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南京医科大学学报(自然科学版)                                  第44卷第6期
               ·868 ·                     Journal of Nanjing Medical University(Natural Sciences)   2024年6月


             ·临床研究·

              心脏手术同期迷宫消融术后房颤复发的风险预测模型构建



              蔡岩坡,顾嘉玺,刘          鸿,邵永丰    *
              南京医科大学第一附属医院心脏大血管外科,江苏                   南京 210029




             [摘    要] 目的:探讨心脏手术同期行迷宫Ⅳ(Maze Ⅳ)消融治疗房颤术后复发的危险因素并构建其风险预测的列线图模
              型。方法:收集2014年1月—2022年12月南京医科大学第一附属医院心脏大血管外科收治的596例心脏手术同期行Maze Ⅳ
              消融的患者资料,依据术后1年内是否房颤复发将患者分为两组,采用单因素卡方检验、多因素Logistic回归、Cox回归模型分
              析术后1年房颤复发的独立危险因素,并构建房颤复发风险预测模型。结果:596例患者术后1年内有150例复发(25.2%)。单
              因素分析显示,年龄、房颤持续时间、左房内径(left atrium diameter,LAD)、右房内径(right atrium diameter,RAD)、既往起搏器置
              入、早期房颤复发是术后房颤复发的危险因素;多因素分析显示,年龄、房颤持续时间、LAD、早期房颤复发为独立危险因素;
              Cox 回归分析显示,年龄[HR=1.035(1.025~1.068),P < 0.001]、房颤持续时间[HR=1.003(1.001~1.006),P=0.003]、LAD[HR=
              1.025(1.006~1.044),P=0.009]、使用铰链式消融钳[HR=3.269(2.083~5.130),P < 0.001]、术后早期房颤复发[HR=3.592(2.532~
              5.095),P < 0.001]与房颤复发显著相关。根据受试者特征曲线分析年龄、房颤持续时间、LAD的截断值分别为59.5岁、9.5个
              月、51.5 cm。结论:Maze Ⅳ在维持窦性心律方面具有良好的长期疗效。对于接受心脏手术的房颤患者,较大的年龄、较长的
              LAD、房颤持续时间会对消融结果产生不利影响,依据此结果构建的危险模型有良好的预测效能。
             [关键词] 房颤;Maze Ⅳ手术;列线图模型
             [中图分类号] R654.2                    [文献标志码] A                      [文章编号] 1007⁃4368(2024)06⁃868⁃08
              doi:10.7655/NYDXBNSN240031


              Construction of a risk prediction model for recurrence of atrial fibrillation after
              simultaneous maze ablation for cardiac surgery

                                                        *
              CAI Yanpo,GU Jiaxi,LIU Hong,SHAO Yongfeng
              Department of Chest Surgery,the First Affiliated Hospital of Nanjing Medical University,Nanjing 210029,China


             [Abstract] Objective:To explore the risk factors for postoperative recurrence of atrial fibrillation after concomitant Maze Ⅳ
              ablation for cardiac surgery and construct a nomogram model for risk prediction. Methods:Data were collected from 596 patients who
              underwent concurrent cardiac surgery and Maze Ⅳ ablation at the Department of Cardiac Macrovascular Surgery of the First Affiliated
              Hospital of Nanjing Medical University,from January 2014 to December 2022. Patients were divided into two groups based on whether
              atrial fibrillation recurred within 1 year postoperatively. Univariate chi⁃square test,multivariate logistic regression,and Cox regression
              models were used to analyze the independent risk factors for atrial fibrillation recurrence within 1 year postoperatively and to construct
              a risk prediction model. Results:Among the 596 patients,150(25.2%)experienced recurrences. Univariate analysis showed that age,
              duration of atrial fibrillation,left atrium diameter(LAD),right atrium diameter(RAD),prior pacemaker placement,and early atrial
              fibrillation recurrence were risk factors for postoperative recurrence of atrial fibrillation,while multifactorial analysis showed that age,
              duration of atrial fibrillation,LAD,and early atrial fibrillation recurrence were independent risk factors. Cox regression analysis
              demonstrated that age[HR=1.035(1.025-1.068),P < 0.001],duration of atrial fibrillation(HR=1.003[1.001-1.006],P=0.003),LAD
             [HR=1.025(1.006- 1.044),P=0.009],use of a hinged ablation forceps[HR=3.269(2.083- 5.130],P < 0.001],and early
              postoperative atrial fibrillation recurrence[HR=3.592(2.532-5.095),P < 0.001]were significantly associated with atrial fibrillation
              recurrence. According to the receiver operating characteristic curve analysis,the cut⁃off values for age,duration of atrial fibrillation,
              and LAD were 59.5 years,9.5 months,and 51.5 cm,respectively. Conclusion:Maze Ⅳ has good long⁃term efficacy in maintaining

             [基金项目] 国家自然科学基金(82100254)
              ∗
              通信作者(Corresponding author),E⁃mail:shaoyongfeng@jsph.org.cn
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