| 摘要: |
| 目的 探讨“大拇指”心电监测仪对消融术后心房颤动复发的监测效果，以及人工智能算法在心房颤动诊断中的优势。方法 前瞻性选择2019年3月至2019年8月我院收治的符合条件的185例行介入消融治疗的非瓣膜性心房颤动患者，利用随机数表法随机分为两组：大拇指组（94例）和传统随访组（91例）。随访并比较两组患者的无心房颤动生存率，比较人工智能算法与传统心电算法诊断心房颤动的灵敏度和特异度。结果 至随访截止日大拇指组和传统随访组分别随访98～263（157.0±38.4） d和91～268（158.8±54.7）d， log-rank检验显示两组无心房颤动生存率分别为79.8%（75/94）和92.3%（84/91）（P<0.05）。术后3个月后大拇指组改变治疗策略的患者比例高于传统随访组（P<0.05）。大拇指组94例患者共记录了18 981份心电图，1 520份（8.0%）由心电图医师诊断为心房颤动，人工智能算法诊断心房颤动的灵敏度和特异度分别为96.5%（1 467/1 520）和99.6%（17 391/17 461），均高于传统心电算法的灵敏度和特异度［分别为89.7%（1 363/1 520）和97.2%（16 972/17 461）］，差异均有统计学意义（P均<0.05）。结论 “大拇指”心电监测仪在消融术后能更早地检出心房颤动复发，利于及时改变治疗策略。人工智能算法能提高心房颤动诊断的准确性，利用人工智能算法的“大拇指”心电监测仪用于心房颤动消融术后的随访结果可靠。 |
| 关键词: 便携式设备 人工智能 便携式心电描记术 导管消融术 心房颤动 |
| DOI：10.16781/j.0258-879x.2021.01.0035 |
| 投稿时间：2020-06-12修订日期：2020-09-27 |
| 基金项目:上海市自然科学基金面上项目（20ZR1456700）. |
|
| Follow-up of atrial fibrillation recurrence after ablation with a BigThumb® electrocardiogram monitor |
| GU Sai-nan△,QIN Ai-hong△,ZHAO Yao,ZUO Wen-zhang,CAO Jiang,HUANG Song-qun* |
(Department of Cardiovasology, Changhai Hospital, Naval Medical University(Second Military Medical University), Shanghai 200433, China
△Co-first authors.
* Corresponding author) |
| Abstract: |
| Objective To investigate the monitoring efficacy of the BigThumb® electrocardiogram (ECG) monitor for the recurrence of atrial fibrillation (AF) after ablation and the advantages of artificial intelligence algorithm in the diagnosis of AF. Methods A total of 185 eligible patients with nonvalvular AF who underwent interventional ablation in our hospital from Mar. 2019 to Aug. 2019 were prospectively selected and randomly divided into two groups:BigThumb® group (BT group, n=94) and traditional follow-up group (TF group, n=91). The AF-free survival rates of the two groups were followed up and compared, and the sensitivity and specificity of artificial intelligence algorithm and traditional ECG algorithm in the diagnosis of AF were also analyzed. Results The patients in BT group and TF group were followed up for 98-263 (157.0±38.4) d and 91-268 (158.8±54.7) d, respectively. Log-rank test results showed that the AF-free survival rates were 79.8% (75/94) and 92.3% (84/91), respectively (P<0.05). Three months after ablation the proportion of patients who changed treatment strategy in BT group was higher than that in TF group (P<0.05). A total of 18 981 ECGs were recorded in BT group, of which 1 520 (8.0%) were diagnosed as AF by electrocardiographists. The sensitivity and specificity of the artificial intelligence algorithm were 96.5% (1 467/1 520) and 99.6% (17 391/17 461), respectively, which were significantly higher than those of the traditional ECG algorithm (89.7%[1 363/1 520] and 97.2%[16 972/17 461]), respectively (both P<0.05). Conclusion The BigThumb® ECG monitor improves the detection of AF recurrence after ablation, which is helpful to change the treatment strategy in time. Artificial intelligence algorithm can improve the accuracy of AF diagnosis and the BigThumb® ECG monitor based on this algorithm is reliable in the follow-up for AF after ablation. |
| Key words: portable device artificial intelligence ambulatory electrocardiography catheter ablation atrial fibrillation |
