Liu Mengdie, Li Qianqian, Zhang Junbao, Chen Yanjun
Medicine School, Shenzhen University, Shenzhen 518000, China.
Department of Cardiovascular Medicine, Peking University Shenzhen Hospital, Shenzhen 518000, China.
Diagnostics (Basel). 2023 Nov 8;13(22):3403. doi: 10.3390/diagnostics13223403.
Although recurrence rates after radiofrequency catheter ablation (RFCA) in patients with atrial fibrillation (AF) remain high, there are a limited number of novel, high-quality mathematical predictive models that can be used to assess early recurrence after RFCA in patients with AF.
To identify the preoperative serum biomarkers and clinical characteristics associated with post-RFCA early recurrence of AF and develop a novel risk model based on least absolute shrinkage and selection operator (LASSO) regression to select important variables for predicting the risk of early recurrence of AF after RFCA.
This study collected a dataset of 136 atrial fibrillation patients who underwent RFCA for the first time at Peking University Shenzhen Hospital from May 2016 to July 2022. The dataset included clinical characteristics, laboratory results, medication treatments, and other relevant parameters. LASSO regression was performed on 100 cycles of data. Variables present in at least one of the 100 cycles were selected to determine factors associated with the early recurrence of AF. Then, multivariable logistic regression analysis was applied to build a prediction model introducing the predictors selected from the LASSO regression analysis. A nomogram model for early post-RFCA recurrence in AF patients was developed based on visual analysis of the selected variables. Internal validation was conducted using the bootstrap method with 100 resamples. The model's discriminatory ability was determined by calculating the area under the curve (AUC), and calibration analysis and decision curve analysis (DCA) were performed on the model.
In a 3-month follow-up of AF patients ( = 136) who underwent RFCA, there were 47 recurrences of and 89 non-recurrences of AF after RFCA. P, PLR, RDW, LDL, and CRI-II were associated with early recurrence of AF after RFCA in patients with AF ( < 0.05). We developed a predictive model using LASSO regression, incorporating four robust factors (PLR, RDW, LDL, CRI-II). The AUC of this prediction model was 0.7248 (95% CI 0.6342-0.8155), and the AUC of the internal validation using the bootstrap method was 0.8403 (95% CI 0.7684-0.9122). The model demonstrated a strong predictive capability, along with favorable calibration and clinical applicability. The Hosmer-Lemeshow test indicated that there was good consistency between the predicted and observed values. Additionally, DCA highlighted the model's advantages in terms of its clinical application.
We have developed and validated a risk prediction model for the early recurrence of AF after RFCA, demonstrating strong clinical applicability and diagnostic performance. This model plays a crucial role in guiding physicians in preoperative assessment and clinical decision-making. This novel approach also provides physicians with personalized management recommendations.
尽管心房颤动（AF）患者经射频导管消融（RFCA）后的复发率仍然很高，但可用于评估AF患者RFCA后早期复发的新型高质量数学预测模型数量有限。
识别与AF患者RFCA后早期复发相关的术前血清生物标志物和临床特征，并基于最小绝对收缩和选择算子（LASSO）回归开发一种新型风险模型，以选择预测AF患者RFCA后早期复发风险的重要变量。
本研究收集了2016年5月至2022年7月在北京大学深圳医院首次接受RFCA的136例心房颤动患者的数据集。该数据集包括临床特征、实验室检查结果、药物治疗及其他相关参数。对100个周期的数据进行LASSO回归。选择在100个周期中至少出现一次的变量，以确定与AF早期复发相关的因素。然后，应用多变量逻辑回归分析建立预测模型，引入从LASSO回归分析中选择的预测因子。基于对所选变量的可视化分析，开发了AF患者RFCA后早期复发的列线图模型。使用自举法进行100次重采样的内部验证。通过计算曲线下面积（AUC）确定模型的鉴别能力，并对模型进行校准分析和决策曲线分析（DCA）。
在对接受RFCA的AF患者（n = 136）进行的3个月随访中，RFCA后有47例AF复发和89例未复发。P、PLR、RDW、LDL和CRI-II与AF患者RFCA后AF的早期复发相关（P < 0.05）。我们使用LASSO回归开发了一个预测模型，纳入了四个稳健因素（PLR、RDW、LDL、CRI-II）。该预测模型的AUC为0.7248（95%CI 0.6342 - 0.8155），使用自举法进行内部验证的AUC为0.8403（95%CI 0.7684 - 0.9122）。该模型显示出强大的预测能力，以及良好的校准和临床适用性。Hosmer-Lemeshow检验表明预测值与观察值之间具有良好的一致性。此外，DCA突出了该模型在临床应用方面的优势。
我们开发并验证了一种用于预测AF患者RFCA后早期复发的风险预测模型，显示出强大的临床适用性和诊断性能。该模型在指导医生进行术前评估和临床决策方面发挥着关键作用。这种新方法还为医生提供了个性化的管理建议。
