Prospective Validation of a Machine-Learning Algorithm Using Photoplethysmography Signals for Early Detection of Atrial Fibrillation During Remote Telemonitoring
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 200
- 试验地点
- 1
- 主要终点
- Diagnostic accuracy (area under the ROC curve) of the PPG-based machine-learning algorithm for detecting clinically relevant AF (≥ 30s), compared with gold-standard 12-lead ECG
研究概览
简要总结
This is a prospective study validating a new machine-learning algorithm that detects atrial fibrillation (AF) from photoplethysmography (PPG) signals, developed for integration into the Seerlinq remote monitoring platform. This algorithm builds on the same core PPG signal-processing technology as Seerlinq's HeartCore device, a CE-certified (Class IIb, MDR) device that monitors left ventricular filling pressures in heart failure patients. The algorithm will be validated through internal cross-validation, external validation against an independent cohort with paired PPG-ECG recordings, and validation in a cohort of patients with paroxysmal atrial fibrillation and frequent sinus-AF transitions.
详细描述
Atrial fibrillation (AF) and heart failure (HF) frequently coexist and share a bidirectional causal relationship; their concurrence is associated with worse clinical outcomes. Early detection of AF may enable timely intervention and improve outcomes. This study is prospectively validating a machine-learning algorithm for AF detection from PPG signals, intended for integration into the Seerlinq remote monitoring platform. This algorithm builds on the same core PPG signal-processing technology as Seerlinq's HeartCore device (a CE-certified, Class IIb device under the EU MDR that monitors left ventricular filling pressures in heart failure patients). It is a stand-alone algorithm designed specifically to detect clinically relevant (≥ 30s) atrial fibrillation.
Validation of the algorithm will proceed in three stages: (1) internal cross-validation; (2) external validation against an independent cohort with paired PPG-ECG recordings, to confirm generalizability; and (3) validation in a cohort of patients with paroxysmal atrial fibrillation and frequent sinus-AF transitions, to assess performance during clinically challenging rhythm changes.
The study is enrolling toward an estimated 1,000 unique PPG recordings. A 12-lead ECG is used to confirm cardiac rhythm classification (gold standard) as the reference for evaluating algorithm performance.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Adults ≥18 years with a diagnosis of heart failure (HFrEF, HFmrEF, or HFpEF)
- •12-lead ECG performed to confirm cardiac rhythm classification (AF vs. non-AF)
排除标准
- •Missing a valid PPG recording
研究组 & 干预措施
Documented AF
HF patients with a history of permanent/paroxysmal AF and AF documented on 12-lead ECG at enrollment
干预措施: PPG-based AF detection algorithm (Other)
Non-AF
HF patients in sinus rhythm on the index 12-lead ECG with no prior documented AF episodes
干预措施: PPG-based AF detection algorithm (Other)
结局指标
主要结局
Diagnostic accuracy (area under the ROC curve) of the PPG-based machine-learning algorithm for detecting clinically relevant AF (≥ 30s), compared with gold-standard 12-lead ECG
时间窗: Through study completion (estimated November 2026)
次要结局
- Positive predictive value and negative predictive value(Through study completion (estimated November 2026))
- Sensitivity and specificity of the algorithm at the Youden-optimal threshold(Through study completion (estimated November 2026))
- Average precision(Through study completion (estimated November 2026))
- Model calibration(Through study completion (estimated November 2026))
- Matthews correlation coefficient(Through study completion (estimated November 2026))
- Overall classification accuracy(Through study completion (estimated November 2026))
- Specificity and false-positive rate in the subgroup with frequent atrial/ventricular extrasystoles(Through study completion (estimated November 2026))
- Accuracy of AF detection during sinus-AF transitions at the individual patient level(Through study completion (estimated November 2026))