Schoels Maximilian, Krumm Laura, Nelde Alexander, Olma Manuel C, Nolte Christian H, Scheitz Jan F, Klammer Markus G, Leithner Christoph, Meisel Andreas, Scheibe Franziska, Krämer Michael, Haeusler Karl Georg, Endres Matthias, Meisel Christian
Computational Neurology, Department of Neurology, Charité - Universitätsmedizin Berlin, Germany; Berlin Institute of Health, Berlin, Germany; Center for Stroke Research Berlin, Berlin, Germany.
Computational Neurology, Department of Neurology, Charité - Universitätsmedizin Berlin, Germany; Berlin Institute of Health, Berlin, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany; Einstein Center for Neurosciences, Berlin, Germany; Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu Berlin, Berlin, Germany.
EBioMedicine. 2025 Aug;118:105869. doi: 10.1016/j.ebiom.2025.105869. Epub 2025 Aug 5.
Paroxysmal atrial fibrillation (AF) is a major cause of stroke but is often undetected in routine clinical practice. Effective stratification is needed to identify patients with stroke who might benefit the most from intensified AF screening. Several artificial intelligence models have been proposed to predict AF based on ECG in sinus rhythm, but broad implementation has been limited. The most valuable input features and most effective model design for AF prediction are also unclear.
We developed and tested AF prediction models utilising continuous electrocardiogram monitoring (CEM) recordings from the first 72 h after admission and multiple clinical input features from patients with stroke hospitalised at Charité, Berlin, Germany, between September 2020 and August 2023. We compared different models and input data to identify the best-performing model for prediction of AF. The relative contributions of different input data sources were assessed for explainability. A final model was externally validated using the first hour of monitoring data from the intervention group of the prospective multicentre MonDAFIS study.
The derivation dataset included 2068 patients with acute ischaemic stroke, of whom 469 (22.7%) had AF, first detected before or during the index hospital stay (366 vs. 103). In predicting newly detected AF, a Bayesian fusion model emerged as best, achieving a ROC-AUC of 0.89 (95% CI: 0.80, 0.96). Model introspection indicated that HRV was the main driver of the model's predictions. A final, simplified tree-based ensemble model using age and HRV parameters of the first hour of CEM data achieved similar performance (ROC-AUC 0.88, 95% CI: 0.79, 0.95). The final model consistently outperformed the AS5F score in a real-world scenario external validation on the MonDAFIS dataset (1519 patients, thereof 36 (2.37%) with AF; ROC-AUC 0.79 vs. ROC-AUC 0.69, p = 4.69e-03).
HRV appears to be the most informative variable for predicting AF. A computationally inexpensive model requiring only 1 h of single-lead CEM data and patients' age supports prediction of AF after acute ischaemic stroke for up to seven days. Such a model may enable risk-based stratification for cardiac monitoring, prioritising efforts where most needed to enhance AF screening efficiency and, ultimately, secondary stroke prevention.
This study was supported by the German Federal Ministry of Education and Research and the German Research Foundation.
阵发性心房颤动（AF）是中风的主要原因，但在常规临床实践中常未被发现。需要有效的分层来识别可能从强化AF筛查中获益最大的中风患者。已经提出了几种基于窦性心律心电图预测AF的人工智能模型，但广泛应用受到限制。AF预测最有价值的输入特征和最有效的模型设计也不清楚。
我们利用德国柏林夏里特医院2020年9月至2023年8月期间住院的中风患者入院后前72小时的连续心电图监测（CEM）记录和多个临床输入特征，开发并测试了AF预测模型。我们比较了不同的模型和输入数据，以确定预测AF的最佳模型。评估不同输入数据源的相对贡献以进行可解释性分析。使用前瞻性多中心MonDAFIS研究干预组的监测数据的第一个小时对最终模型进行外部验证。
推导数据集包括2068例急性缺血性中风患者，其中469例（22.7%）有AF，在索引住院期间或之前首次检测到（366例对103例）。在预测新检测到的AF方面，贝叶斯融合模型表现最佳，ROC-AUC为0.89（95%CI：0.80，0.96）。模型内省表明，心率变异性（HRV）是模型预测的主要驱动因素。使用CEM数据第一个小时的年龄和HRV参数的最终简化基于树的集成模型取得了类似的性能（ROC-AUC 0.88，95%CI：0.79，0.95）。在MonDAFIS数据集的真实场景外部验证中，最终模型始终优于AS5F评分（1519例患者，其中36例（2.37%）有AF；ROC-AUC 0.79对ROC-AUC 0.69，p = 4.69e-03）。
HRV似乎是预测AF最具信息性的变量。一个计算成本低的模型，仅需要1小时的单导联CEM数据和患者年龄，支持预测急性缺血性中风后长达7天的AF。这样的模型可以实现基于风险的心脏监测分层，在最需要的地方优先进行努力，以提高AF筛查效率，并最终预防继发性中风。
本研究由德国联邦教育与研究部和德国研究基金会支持。
