and prediction of incident atrial fibrillation in poststroke patients: a French nationwide study. J Am Heart Assoc. 2019;8(13):e012546. doi:10.1161/JAHA.119.012546

24. Chua S-K, Shyu K-G, Lu M-J, et al. Clinical utility of CHADS2 and CHA2DS2-VASc scoring systems for predicting postoperative atrial fibrillation after cardiac surgery. J Thorac Cardiovasc Surg. 2013;146(4):919–926 e1. doi:10.1016/j.jtcvs.2013.03.040

25. von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453–1457. doi:10.1016/S0140-6736(07)61602-X

26. María Burgos L, Gil RA, Brito VG, et al. Development and validation of a simple clinical risk prediction model for new-onset postoperative atrial fibrillation after cardiac surgery: nopaf score. J Atr Fibrillation. 2020;13(2):2249. doi:10.4022/jafib.2249

27. Conen D, Alonso-Coello P, Douketis J, et al. Risk of stroke and other adverse outcomes in patients with perioperative atrial fibrillation 1 year after non-cardiac surgery. Eur Heart J. 2020;41(5):645–651. doi:10.1093/eurheartj/ehz431

28. Fernando SM, Mathew R, Hibbert B, et al. New-onset atrial fibrillation and associated outcomes and resource use among critically ill adults—a multicenter retrospective cohort study. Crit Care. 2020;24(1):15. doi:10.1186/s13054-020-2730-0

29. Higuchi S, Kabeya Y, Matsushita K, et al. Incidence and complications of perioperative atrial fibrillation after non-cardiac surgery for malignancy. PLoS One. 2019;14(5):e0216239. doi:10.1371/journal.pone.0216239

30. Zoghbi WA, Adams D, Bonow RO, et al. Recommendations for noninvasive evaluation of native valvular regurgitation: a report from the American Society of Echocardiography developed in collaboration with the Society for Cardiovascular Magnetic Resonance. J Am Soc Echocardiogr. 2017;30(4):303–371. doi:10.1016/j.echo.2017.01.007

31. Rudski LG, Lai WW, Afilalo J, et al. Guidelines for the echocardiographic assessment of the right heart in adults: a report from the American Society of Echocardiography endorsed by the European Association of Echocardiography, a registered branch of the European Society of Cardiology, and the Canadian Society of Echocardiography. J Am Soc Echocardiogr. 2010;23(7):685–688. doi:10.1016/j.echo.2010.05.010

32. Lip GY, Lane DA. Modern management of atrial fibrillation requires initial identification of “low-risk” patients using the CHA2DS2-VASc score, and not focusing on “high-risk” prediction. Circ J. 2014;78(8):1843–1845. doi:10.1253/circj.cj-14-0584

33. Han J, Li G, Zhang D, Wang X, Guo X. Predicting late recurrence of atrial fibrillation after radiofrequency ablation in patients with atrial fibrillation: comparison of C2HEST and HATCH Scores. Front Cardiovasc Med. 2022;9:907817. doi:10.3389/fcvm.2022.907817

34. de Vos CB, Pisters R, Nieuwlaat R, et al. Progression from paroxysmal to persistent atrial fibrillation clinical correlates and prognosis. J Am Coll Cardiol. 2010;55(8):725–731. doi:10.1016/j.jacc.2009.11.040

35. Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37–48. doi:10.1097/00001648-199901000-00008

36. Ananth CV, Schisterman EF. Confounding, causality, and confusion: the role of intermediate variables in interpreting observational studies in obstetrics. Am J Obstet Gynecol. 2017;217(2):167–175. doi:10.1016/j.ajog.2017.04.016

37. Sobolev B, Guy P, Sheehan KJ, et al. Mortality effects of timing alternatives for Hip fracture surgery. CMAJ. 2018;190(31):E923–E932. doi:10.1503/cmaj.171512

38. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–845. doi:10.2307/2531595

39. Glassford NJ, Schneider AG, Xu S, et al. The nature and discriminatory value of urinary neutrophil gelatinase-associated lipocalin in critically ill patients at risk of acute kidney injury. Inten Care Med. 2013;39(10):1714–1724. doi:10.1007/s00134-013-3040-7

40. Pencina MJ, D’Agostino RB Sr., D’Agostino RB Jr., Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med. 2008;27(2):157–212. doi:10.1002/sim.2929

41. Orhan AL, Çınar T, Hayıroğlu Mİ, et al. Atrial fibrillation as a preoperative risk factor predicts long-term mortality in elderly patients without heart failure and undergoing Hip fracture surgery. Rev Assoc Med Bras. 2021;67(11):1633–1638. doi:10.1590/1806-9282.20210686

42. Çiçek V, Cinar T, Hayiroglu MI, et al. Preoperative cardiac risk factors associated with in-hospital mortality in elderly patients without heart failure undergoing Hip fracture surgery: a single-centre study. Postgrad Med J. 2021;97(1153):701–705. doi:10.1136/postgradmedj-2020-138679

43. AlTurki A, Marafi M, Proietti R, et al. Major adverse cardiovascular events associated with postoperative atrial fibrillation after noncardiac surgery, a systematic review and meta-analysis. Circ Arrhythm Electrophysiol. 2020;13(1):e007437. doi:10.1161/CIRCEP.119.007437

44. Kanji S, Williamson DR, Yaghchi BM, Albert M, McIntyre L; Canadian Critical Care Trials G. Epidemiology and management of atrial fibrillation in medical and noncardiac surgical adult intensive care unit patients. J Crit Care. 2012;27(3):326e1–8. doi:10.1016/j.jcrc.2011.10.011

45. Lopes LA, Agrawal DK. Post-operative atrial fibrillation: current treatments and etiologies for a persistent surgical complication. J Surg Res. 2022;5(1):159–172. doi:10.26502/jsr.10020209

46. Smith HA, Kanji S, Tran DTT, et al. Prophylaxis for patients at Risk to Eliminate Post-operative Atrial Fibrillation (PREP-AF trial): a protocol for a feasibility randomized controlled study. Trials. 2021;22(1):384. doi:10.1186/s13063-021-05318-1

47. Douedi S, Douedi H. Mitral regurgitation. In: StatPearls. Treasure Island (FL): StatPearls Publishing Copyright © 2022, StatPearls Publishing LLC.; 2022.

48. Gomes NFA, Silva VR, Levine RA, et al. Progression of mitral regurgitation in rheumatic valve disease: role of left atrial remodeling. Front Cardiovasc Med. 2022;9:862382. doi:10.3389/fcvm.2022.862382

49. Qiao Y, Wu L, Hou B, et al. Functional mitral regurgitation, predictor for atrial substrate remodeling and poor ablation outcome in paroxysmal atrial fibrillation. Medicine. 2016;95(30):e4333. doi:10.1097/MD.0000000000004333

50. Abhayaratna WP, Seward JB, Appleton CP, et al. Left atrial size, physiologic determinants and clinical applications. J Am Coll Cardiol. 2006;47(12):2357–2363. doi:10.1016/j.jacc.2006.02.048

51. Thomas L, Abhayaratna WP. Left atrial reverse remodeling, mechanisms, evaluation, and clinical significance. JACC Cardiovasc Imaging. 2017;10(1):65–77. doi:10.1016/j.jcmg.2016.11.003

52. Maiello M, Sharma RK, Matteo CM, Reddy HK, Palmiero P. Differential left atrial remodeling in LV diastolic dysfunction and mitral regurgitation. Echocardiography. 2009;26(7):772–778. doi:10.1111/j.1540-8175.2008.00889.x

53. Simmers D, Potgieter D, Ryan L, Fahrner R, Rodseth RN. The use of preoperative B-type natriuretic peptide as a predictor of atrial fibrillation after thoracic surgery: systematic review and meta-analysis. J Cardiothorac Vasc Anesth. 2015;29(2):389–395. doi:10.1053/j.jvca.2014.05.015

54. Zhao B-C, Huang T-Y, Deng Q-W, et al. Prophylaxis against atrial fibrillation after general thoracic surgery, trial sequential analysis and network meta-analysis. Chest. 2017;151(1):149–159. doi:10.1016/j.chest.2016.08.1476

55. Manfrini O, Cenko E, Ricci B, Bugiardini R. Post cardiovascular surgery atrial fibrillation. biomarkers determining prognosis. Curr Med Chem. 2019;26(5):916–924. doi:10.2174/0929867324666170727104930

56. Ishibashi H, Wakejima R, Asakawa A, et al. Postoperative atrial fibrillation in lung cancer lobectomy—analysis of risk factors and prognosis. World J Surg. 2020;44(11):3952–3959. doi:10.1007/s00268-020-05694-w

57. Hayıroğlu Mİ, Çınar T, Selçuk M, et al. The significance of the morphology-voltage-P-wave duration (MVP) ECG score for prediction of in-hospital and long-term atrial fibrillation in ischemic stroke. J Electrocardiol. 2021;69:44–50. doi:10.1016/j.jelectrocard.2021.09.006

58. Çinar T, Hayiroğlu Mİ, Selçuk M, et al. Evaluation of electrocardiographic P wave parameters in predicting long-term atrial fibrillation in patients with acute ischemic stroke. Arq Neuropsiquiatr. 2022;80(09):877–884. doi:10.1055/s-0042-1755322

59. Lee CT, Strauss DM, Stone LE, Stoltzfus JC, Puc MM, Burfeind WR. Preoperative CHA2DS2-VASc score predicts postoperative atrial fibrillation after lobectomy. Thorac Cardiovasc Surg. 2019;67(02):125–130. doi:10.1055/s-0038-1675638

60. Fuster V, Ryden LE, Cannom DS, et al. ACC/AHA/ESC 2006 guidelines for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the European Society of Cardiology Committee for Practice Guidelines (writing committee to revise the 2001 guidelines for the management of patients with atrial fibrillation): developed in collaboration with the European Heart Rhythm Association and the Heart Rhythm Society. Circulation. 2006;114(7):e257–354. doi:10.1161/CIRCULATIONAHA.106.177292

61. You JJ, Singer DE, Howard PA, et al. Antithrombotic therapy for atrial fibrillation, antithrombotic therapy and prevention of thrombosis, 9th ed, American college of chest physicians evidence-based clinical practice guidelines. Chest. 2012;141(2):e531S–e575S. doi:10.1378/chest.11-2304

62. Lip GYH, Nieuwlaat R, Pisters R, Lane DA, Crijns HJGM. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach, the euro heart survey on atrial fibrillation. Chest. 2010;137(2):263–272. doi:10.1378/chest.09-1584

63. Hu W-S, Lin C-L. Comparison of CHA2DS2-VASc and C2HEST scores for predicting the incidence of atrial fibrillation among patients with end-stage renal disease. Perfusion. 2020;35(8):842–846. doi:10.1177/0267659120930931

64. Tischer TS, Schneider R, Lauschke J, Diedrich D, Kundt G, Bansch D. Prävalenz von Vorhofflimmern und der HATCH-Score. Herz. 2015;40(5):803–808. doi:10.1007/s00059-015-4305-4

65. Emren V, Aldemir M, Duygu H, et al. Usefulness of HATCH score as a predictor of atrial fibrillation after coronary artery bypass graft. Kardiol Pol. 2016;74(8):749–753. doi:10.5603/KP.a2016.0045

66. Selvi M, Gungor H, Zencir C, et al. A new predictor of atrial fibrillation after coronary artery bypass graft surgery: HATCH score. J Investig Med. 2018;66(3):648–652. doi:10.1136/jim-2017-000525

67. Kristensen SD, Knuuti J. New ESC/ESA guidelines on non-cardiac surgery: cardiovascular assessment and management. Eur Heart J. 2014;35(35):2344–2345. doi:10.1093/eurheartj/ehu285

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