A A Novel Risk Score to Predict New Onset Atrial Fibrillation in Patients Undergoing Isolated Coronary Artery Bypass Grafting

Lin, Sophie; Crawford, Todd C.; Suarez‐Pierre, Alejandro; Magruder, J. Trent; Carter, Michael V.; Cameron, Duke E.; Whitman, Glenn J.R.; Lawton, Jennifer S.; Baumgartner, William A.; Mandal, Kaushik

doi:10.1532/hsf.2151

Cited by 9 publications

(13 citation statements)

References 26 publications

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“…within the incidence of prior studies [1][2] [3] . Age is one of the most consistent risk factors for postoperative AF in many previous literature [4] [15] , which was also supported by our research. The effect of advanced age on AF may relate to degenerative changes in atrial structure and function [11] .…”

Section: Discussionsupporting

confidence: 91%

New-Onset Atrial Fibrillation after Off-Pump Coronary Artery Bypass Grafting: Predictors and In-hospital Complications

Xu¹,

Fan²,

Chen³

2020

Preprint

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Objective To explore risk factors and in-hospital complications of new-onset atrial fibrillation (AF) after off-pump coronary artery bypass grafting (OPCAB). Methods In this study of 1344 patients who underwent isolated OPCAB from 2012 to 2015, patients were divided into AF and non-AF group according to whether new-onset postoperative AF occurred. Results The incidence of new-onset AF after OPCAB was 28.57%, mainly appeared within the first 4 days after surgery. After binary logistic regression analysis, age, peripheral vascular disease, median-sternotomy OPCAB, mechanical ventilation time, IABP were independent predictors of AF( p <0.001,OR 1.039,95%CI 1.023-1.055; p =0.007,OR 2.450,95%CI 1.282-4.684; p =0.044,OR 0.589,95%CI 0.351-0.987; p =0.013,OR 1.006,95%CI 1.001-1.011; p =0.007,OR 3.001,95%CI 1.356-6.642, respectively). Patients with AF have a significant higher risk of reoperation, re-entry into ICU, re-intubation, postoperative myocardial infarction, renal failure, and death ( p =0.013, p =0.015, p <0.001, p =0.037, p <0.001, p <0.001, respectively), also a longer re-ICU time ( p =0.014). Conclusion Advanced age, peripheral vascular disease, median-sternotomy OPCAB, mechanical ventilation time, IABP were independent predictors for new-onset AF after OPCAB. Postoperative AF was clearly associated with more in-hospital complications.

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Section: Discussionsupporting

confidence: 91%

New-Onset Atrial Fibrillation after Off-Pump Coronary Artery Bypass Grafting: Predictors and In-hospital Complications

Xu¹,

Fan²,

Chen³

2020

Preprint

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“…Among echocardiographic markers, left atrial volume index (LAVI) was associated with PAOF in most studies carried out assessing LAVI, 14,26,30–35 while left ventricular ejection function was not. 14–16,19,23,31,35,37,41,42,49,60,61,67,69,71,72,77,78,83,85,88,93,97,98,103,106–108,112,117 The ECG parameter p wave duration was also associated with AF in some studies. 22,54,69,103,117,118 Mitral valve disease was strongly associated with POAF.…”

Section: Resultsmentioning

confidence: 99%

“…22,54,69,103,117,118 Mitral valve disease was strongly associated with POAF. 9,12,25,44,49,73,82,89,103 Other surgical factors, including RBC use, 5,19,23–26,41,55,67,105 on versus off pump CABG 1,15,23,24,40,45,56,76,85,90,91,93,95,96,109,114 and clamp time 1,7,10,14,21,22,24,26,27,31,35,38,39,41,50,54–56,65,66,68,69,71,73,…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2021

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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.

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“…), что иллюстрировалось наличием их взаимосвязей с вероятностью развития аритмии [10]. На основе методов математической статистики был разработан ряд прогностических шкал, в которых для оценки вероятности развития ПоФП были использованы предикторы, характеризующие предоперационный статус больных: POAF (Post-operative atrial fibrillation), Kolec и PAFAC (Predictors of AF After CABG) [11][12][13]. Кроме того, в ряде работ прогнозирование ПоФП осуществлялось по шкалам CHA 2 DS 2 -VASc и HAS-BLED, клинические показатели которых (женский пол, возраст, АГ и др.)…”

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“…Обработка и анализ данных выполнялись на языке R в среде R-studio и языке Python с помощью пакетов keras и tensorflow. [11][12][13]24]. В нашей работе результаты прогнозирования были существенно выше, что обеспечивалось много-в 2 раза выше, чем у лиц без нарушения сердечного ритма.…”

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Algorithm for selecting predictors and prognosis of atrial fibrillation in patients with coronary artery disease after coronary artery bypass grafting

et al. 2021

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Aim. To develop an algorithm for selecting predictors and prognosis of atrial fibrillation (AF) in patients with coronary artery disease (CAD) after coronary artery bypass grafting (CABG).Material and methods. This retrospective study included 886 case histories of patients with CAD aged 35 to 81 years (median age, 63 years; 95% confidence interval [63; 64]), who underwent isolated CABG under cardiopulmonary bypass. Eighty-five patients with prior AF were excluded from the study. Two groups of persons were identified, the first of which consisted of 153 (19,1%) patients with newly recorded AF episodes, the second — 648 (80,9%) patients without cardiac arrhythmias. Preoperative clinical and functional status was assessed using 100 factors. Chi-squared, Fisher, and Mann-Whitney tests, as well as univariate logistic regression (LR) were used for data processing and analysis. Multivariate LR and artificial neural networks (ANN) were used to develop predictive models. The boundaries of significant ranges of potential predictors were determined by stepwise assessment of the odds ratio and p-value. The model accuracy was assessed using 4 metrics: area under the ROC-curve (AUC), sensitivity, specificity, and accuracy.Results. A comprehensive analysis of preoperative status of patients made it possible to identify 11 factors with the highest predictive potential, linearly and nonlinearly associated with postoperative AF (PAF). These included age (55-74 years for men and 60-78 years for women), anteroposterior and superior-inferior left atrial dimensions, transverse and longitudinal right atrial dimensions, tricuspid valve regurgitation, left ventricular end systolic dimension >49 mm, RR length of 1000-1100 ms, PQ length of 170-210 ms, QRS length of 50-80 ms, QT >420 ms for men and >440 ms for women, and heart failure with ejection fraction of 4560%. The metrics of the best predictive ANN model were as follows: AUC — 0,75, specificity — 0,73, sensitivity — 0,74, and accuracy — 0,73. These values in best model based on multivariate LR were lower (0,75; 0,7; 0,68 and 0,7, respectively).Conclusion. The developed algorithm for selecting predictors made it possible to verify significant predictive ranges and weight coefficients characterizing their influence on PAF development. The predictive model based on ANN has a higher accuracy than multivariate HR.

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A A Novel Risk Score to Predict New Onset Atrial Fibrillation in Patients Undergoing Isolated Coronary Artery Bypass Grafting

Cited by 9 publications

References 26 publications

New-Onset Atrial Fibrillation after Off-Pump Coronary Artery Bypass Grafting: Predictors and In-hospital Complications

New-Onset Atrial Fibrillation after Off-Pump Coronary Artery Bypass Grafting: Predictors and In-hospital Complications

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

Algorithm for selecting predictors and prognosis of atrial fibrillation in patients with coronary artery disease after coronary artery bypass grafting

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