Economou Lundeberg Johan, Måneheim Alexandra, Persson Anders, Dziubinski Marek, Sridhar Arun, Healey Jeffrey S, Slusarczyk Magdalena, Engström Gunnar, Johnson Linda S
Department of Clinical Physiology, Skåne University Hospital, Lund, Sweden.
Department of Clinical Sciences, Lund University, Malmö, Sweden.
Heart Rhythm O2. 2023 Jun 30;4(8):500-505. doi: 10.1016/j.hroo.2023.06.009. eCollection 2023 Aug.
Ventricular tachycardia (VT) occurs intermittently, unpredictably, and has potentially lethal consequences.
Our aim was to derive a risk prediction model for VT episodes ≥10 beats detected on 30-day mobile cardiac telemetry based on the first 24 hours of the recording.
We included patients who were monitored for 2 to 30 days in the United States using full-disclosure mobile cardiac telemetry, without any VT episode ≥10 beats on the first full recording day. An elastic net prediction model was derived for the outcome of VT ≥10 beats on monitoring days 2 to 30. Potential predictors included age, sex, and electrocardiographic data from the first 24 hours: heart rate; premature atrial and ventricular complexes occurring as singlets, couplets, triplets, and runs; and the fastest rate for each event. The population was randomly split into training (70%) and testing (30%) samples.
In a population of 19,781 patients (mean age 65.3 ± 17.1 years, 43.5% men), with a median recording time of 18.6 ± 9.6 days, 1510 patients had at least 1 VT ≥10 beats. The prediction model had good discrimination in the testing sample (area under the receiver-operating characteristic curve 0.7584, 95% confidence interval 0.7340-0.7829). A model excluding age and sex had an equally good discrimination (area under the receiver-operating characteristic curve 0.7579, 95% confidence interval 0.7332-0.7825). In the top quintile of the score, more than 1 in 5 patients had a VT ≥10 beats, while the bottom quintile had a 98.2% negative predictive value.
Our model can predict risk of VT ≥10 beats in the near term using variables derived from 24-hour electrocardiography, and could be used to triage patients to extended monitoring.
室性心动过速（VT）发作具有间歇性、不可预测性，且可能产生致命后果。
我们的目标是基于记录的前24小时数据，推导一个用于预测30天动态心电图监测中检测到的室性心动过速发作≥10次的心搏的风险预测模型。
我们纳入了在美国使用全披露动态心电图监测2至30天的患者，且在首个完整记录日无任何室性心动过速发作≥10次的心搏。针对监测第2至30天室性心动过速≥10次的心搏这一结局，推导了弹性网络预测模型。潜在预测因素包括年龄、性别以及前24小时的心电图数据：心率；单发、成对、三联律和连续出现的房性和室性早搏；以及每个事件的最快心率。将总体随机分为训练样本（70%）和测试样本（30%）。
在19781例患者（平均年龄65.3±17.1岁，43.5%为男性）中，记录时间中位数为18.6±9.6天，1510例患者至少有1次室性心动过速发作≥10次的心搏。预测模型在测试样本中具有良好的区分度（受试者工作特征曲线下面积为0.7584，95%置信区间为0.7340 - 0.7829）。一个排除年龄和性别的模型具有同样良好的区分度（受试者工作特征曲线下面积为0.7579，95%置信区间为0.7332 - 0.7825）。在得分最高的五分之一患者中，超过五分之一的患者有室性心动过速发作≥10次的心搏，而得分最低的五分之一患者的阴性预测值为98.2%。
我们的模型可以使用从24小时心电图得出的变量预测近期室性心动过速发作≥10次的心搏的风险，并可用于对患者进行分类以便延长监测。
