An Artificial Intelligence-Enabled ECG Algorithm for Predicting the Risk of Recurrence in Patients with Paroxysmal Atrial Fibrillation after Catheter Ablation
<b>Background:</b> Catheter ablation (CA) is an important treatment strategy to reduce the burden and complications of atrial fibrillation (AF). This study aims to predict the risk of recurrence in patients with paroxysmal AF (pAF) after CA by an artificial intelligence (AI)-enabled elec...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Journal of Clinical Medicine |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0383/12/5/1933 |
| _version_ | 1797615021704871936 |
|---|---|
| author | Junrong Jiang Hai Deng Hongtao Liao Xianhong Fang Xianzhang Zhan Wei Wei Shulin Wu Yumei Xue |
| author_facet | Junrong Jiang Hai Deng Hongtao Liao Xianhong Fang Xianzhang Zhan Wei Wei Shulin Wu Yumei Xue |
| author_sort | Junrong Jiang |
| collection | DOAJ |
| description | <b>Background:</b> Catheter ablation (CA) is an important treatment strategy to reduce the burden and complications of atrial fibrillation (AF). This study aims to predict the risk of recurrence in patients with paroxysmal AF (pAF) after CA by an artificial intelligence (AI)-enabled electrocardiography (ECG) algorithm. <b>Methods and Results:</b> 1618 ≥ 18 years old patients with pAF who underwent CA in Guangdong Provincial People’s Hospital from 1 January 2012 to 31 May 2019 were enrolled in this study. All patients underwent pulmonary vein isolation (PVI) by experienced operators. Baseline clinical features were recorded in detail before the operation and standard follow-up (≥12 months) was conducted. The convolutional neural network (CNN) was trained and validated by 12-lead ECGs within 30 days before CA to predict the risk of recurrence. A receiver operating characteristic curve (ROC) was created for the testing and validation sets, and the predictive performance of AI-enabled ECG was assessed by the area under the curve (AUC). After training and internal validation, the AUC of the AI algorithm was 0.84 (95% CI: 0.78–0.89), with a sensitivity, specificity, accuracy, precision and balanced F Score (F1 score) of 72.3%, 95.0%, 92.0%, 69.1% and 0.707, respectively. Compared with current prognostic models (APPLE, BASE-AF2, CAAP-AF, DR-FLASH and MB-LATER), the performance of the AI algorithm was better (<i>p</i> < 0.01). <b>Conclusions:</b> The AI-enabled ECG algorithm seemed to be an effective method to predict the risk of recurrence in patients with pAF after CA. This is of great clinical significance in decision-making for personalized ablation strategies and postoperative treatment plans in patients with pAF. |
| first_indexed | 2024-03-11T07:20:34Z |
| format | Article |
| id | doaj.art-65a3b0498ec94710818d8145a0889eca |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-11T07:20:34Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-65a3b0498ec94710818d8145a0889eca2023-11-17T08:00:18ZengMDPI AGJournal of Clinical Medicine2077-03832023-03-01125193310.3390/jcm12051933An Artificial Intelligence-Enabled ECG Algorithm for Predicting the Risk of Recurrence in Patients with Paroxysmal Atrial Fibrillation after Catheter AblationJunrong Jiang0Hai Deng1Hongtao Liao2Xianhong Fang3Xianzhang Zhan4Wei Wei5Shulin Wu6Yumei Xue7Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, ChinaGuangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, ChinaGuangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, ChinaGuangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, ChinaGuangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, ChinaGuangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, ChinaGuangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, ChinaGuangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China<b>Background:</b> Catheter ablation (CA) is an important treatment strategy to reduce the burden and complications of atrial fibrillation (AF). This study aims to predict the risk of recurrence in patients with paroxysmal AF (pAF) after CA by an artificial intelligence (AI)-enabled electrocardiography (ECG) algorithm. <b>Methods and Results:</b> 1618 ≥ 18 years old patients with pAF who underwent CA in Guangdong Provincial People’s Hospital from 1 January 2012 to 31 May 2019 were enrolled in this study. All patients underwent pulmonary vein isolation (PVI) by experienced operators. Baseline clinical features were recorded in detail before the operation and standard follow-up (≥12 months) was conducted. The convolutional neural network (CNN) was trained and validated by 12-lead ECGs within 30 days before CA to predict the risk of recurrence. A receiver operating characteristic curve (ROC) was created for the testing and validation sets, and the predictive performance of AI-enabled ECG was assessed by the area under the curve (AUC). After training and internal validation, the AUC of the AI algorithm was 0.84 (95% CI: 0.78–0.89), with a sensitivity, specificity, accuracy, precision and balanced F Score (F1 score) of 72.3%, 95.0%, 92.0%, 69.1% and 0.707, respectively. Compared with current prognostic models (APPLE, BASE-AF2, CAAP-AF, DR-FLASH and MB-LATER), the performance of the AI algorithm was better (<i>p</i> < 0.01). <b>Conclusions:</b> The AI-enabled ECG algorithm seemed to be an effective method to predict the risk of recurrence in patients with pAF after CA. This is of great clinical significance in decision-making for personalized ablation strategies and postoperative treatment plans in patients with pAF.https://www.mdpi.com/2077-0383/12/5/1933artificial intelligenceatrial fibrillationcatheter ablationrecurrence |
| spellingShingle | Junrong Jiang Hai Deng Hongtao Liao Xianhong Fang Xianzhang Zhan Wei Wei Shulin Wu Yumei Xue An Artificial Intelligence-Enabled ECG Algorithm for Predicting the Risk of Recurrence in Patients with Paroxysmal Atrial Fibrillation after Catheter Ablation Journal of Clinical Medicine artificial intelligence atrial fibrillation catheter ablation recurrence |
| title | An Artificial Intelligence-Enabled ECG Algorithm for Predicting the Risk of Recurrence in Patients with Paroxysmal Atrial Fibrillation after Catheter Ablation |
| title_full | An Artificial Intelligence-Enabled ECG Algorithm for Predicting the Risk of Recurrence in Patients with Paroxysmal Atrial Fibrillation after Catheter Ablation |
| title_fullStr | An Artificial Intelligence-Enabled ECG Algorithm for Predicting the Risk of Recurrence in Patients with Paroxysmal Atrial Fibrillation after Catheter Ablation |
| title_full_unstemmed | An Artificial Intelligence-Enabled ECG Algorithm for Predicting the Risk of Recurrence in Patients with Paroxysmal Atrial Fibrillation after Catheter Ablation |
| title_short | An Artificial Intelligence-Enabled ECG Algorithm for Predicting the Risk of Recurrence in Patients with Paroxysmal Atrial Fibrillation after Catheter Ablation |
| title_sort | artificial intelligence enabled ecg algorithm for predicting the risk of recurrence in patients with paroxysmal atrial fibrillation after catheter ablation |
| topic | artificial intelligence atrial fibrillation catheter ablation recurrence |
| url | https://www.mdpi.com/2077-0383/12/5/1933 |
| work_keys_str_mv | AT junrongjiang anartificialintelligenceenabledecgalgorithmforpredictingtheriskofrecurrenceinpatientswithparoxysmalatrialfibrillationaftercatheterablation AT haideng anartificialintelligenceenabledecgalgorithmforpredictingtheriskofrecurrenceinpatientswithparoxysmalatrialfibrillationaftercatheterablation AT hongtaoliao anartificialintelligenceenabledecgalgorithmforpredictingtheriskofrecurrenceinpatientswithparoxysmalatrialfibrillationaftercatheterablation AT xianhongfang anartificialintelligenceenabledecgalgorithmforpredictingtheriskofrecurrenceinpatientswithparoxysmalatrialfibrillationaftercatheterablation AT xianzhangzhan anartificialintelligenceenabledecgalgorithmforpredictingtheriskofrecurrenceinpatientswithparoxysmalatrialfibrillationaftercatheterablation AT weiwei anartificialintelligenceenabledecgalgorithmforpredictingtheriskofrecurrenceinpatientswithparoxysmalatrialfibrillationaftercatheterablation AT shulinwu anartificialintelligenceenabledecgalgorithmforpredictingtheriskofrecurrenceinpatientswithparoxysmalatrialfibrillationaftercatheterablation AT yumeixue anartificialintelligenceenabledecgalgorithmforpredictingtheriskofrecurrenceinpatientswithparoxysmalatrialfibrillationaftercatheterablation AT junrongjiang artificialintelligenceenabledecgalgorithmforpredictingtheriskofrecurrenceinpatientswithparoxysmalatrialfibrillationaftercatheterablation AT haideng artificialintelligenceenabledecgalgorithmforpredictingtheriskofrecurrenceinpatientswithparoxysmalatrialfibrillationaftercatheterablation AT hongtaoliao artificialintelligenceenabledecgalgorithmforpredictingtheriskofrecurrenceinpatientswithparoxysmalatrialfibrillationaftercatheterablation AT xianhongfang artificialintelligenceenabledecgalgorithmforpredictingtheriskofrecurrenceinpatientswithparoxysmalatrialfibrillationaftercatheterablation AT xianzhangzhan artificialintelligenceenabledecgalgorithmforpredictingtheriskofrecurrenceinpatientswithparoxysmalatrialfibrillationaftercatheterablation AT weiwei artificialintelligenceenabledecgalgorithmforpredictingtheriskofrecurrenceinpatientswithparoxysmalatrialfibrillationaftercatheterablation AT shulinwu artificialintelligenceenabledecgalgorithmforpredictingtheriskofrecurrenceinpatientswithparoxysmalatrialfibrillationaftercatheterablation AT yumeixue artificialintelligenceenabledecgalgorithmforpredictingtheriskofrecurrenceinpatientswithparoxysmalatrialfibrillationaftercatheterablation |