Predicting New-Onset Postoperative Atrial Fibrillation Following Isolated Coronary Artery Bypass Grafting: Development and Validation of a Novel Nomogram

Nomogram to predict recurrence risk factors in patients with non-valvular paroxysmal atrial fibrillation after catheter radiofrequency ablation

BACKGROUND

Radiofrequency catheter ablation (RFCA) is an effective method for controlling the heart rate of paroxysmal atrial fibrillation (PAF). However, recurrence is trouble under the RFCA. To gain a deeper understanding of the risk factors for recurrence in patients, we created a nomogram model to provide clinicians with treatment recommendations.

METHODS

A total of two hundred thirty-three patients with PAF treated with RFCA at Guizhou Medical University Hospital between January 2021 and December 2022 were consecutively included in this study, and after 1 year of follow-up coverage, 166 patients met the nadir inclusion criteria. Patients with AF were divided into an AF recurrence group and a non-recurrence group. The nomogram was constructed using univariate and multivariate logistic regression analyses. By calculating the area under the curve, we analyzed the predictive ability of the risk scores (AUC). In addition, the performance of the nomogram in terms of calibration, discrimination, and clinical utility was evaluated.

RESULTS

At the 12-month follow-up, 48 patients (28.92%) experienced a recurrence of AF after RFCA, while 118 patients (71.08%) maintained a sinus rhythm. In addition to age, sex, and TRV, LAD, and TTPG were independent predictors of recurrence of RFCA. The c-index of the nomogram predicted AF recurrence with an accuracy of .723, showing good decision curves and a calibrated nomogram, as determined by internal validation using a bootstrap sample size of 1000.

CONCLUSION

We created a nomogram based on multifactorial logistic regression analysis to estimate the probability of recurrence in patients with atrial fibrillation 1 year after catheter ablation. This plot can be utilized by clinicians to predict the likelihood of recurrence.

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.

A prediction model for new-onset atrial fibrillation following coronary artery bypass graft surgery: A multicenter retrospective study

Objective

Developing and assessing a risk prediction model of postoperative atrial fibrillation (POAF) after coronary artery bypass grafting (CABG), and aims to provide a reference for the prediction and prevention.

Design

A retrospective case-control study.

Setting

Three major urban teaching and university hospitals and tertiary referral centers.

Participants

consecutive patients undergoing CABG.

Interventions

The study was retrospective and no interventions were administered to patients.

Measurements and main results

In the study, the overall new-onset POAF prevalence was approximately 28%. A prediction model for POAF with nine significant indicators was developed, and identified new predictors of POAF: left ventricular end diastolic diameter (LVEDD), intraoperative defibrillation, and intraoperative temporary pacing lead implantation. The model had good discrimination in both the derivation and validation cohorts, with the area under the receiver operating characteristic curves (AUCs) of 0.621 (95% CI = 0.602-0.640) and 0.616 (95% CI = 0.579-0.651), respectively, and showed good calibration. Compared with CHA₂DS₂-VASc, HATCH score, and the prediction model of POAF after CABG developed based on a small sample of clinical data from a single center in China, the model in this study had better discrimination.

Conclusion

We have developed and validated a new prediction model of POAF after CABG using multicenter data that can be used in the clinic for early identification of high-risk patients of POAF, and to help effectively prevent POAF in postoperative patients.

Machine Learning to Identify Patients at Risk of Developing New-Onset Atrial Fibrillation after Coronary Artery Bypass

BACKGROUND

This study aims to get an effective machine learning (ML) prediction model of new-onset postoperative atrial fibrillation (POAF) following coronary artery bypass grafting (CABG) and to highlight the most relevant clinical factors.

METHODS

Four ML algorithms were employed to analyze 394 patients undergoing CABG, and their performances were compared: Multivariate Adaptive Regression Spline, Neural Network, Random Forest, and Support Vector Machine. Each algorithm was applied to the training data set to choose the most important features and to build a predictive model. The better performance for each model was obtained by a hyperparameters search, and the Receiver Operating Characteristic Area Under the Curve metric was selected to choose the best model. The best instances of each model were fed with the test data set, and some metrics were generated to assess the performance of the models on the unseen data set. A traditional logistic regression was also performed to be compared with the machine learning models.

RESULTS

Random Forest model showed the best performance, and the top five predictive features included age, preoperative creatinine values, time of aortic cross-clamping, body surface area, and Logistic Euro-Score.

CONCLUSIONS

The use of ML for clinical predictions requires an accurate evaluation of the models and their hyperparameters. Random Forest outperformed all other models in the clinical prediction of POAF following CABG.

Comparison of in-hospital outcomes after coronary artery bypass graft surgery in elders and younger patients: a multicenter retrospective study

OBJECTIVES

We aimed to identify in-hospital outcomes in young (≤ 65 years) and old (> 65 years) patients after coronary artery bypass grafting (CABG) by analyzing the effect of age on adverse events after on-pump or off-pump CABG.

METHODS

Patients older than 65 years were defined as older patients and others were defined as younger patients. The qualitative data were compared by chi-square or Fisher's exact tests. The quantitative data were compared by the two-sample independent t-test or Mann-Whitney U test. Multifactor binary logistic regression was used to control for confounders and to investigate the effect of age on dichotomous outcome variables such as death.

RESULTS

In the on-pump CABG population, the postoperative in-hospital mortality, the incidence of postoperative symptomatic cerebral infarction (POSCI) and postoperative atrial fibrillation (POAF) was higher in older patients than in younger patients (P value < 0.05), and age > 65 years was associated with postoperative in-hospital mortality (OR = 2.370, P value = 0.031), POSCI (OR = 5.033, P value = 0.013), and POAF (OR = 1.499, P value < 0.001). In the off-pump CABG population, the incidence of POAF was higher in older patients than in younger patients (P value < 0.05), and age > 65 years was associated with POAF (OR = 1.392, P value = 0.011).

CONCLUSION

In-hospital outcomes after CABG are strongly influenced by age. In on-pump CABG, the risk of postoperative death, POSCI, and POAF was higher in older patients, and in off-pump CABG, the risk of POAF was higher in older patients.

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.