Scientific Papers

Machine learning-based risk models for procedural complications of radiofrequency ablation for atrial fibrillation | BMC Medical Informatics and Decision Making

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The present study included 3187 patients undergoing RFA (3365 procedures) in a large center that captured real-world clinical information and was used to develop a risk model for complications associated with the procedure.

In this study, the most common complication was cardiac effusion or tamponade (0.83%), similar to the results ranging from 0.5% ~ 1.3% previously reported [8, 14,15,16,17,18,19]. For vascular complications, previous studies reported incidences from 1.1% to 2.3% [16,17,18, 20, 21]. In our study, the incidence of access site hemorrhage was 0.62%; hemorrhage requiring transfusion, 0.27%; thromboembolic events, 0.12%; arteriovenous fistulas/pseudoaneurysm, 0.06%; and pulmonary vein stenosis, 0.03%. The overall rate of procedural complications in this study was 1.84%, which is a lower level compared to the complication rates previously reported ranging from 3.3%-6.84% [7, 8, 15,16,17,18,19,20,21,22,23,24,25,26,27] to as high as 9.1% [28] in a survey of U.S. medicare patients. Several potential reasons were contributing to the low incidence of postoperative complications in this study. First, we excluded patients undergoing concomitant other surgeries like left atrial appendage closure, leading to a lower incidence of postoperative complications. Second, this study was conducted at a high-volume center, with more than 1000 RFA procedures performed annually. Complication risk was reduced when the surgery occurred in hospitals with high surgery volumes, similar to those reported previously [14, 21, 24]. Finally, the outcome of this study was only based on the in-hospital data.

Using 20 variables identified by machine learning techniques, we developed a predictive model for postoperative complications with good predictive power in AF patients undergoing RFA. According to the definition of the literature [29, 30], the AUC value between 0.7 and 0.8 is acceptable. The model shows better performance (AUC = 0.721) than the model reported previously [11] (AUC = 0.64) and has the potential to be used in clinical practice, particularly for the outcome of hemorrhage, where the AUC reaches 0.839. To evaluate the clinical applicability of the model, patients was stratified into high-risk and low-risk groups according to the probability of the best performed machine learning model. The incidence of postoperative complications difference between two groups was statistically significant.

This study not only developed a more accurate risk model and identified previously unrecognized important risk factors but also made it “explainable”. Our study benefits from the utilization of SHAP values to unveil the “black box” of machine learning models, thus, our model can furnish implications for patient management even when implemented on individual patients. We employed radar plot and as well as SHAP dependence plot for visualized at the feature and the individual level. Among the 10 most important features, most had an obvious cut-point at which the predicted risk abruptly changed. For example, Ccr < 50 ml/(min × 1.73m2), ALB > 50 g/L or < 35 g/L, CHA_2DS_2-VACs score ≥ 4, DD > 5 mg/L, AST > 100 U/L, NT-pro-BNP > 2000 ng/L, CREA < 50 μmol/L, or older than 80 resulted in a significant increase in postoperative complication risk.

Ccr is accepted as the best overall measurement for assessing renal function [31], a Ccr < 60 ml/(min × 1.73m2) is considered compromised renal function. From the shap dependence plot, reduction of Ccr is shown to increase the risk of postoperative complication, which is consistent with previous research fundings [7, 14]. In our study, ALB is another key predictor for postoperative complication. An obvious U-shaped relationship exists between ALB and the risk of postoperative complication, as both lower than 35 and higher than 50 g/L were associated with an increased risk. Serum ALB is usually used to reflect nutritional status and the ability of the liver to synthesize protein. Decrease in ALB level is indicative liver damage or malnutrition. Meanwhile, several novel findings have been disclosed in our study. Preoperative elevated D-dimer was essential predictors of postoperative complications. Elevated D-dimer indicate a hypercoagulable state and secondary fibrinolysis, which may result in thrombotic disease [32, 33]. Whereas thromboembolic events were infrequent in this study, this could be due to the relatively short length of postoperative hospital stay. Patients with postoperative complications were at a hypercoagulable state at the early stage after ablation procedure but have not yet shown thromboembolic symptoms. Furthermore, preoperative elevated AST, and NT-pro-BNP were essential predictors of postoperative complications in our study. Patients with more comorbidities are more likely to exhibit dysregulated hepatic function, or myocardial function and significantly higher AST, or NT-pro-BNP levels.

The independent factors of procedural complications that have been reported previously were the gender of female [11, 15, 17, 18, 24, 25], older age [11, 16, 20, 24, 25], longer procedural duration [18, 34], the complexity of the procedure [20], CHA_2DS_2-VASc score [8, 9], smaller left atrium dimension [34], and comorbidities like congestive heart failure [11, 16], renal insufficiency [7, 14], coagulopathy [11], peripheral vascular disease [9, 11], chronic obstructive pulmonary disease [11], hypertension [14], mild liver disease [14], diabetes with chronic complications [14], and coronary artery disease [26]. Risk factors like CHA_2DS_2-VACs score, CREA, Ccr, and older age, which are in accordance with previous studies, play an essential role in our model. The inconsistencies between our findings and previous studies are primarily due to the following reasons. Firstly, the differences between studies could result from differences in inclusion criteria or the number of subjects enrolled. Secondly, previous studies mostly included limited variables and included few laboratory indicators. Compared to comorbidities or prior diseases, laboratory indicators for short-term outcome prediction were more objective and sensitive.

To reduce the risk of postoperative complications for AF patients requiring RFA, it is recommended to take the following measures. Firstly, preoperative comprehensive assessment and optimal control of correctable risk factors such as coagulation capability or renal function should be effectively and efficiently implemented in advance to achieve better outcomes. Secondly, the patient’s vital signs and cardiac function throughout the procedure should be closely monitored. Finally, for patients with high risk after RFA, appropriate postoperative care or surveillance is necessary for detecting early complications. Additionally, schedule regular follow-up visits for discharged patients are recommended to assess the patient’s recovery and to provide cardiac rehabilitation and health education.

This study provides additional evidence that can contribute to further research in this field. In this retrospective study, we developed and evaluated different machine learning algorithms using a wide range of features to predict postoperative complications of RFA. Considering the composite outcome of any complication, we conducted sub-models of the most common complication to investigate whether the predictors were different between those two groups. Moreover, for any complication, cardiac effusion, or hemorrhage, over half of the top 10 features were laboratory features. This study demonstrated that the laboratory features, which instantly reflect physical conditions and have been ignored by previous studies, may be more sensitive and more relevant to postoperative complication prediction. One of the advantages of this finding is that it uses variables that are easily accessible within the electronic medical records (EMR). As a result, the model can be integrated into a decision support system under the EMR framework. In practice, this decision support system would access the clinical information of a new patient and calculate the risk of the patient experiencing a postoperative complication.

The present study also has several limitations. Firstly, generalizability is a potential limitation because all patients were included in a single center. Although 3365 procedures were included in this study, with the data collected for patients who presented between 2018 and 2021, the data from a single center, which could not represent the population of Chinese RFA patients, a multi-center study is needed to validate this result. Secondly, this was an in-hospital outcome prediction study based on retrospective use of electronic medical record data, the complications that are known to occur late such as atrio-esophageal fistula might not be captured. The complication rate might be underestimated. However, the majority of the complications occurred in a short period after the RFA procedure, so it is unlikely that a significant number of complications were missed. Finally, although we have included more variables than in previous studies, potential factors such as ablation duration and other intraoperative variables were not available in our database.

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