Postoperative Atrial Fibrillation in Lung Cancer Lobectomy-Analysis of Risk Factors and Prognosis

Impact of preoperative inflammatory indices and postoperative pneumonia on postoperative atrial fibrillation in patients with non-small cell lung cancer: a retrospective study

BACKGROUND

This study aimed to evaluate the impact of preoperative inflammatory indices and postoperative pneumonia (POP) on postoperative atrial fibrillation (POAF) in non-small cell lung cancer (NSCLC) patients.

METHODS

All consecutive patients who underwent pulmonary resection at our hospital (January 2016-October 2019) were enrolled. Preoperative inflammatory indices, demographic data, surgical details, and postoperative conditions were analyzed. Univariate and multivariate analyses of risk factors associated with POAF were also conducted.

RESULTS

Among the 382 patients included in the study, 32 (8.38%) developed POAF. Compared to non-POAF patients, POAF patients had greater incidence of POP (P = 0.09). Approximately 31 patients (96.9%) developed atrial fibrillation within three days after surgery. The POAF group had a significantly greater mean age (68.94 years) than did the non-POAF group (63 years) (P = 0.002). Additionally, compared to non-POAF patients, POAF patients exhibited an increased number of resected mediastinal lymph nodes (P < 0.001) and mediastinal lymph node stations (P < 0.001).The POAF group also had a greater intraoperative blood volume (P = 0.006), longer surgical duration (P = 0.022), and greater drainage volume (P = 0.003). IA/B stage (P < 0.001) and IIIA/B stage(P < 0.001), and lobectomy resection (P = 0.008) and wedge resection (P = 0.023) were also associated with POAF. Compared to those in the non-POAF group, the POAF group had longer postoperative hospital stays (10.54 days vs. 9 days; P = 0.001) and longer drainage times (7 days vs. 5 days; P = 0.004). Multivariate analysis revealed age, POP, and stage IIIA/B as independent influencing factors of POAF in NSCLC patients.

CONCLUSION

Preoperative inflammatory indices were not significantly associated with POAF, but age, POP, and stage IIIA/B were identified as independent influencing factors. Advanced-stage NSCLC patients may have a greater susceptibility to POAF than early-stage patients, although further validation is needed. Additionally, POAF was linked to a longer postoperative hospital stay.

Development and validation a nomogram for predicting new-onset postoperative atrial fibrillation following pulmonary resection

BACKGROUND

The new-onset postoperative atrial fibrillation (NOPAF) following pulmonary resection is a common clinical concern. The aim of this study was to construct a nomogram to intuitively predict the risk of NOPAF and offered protective treatments.

METHODS

Patients who underwent pulmonary resection between January 2018 and December 2020 were consecutively enrolled. Forward stepwise multivariable logistic regression analyses were used to screen independent predictors, and a derived nomogram model was built. The model performance was evaluated in terms of calibration, discrimination and clinical utility and validated with bootstrap resampling.

RESULTS

A total of 3583 patients who met the research criteria were recruited for this study. The incidence of NOPAF was 1.507% (54/3583). A nomogram, composed of five independent predictors, namely age, admission heart rate, extent of resection, laterality, percent maximum ventilation volume per minute (%MVV), was constructed. The concordance index (C-index) was 0.811. The nomogram showed substantial discriminative ability, with an area under the receiver operating characteristic curve of 0.811 (95% CI 0.758-0.864). Moreover, the model shows prominent calibration performance and higher net clinical benefits.

CONCLUSION

We developed a novel nomogram that can predict the risk of NOPAF following pulmonary resection, which may assist clinicians predict the individual probability of NOPAF and perform available prophylaxis. By using bootstrap resampling for validation, the optimal discrimination and calibration were demonstrated, indicating that the nomogram may have clinical practicality.

Machine Learning for Predicting Postoperative Atrial Fibrillation After Cardiac Surgery: A Scoping Review of Current Literature

Postoperative atrial fibrillation (POAF) occurs in up to 20% to 55% of patients who underwent cardiac surgery. Machine learning (ML) has been increasingly employed in monitoring, screening, and identifying different cardiovascular clinical conditions. It was proposed that ML may be a useful tool for predicting POAF after cardiac surgery. An electronic database search was conducted on Medline, EMBASE, Cochrane, Google Scholar, and ClinicalTrials.gov to identify primary studies that investigated the role of ML in predicting POAF after cardiac surgery. A total of 5,955 citations were subjected to title and abstract screening, and ultimately 5 studies were included. The reported incidence of POAF ranged from 21.5% to 37.1%. The studied ML models included: deep learning, decision trees, logistic regression, support vector machines, gradient boosting decision tree, gradient-boosted machine, K-nearest neighbors, neural network, and random forest models. The sensitivity of the reported ML models ranged from 0.22 to 0.91, the specificity from 0.64 to 0.84, and the area under the receiver operating characteristic curve from 0.67 to 0.94. Age, gender, left atrial diameter, glomerular filtration rate, and duration of mechanical ventilation were significant clinical risk factors for POAF. Limited evidence suggest that machine learning models may play a role in predicting atrial fibrillation after cardiac surgery because of their ability to detect different patterns of correlations and the incorporation of several demographic and clinical variables. However, the heterogeneity of the included studies and the lack of external validation are the most important limitations against the routine incorporation of these models in routine practice. Artificial intelligence, cardiac surgery, decision tree, deep learning, gradient-boosted machine, gradient boosting decision tree, k-nearest neighbors, logistic regression, machine learning, neural network, postoperative atrial fibrillation, postoperative complications, random forest, risk scores, scoping review, support vector machine.

Exploring postoperative atrial fibrillation after non-cardiac surgery: mechanisms, risk factors, and prevention strategies

Atrial fibrillation (AF) stands as the most prevalent persistent arrhythmia and a common complication after surgical procedures. Although the majority of non-cardiac surgery patients experience postoperative AF (POAF) and the condition is typically self-limited and asymptomatic, its detrimental impact on patient outcomes, prolonged hospitalization, and heightened risk of stroke and overall mortality has become increasingly evident. Of significant concern, POAF emerges as a noteworthy risk factor for stroke, myocardial infarction, and mortality in comparison to patients with non-surgical atrial fibrillation. Multiple studies have corroborated the association between POAF and an elevated risk of stroke and mortality. The development of postoperative atrial fibrillation is multifactorial, with the inflammatory response being a primary contributor; additionally, factors such as hypovolemia, intraoperative hypotension, anemia, trauma, and pain can trigger POAF. Risk factors for POAF in non-cardiac surgery primarily relate to age, hypertension, obesity, prior cardiac disease, obstructive sleep apnea, and male sex. Prophylactic treatment with β-blockers, amiodarone, or magnesium has demonstrated efficacy, but further trials are warranted, especially in high-risk populations. This review provides an account of the incidence rate, pathophysiology, and prognosis of atrial fibrillation after non-cardiac surgery, elucidates the underlying mechanisms of its occurrence, and explores various preventive strategies investigated in this domain.

Postoperative cerebral infarction and arrhythmia after pulmonary lobectomy in Japan: a retrospective analysis of 77,060 cases in a national clinical database

PURPOSE

To investigate the incidence of postoperative cerebral infarction after curative lobectomy, its association with the type of lobectomy, and how postoperative new-onset arrhythmia contributes to postoperative cerebral infarction.

METHODS

The subjects of this analysis were 77,060 patients who underwent curative lobectomy for lung cancer between 2016 and 2018 according to the National Clinical Database. Incidences of postoperative cerebral infarction and postoperative new-onset arrhythmia were analyzed. Moreover, mediation analysis was performed to evaluate the causal pathway between postoperative new-onset arrhythmia and postoperative cerebral infarction.

RESULTS

Postoperative cerebral infarction occurred in 110 (0.7%) patients after left upper lobectomy and in 85 (0.7%) patients after left lower lobectomy. Left upper lobectomy and left lower lobectomy were associated with a higher likelihood of postoperative cerebral infarction than right lower lobectomy. Left upper lobectomy was the strongest independent predictor of postoperative new-onset arrhythmia. However, in the mediation analysis, the odds ratio for cerebral infarction did not change after the addition of the factor of postoperative new-onset arrhythmia.

CONCLUSION

Cerebral infarction occurred significantly more often not only after left upper lobectomy, but also after left lower lobectomy. Postoperative new-onset arrhythmia was less likely to be related to cerebral infarction after left upper lobectomy.

Relationship between postoperative atrial fibrillation and its recurrence after lung resection

PURPOSE

Atrial fibrillation (AF) frequently occurs after pulmonary resection and is commonly referred to as postoperative atrial fibrillation (POAF). This study explored whether or not POAF is related to the recurrence of AF in the chronic phase.

METHODS

A total of 1311 consecutive patients without a history of AF who underwent lung resection based on a diagnosis of lung tumor were retrospectively analyzed.

RESULTS

POAF occurred in 46 patients (3.5%), and a logistic regression analysis revealed that the age (p < 0.05), history of hyperthyroidism (p < 0.05), and major lung resection (p < 0.05) were independent predictors of POAF. AF events in the chronic phase were observed in 15 (32.6%) and 45 (3.6%) patients with and without POAF, respectively. A Cox regression analysis revealed that POAF was the only independent predictor of AF development in the chronic phase (p < 0.01). The Kaplan-Meier curve and log-rank test revealed that the cumulative incidence of AF in the chronic phase was significantly higher in patients with POAF than in those without POAF (p < 0.01).

CONCLUSION

POAF was an independent predictor for AF in the chronic phase after lung resection. Further investigations including cases of catheter ablation and optimal medical therapy for patients with POAF after lung resection are needed.

Interpretable prediction of cardiopulmonary complications after non-small cell lung cancer surgery based on machine learning and SHapley additive exPlanations

Introduction

Lung cancer is a prevalent malignancy globally, with approximately 20% of patients developing cardiopulmonary complications after lobectomy. In order to prevent complications, an accurate and personalized method based on machine learning (ML) is required.

Methods

During the period of 2017-2021, a retrospective analysis was conducted on the medical records of patients who had undergone lobectomy for non-small cell lung cancer (NSCLC). We performed logical regression, decision tree (DT), random forest (RF), gradient boost DT, and eXtreme gradient boosting analyses to establish an ML model. The ten-fold cross-validation was used to evaluate the performance of multiple ML models based on various evaluation metrics, including accuracy, precision, recall, F1 score, and area under the receiver operating (AUC). Additionally, we also calculated the Kappa value of these model. Each model used grid search to optimize hyper-parameters and then used the interpretability method to provide explanations for the model's Decisions.

Results

The study included 718 eligible patients, among whom the incidence of postoperative cardiopulmonary complications was 20.89%. The RF model showed the best comprehensive performance among all models, and its ten-fold cross-validation accuracy, precision, recall, F1 score, and AUC were (OR and 95% confidence interval [CI]) 0.786 (0.738-0.834), 0.803 (0.735-0.872), 0.738 (0.678-0.797), 0.766 (0.714-0.818), 0.856 (0.815-0.898), respectively. The kappa value of the RF model was 0.696 (0.617-0.768). The SHAP method showed that gender, age, and intraoperative blood loss were closely associated with postoperative cardiopulmonary complications.

Conclusion

The application of ML methods for predicting postoperative cardiopulmonary complications based on clinical data of patients with NSCLC showed a good performance. The results indicate that ML combined with the SHAP individualized interpretation method has practical clinical value.

Risk Factors and In-Hospital Outcomes of Perioperative Atrial Fibrillation for Patients with Cancer: A Meta-Analysis

BACKGROUND

Perioperative atrial fibrillation is a common postoperative complication. Adverse consequences associated with POAF include hemodynamic instability, increased risk of stroke, extended hospital stays, and increased mortality.

METHODS

To determine the risk factors for POAF and to investigate the outcomes of POAF for patients with cancer, a systematic search of the PubMed and Cochrane Library databases was conducted from inception of the study to 1 September 2021. The inclusion criteria specified studies reporting the prevalence of POAF among patients with cancer. The study excluded articles not written in English, review articles, case reports, letters, commentaries, systematic reviews, meta-analyses, and conference abstracts.

RESULTS

The search identified 49 studies with 201,081 patients, and the pooled prevalence of POAF was 13.5% (95% confidence interval [CI], 11.6-15.7%). Meta-analyses showed that the incidence of POAF among patients with cancer was associated with age (mean difference [MD], 4.31; 95%CI, 3.16-5.47), male sex (odds ratio [OR], 1.39; 95% CI, 1.19-1.62), chronic obstructive pulmonary disease (OR, 2.47; 95% CI, 1.71-3.56), hypertension (OR, 1.47; 95% CI, 1.23-1.75), intraoperative blood transfusion (OR, 4.58; 95% CI, 2.31-9.10), and open surgery (OR, 1.51; 95% CI, 1.26-1.81). Patients with POAF had significantly higher in-hospital mortality (OR, 4.25; 95% CI, 2.79-6.45), longer hospital stays (MD, 3.07; 95% CI, 1.63-4.51), and higher incidences of pneumonia (OR, 3.32; 95% CI, 2.85-3.86), stroke (OR, 6.57; 95% CI, 1.56-26.00), and myocardial infarction (OR, 3.00; 95% CI, 1.45-6.20) than those without POAF.

CONCLUSIONS

For patients with cancer, POAF is associated with an increased burden of comorbidities and worse outcomes.

Perioperative cardiovascular pathophysiology in patients undergoing lung resection surgery: a narrative review

Although thoracic surgery is understood to confer a high risk of postoperative respiratory complications, the substantial haemodynamic challenges posed are less well appreciated. This review highlights the influence of cardiovascular comorbidity on outcome, reviews the complex pathophysiological changes inherent in one-lung ventilation and lung resection, and examines their influence on cardiovascular complications and postoperative functional limitation. There is now good evidence for the presence of right ventricular dysfunction postoperatively, a finding that persists to at least 3 months. This dysfunction results from increased right ventricular afterload occurring both intraoperatively and persisting postoperatively. Although many patients adapt well, those with reduced right ventricular contractile reserve and reduced pulmonary vascular flow reserve might struggle. Postoperative right ventricular dysfunction has been implicated in the aetiology of postoperative atrial fibrillation and perioperative myocardial injury, both common cardiovascular complications which are increasingly being appreciated to have impact long into the postoperative period. In response to the physiological demands of critical illness or exercise, contractile reserve, flow reserve, or both can be overwhelmed resulting in acute decompensation or impaired long-term functional capacity. Aiding adaptation to the unique perioperative physiology seen in patients undergoing thoracic surgery could provide a novel therapeutic avenue to prevent cardiovascular complications and improve long-term functional capacity after surgery.

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

Purpose

Atrial fibrillation (AF) is common in critically ill patients and can have serious consequences. Postoperative AF (POAF) in critically ill patients following noncardiac surgery has been understudied, contrary to cardiac procedures. Mitral regurgitation (MR) is associated with left ventricular dysfunction, which might contribute to the occurrence of AF in postoperative critically ill patients. This study aimed to investigate the association between MR and POAF in critically ill noncardiac surgery patients and establish a new nomogram for the prediction of POAF in critically ill noncardiac surgery patients.

Patients and Methods

A prospective cohort of 2474 patients who underwent thoracic and general surgery was enrolled in this study. Data on preoperative transthoracic echocardiography (TTE), electrocardiogram (ECG), and several commonly utilized scoring systems (CHA2DS2-VASc, HATCH, COM-AF, HART, and C2HEST) and baseline clinical data were collected. Independent predictors were selected by univariate and multivariable logistic regression analysis, and a nomogram was constructed for POAF within 7 days after postoperative intensive care unit (ICU) admission. The ability of the MR-nomogram and other scoring systems to predict POAF was compared by receiver operator characteristic (ROC) curve analysis and decision curve analysis (DCA). Additional contributions were evaluated by integrated discrimination improvement (IDI) and net reclassification improvement (NRI) analysis.

Results

A total of 213 (8.6%) patients developed POAF within 7 days after ICU admission. Compared to CHA2DS2-VASc, HATCH, COM-AF, HART, and C2HEST scoring systems, MR-nomogram showed better predictive ability for POAF with an area under the ROC curve of 0.824 (95% confidence interval: 0.805-0.842, p < 0.001). The improvement of the MR-nomogram in predictive value was supported by NRI and IDI analysis. The net benefit of the MR nomogram was maximal in DCA.

Conclusion

MR is an independent risk factor of POAF in critically ill noncardiac surgery patients. The nomogram predicted POAF better than other scoring systems.

Evaluation of gastroesophageal reflux disease and hiatal hernia as risk factors for lobectomy complications

Objective

Up to 40% of lobectomies are complicated by adverse events. Gastroesophageal reflux disease (GERD) and hiatal hernia have been associated with morbidity across a range of clinical scenarios, yet their relation to recovery from pulmonary resection is understudied. We evaluated GERD and hiatal hernia as predictors of complications after lobectomy for lung cancer.

Methods

Lobectomy patients at Yale-New Haven Hospital between January 2014 and April 2021 were evaluated for predictors of 30-day postoperative complications, pneumonia, atrial arrhythmia, readmission, and mortality. Multivariable regression models included sociodemographic characteristics, body mass index, surgical approach, cardiopulmonary comorbidities, hiatal hernia, GERD, and preoperative acid-suppressive therapy as predictors.

Results

Overall, 824 patients underwent lobectomy, including 50.5% with a hiatal hernia and 38.7% with GERD. The median age was 68 [interquartile range, 61-74] years, and the majority were female (58.4%). At least 1 postoperative complication developed in 39.6% of patients, including atrial arrhythmia (11.7%) and pneumonia (4.1%). Male sex (odds ratio [OR], 1.51; 95% confidence interval [CI], 1.11-2.06, P = .01), age ≥70 years (OR, 1.55; 95% CI, 1.13-2.11, P = .01), hiatal hernia (OR, 1.40; 95% CI, 1.03-1.90, P = .03), and intraoperative packed red blood cells (OR, 4.80; 95% CI, 1.51-15.20, P = .01) were significant risk factors for developing at least 1 postoperative complication. Hiatal hernia was also a significant predictor of atrial arrhythmia (OR, 1.64; 95% CI, 1.02-2.62, P = .04) but was not associated with other adverse events.

Conclusions

Our findings indicate that hiatal hernia may be a novel risk factor for complications, especially atrial arrhythmia, following lobectomy that should be considered in the preoperative evaluation of lung cancer patients.

Higher Mortality Associated With New-Onset Atrial Fibrillation in Cancer Patients: A Systematic Review and Meta-Analysis

Aim

This research was conducted to evaluate the mortality outcome of cancer patients with new-onset atrial fibrillation. We also aimed to assess if there was any confounding relation between the mortality of these patients and surgical intervention.

Materials and Methods

A systemic search was conducted from electronic databases (PubMed/Medline, Cochrane Library, and Google Scholar) from inception to 7 February 2022. All statistical analyses were conducted in Review Manager 5.4.1. Studies meeting inclusion criteria were selected. Only those studies that involved cancer patients without pre-existing atrial fibrillation were selected, and mortality rate was compared between the patients who developed atrial fibrillation and those who did not. A random-effect model was used when heterogeneity was seen to pool the studies, and the result was reported in the odds ratio (OR) and the corresponding 95% confidence interval (CI).

Results

Eighteen studies were selected for meta-analysis. Statistical analysis showed that the cancer patients who subsequently developed atrial fibrillation had a significantly higher mortality rate as compared to those who did not (OR = 1.90 [1.65, 2.19]; p &lt; 0.00001; I2 = 100%). We also separately analyzed the mortality risk in the surgery group and the non-surgery group. Statistical analysis showed that there was significantly higher mortality rate associated with new-onset atrial fibrillation in cancer patients in the surgery group (OR= 3.68 [2.29, 5.94]; p &lt; 0.00001; I2 = 61%) as well as in the non-surgery group (OR = 1.64 [1.39, 1.93]; p &lt; 0.00001; I2 = 100%).

Conclusion

Cancer patients, who subsequently developed atrial fibrillation, had a higher mortality rate as compared to those cancer patients who did not develop atrial fibrillation. A higher mortality rate was seen in both surgical and non-surgical subgroups. This implies that extra care and specific measures must be taken in the management of cancer patients with new-onset atrial fibrillation.

Atrial Fibrillation after Lung Cancer Surgery: Prediction, Prevention and Anticoagulation Management

Simple Summary

Atrial fibrillation that occurs after surgery raises further questions with respect to spontaneous atrial fibrillation, being an event unquestionably related to the surgical act itself and always quite self-limiting. The purpose of this review is to present the knowledge gained so far, including the most recent findings, regarding this peculiar form of arrhythmia. Its prognostic impact and the possibility of predicting and preventing it were the subject of our analysis, as well as the similarities and differences with spontaneous atrial fibrillation in relation to anticoagulation. Where possible, the search for evidence has focused on studies involving lung cancer patients undergoing thoracic surgery, highlighting any differences with cardiac surgery.

Abstract

Atrial fibrillation (AF) is a common complication of the early postoperative period of various types of surgery, including that for lung cancer. Although induced by the homeostatic alterations related to surgery, there is evidence that it is not a mere stand-alone transitory event, but it represents a relevant complication of surgery, bearing considerable prognostic consequences. Different methods have therefore been explored to predict the occurrence of postoperative atrial fibrillation (POAF) and prevent it. In particular, the age among clinical factors, and N-terminal prohormone of brain natriuretic peptide (NT-proBNP), as a marker, have proven to be good predictors, and the use of beta-blockers or amiodarone in primary prevention seems to reduce its incidence significantly. There is growing evidence that POAF significantly increases the risk of stroke and global mortality in the long term; therefore, it should be managed in the same way as spontaneous atrial fibrillation. In this review, we will present the strongest evidence found so far and the most recent findings regarding the management of POAF, with a special focus on patients undergoing thoracic surgery for lung cancer.

Postoperative atrial fibrillation in pneumonectomy for primary lung cancer

Background

This study assessed the incidence and risk factors (RFs) of postoperative atrial fibrillation (POAF) and its impact on clinical outcomes in patients undergoing pneumonectomy for lung cancer.

Methods

Between 2013 and 2018, this monocentric retrospective study enrolled 324 consecutive pneumonectomy patients for primary lung cancer from our institution and 350 lobectomy and 349 segmentectomy cases matched by age, sex and body mass index (BMI). RF for POAF and postoperative death in pneumonectomy patients were assessed by logistic regression, and long-term outcomes after a median follow-up of 30 (range, 2–61) months by Cox proportional hazard model. Electrophysiology study (EPS) files of 30 AF patients with lung resection history were reviewed.

Results

POAF developed more often after pneumonectomy than lobectomy and segmentectomy (23.2% vs. 6.6% vs. 1.4%, respectively; P<0.001). Among 75 pneumonectomy patients with POAF, POAF was solitary in 55 patients (73.3%) and concurrent with other complications in 3 patients (4%). POAF risk after pneumonectomy was 4 and 22 times that after lobectomy and segmentectomy, respectively, with age >60 years and left atrial diameter (LAd) ≥35 mm as independent predictors. POAF, infection and hemorrhage were independent RFs for perioperative death after pneumonectomy; however, POAF was not RF for long-term death. Pulmonary vein (PV) trigger was identified in 60% (18/30) of AF patients with lung resection history, with stump PVs being more active than non-stump PVs (38.2% vs. 10.5%, P<0.001).

Conclusions

Post-pneumonectomy AF, with remarkable incidence, risk and independent predictors including age >60 years and LAd ≥35 mm, was mostly solitary and possibly secondary to stump and non-stump PV triggers. POAF, along with infection and hemorrhage, was a RF for perioperative death.