School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom; Cardiology Department, Guy's and St Thomas' Hospital, London, United Kingdom.
School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, United Kingdom.
Heart Rhythm. 2022 Jun;19(6):885-893. doi: 10.1016/j.hrthm.2021.12.036. Epub 2022 Apr 28.
Transvenous lead extraction (TLE) remains a high-risk procedure.
The purpose of this study was to develop a machine learning (ML)-based risk stratification system to predict the risk of major adverse events (MAEs) after TLE. A MAE was defined as procedure-related major complication and procedure-related death.
We designed and evaluated an ML-based risk stratification system trained using the European Lead Extraction ConTRolled (ELECTRa) registry to predict the risk of MAEs in 3555 patients undergoing TLE and tested this on an independent registry of 1171 patients. ML models were developed, including a self-normalizing neural network (SNN), stepwise logistic regression model ("stepwise model"), support vector machines, and random forest model. These were compared with the ELECTRa Registry Outcome Score (EROS) for MAEs.
There were 53 MAEs (1.7%) in the training cohort and 24 (2.4%) in the test cohort. Thirty-two clinically important features were used to train the models. ML techniques were similar to EROS by balanced accuracy (stepwise model: 0.74 vs EROS: 0.70) and superior by area under the curve (support vector machines: 0.764 vs EROS: 0.677). The SNN provided a finite risk for MAE and accurately identified MAE in 14 of 169 "high (>80%) risk" patients (8.3%) and no MAEs in all 198 "low (<20%) risk" patients (100%).
ML models incrementally improved risk prediction for identifying those at risk of MAEs. The SNN has the additional advantage of providing a personalized finite risk assessment for patients. This may aid patient decision making and allow better preoperative risk assessment and resource allocation.
经静脉导线拔除术（TLE）仍然是一项高风险的操作。
本研究旨在开发一种基于机器学习（ML）的风险分层系统，以预测 TLE 后发生主要不良事件（MAEs）的风险。MAE 定义为与操作相关的严重并发症和与操作相关的死亡。
我们设计并评估了一种基于 ML 的风险分层系统，该系统使用欧洲经静脉导线拔除术控制（ELECTRa）注册中心的数据进行训练，以预测 3555 例 TLE 患者发生 MAEs 的风险，并在 1171 例患者的独立注册中心进行了测试。开发了 ML 模型，包括自归一化神经网络（SNN）、逐步逻辑回归模型（“逐步模型”）、支持向量机和随机森林模型。将这些模型与 MAEs 的 ELECTRa 注册中心结果评分（EROS）进行比较。
在训练队列中发生 53 例 MAEs（1.7%），在测试队列中发生 24 例（2.4%）。使用 32 个有临床意义的特征来训练模型。ML 技术在平衡准确性方面与 EROS 相似（逐步模型：0.74 vs EROS：0.70），在曲线下面积方面优于 EROS（支持向量机：0.764 vs EROS：0.677）。SNN 提供了 MAE 的有限风险，并在 169 例“高（>80%）风险”患者中的 14 例（8.3%）中准确识别 MAE，在所有 198 例“低（<20%）风险”患者中均未发生 MAE（100%）。
ML 模型可逐步提高识别 MAE 风险的能力。SNN 具有提供个性化有限风险评估的额外优势。这可能有助于患者决策，并允许更好地进行术前风险评估和资源分配。
