Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America.
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America.
J Electrocardiol. 2022 Sep-Oct;74:32-39. doi: 10.1016/j.jelectrocard.2022.07.070. Epub 2022 Aug 2.
Timely and accurate discrimination of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular WCT (SWCT) is critically important. Previously we developed and validated an automated VT Prediction Model that provides a VT probability estimate using the paired WCT and baseline 12-lead ECGs. Whether this model improves physicians' diagnostic accuracy has not been evaluated.
We sought to determine whether the VT Prediction Model improves physicians' WCT differentiation accuracy.
Over four consecutive days, nine physicians independently interpreted fifty WCT ECGs (25 VTs and 25 SWCTs confirmed by electrophysiological study) as either VT or SWCT. Day 1 used the WCT ECG only, Day 2 used the WCT and baseline ECG, Day 3 used the WCT ECG and the VT Prediction Model's estimation of VT probability, and Day 4 used the WCT ECG, baseline ECG, and the VT Prediction Model's estimation of VT probability.
Inclusion of the VT Prediction Model data increased diagnostic accuracy versus the WCT ECG alone (Day 3: 84.2% vs. Day 1: 68.7%, p 0.009) and WCT and baseline ECGs together (Day 3: 84.2% vs. Day 2: 76.4%, p 0.003). There was no further improvement of accuracy with addition of the baseline ECG comparison to the VT Prediction Model (Day 3: 84.2% vs. Day 4: 84.0%, p 0.928). Overall sensitivity (Day 3: 78.2% vs. Day 1: 67.6%, p 0.005) and specificity (Day 3: 90.2% vs. Day 1: 69.8%, p 0.016) for VT were superior after the addition of the VT Prediction Model.
The VT Prediction Model improves physician ECG diagnostic accuracy for discriminating WCTs.
及时准确地区分宽复杂心动过速（WCT）为室性心动过速（VT）或室上性 WCT（SWCT）至关重要。此前，我们开发并验证了一种自动 VT 预测模型，该模型使用配对的 WCT 和基线 12 导联心电图提供 VT 概率估计。该模型是否能提高医生的诊断准确性尚未得到评估。
我们旨在确定 VT 预测模型是否能提高医生对 WCT 区分的准确性。
在连续四天内，九位医生独立分析了五十份 WCT 心电图（通过电生理研究证实了 25 份 VT 和 25 份 SWCT），将其分为 VT 或 SWCT。第一天仅使用 WCT 心电图，第二天使用 WCT 和基线心电图，第三天使用 WCT 心电图和 VT 预测模型的 VT 概率估计，第四天使用 WCT 心电图、基线心电图和 VT 预测模型的 VT 概率估计。
与仅使用 WCT 心电图相比，纳入 VT 预测模型数据可提高诊断准确性（第 3 天：84.2%比第 1 天：68.7%，p 0.009），与 WCT 和基线心电图一起使用时（第 3 天：84.2%比第 2 天：76.4%，p 0.003）。与单独使用 VT 预测模型相比，添加基线心电图比较并未进一步提高准确性（第 3 天：84.2%比第 4 天：84.0%，p 0.928）。添加 VT 预测模型后，VT 的整体敏感性（第 3 天：78.2%比第 1 天：67.6%，p 0.005）和特异性（第 3 天：90.2%比第 1 天：69.8%，p 0.016）更高。
VT 预测模型可提高医生心电图诊断区分 WCT 的准确性。
