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Naji FH, Alatic J, Balevski I, Suran D. Left Atrial Volume Index Predicts Atrial Fibrillation Recurrence after Catheter Ablation Only in Obese Patients-Brief Report. Diagnostics (Basel) 2024; 14:1570. [PMID: 39061707 PMCID: PMC11275257 DOI: 10.3390/diagnostics14141570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 07/08/2024] [Accepted: 07/11/2024] [Indexed: 07/28/2024] Open
Abstract
BACKGROUND It has been shown that obesity and a higher body mass index (BMI) are associated with a higher recurrence rate of atrial fibrillation (AF) after successful catheter ablation (CA). The same has been proven for the left atrial volume index (LAVI). It has also been shown that there is a correlation between LAVI and BMI. However, whether the LAVI's prognostic impact on AF recurrence is BMI-independent remains unclear. METHODS We prospectively included 62 patients with paroxysmal AF who were referred to our institution for CA. All patients underwent radiofrequency CA with standard pulmonary veins isolation. Transthoracic 2-D echocardiography was performed one day after CA to obtain standard measures of cardiac function and morphology. Recurrence was defined as documented AF within 6 months of the follow-up period. Patients were also instructed to visit our outpatient clinic earlier in case of symptoms suggesting AF recurrence. RESULTS We observed AF recurrence in 27% of patients after 6 months. The mean BMI in our cohort was 29.65 ± 5.08 kg/cm2 and the mean LAVI was 38.04 ± 11.38 mL/m2. We further divided patients into two groups according to BMI. Even though the LAVI was similar in both groups, we found it to be a significant predictor of AF recurrence only in obese patients (BMI ≥ 30) and not in the non-obese group (BMI < 30). There was also no significant difference in AF recurrence between both cohorts. The significance of the LAVI as an AF recurrence predictor in the obesity group was also confirmed in a multivariate model. CONCLUSIONS According to our results, the LAVI tends to be a significant predictor of AF recurrence after successful catheter ablation in obese patients, but not in normal-weight or overweight patients. This would suggest different mechanisms of AF in non-obese patients in comparison to obese patients. Further studies are needed in this regard.
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Affiliation(s)
- Franjo Husam Naji
- University Clinical Center, 2000 Maribor, Slovenia
- Faculty of Medicine, University of Maribor, 2000 Maribor, Slovenia
| | - Jan Alatic
- University Clinical Center, 2000 Maribor, Slovenia
| | | | - David Suran
- University Clinical Center, 2000 Maribor, Slovenia
- Faculty of Medicine, University of Maribor, 2000 Maribor, Slovenia
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Sun S, Wang L, Lin J, Sun Y, Ma C. An effective prediction model based on XGBoost for the 12-month recurrence of AF patients after RFA. BMC Cardiovasc Disord 2023; 23:561. [PMID: 37974062 PMCID: PMC10655386 DOI: 10.1186/s12872-023-03599-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 11/07/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND Atrial fibrillation (AF) is a common heart rhythm disorder that can lead to complications such as stroke and heart failure. Radiofrequency ablation (RFA) is a procedure used to treat AF, but it is not always successful in maintaining a normal heart rhythm. This study aimed to construct a clinical prediction model based on extreme gradient boosting (XGBoost) for AF recurrence 12 months after ablation. METHODS The 27-dimensional data of 359 patients with AF undergoing RFA in the First Affiliated Hospital of Soochow University from October 2018 to November 2021 were retrospectively analysed. We adopted the logistic regression, support vector machine (SVM), random forest (RF) and XGBoost methods to conduct the experiment. To evaluate the performance of the prediction, we used the area under the receiver operating characteristic curve (AUC), the area under the precision-recall curve (AP), and calibration curves of both the training and testing sets. Finally, Shapley additive explanations (SHAP) were utilized to explain the significance of the variables. RESULTS Of the 27-dimensional variables, ejection fraction (EF) of the left atrial appendage (LAA), N-terminal probrain natriuretic peptide (NT-proBNP), global peak longitudinal strain of the LAA (LAAGPLS), left atrial diameter (LAD), diabetes mellitus (DM) history, and female sex had a significant role in the predictive model. The experimental results demonstrated that XGBoost exhibited the best performance among these methods, and the accuracy, specificity, sensitivity, precision and F1 score (a measure of test accuracy) of XGBoost were 86.1%, 89.7%, 71.4%, 62.5% and 0.67, respectively. In addition, SHAP analysis also proved that the 6 parameters were decisive for the effect of the XGBoost-based prediction model. CONCLUSIONS We proposed an effective model based on XGBoost that can be used to predict the recurrence of AF patients after RFA. This prediction result can guide treatment decisions and help to optimize the management of AF.
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Affiliation(s)
- ShiKun Sun
- The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Li Wang
- The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - Jia Lin
- The First Affiliated Hospital of Soochow University, Suzhou, 215006, China
| | - YouFen Sun
- The Shengcheng Street Health Center, Shouguang, 262700, China.
| | - ChangSheng Ma
- The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
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Fan X, Li Y, He Q, Wang M, Lan X, Zhang K, Ma C, Zhang H. Predictive Value of Machine Learning for Recurrence of Atrial Fibrillation after Catheter Ablation: A Systematic Review and Meta-Analysis. Rev Cardiovasc Med 2023; 24:315. [PMID: 39076446 PMCID: PMC11272879 DOI: 10.31083/j.rcm2411315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 07/03/2023] [Accepted: 07/17/2023] [Indexed: 07/31/2024] Open
Abstract
Background Accurate detection of atrial fibrillation (AF) recurrence after catheter ablation is crucial. In this study, we aimed to conduct a systematic review of machine-learning-based recurrence detection in the relevant literature. Methods We conducted a comprehensive search of PubMed, Embase, Cochrane, and Web of Science databases from 1980 to December 31, 2022 to identify studies on prediction models for AF recurrence risk after catheter ablation. We used the prediction model risk of bias assessment tool (PROBAST) to assess the risk of bias, and R4.2.0 for meta-analysis, with subgroup analysis based on model type. Results After screening, 40 papers were eligible for synthesis. The pooled concordance index (C-index) in the training set was 0.760 (95% confidence interval [CI] 0.739 to 0.781), the sensitivity was 0.74 (95% CI 0.69 to 0.77), and the specificity was 0.76 (95% CI 0.72 to 0.80). The combined C-index in the validation set was 0.787 (95% CI 0.752 to 0.821), the sensitivity was 0.78 (95% CI 0.73 to 0.83), and the specificity was 0.75 (95% CI 0.65 to 0.82). The subgroup analysis revealed no significant difference in the pooled C-index between models constructed based on radiomics features and those based on clinical characteristics. However, radiomics based showed a slightly higher sensitivity (training set: 0.82 vs. 0.71, validation set: 0.83 vs. 0.73). Logistic regression, one of the most common machine learning (ML) methods, exhibited an overall pooled C-index of 0.785 and 0.804 in the training and validation sets, respectively. The Convolutional Neural Networks (CNN) models outperformed these results with an overall pooled C-index of 0.862 and 0.861. Age, radiomics features, left atrial diameter, AF type, and AF duration were identified as the key modeling variables. Conclusions ML has demonstrated excellent performance in predicting AF recurrence after catheter ablation. Logistic regression (LR) being the most widely used ML algorithm for predicting AF recurrence, also showed high accuracy. The development of risk prediction nomograms for wide application is warranted.
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Affiliation(s)
- Xingman Fan
- Graduate School, Hebei North University, 075000 Zhangjiakou, Hebei, China
- Department of Cardiology, Air Force Medical Center, Air Force Medical
University, PLA,100142 Beijing, China
| | - Yanyan Li
- Department of Cardiology, Air Force Medical Center, Air Force Medical
University, PLA,100142 Beijing, China
| | - Qiongyi He
- Air Force Clinical medical college, Fifth Clinical College of Anhui
Medical University, 230032 Hefei, Anhui, China
| | - Meng Wang
- Graduate School, Hebei North University, 075000 Zhangjiakou, Hebei, China
- Department of Cardiology, Air Force Medical Center, Air Force Medical
University, PLA,100142 Beijing, China
| | - Xiaohua Lan
- Graduate School, Hebei North University, 075000 Zhangjiakou, Hebei, China
| | - Kaijie Zhang
- Graduate School, Hebei North University, 075000 Zhangjiakou, Hebei, China
| | - Chenyue Ma
- Air Force Clinical medical college, Fifth Clinical College of Anhui
Medical University, 230032 Hefei, Anhui, China
| | - Haitao Zhang
- Graduate School, Hebei North University, 075000 Zhangjiakou, Hebei, China
- Department of Cardiology, Air Force Medical Center, Air Force Medical
University, PLA,100142 Beijing, China
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Zheng D, Zhang Y, Huang D, Wang M, Guo N, Zhu S, Zhang J, Ying T. Incremental predictive utility of a radiomics signature in a nomogram for the recurrence of atrial fibrillation. Front Cardiovasc Med 2023; 10:1203009. [PMID: 37636308 PMCID: PMC10451088 DOI: 10.3389/fcvm.2023.1203009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 07/19/2023] [Indexed: 08/29/2023] Open
Abstract
Background Recurrence of atrial fibrillation (AF) after catheter ablation (CA) remains a challenge today. Although it is believed that evaluating the structural and functional remodeling of the left atrium (LA) may be helpful in predicting AF recurrence, there is a lack of consensus on prediction accuracy. Ultrasound-based radiomics is currently receiving increasing attention because it might aid in the diagnosis and prognosis prediction of AF recurrence. However, research on LA ultrasound radiomics is limited. Objective We aim to investigate the incremental predictive utility of LA radiomics and construct a radiomics nomogram to preoperatively predict AF recurrence following CA. Methods A training cohort of 232 AF patients was designed for nomogram construction, while a validation cohort (n = 100) served as the model performance test. AF recurrence during a follow-up period of 3-12 months was defined as the endpoint. The radiomics features related to AF recurrence were extracted and selected to create the radiomics score (rad score). These rad scores, along with other morphological and functional indicators for AF recurrence, were included in the multivariate Cox analysis to establish a nomogram for the prediction of the likelihood of AF recurrence within 1 year following CA. Results In the training and validation cohorts, AF recurrence rates accounted for 32.3% (75/232) and 25.0% (25/100), respectively. We extracted seven types of radiomics features associated with AF recurrence from apical four-chamber view echocardiography images and established a rad score for each patient. The radiomics nomogram was built with the rad score, AF type, left atrial appendage emptying flow velocity, and peak atrial longitudinal strain. It outperformed the nomogram building without the rad score in terms of the predictive efficacy of CA outcome and showed favorable performance in both cohorts. Conclusion We revealed the incremental utility of a radiomics signature in the prediction of AF recurrence and preliminarily developed and validated a radiomics nomogram for identifying patients who were at high risk of post-CA recurrence, which contributed to an appropriate management strategy for AF.
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Affiliation(s)
- Dongyan Zheng
- Department of Ultrasound, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Yueli Zhang
- Department of Ultrasound, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Dong Huang
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Man Wang
- Department of Ultrasound, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Ning Guo
- Department of Ultrasound, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Shu Zhu
- Department of Ultrasound, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Juanjuan Zhang
- Department of Cardiology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Tao Ying
- Department of Ultrasound, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
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Jing M, Li D, Xi H, Zhang Y, Zhou J. Value of Imaging in the Non-Invasive Prediction of Recurrence after Catheter Ablation in Patients with Atrial Fibrillation: An Up-to-Date Review. Rev Cardiovasc Med 2023; 24:241. [PMID: 39076720 PMCID: PMC11266785 DOI: 10.31083/j.rcm2408241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 03/06/2023] [Accepted: 03/14/2023] [Indexed: 07/31/2024] Open
Abstract
Catheter ablation (CA) is the first-line treatment for atrial fibrillation (AF) patients. However, the risk of recurrence associated with CA treatment should not be ignored. Therefore, the preoperative identification of patients at risk of recurrence is essential for identifying patients who will benefit from non-invasive surgery. Echocardiography, computed tomography (CT), and magnetic resonance imaging (MRI) are essential for the preoperative non-invasive prediction of AF recurrence after CA. Compared to laboratory examinations and other examination methods, these modalities can identify structural changes in the heart and assess functional variations. Accordingly, in past studies, morphological features, quantitative parameters, and imaging information of the heart, as assessed by echocardiography, CT, and MRI, have been used to predict AF recurrence after CA noninvasively. This review summarizes and discusses the current research on echocardiography, CT, MRI, and machine learning for predicting AF recurrence following CA. Recommendations for future research are also presented.
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Affiliation(s)
- Mengyuan Jing
- Department of Radiology, Lanzhou University Second Hospital, 730030 Lanzhou, Gansu, China
- Second Clinical School, Lanzhou University, 730030 Lanzhou, Gansu, China
- Key Laboratory of Medical Imaging of Gansu Province, 730030 Lanzhou, Gansu, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, 730030 Lanzhou, Gansu, China
| | - Dong Li
- Department of Cardiovascular Medicine, Lanzhou University Second Hospital, 730030 Lanzhou, Gansu, China
| | - Huaze Xi
- Department of Radiology, Lanzhou University Second Hospital, 730030 Lanzhou, Gansu, China
- Second Clinical School, Lanzhou University, 730030 Lanzhou, Gansu, China
- Key Laboratory of Medical Imaging of Gansu Province, 730030 Lanzhou, Gansu, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, 730030 Lanzhou, Gansu, China
| | - Yuting Zhang
- Department of Radiology, Lanzhou University Second Hospital, 730030 Lanzhou, Gansu, China
- Second Clinical School, Lanzhou University, 730030 Lanzhou, Gansu, China
- Key Laboratory of Medical Imaging of Gansu Province, 730030 Lanzhou, Gansu, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, 730030 Lanzhou, Gansu, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, 730030 Lanzhou, Gansu, China
- Second Clinical School, Lanzhou University, 730030 Lanzhou, Gansu, China
- Key Laboratory of Medical Imaging of Gansu Province, 730030 Lanzhou, Gansu, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, 730030 Lanzhou, Gansu, China
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Razeghi O, Kapoor R, Alhusseini MI, Fazal M, Tang S, Roney CH, Rogers AJ, Lee A, Wang PJ, Clopton P, Rubin DL, Narayan SM, Niederer S, Baykaner T. Atrial fibrillation ablation outcome prediction with a machine learning fusion framework incorporating cardiac computed tomography. J Cardiovasc Electrophysiol 2023; 34:1164-1174. [PMID: 36934383 PMCID: PMC10857794 DOI: 10.1111/jce.15890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 03/06/2023] [Accepted: 03/14/2023] [Indexed: 03/20/2023]
Abstract
BACKGROUND Structural changes in the left atrium (LA) modestly predict outcomes in patients undergoing catheter ablation for atrial fibrillation (AF). Machine learning (ML) is a promising approach to personalize AF management strategies and improve predictive risk models after catheter ablation by integrating atrial geometry from cardiac computed tomography (CT) scans and patient-specific clinical data. We hypothesized that ML approaches based on a patient's specific data can identify responders to AF ablation. METHODS Consecutive patients undergoing AF ablation, who had preprocedural CT scans, demographics, and 1-year follow-up data, were included in the study for a retrospective analysis. The inputs of models were CT-derived morphological features from left atrial segmentation (including the shape, volume of the LA, LA appendage, and pulmonary vein ostia) along with deep features learned directly from raw CT images, and clinical data. These were merged intelligently in a framework to learn their individual importance and produce the optimal classification. RESULTS Three hundred twenty-one patients (64.2 ± 10.6 years, 69% male, 40% paroxysmal AF) were analyzed. Post 10-fold nested cross-validation, the model trained to intelligently merge and learn appropriate weights for clinical, morphological, and imaging data (AUC 0.821) outperformed those trained solely on clinical data (AUC 0.626), morphological (AUC 0.659), or imaging data (AUC 0.764). CONCLUSION Our ML approach provides an end-to-end automated technique to predict AF ablation outcomes using deep learning from CT images, derived structural properties of LA, augmented by incorporation of clinical data in a merged ML framework. This can help develop personalized strategies for patient selection in invasive management of AF.
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Affiliation(s)
- Orod Razeghi
- King’s College, London, UK
- University College London, London, UK
| | | | | | | | - Siyi Tang
- Stanford University, California, USA
| | | | | | - Anson Lee
- Stanford University, California, USA
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