Prediction of pacing using HRV

Pacemaker use due to conduction abnormalities is a common complication following surgical aortic valve replacement (AVR). Heart rate variability (HRV) is associated with sinus node dysfunction and significant dysrhythmias. However, its predictive value for postoperative electrical pacing requirements after AVR remains unclear. This retrospective study reviewed pre-registered electrical records from 194 adult patients who underwent isolated AVR. HRV parameters in both time and frequency domains were obtained prior to anesthesia induction and before initiating cardiopulmonary bypass. Tree-based machine learning (ML) models, including RandomForest, LightGBM, and ExtraTrees, were developed using HRV parameters and clinical variables to predict postoperative pacing needs. The incidence of temporary electrical pacing postoperatively was 35.1% (34.8% in the training set and 35.9% in the test set). The RandomForest model incorporating both HRV and clinical features achieved an area under the receiver operating characteristic curve of 0.731 (95%CI, 0.681–0.781) and an area under the precision-recall curve of 0.687 (95%CI, 0.619–0.746). In conclusion, ML models leveraging HRV demonstrated potential for predicting postoperative pacemaker requirements following isolated surgical AVR. Accurate prediction of significant conduction disturbances through HRV-based ML algorithms may enable timely interventions and improved management for at-risk patients in clinical practice.

传导异常导致的起搏器使用是外科主动脉瓣置换术（AVR）后常见的并发症。心率变异性（HRV）与窦房结功能障碍及严重心律失常相关，但它对AVR术后电起搏需求的预测价值仍不明确。本回顾性研究分析了194例接受孤立性AVR的成年患者的预先登记电生理记录。在麻醉诱导前和体外循环启动前获取了时域和频域的HRV参数。研究人员利用HRV参数和临床变量开发了基于树的机器学习（ML）模型，包括随机森林、LightGBM和极端随机树，以预测术后起搏需求。术后临时电起搏的发生率为35.1%（训练集34.8%，测试集35.9%）。整合HRV和临床特征的随机森林模型获得了0.731的受试者工作特征曲线下面积（95%置信区间0.681–0.781）及0.687的精确召回曲线下面积（95%置信区间0.619–0.746）。综上，利用HRV的ML模型在预测孤立性外科AVR术后起搏器需求方面显示出潜力。通过基于HRV的ML算法准确预测严重传导障碍，可为临床实践中高危患者的及时干预和优化管理提供支持。