Development and Validation of a Risk Nomogram Model for Predicting Recurrence in Patients with Atrial Fibrillation After Radiofrequency Catheter Ablation

Zhao, Zhihao; Zhang, Fengyun; Ma, Ruicong; Bo, Lin; Zhang, Zeqing; Zhang, Chaoqun; Wang, Zhirong; Li, Chengzong; Yu, Yang

doi:10.2147/cia.s376091

Cited by 9 publications

(10 citation statements)

References 34 publications

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“…Among them, one article was unavailable in full text, 6 articles were excluded for other reasons, and one article was deleted due to duplication of an identical cohort. Finally, 40 studies were included in this systematic review and meta-analysis [ 12 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 ]. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The majority of catheter ablation procedures were performed using radiofrequency ablation or cryoablation, and the average follow-up time ranged from 4 months to 120 months. Patients from the United States were represented in 6 studies [ 12 , 17 , 18 , 19 , 20 , 21 ], Europe in 11 studies [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ], and the Asia-Pacific region in 23 studies [ 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 ]. Regarding the ML algorithms, logistic regression was the most commonly used method for predicting AF recurrence after catheter ablation, accounting for 24 out of 40 studies (60%).…”

Section: Resultsmentioning

confidence: 99%

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Predictive Value of Machine Learning for Recurrence of Atrial Fibrillation after Catheter Ablation: A Systematic Review and Meta-Analysis

Fan,

Li,

et al. 2023

Rev. Cardiovasc. Med.

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Predictive Value of Machine Learning for Recurrence of Atrial Fibrillation after Catheter Ablation: A Systematic Review and Meta-Analysis

Fan,

Li,

et al. 2023

Rev. Cardiovasc. Med.

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“…Including our model, recent advances in radiomics-inclusive models have exhibited increased accuracy in predicting post-ablation AF recurrence ( 44 , 47 ). These models seem to surpass traditional clinical prediction models ( 48 – 51 ). Even models utilizing advanced machine learning techniques are not immune to the limitations of relying purely on clinical indicators ( 48 , 49 ).…”

Section: Discussionmentioning

confidence: 96%

Incremental predictive utility of a radiomics signature in a nomogram for the recurrence of atrial fibrillation

Zheng

Zhang

Huang

et al. 2023

Front. Cardiovasc. Med.

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BackgroundRecurrence 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.ObjectiveWe aim to investigate the incremental predictive utility of LA radiomics and construct a radiomics nomogram to preoperatively predict AF recurrence following CA.MethodsA 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.ResultsIn 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.ConclusionWe 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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“…With the publication of various predictors for AF recurrence, the development of prediction models becomes an emerging scene. For instance, Zhao et al reported a four-factor nomogram consisting of AF type, LA diameter, LVEF, and systemic inflammation score [ 25 ]. Based on NTproBNP, AF type, LAA volume, and LA volume, Zhou et al built a deep learning-based model via a convolutional neural network [ 26 ].…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Novel Prognostic Tool to Predict Recurrence of Paroxysmal Atrial Fibrillation after the First-Time Catheter Ablation: A Retrospective Cohort Study

et al. 2023

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There is no gold standard to tell frustrating outcomes after the catheter ablation of paroxysmal atrial fibrillation (PAF). The study aims to construct a prognostic tool. We retrospectively analyzed 315 patients with PAF who underwent first-time ablation at the Second Xiangya Hospital of Central South University. The endpoint was identified as any documented relapse of atrial tachyarrhythmia lasting longer than 30 s after the three-month blanking period. Univariate Cox regression analyzed eleven preablation parameters, followed by two supervised machine learning algorithms and stepwise regression to construct a nomogram internally validated. Five factors related to ablation failure were as follows: female sex, left atrial appendage emptying flow velocity ≤31 cm/s, estimated glomerular filtration rate <65.8 mL/(min·1.73 m2), P wave duration in lead aVF ≥ 120 ms, and that in lead V1 ≥ 100 ms, which constructed a nomogram. It was correlated with the CHA2DS2-VASc score but outperformed the latter evidently in discrimination and clinical utility, not to mention its robust performances in goodness-of-fit and calibration. In addition, the nomogram-based risk stratification could effectively separate ablation outcomes. Patients at risk of relapse after PAF ablation can be recognized at baseline using the proposed five-factor nomogram.

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Development and Validation of a Risk Nomogram Model for Predicting Recurrence in Patients with Atrial Fibrillation After Radiofrequency Catheter Ablation

Cited by 9 publications

References 34 publications

Predictive Value of Machine Learning for Recurrence of Atrial Fibrillation after Catheter Ablation: A Systematic Review and Meta-Analysis

Predictive Value of Machine Learning for Recurrence of Atrial Fibrillation after Catheter Ablation: A Systematic Review and Meta-Analysis

Incremental predictive utility of a radiomics signature in a nomogram for the recurrence of atrial fibrillation

Development and Validation of a Novel Prognostic Tool to Predict Recurrence of Paroxysmal Atrial Fibrillation after the First-Time Catheter Ablation: A Retrospective Cohort Study

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