Prospective External Validation of Three Preoperative Risk Scores for Prediction of New Onset Atrial Fibrillation After Cardiac Surgery

Prevention of Atrial Fibrillation After Cardiac Surgery: A Review of Literature and Comparison of Different Treatment Modalities

Atrial fibrillation is the most common arrhythmia to occur after cardiac surgery, with an incidence of 10% to 50%. It is associated with postoperative complications including increased risk of stroke, prolonged hospital stays and increased costs. Despite new insights into the mechanisms of atrial fibrillation, no specific etiologic factor has been identified as the sole perpetrator of the arrhythmia. Current evidence suggests that the pathophysiology of atrial fibrillation in general, as well as after cardiac surgery, is multifactorial. Studies have also shown that new-onset postoperative atrial fibrillation following cardiac surgery is associated with a higher risk of short-term and long-term mortality. Furthermore, it has been demonstrated that prophylactic medical therapy decreases the incidence of postoperative atrial fibrillation after cardiac surgery. Of note, the incidence of postoperative atrial fibrillation has not changed during the last decades despite the numerous preventive strategies and operative techniques proposed, although the perioperative and postoperative care of cardiac patients as such has improved.

Prospective Study of Risk Factors for Postoperative Atrial Fibrillation After Cardiac Surgery

INTRODUCTION

To examine risk factors for new-onset postoperative atrial fibrillation (POAF) after cardiac surgery.

METHODS

Patients enrolled in the Cardiothoracic Surgical Trials Network multicenter, randomized trial of rate control versus rhythm control for POAF were included. Predictors of POAF were determined using multivariable logistic regression.

RESULTS

Among the 2104 patients who were enrolled preoperatively, 695 developed POAF (33.0%). Rates of POAF were 28.1% after isolated coronary artery bypass grafting (CABG), 33.7% after isolated valve repair or replacement, and 47.3% after CABG plus valve repair or replacement. Baseline characteristics associated with an increased risk of POAF identified on multivariable analysis included older age (odds ratio [OR] 1.57; 95% confidence interval [CI] 1.42-1.73, per 10 y), White race or non-Hispanic ethnicity (OR 1.52; CI: 1.11-2.07), history of heart failure (OR 1.55; CI: 1.16-2.08), and history of hypothyroidism (OR 1.42; CI 1.04-1.94). The type of cardiac procedure was associated with an increased risk of POAF with both isolated valve repair or replacement (OR 1.33, CI 1.08-1.64) and combined CABG plus valve repair or replacement (OR 1.64, CI 1.24-2.17) having increased risk of POAF compared to isolated CABG. No preoperative cardiac medication was associated with POAF.

CONCLUSIONS

In this prospective cohort of patients, older age, a history of hypothyroidism, a history of heart failure, and valve repair or replacement, with or without CABG, and White non-Hispanic race were associated with an increased risk of POAF.

Risk Scores for Prediction of Postoperative Atrial Fibrillation After Cardiac Surgery: A Systematic Review and Meta-Analysis

Postoperative atrial fibrillation (POAF) is a common complication after cardiac surgery and is associated with poor clinical outcomes. The objective of this systematic review and meta-analysis was to assess the performance of risk scores to predict POAF in cardiac surgery patients. We searched MEDLINE, Embase, and Cochrane CENTRAL for studies that developed/evaluated a POAF risk prediction model. Pairs of reviewers independently screened studies and extracted data. We pooled area under the receiver operating curves (AUCs), sensitivity and specificity, and adjusted odds ratios from multivariable regression analyses using the generic inverse variance method and random effects models. Forty-three studies (n = 63,847) were included in the quantitative synthesis. Most scores were originally developed for other purposes but evaluated for predicting POAF. Pooled AUC revealed moderate POAF discrimination for the EuroSCORE II (AUC 0.59, 95% confidence interval [CI] 0.54 to 0.65), Society of Thoracic Surgeons (AUC 0.60, 95% CI 0.56 to 0.63), EuroSCORE (AUC 0.63, 95% CI 0.58 to 0.68), CHADS2 (AUC 0.66, 95% CI 0.57 to 0.75), POAF Score (AUC 0.66, 95% CI 0.63 to 0.68), HATCH (AUC 0.67, 95% CI 0.57 to 0.75), CHA2DS2-VASc (AUC 0.68, 95% CI 0.60 to 0.75) and SYNTAX scores (AUC 0.74, 95% CI 0.71 to 0.78). Pooled analyses at specific cutoffs of the CHA2DS2-VASc, CHADS2, HATCH, and POAF scores demonstrated moderate-to-high sensitivity (range 46% to 87%) and low-to-moderate specificity (range 31% to 70%) for POAF prediction. In conclusion, existing clinical risk scores offer at best moderate prediction for POAF after cardiac surgery. Better models are needed to guide POAF risk stratification in cardiac surgery patients.

Prediction of new onset postoperative atrial fibrillation using a simple Nomogram

BACKGROUND

New onset postoperative atrial fibrillation (POAF) is the most common complication of cardiac surgery, with an incidence ranging from 15 to 50%. This study aimed to develop a new nomogram to predict POAF using preoperative and intraoperative risk factors.

METHODS

We retrospectively analyzed the data of 2108 consecutive adult patients (> 18 years old) who underwent cardiac surgery at our medical institution. The types of surgery included isolated coronary artery bypass grafting, valve surgery, combined valve and coronary artery bypass grafting (CABG), or aortic surgery. Logistic regression or machine learning methods were applied to predict POAF incidence from a subset of 123 parameters. We also developed a simple nomogram based on the strength of the results and compared its predictive ability with that of the CHA2DS2-VASc and POAF scores currently used in clinical practice.

RESULTS

POAF was observed in 414 hospitalized patients. Logistic regression provided the highest area under the receiver operating characteristic curve (ROC) in the validation cohort. A simple bedside tool comprising three variables (age, left atrial diameter, and surgery type) was established, which had a discriminative ability with a ROC of 0.726 (95% CI 0.693-0.759) and 0.727 (95% CI 0.676-0.778) in derivation and validation subsets respectively. The calibration curve of the new model was relatively well-fit (p = 0.502).

CONCLUSIONS

Logistic regression performed better than machine learning in predicting POAF. We developed a nomogram that may assist clinicians in identifying individuals who are prone to POAF.

Prevalence of Postoperative Atrial Fibrillation and Impact to Nursing Practice—A Cross Sectional Study

Background: Atrial fibrillation is the most common clinically significant cardiac arrhythmia, and it might lead to heart failure, which prolongs the duration of hospitalization and consequently increases the cost of treatment. Thus, diagnosing and treating atrial fibrillation should be the first line of defense against further complications. This study aimed to determine the incidence rate of postoperative atrial fibrillation and correlation with cardiac surgery on heart valves. A specific aim was to determine the relationship between the prevalence of atrial fibrillation and socio-demographic features. Methods: The study has a prospective cross-sectional design. The questionnaire was anonymous, requesting socio-demographic information as inclusion criteria, and the data were analyzed using descriptive statistics methods. Results: The sample was 201 patients. χ2 test and t-test were performed where we found that the frequency of atrial fibrillation was higher in the groups that have had valve surgery compared to other cardiac surgeries (χ2 = 7.695, ss = 2, p = 0.021). Atrial fibrillation increased with the age of the patients, but the prevalence of atrial fibrillation was not correlated with body weight. Conclusion: The results of this this study show that atrial fibrillation was higher in the participants who had valve surgery compared to other cardiac surgeries. There was also an increase in atrial fibrillation in the older participants. The results of this study can help to improve nursing practice and the quality of care for cardiac surgery patients with regard to daily activities, or planning nursing care due to the patient’s condition.

Myeloperoxidase in the pericardial fluid improves the performance of prediction rules for postoperative atrial fibrillation

OBJECTIVES

After surgery, inflammation is a prominent factor influencing postoperative atrial fibrillation. Myeloperoxidase is a major contributor to inflammatory responses after surgical tissue damage. We evaluated whether myeloperoxidase is associated with postoperative atrial fibrillation clinically and in an animal model.

METHODS

This prospective cohort study included patients undergoing isolated coronary artery bypass grafting. Myeloperoxidase concentrations in blood and pericardial fluid were determined at baseline and 6, 12, and 18 hours after coronary artery bypass grafting. Myeloperoxidase activity in blood, pericardial fluid, and atrium were also evaluated in a canine coronary artery bypass grafting model. Electrophysiologic, histologic, and immunohistochemistry analyses were performed to explore underlying mechanisms.

RESULTS

Postoperative atrial fibrillation occurred in 45 of 137 patients (32.8%). Patients with postoperative atrial fibrillation had significantly higher serum and pericardial myeloperoxidase levels. Individual clinical and surgical factors had moderate predictive value (area under the curve, 0.760) for postoperative atrial fibrillation. Discrimination improved remarkably when myeloperoxidase was combined with other parameters (area under the curve, 0.901). Pericardial myeloperoxidase at 6 hours postoperatively was the strongest independent predictor of postoperative atrial fibrillation (odds ratio, 19.215). The rate of postoperative atrial fibrillation increased exponentially across pericardial myeloperoxidase grades. Compared with controls, coronary artery bypass grafting-treated dogs showed higher atrial fibrillation vulnerability and maintenance, shorter atrial effective refractory period, attenuated connexin 43 expression, and increased myocardial and pericardial myeloperoxidase activity. Connexin 43 expression and atrial effective refractory period were strongly negatively correlated with myocardial and pericardial myeloperoxidase activity.

CONCLUSIONS

Myeloperoxidase is linked to postoperative atrial fibrillation, and the ability to predict postoperative atrial fibrillation was remarkably improved by adding pericardial myeloperoxidase. Myeloperoxidase-related atrial structural and electrical remodeling is a physiologic substrate for this arrhythmia.

Predicting atrial fibrillation after cardiac surgery: a scoping review of associated factors and systematic review of existing prediction models

INTRODUCTION

Postoperative atrial fibrillation (POAF) is common after cardiac surgery and associated with increased hospital length of stay, patient morbidity and mortality. We aimed to identify factors associated with POAF and evaluate the accuracy of available POAF prediction models.

METHODS

We screened articles from Ovid MEDLINE® and PubMed Central® (PMC) and included studies that evaluated risk factors associated with POAF or studies that designed or validated POAF prediction models. We only included studies in cardiac surgical patients with sample size n ⩾ 50 and a POAF outcome group ⩾20. We summarised factors that were associated with POAF and assessed prediction model performance by reviewing reported calibration and discriminative ability.

RESULTS

We reviewed 232 studies. Of these, 142 fulfilled the inclusion criteria. Age was frequently found to be associated with POAF, while most other variables showed contradictory findings, or were assessed in few studies. Overall, 15 studies specifically developed and/or validated 12 prediction models. Of these, all showed poor discrimination or absent calibration in predicting POAF in externally validated cohorts.

CONCLUSIONS

Except for age, reporting of factors associated with POAF is inconsistent and often contradictory. Prediction models have low discrimination, missing calibration statistics, are at risk of bias and show limited clinical applicability. This suggests the need for studies that prospectively collect AF relevant data in large cohorts and then proceed to validate findings in external data sets.

Prognostic model for atrial fibrillation after cardiac surgery: a UK cohort study

OBJECTIVE

To develop a validated clinical prognostic model to determine the risk of atrial fibrillation after cardiac surgery as part of the PARADISE project (NIHR131227).

METHODS

Prospective cohort study with linked electronic health records from a cohort of 5.6 million people in the United Kingdom Clinical Practice Research Datalink from 1998 to 2016. For model development, we considered a priori candidate predictors including demographics, medical history, medications, and clinical biomarkers. We evaluated associations between covariates and the AF incidence at the end of follow-up using logistic regression with the least absolute shrinkage and selection operator. The model was validated internally with the bootstrap method; subsequent performance was examined by discrimination quantified with the c-statistic and calibration assessed by calibration plots. The study follows TRIPOD guidelines.

RESULTS

Between 1998 and 2016, 33,464 patients received cardiac surgery among the 5,601,803 eligible individuals. The final model included 13-predictors at baseline: age, year of index surgery, elevated CHA₂DS₂-VASc score, congestive heart failure, hypertension, acute coronary syndromes, mitral valve disease, ventricular tachycardia, valve surgery, receiving two combined procedures (e.g., valve replacement + coronary artery bypass grafting), or three combined procedures in the index procedure, statin use, and ethnicity other than white or black (statins and ethnicity were protective). This model had an optimism-corrected C-statistic of 0.68 both for the derivation and validation cohort. Calibration was good.

CONCLUSIONS

We developed a model to identify a group of individuals at high risk of AF and adverse outcomes who could benefit from long-term arrhythmia monitoring, risk factor management, rhythm control and/or thromboprophylaxis.

Nomogram predicts atrial fibrillation after coronary artery bypass grafting

Objective

Using the nomogram to intuitively predict atrial fibrillation after coronary artery bypass grafting. Identify high-risk patients with atrial fibrillation and provide preoperative protective therapy.

Methods

A total of 397 patients that underwent coronary artery bypass grafting were consecutively enrolled. Independent predictors of patients were analyzed by multivariate logistic regression. Two nomograms were constructed to predict postoperative atrial fibrillation.

Results

The incidence of postoperative atrial fibrillation in this study was 29% (115/397). Multivariate Logistic showed that Age, Operative Time > 4 h, Left Atrial Diameter > 40 mm, Mean Arterial Pressure, Body Mass Index > 23 kg/m², Insulins, and Statins were independently associated with atrial fibrillation after isolated coronary artery bypass grafting. The nomogram of postoperative atrial fibrillation in patients was constructed using total predictor variables (AUC = 0.727, 95% CI 0.673–0.781). The model was internally validated (AUC = 0.701) by K-fold Cross-validation resampling (K = 5, Times = 400). To make an early intervention, the intraoperative information of the patients was excluded. Only 6 variables before surgery were used to establish the brief nomogram to predict postoperative atrial fibrillation (AUC = 0.707, 95% CI 0.651–0.764). The brief model was internally validated (AUC = 0.683) by resampling with K-fold Cross-validation resampling.

Conclusions

These two nomograms could be used to predict patients at high risk for atrial fibrillation after isolated coronary artery bypass grafting.

Nurse Staffing Practices and Postoperative Atrial Fibrillation Among Cardiac Surgery Patients: A Multisite Cohort Study

Background

Postoperative atrial fibrillation (POAF) is a frequent complication of cardiac surgery that is associated with increased morbidity, mortality, and costs. Recent studies suggest that nurse staffing practices are associated with adverse postoperative events, but whether these practices are also related to POAF occurrence is unknown.

Methods

To fill this knowledge gap, a cohort of 6401 cardiac surgery patients admitted to 2 Canadian university health centres (UHC A and UHC B) between 2014 and 2018 was studied. Patients’ cumulative exposure to 4 staffing practices (registered nurse [RN] understaffing, education, experience, and non-RN skill mix) was measured every shift over the first 6 postoperative days, during which 96% of POAF cases occur. The associations of these exposures with in-hospital POAF occurrence were estimated using site-specific multivariable logistic regression models and a mixed-effect model combining data from both sites.

Results

Overall, 563 (27.2%) and 1336 (30.8%) cases of POAF occurred at UHC A and UHC B, respectively. In site-specific models, every 5% increase in the cumulative proportion of understaffed shifts over the first 6 postoperative days was associated with a 3.5% increase in the odds of POAF (adjusted odds ratio [aOR] for UHC A: 1.035; 95% confidence interval [CI]: 1.000-1.070, P = 0.0472; aOR for UHC B: 1.035; 95% CI: 1.013-1.057, P = 0.0019). In the mixed-effect model combining data from both sites, RN understaffing remained significant and was associated with a 3.1% increase in the odds of POAF (aOR: 1.031; 95% CI: 1.014-1.048, P = 0.0003). No other staffing practices were significantly associated with POAF occurrence.

Conclusion

Higher RN understaffing postoperatively is associated with increased POAF occurrence among cardiac surgery patients.

Perioperative risk factors for new-onset postoperative atrial fibrillation after coronary artery bypass grafting: a systematic review

Background

Postoperative atrial fibrillation (POAF) is the most common cardiac dysrhythmia to occur after coronary artery bypass grafting (CABG). However, the risk factors for new-onset POAF after CABG during the perioperative period have yet to be clearly defined. Accordingly, the aim of our systematic review was to evaluate the perioperative predictors of new-onset POAF after isolated CABG.

Method

Our review methods adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guideline. We searched seven electronic databases (PubMed, Embase, CINAHL, PsycArticles, Cochrane, Web of Science, and SCOPUS) to identify all relevant English articles published up to January 2020. Identified studies were screened independently by two researchers for selection, according to predefined criteria. The Newcastle–Ottawa Scale was used to evaluate the quality of studies retained.

Results

After screening, nine studies were retained for analysis, including 4798 patients, of whom 1555 (32.4%) experienced new-onset POAF after CABG. The incidence rate of new-onset POAF ranged between 17.3% and 47.4%. The following risk factors were identified: old age (p < 0.001), a high preoperative serum creatinine level (p = 0.001), a low preoperative hemoglobin level (p = 0.007), a low left ventricle ejection fraction in Asian patients (p = 0.001), essential hypertension (p < 0.001), chronic obstructive pulmonary disease (p = 0.010), renal failure (p = 0.009), cardiopulmonary bypass use (p = 0.002), perfusion time (p = 0.017), postoperative use of inotropes (p < 0.001), postoperative renal failure (p = 0.001), and re-operation (p = 0.005). All studies included in the analysis were of good quality.

Conclusions

The risk factors identified in our review could be used to improve monitoring of at-risk patients for early detection and treatment of new-onset POAF after CABG, reducing the risk of other complications and negative clinical outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-021-02224-x.

Machine Learning Outperforms Existing Clinical Scoring Tools in the Prediction of Postoperative Atrial Fibrillation During Intensive Care Unit Admission After Cardiac Surgery

OBJECTIVE(S)

Using the Medical Information Mart for Intensive Care III (MIMIC-III) database, we compared the performance of machine learning (ML) to the to the established gold standard scoring tool (POAF Score) in predicting postoperative atrial fibrillation (POAF) during intensive care unit (ICU) admission after cardiac surgery.

METHODS

Random forest classifier (RF), decision tree classifier (DT), logistic regression (LR), K neighbours classifier (KNN), support vector machine (SVM), and gradient boosted machine (GBM) were compared to the POAF Score. Cross-validation was used to assess the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of ML models. POAF Score performance confidence intervals were generated using 1,000 bootstraps. Risk profiles for GBM were generated using Shapley additive values.

RESULTS

A total of 6,349 ICU admissions encompassing 6,040 patients were included. POAF occurred in 1,364 of the 6,349 admissions (21.5%). For predicting POAF during ICU admission after cardiac surgery, GBM, LR, RF, KNN, SVM and DT achieved an AUC of 0.74 (0.71-0.77), 0.73 (0.71-0.75), 0.72 (0.69-0.75), 0.68 (0.67-0.69), 0.67 (0.66-0.68) and 0.59 (0.55-0.63) respectively. The POAF Score AUC was 0.63 (0.62-0.64). Shapley additive values analysis of GBM generated patient level explanations for each prediction.

CONCLUSION

Machine learning models based on readily available preoperative data can outperform clinical scoring tools for predicting POAF during ICU admission after cardiac surgery. Explanatory models are shown to have potential in personalising POAF risk profiles for patients by illustrating probabilistic input variable contributions. Future research is required to evaluate the clinical utility and safety of implementing ML-driven tools for POAF prediction.

Atrial Fibrillation after Cardiac Surgery-To Infinity and Beyond!

No Abstract

Long-term outcomes of postoperative atrial fibrillation following non cardiac surgery: A systematic review and metanalysis

BACKGROUND

New-onset atrial fibrillation (AF) in non-cardiac postoperative setting is common and is associated with a high risk of in-hospital mortality and morbidity. The long-term risks of stroke, mortality and AF recurrence rate in patients with postoperative AF (POAF) are unclear.

METHODS

We performed a systematic literature review in electronic databases from inception to March 5th, 2020 of studies reporting the incidence of stroke, mortality and AF recurrence in patients with POAF. We confined our analysis to studies with a cohort of at least 150 patients with POAF and with a median follow-up of 12 months as a minimum. Odds Ratios (OR) were pooled using a random-effects model.

RESULTS

Qualitative analysis included 8 studies (7 observational cohort studies and 1 randomized controlled trial) enrolling 3,718,587 patients. Six studies underwent metanalysis comprising 17,684 postoperative patients with POAF and 2,169,248 postoperative patients without POAF. The development of POAF conferred a four-fold increased risk of stroke in the long-term [OR 4.05; 95% confidence interval (CI) 2.91-5.62]. Mortality in the two studies reporting long-term data was higher in patients with POAF compared to those without POAF (OR 3.59; CI 95% 2.84-4.53). Data about recurrence were too heterogeneous to undergo metanalysis.

CONCLUSIONS

POAF is associated with a greater risk of stroke and mortality over the long-term period. Studies focusing on AF recurrence are needed to address the perception of POAF as a benign transient entity. The increased mortality risk following POAF should encourage systematic detection and prevention of this arrhythmia.

Postoperative Atrial Fibrillation: Features, Mechanisms, and Clinical Management

Advances in atrial fibrillation (AF) management, perioperative medicine, and surgical techniques have reignited an interest in postoperative AF (POAF). POAF results from the interaction among subclinical atrial substrate, surgery-induced substrate, and transient postoperative factors. Prophylaxis for POAF after cardiac surgery is well established but the indications for preoperative treatment in noncardiac surgery need further investigation. A rate-control strategy is adequate for most asymptomatic patients with POAF and anticoagulation should be initiated for POAF more than 48 to 72 hours postsurgery. Research is needed to improve evidence-based management of POAF and guide long-term management in view of the substantial late recurrence-rate.

Assessment of the ability of the CHA2DS2-VASc scoring system to grade left atrial function by 2D speckle-tracking echocardiography

Background

The CHA₂DS₂-VASc scoring system is correlated with left atrial (LA) reservoir function in patients with atrial fibrillation (AF) rhythm or paroxysmal AF. We assessed the ability of CHA₂DS₂-VASc to grade LA function in patients with sinus rhythm who were candidates for coronary artery bypass grafting (CABG).

Methods

This cross-sectional study recruited 340 consecutive candidates for CABG and categorized them according to their CHA₂DS₂-VASc scores as mild-, moderate-, and high-risk score groups with 34 (10%), 83 (24%), and 223 (66%) patients, respectively. LA function was evaluated via 2D speckle-tracking echocardiography in terms of global longitudinal strain and strain rate during the reservoir, conduit, and contraction phases. In-hospital mortality, postoperative AF, prolonged intensive care unit (ICU) stay, and prolonged mechanical ventilation were assessed.

Results

LA strain and strain rate during the reservoir phase was statistically significantly lower in the high-risk score group than the low- and moderate-risk score groups (27.8 ± 6.9% vs 31.0 ± 5.0% vs 29.8 ± 6.1%, respectively; P = 0.004 and 2.6 ± 0.7 s⁻¹ vs 2.9 ± 0.6 s⁻¹ vs 2.9 ± 0.6 s⁻¹, correspondingly; P = 0.009) and regarding LA strain and strain rate during the conduit phase (9.7 [7.1–12.5]% vs 12.9 [9.4–15.1]% vs 11.5 [9.1–13.8]%, correspondingly; P < 0.001 and 2.1 [1.6–2.7] s⁻¹ vs 2.8 [2.4–3.6] s⁻¹ vs 2.6 [2.2–3.0] s⁻¹, respectively; P < 0.001). In addition, LA strain rate during the conduit phase was lower in the moderate-risk score group than the low-risk score group. After adjustments for possible confounders, these differences remained statistically significant. The risk of postoperative AF and prolonged ICU stay was highest in the high-risk score group (relative risk = 9.67 (1.31–71.43) and 8.05 (1.08–60.16), respectively; P = 0.026 and P = 0.042, respectively).

Conclusions

LA reservoir and conduit functions decreased in the high-risk score group, which was accompanied by an increased risk of postoperative AF and prolonged ICU stay.

How Does the Skeletal Oncology Research Group Algorithm's Prediction of 5-year Survival in Patients with Chondrosarcoma Perform on International Validation?

BACKGROUND

The Skeletal Oncology Research Group (SORG) machine learning algorithm for predicting survival in patients with chondrosarcoma was developed using data from the Surveillance, Epidemiology, and End Results (SEER) registry. This algorithm was externally validated on a dataset of patients from the United States in an earlier study, where it demonstrated generally good performance but overestimated 5-year survival. In addition, this algorithm has not yet been validated in patients outside the United States; doing so would be important because external validation is necessary as algorithm performance may be misleading when applied in different populations.

QUESTIONS/PURPOSES

Does the SORG algorithm retain validity in patients who underwent surgery for primary chondrosarcoma outside the United States, specifically in Italy?

METHODS

A total of 737 patients were treated for chondrosarcoma between January 2000 and October 2014 at the Italian tertiary care center which was used for international validation. We excluded patients whose first surgical procedure was performed elsewhere (n = 25), patients who underwent nonsurgical treatment (n = 27), patients with a chondrosarcoma of the soft tissue or skull (n = 60), and patients with peripheral, periosteal, or mesenchymal chondrosarcoma (n = 161). Thus, 464 patients were ultimately included in this external validation study, as the earlier performed SEER study was used as the training set. Therefore, this study-unlike most of this type-does not have a training and validation set. Although the earlier study overestimated 5-year survival, we did not modify the algorithm in this report, as this is the first international validation and the prior performance in the single-institution validation study from the United States may have been driven by a small sample or non-generalizable patterns related to its single-center setting. Variables needed for the SORG algorithm were manually collected from electronic medical records. These included sex, age, histologic subtype, tumor grade, tumor size, tumor extension, and tumor location. By inputting these variables into the algorithm, we calculated the predicted probabilities of survival for each patient. The performance of the SORG algorithm was assessed in this study through discrimination (the ability of a model to distinguish between a binary outcome), calibration (the agreement of observed and predicted outcomes), overall performance (the accuracy of predictions), and decision curve analysis (establishment on the ability of a model to make a decision better than without using the model). For discrimination, the c-statistic (commonly known as the area under the receiver operating characteristic curve for binary classification) was calculated; this ranged from 0.5 (no better than chance) to 1.0 (excellent discrimination). The agreement between predicted and observed outcomes was visualized with a calibration plot, and the calibration slope and intercept were calculated. Perfect calibration results in a slope of 1 and an intercept of 0. For overall performance, the Brier score and the null-model Brier score were calculated. The Brier score ranges from 0 (perfect prediction) to 1 (poorest prediction). Appropriate interpretation of the Brier score requires comparison with the null-model Brier score. The null-model Brier score is the score for an algorithm that predicts a probability equal to the population prevalence of the outcome for every patient. A decision curve analysis was performed to compare the potential net benefit of the algorithm versus other means of decision support, such as treating all or none of the patients. There were several differences between this study and the earlier SEER study, and such differences are important because they help us to determine the performance of the algorithm in a group different from the initial study population. In this study from Italy, 5-year survival was different from the earlier SEER study (71% [319 of 450 patients] versus 76% [1131 of 1487 patients]; p = 0.03). There were more patients with dedifferentiated chondrosarcoma than in the earlier SEER study (25% [118 of 464 patients] versus 8.5% [131 of 1544 patients]; p < 0.001). In addition, in this study patients were older, tumor size was larger, and there were higher proportions of high-grade tumors than the earlier SEER study (age: 56 years [interquartile range {IQR} 42 to 67] versus 52 years [IQR 40 to 64]; p = 0.007; tumor size: 80 mm [IQR 50 to 120] versus 70 mm [IQR 42 to 105]; p < 0.001; tumor grade: 22% [104 of 464 had Grade 1], 42% [196 of 464 had Grade 2], and 35% [164 of 464 had Grade 3] versus 41% [592 of 1456 had Grade 1], 40% [588 of 1456 had Grade 2], and 19% [276 of 1456 had Grade 3]; p ≤ 0.001).

RESULTS

Validation of the SORG algorithm in a primarily Italian population achieved a c-statistic of 0.86 (95% confidence interval 0.82 to 0.89), suggesting good-to-excellent discrimination. The calibration plot showed good agreement between the predicted probability and observed survival in the probability thresholds of 0.8 to 1.0. With predicted survival probabilities lower than 0.8, however, the SORG algorithm underestimated the observed proportion of patients with 5-year survival, reflected in the overall calibration intercept of 0.82 (95% CI 0.67 to 0.98) and calibration slope of 0.68 (95% CI 0.42 to 0.95). The Brier score for 5-year survival was 0.15, compared with a null-model Brier of 0.21. The algorithm showed a favorable decision curve analysis in the validation cohort.

CONCLUSIONS

The SORG algorithm to predict 5-year survival for patients with chondrosarcoma held good discriminative ability and overall performance on international external validation; however, it underestimated 5-year survival for patients with predicted probabilities from 0 to 0.8 because the calibration plot was not perfectly aligned for the observed outcomes, which resulted in a maximum underestimation of 20%. The differences may reflect the baseline differences noted between the two study populations. The overall performance of the algorithm supports the utility of the algorithm and validation presented here. The freely available digital application for the algorithm is available here: https://sorg-apps.shinyapps.io/extremitymetssurvival/.

LEVEL OF EVIDENCE

Level III, prognostic study.

Development and Validation of A Simple Clinical Risk Prediction Model for New-Onset Postoperative Atrial Fibrillation After Cardiac Surgery: Nopaf Score

Introduction

Postoperative atrial fibrillation (POAFib) occurs in 20 to 40% of patients following cardiac surgery, and is associated with an increased perioperative morbidity and mortality. We aimed to develop and validate a simple clinical risk model for the prediction of POAFib after cardiac surgery.

Methods

An analytical single center retrospective cohort study was conducted, including consecutive patients undergoing cardiac surgery between 2004 and 2017 with POAFib. To create the predictive risk score, a logistic regression model was performed using a random sample of 75% of the population. Coefficients of the model were then converted to a numerical risk score, and three groups were defined: low risk (≤1 point), intermediate risk (2-5 points) and high risk (≥6 points). The score was validated using the remaining 25% of the patients. Discrimination was evaluated through the area under the curve (AUC) ROC, and calibration using the Hosmer-Lemeshow (HL) test, calibration plots, and ratio of expected and observed events (E/O).

Results

Six thousand five hundred nine patients underwent cardiac surgery: 52% coronary artery bypass grafting (CABG), 20% valve surgery, 14% combined (CABG and valve surgery) and 12% other. New-onset AF occurred in 1222 patients (18.77%). In the multivariate analysis, age, use of cardiopulmonary bypass pump, severe reduction in left ventricular ejection fraction (LVEF), chronic renal disease and heart failure were independent risk factors for POAFib, while the use of statins was a protective factor. The NOPAF score was calculated by adding points for each independent risk predictor. In the derivation cohort, the AUC was 0.71 (CI95% 0.69-0.72), and in the validation cohort the model also showed good discrimination (AUC 0.67 IC 0.64-0.70) and excellent calibration (HL P = 0.24). The E/O ratio was 1 (CI 95%: 0.89-1.12). According to the risk category, POAFib occurred in 5% of low; 11% of intermediate and 27.7% of high risk patients in the derivation cohort (P <0.001), and 5.7%; 12.6%; and 23.6% in the validation cohort respectively (P <0.001).

Conclusion

From a large hospitalized population, we developed and validated a simple risk score named NOPAF, based on clinical variables that accurately stratifies the risk of POAFib. This score may help to identify high-risk patients prior to cardiac surgery, in order to strengthen postoperative atrial fibrillation prophylaxis.

The C-reactive protein/albumin ratio as a new predictor for postoperative atrial fibrillation after coronary artery bypass graft surgery

BACKGROUND

This study aimed to investigate the predictive significance of C-reactive protein/albumin ratio for postoperative atrial fibrillation occurrence in patients who were underwent coronary artery bypass graft surgery.

METHODS

Among 830 patients who underwent coronary artery bypass grafting with cardiopulmonary bypass between January 2016 and February 2020, 137 patients with no prior arrhythmia history were included in this cross sectional study.

RESULTS

One hundred and thirty-seven (16.5%) patients developed atrial fibrillation in postoperative period. Patients who experienced postoperative atrial fibrillation were more likely to be older but displayed similar rates of diabetes mellitus, hypertension, hypercholesterolemia, cerebrovascular disease, peripheral vascular disease and chronic obstructive pulmonary disease. For prediction of postoperative atrial fibrillation development, diagnostic odds ratio (OR) and positive likelihood ratio of C-reactive protein/albumin ratio value (OR: 1.854; confidence interval [CI]: 1.598-2.142; P < .001) was higher than serum C-reactive protein and albumin levels. (OR: 1.159; CI: 1.115-1.201; P < .001; OR: 0.438; CI: 0.258-0.865; P < .001, respectively). Which means that C-reactive protein/albumin ratio may detect postoperative atrial fibrillation development better C-reactive protein itself.

CONCLUSION

Based on our results, patients who developed postoperative atrial fibrillation after coronary artery bypass grafting had significantly higher preoperative C-reactive protein/albumin ratio levels than patients who remained in normal sinus rhythm in the postoperative period. Also, higher C-reactive protein/albumin ratio value was one of the independent predictive factors for postoperative atrial fibrillation. Therefore, we concluded that evaluating preoperative C-reactive protein/albumin ratio value might provide early identification of patients with high risk for postoperative atrial fibrillation.

Postoperative Atrial Fibrillation Following Cardiac Surgery: From Pathogenesis to Potential Therapies

Postoperative atrial fibrillation (POAF) is a major complication after cardiac surgery which can lead to high rates of morbidity and mortality, an enhanced length of hospital stay, and an increased cost of care. POAF is postulated to be a multifactorial phenomenon; however, some major pathogeneses have been proposed, including inflammatory pathways, oxidative stress, and autonomic dysfunction. Genetic studies also showed that inflammatory pathways, beta-1 adrenoreceptor variants, G protein-coupled receptor kinase 5 gene variants, and non-coding single-nucleotide polymorphisms in the 4q25 chromosomal locus are involved in this phenomenon. Moreover, several predisposing factors lead to the development of POAF, consisting of pre-, intra-, and postoperative contributors. The main predisposing factors comprise age, prior history of major cardiovascular risk factors, and ischemia-reperfusion injury during surgery. The management of POAF is based on the usual therapies used for non-surgical AF, including medications for either rate control or rhythm control in hemodynamically unstable patients. The perioperative administration of β-blockers and some antiarrhythmic agents has been recommended in major international guidelines. In addition, upstream therapies consisting of colchicine, magnesium, statins, and antioxidants have attenuated the incidence of POAF; however, some uncomfortable side effects developed in large randomized trials. The use of anticoagulation has also resulted in less mortality in patients with POAF at higher risk of thromboembolic events. Despite these recommendations, the actual regimen for the prevention of POAF remains controversial. In this review, we highlight the pathogenesis, predisposing factors, and potential therapeutic options for the management of patients at risk for or with POAF following cardiac surgery.

Comparative Performance of Prediction Models for Contrast-Associated Acute Kidney Injury After Percutaneous Coronary Intervention

BACKGROUND

Identifying patients at increased risk of contrast-associated acute kidney injury (CA-AKI) can help target risk mitigation strategies toward these individuals during percutaneous coronary intervention. Illuminating which risk models best stratify risk is an important foundation for such quality improvement efforts.

METHODS AND RESULTS

Seven previously published risk prediction models for CA-AKI and 3 models for kidney injury requiring dialysis were validated using 2 definitions for CA-AKI (the Kidney Disease: Improving Global Outcomes definition of ≥0.3 mg/dL within 48 hours or ≥50% increase in serum creatinine from baseline within 7 days and the historical definition of ≥0.5 mg/dL or ≥25% increase in serum creatinine from baseline within 48 hours), and AKI requiring dialysis within 30 days of percutaneous coronary intervention. Model performance was compared based on discrimination, calibration, and categorical net reclassification index before and after model recalibration. Among 7888 patients who underwent percutaneous coronary intervention in Alberta Canada, CA-AKI occurred in 330 patients (4.2%) when CA-AKI was defined using the Kidney Disease: Improving Global Outcomes definition and 571 (7.3%) when using the historical definition. CA-AKI requiring dialysis occurred in 42 (0.6%) patients. When validated using the Kidney Disease: Improving Global Outcomes definition for CA-AKI, the 2 most recently published models for CA-AKI showed better discrimination (C statistics, 0.75-0.76) than older models (C statistics, 0.61-0.68). C statistics of models for kidney injury requiring dialysis ranged from 0.70 to 0.86. The calibration of all models for CA-AKI deviated from ideal, and the proportion of patients classified into different risk categories for CA-AKI differed substantially for the 2 most recent models. Recalibration significantly improved risk stratification of patients into clinical risk categories for some models.

CONCLUSIONS

Recent prediction models for CA-AKI show better discrimination compared with older models; however, model recalibration should be examined in external cohorts to improve the accuracy of predictions, particularly if predicted risk strata are used to guide management approaches.

Are Serum Potassium and Magnesium Levels Associated with Atrial Fibrillation After Cardiac Surgery?

OBJECTIVES

Potassium and magnesium are frequently administered after cardiac surgery to reduce the risk of atrial fibrillation (AF). The evidence for this practice is unclear. This study was designed to evaluate the relationship between serum potassium and magnesium levels and AF after cardiac surgery.

DESIGN

Observational cohort study.

SETTING

A cardiac intensive care unit in the United Kingdom.

PARTICIPANTS

Patients undergoing cardiac surgery between January 2013 and November 2017.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Cardiac rhythm was assessed using continuous electrocardiogram (ECG) monitoring in 3,068 patients on the cardiac intensive care unit. Associations between serum potassium and magnesium concentrations extracted from hospital databases and postoperative AF were assessed using univariable and multivariable analyses. The association between electrolyte supplementation therapy and AF was also analyzed. AF developed within 72 hours of cardiac surgery in 545 (17.8%) of the 3,068 patients. After adjusting for logistic EuroSCORE, surgery type, cardiopulmonary bypass time and age, mean serum potassium concentration <4.5 mmol/L was associated with an increased risk of AF (odds ratio [OR] 1.43 (95% confidence interval (CI): 1.17-1.75), p < 0.001). Mean magnesium concentration <1.0 mmol/L was not associated with an increased risk of AF (OR 0.89, 0.71-1.13, p = 0.342), but the administration of magnesium was associated with increased risk of developing AF (OR 1.61, 1.33-1.96, p < 0.001).

CONCLUSIONS

Maintaining a serum potassium concentration ≥4.5 mmol/L after cardiac surgery may reduce the incidence of postoperative AF. Magnesium supplementation was associated with an increased risk of postoperative AF. Prospective randomized trials are required to clarify these associations.

Immediate Preoperative Transthoracic Echocardiography for the Prediction of Postoperative Atrial Fibrillation in High-Risk Cardiac Surgery

OBJECTIVE

The present study aimed to validate the utility of bedside cardiac ultrasound to identify patients for the risk of postoperative atrial fibrillation (POAF).

DESIGN

A prospective cohort study of consecutive patients.

SETTING

Single-center tertiary referral center.

PARTICIPANTS

After Institutional Review Board consent, 169 patients undergoing elective cardiac surgery were enrolled in the study.

INTERVENTIONS

A preoperative transthoracic echocardiographic interrogation assessing diastolic function was performed. Measurements were assessed offline with experienced echocardiographers blinded to clinical outcomes.

MEASUREMENTS AND MAIN RESULTS

The primary outcome was POAF during the first 72 hours after surgery. A total of 169 patients completed the study, 44 of whom (26.0%) developed POAF, and 39 (25.2%) had diastolic dysfunction. Patients with POAF had a higher rate of postoperative heart failure, reintubation within 24 hours of surgery, and length of stay (p = 0.002, 0.01, and 0.0006, respectively). Predictors significant for POAF included increasing age, left atrial volume indexed to body surface area (LAVI), and diastolic dysfunction (p = 0.02, 0.0001, and 0.001, respectively). Multivariate spline regressions demonstrated a nonlinear correlation between increasing LAVI and risk of POAF.

CONCLUSION

Left atrial volume can be assessed efficiently preoperatively to provide superior risk stratification over clinical factors and diastolic parameters alone for the prediction of POAF. Furthermore, the present study demonstrated that the cutoffs of chamber quantification currently used do not appropriately capture the increased risk of POAF. Thus, LAVI provides a simple measure to identify patients who are in need of targeted prophylaxis for POAF.

CHA2DS2-VASc Score for Identifying Patients at High Risk of Postoperative Atrial Fibrillation After Cardiac Surgery: A Meta-analysis

BACKGROUND

Postoperative atrial fibrillation (POAF) is a common complication after cardiac surgery, resulting in an increased risk of morbidity and longer hospital stay. Pharmacologic prophylaxis has been recommended to improve the outcome in patients at high risk of developing POAF after cardiac surgery. Several studies have applied the CHA₂DS₂-VASc (Congestive heart failure, Hypertension Age [≥65 = 1 point, ≥75 = 2 points], Diabetes, and Stroke/transient ischemic attack (2 points)-vascular disease [peripheral arterial disease, previous myocardial infarction, aortic atheroma]) score in the risk stratification of POAF but yielded contradicting results. This study aims to determine the association between CHA₂DS₂-VASc score and POAF and further to explore its discriminative ability for the prediction of POAF.

METHODS

We systematically searched the Medline, Embase, Cochrane library, and other data sources with key terms "CHA₂DS₂-VASc," "atrial fibrillation," and "cardiac surgery." Studies designed for CHA₂DS₂-VASc score in stratifying the risks of POAF in patients undergoing cardiac surgery were included. Statistical analyses were performed with R 3.5.1 and STATA 13.0.

RESULTS

Seven hundred twenty-one studies were identified, of which 12 studies with 18,086 patients were finally included in our analysis. The CHA₂DS₂-VASc score was found to be an independent predictor of POAF after cardiac surgery (odds ratio, 1.46; 95% confidence interval [CI], 1.25-1.72) and exhibited a relatively strong specificity (0.70; 95% CI, 0.61-0.78) and sensitivity (0.72; 95% CI, 0.54-0.85) for predicting POAF. The bivariate model-based pooled area under the receiver operating curve was estimated to be 0.76 (95% CI, 0.72-0.79).

CONCLUSIONS

The CHA₂DS₂-VASc score has relatively good performance in predicting POAF after cardiac surgery and may help identify the patients at high risk of POAF.

New-Onset Atrial Fibrillation in Adult Patients After Cardiac Surgery

Purpose of Review

An overview of recent literature regarding pathophysiology, risk factors, prophylaxis, and treatment of new-onset atrial fibrillation (AF) in post-cardiac surgical patients.

Recent Findings

AF is the most frequent adverse event after cardiac surgery with significant associated morbidity, mortality, and financial cost. Its causes are multifactorial, and models to stratify patients into risk categories are progressing but a consistent, evidence-based system has not yet been developed. Pharmacologic and surgical interventions to prevent and treat this complication have been an area of ongoing research and recent societal guidelines reflect this.

Summary

Inconsistencies remain surrounding how to best identify higher-risk AF patients, which interventions should be used to prevent and treat AF, and which patient groups should receive these interventions. The evidence for these available strategies and their place in contemporary guidelines are summarized.

Postoperative atrial fibrillation is associated with higher scores on predictive indices

OBJECTIVE

To compare the performance of the CHADS VASc, POAF, and HATCH scoring systems to predict new-onset atrial fibrillation after cardiac surgery.

METHODS

We conducted a single-center cohort study, performing a retrospective analysis of prospectively collected data. The study included consecutive patients undergoing cardiac surgery between January 2010 and December 2016. The primary outcome was the development of new-onset postoperative atrial fibrillation during hospitalization.

RESULTS

A total of 3113 patients underwent cardiac surgery during the study period: coronary artery bypass graft surgery (45%), valve replacement (24%), combined procedure (revascularization-valve surgery) (15%), and other procedures (16%). Twenty-one percent (n = 654) presented postoperative atrial fibrillation. Median scores in patients with postoperative atrial fibrillation were significantly higher (P < .001). The CHAD2DS2-VASc score demonstrated greater discriminative ability to predict the event (C-statistic, 0.77; 95% confidence interval [CI], 0.75-0.79) versus the POAF score and the HATCH score (C-statistic, 0.71; 95% CI, 0.69-0.73 and C-statistic, 0.70; 95% CI, 0.67-0.72, respectively). All 3 scores presented good calibration according to the Hosmer-Lemeshow test univariate and multivariable analysis demonstrated that the 3 scores were independent predictors of postoperative atrial fibrillation: CHA2DS2-VASc score odds ratio 1.87 (95% CI, 1.64-2.13), POAF score odds ratio 1.18 (95% CI, 1.01-1.36), and HATCH score odds ratio 1.62 (95% CI, 1.37-1.92).

CONCLUSIONS

The POAF, CHA2DS2-VASc, and HATCH scoring systems showed good discrimination and calibration to predict postoperative atrial fibrillation in patients undergoing cardiac surgery. Among them, the CHA2DS2-Vasc score presented the best discriminative ability for postoperative atrial fibrillation and has the advantage of being easy to calculate.