Influencing Factors of Recurrence of Nonvalvular Atrial Fibrillation after Radiofrequency Catheter Ablation and Construction of Clinical Nomogram Prediction Model

Ruan, Zhongbao; Liang, Hong-xia; Wang, Fei; Chen, Gecai; Zhu, Jun‐guo; Ren, Yin; Zhu, Li

doi:10.1155/2022/8521735

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…In recent years, several nomogram models have been constructed to predict the recurrence of the disease early [ 36 , 37 ]. At present, there is no nomogram model for predicting the relapse probability of PMN.…”

Section: Discussionmentioning

confidence: 99%

A preliminary nomogram model for predicting relapse of patients with primary membranous nephropathy

Wang

Lai

et al. 2023

Renal Failure

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Objective To explore the predictive factors and establish a nomogram model for predicting relapse risk in primary membranous nephropathy (PMN). Methods The clinical, laboratory, pathological and follow-up data of patients with biopsy-proven membranous nephropathy were collected in the Affiliated Hospital of Qingdao University. A total of 400 PMN patients who achieved remission were assigned to the development group ( n = 280) and validation group ( n = 120) randomly. Cox regression analysis was performed in the development cohort to determine the predictive factors of relapse in PMN patients, a nomogram model was established based on the multivariate Cox regression analysis and validated in the validation group. C-index and calibration plots were used to evaluate the discrimination and calibration performance of the model respectively. Result Hyperuricemia (HR = 2.938, 95% CI 1.875–4.605, p < 0.001), high C-reactive protein (CRP) (HR = 1.147, 95% CI 1.086–1.211, p < 0.001), and treatment with calcineurin inhibitors with or without glucocorticoids (HR = 2.845, 95%CI 1.361–5.946, p = 0.005) were independent risk factors, while complete remission (HR = 0.420, 95%CI 0.270–0.655, p < 0.001) was a protective factor for relapse of PMN according to multivariate Cox regression analysis, then a nomogram model for predicting relapse of PMN was established combining the above indicators. The C-indices of this model were 0.777 (95%CI 0.729–0.825) and 0.778 (95%CI 0.704–0.853) in the development group and validation group respectively. The calibration plots showed that the predicted relapse probabilities of the model were consistent with the actual probabilities at 1, 2 and 3 years, which indicated favorable performance of this model in predicting the relapse probability of PMN. Conclusions Hyperuricemia, remission status, CRP and therapeutic regimen were predictive factors for relapse of PMN. A novel nomogram model with good discrimination and calibration was constructed to predict relapse risk in patients with PMN early.

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Section: Discussionmentioning

confidence: 99%

A preliminary nomogram model for predicting relapse of patients with primary membranous nephropathy

Wang

Lai

et al. 2023

Renal Failure

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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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Factors predicting the recurrence of atrial fibrillation after catheter ablation: A review

Mo,

Wang,

Zhang

et al. 2024

Heliyon

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Influencing Factors of Recurrence of Nonvalvular Atrial Fibrillation after Radiofrequency Catheter Ablation and Construction of Clinical Nomogram Prediction Model

Cited by 4 publications

References 18 publications

A preliminary nomogram model for predicting relapse of patients with primary membranous nephropathy

A preliminary nomogram model for predicting relapse of patients with primary membranous nephropathy

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

Factors predicting the recurrence of atrial fibrillation after catheter ablation: A review

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