Wei Yue, Lin Changjian, Xie Yun, Bao Yangyang, Luo Qingzhi, Zhang Ning, Wu Liqun
Department of Cardiovascular Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Front Cardiovasc Med. 2023 Aug 11;10:1073108. doi: 10.3389/fcvm.2023.1073108. eCollection 2023.
Few studies have explored the use of machine learning models to predict the recurrence of atrial fibrillation (AF) in patients who have undergone cryoballoon ablation (CBA). We aimed to explore the risk factors for the recurrence of AF after CBA in order to construct a nomogram that could predict this risk.
Data of 498 patients who had undergone CBA at Ruijin Hospital, Shanghai Jiaotong University School of Medicine, were retrospectively collected. Factors such as clinical characteristics and biophysical parameters during the CBA procedure were collected for the selection of variables. Scores for all the biophysical factors-such as time to pulmonary vein isolation (TTI) and balloon temperature-were calculated to enable construction of the model, which was then calibrated and compared with the risk scores.
A 36-month follow-up showed that 177 (35.5%) of the 489 patients experienced AF recurrence. The left atrial volume, TTI, nadir cryoballoon temperature, and number of unsuccessful freezes were related to the recurrence of AF ( < .05). The area under the curve (AUC) of the nomogram's time-dependent receiver operating characteristic curve was 77.6%, 71.6%, and 71.0%, respectively, for the 1-, 2-, and 3-year prediction of recurrence in the training cohort and 77.4%, 74.7%, and 68.7%, respectively, for the same characteristics in the validation cohort. Calibration and data on the nomogram's clinical effectiveness showed it to be accurate for the prediction of recurrence in both the training and validation cohorts as compared with established risk scores.
Biophysical parameters such as TTI and cryoballoon temperature have a great impact on AF recurrence. The predictive accuracy for recurrence of our nomogram was superior to that of conventional risk scores.
很少有研究探索使用机器学习模型来预测接受冷冻球囊消融术（CBA）的患者心房颤动（AF）的复发情况。我们旨在探讨CBA术后AF复发的危险因素，以构建能够预测这种风险的列线图。
回顾性收集上海交通大学医学院附属瑞金医院498例接受CBA的患者的数据。收集CBA手术过程中的临床特征和生物物理参数等因素以选择变量。计算所有生物物理因素的得分，如肺静脉隔离时间（TTI）和球囊温度，以便构建模型，然后对模型进行校准并与风险评分进行比较。
36个月的随访显示，489例患者中有177例（35.5%）发生AF复发。左心房容积、TTI、最低冷冻球囊温度和冷冻失败次数与AF复发相关（P<0.05）。在训练队列中，列线图的时间依赖性受试者操作特征曲线的曲线下面积（AUC）在预测1年、2年和3年复发时分别为77.6%、71.6%和71.0%，在验证队列中，相同特征的AUC分别为77.4%、74.7%和68.7%。校准和列线图临床有效性的数据表明，与既定风险评分相比，它在训练和验证队列中对复发的预测都是准确的。
TTI和冷冻球囊温度等生物物理参数对AF复发有很大影响。我们的列线图对复发的预测准确性优于传统风险评分。
