Luongo Giorgio, Rees Felix, Nairn Deborah, Rivolta Massimo W, Dössel Olaf, Sassi Roberto, Ahlgrim Christoph, Mayer Louisa, Neumann Franz-Josef, Arentz Thomas, Jadidi Amir, Loewe Axel, Müller-Edenborn Björn
Institute of Biomedical Engineering (IBT), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany.
Division of Cardiology and Angiology II, University Heart Center Freiburg-Bad Krozingen, Bad Krozingen, Germany.
Front Cardiovasc Med. 2022 Feb 28;9:812719. doi: 10.3389/fcvm.2022.812719. eCollection 2022.
Atrial fibrillation (AF) and heart failure often co-exist. Early identification of AF patients at risk for AF-induced heart failure (AF-HF) is desirable to reduce both morbidity and mortality as well as health care costs. We aimed to leverage the characteristics of beat-to-beat-patterns in AF to prospectively discriminate AF patients with and without AF-HF.
A dataset of 10,234 5-min length RR-interval time series derived from 26 AF-HF patients and 26 control patients was extracted from single-lead Holter-ECGs. A total of 14 features were extracted, and the most informative features were selected. Then, a decision tree classifier with 5-fold cross-validation was trained, validated, and tested on the dataset randomly split. The derived algorithm was then tested on 2,261 5-min segments from six AF-HF and six control patients and validated for various time segments.
The algorithm based on the spectral entropy of the RR-intervals, the mean value of the relative RR-interval, and the root mean square of successive differences of the relative RR-interval yielded an accuracy of 73.5%, specificity of 91.4%, sensitivity of 64.7%, and PPV of 87.0% to correctly stratify segments to AF-HF. Considering the majority vote of the segments of each patient, 10/12 patients (83.33%) were correctly classified.
Beat-to-beat-analysis using a machine learning classifier identifies patients with AF-induced heart failure with clinically relevant diagnostic properties. Application of this algorithm in routine care may improve early identification of patients at risk for AF-induced cardiomyopathy and improve the yield of targeted clinical follow-up.
心房颤动（AF）与心力衰竭常并存。早期识别有房颤诱发心力衰竭（AF-HF）风险的房颤患者，对于降低发病率、死亡率以及医疗成本是很有必要的。我们旨在利用房颤逐搏模式的特征，前瞻性地区分有和没有AF-HF的房颤患者。
从单导联动态心电图中提取了一个包含10234个长度为5分钟的RR间期时间序列的数据集，该数据集来自26例AF-HF患者和26例对照患者。共提取了14个特征，并选择了信息量最大的特征。然后，在随机划分的数据集上训练、验证和测试了具有5折交叉验证的决策树分类器。然后，在来自6例AF-HF患者和6例对照患者的2261个5分钟片段上测试了所推导的算法，并在不同时间段进行了验证。
基于RR间期的频谱熵、相对RR间期的平均值以及相对RR间期连续差值的均方根的算法，对AF-HF节段进行正确分层的准确率为73.5%，特异性为91.4%，敏感性为64.7%，阳性预测值为87.0%。考虑每个患者节段的多数投票，10/12例患者（83.33%）被正确分类。
使用机器学习分类器进行逐搏分析可识别出具有临床相关诊断特性的房颤诱发心力衰竭患者。该算法在常规护理中的应用可能会改善对房颤诱发心肌病风险患者的早期识别，并提高有针对性的临床随访效果。
