Quick Conclusion: S010 represents a major technical leap for SCG by moving from simple timing estimation to direct Cardiac Output prediction using Deep Learning. Validating against the gold standard RHC in a real-world heart failure cohort makes this a key supporting document for OpenSCG’s clinical monitoring claims.
📊 Key Accuracy Metrics
| Metric | Result |
|---|---|
| CO Bias (CO < 6 L/min) | 0.01 L/min |
| CO LoA (CO < 6 L/min) | 0.88 to 0.87 L/min |
| CI Bias (CI < 2.2 L/min/m2) | 0.07 L/min/m2 |
| CI LoA (CI < 2.2 L/min/m2) | 0.35 to 0.48 L/min/m2 |
| RMSE% | 22% for CO, 20% for CI |
🔍 Study Analysis
Objective & Population
Retrospective / Deep Learning Development & Validation. Cohort: Heart failure patients (NYHA class II-IV, predominantly HFrEF) (N=73).
What it Supports
The study supports the use of Deep Learning and triaxial SCG (combined with ECG and BMI) to non-invasively estimate Cardiac Output in adult heart failure patients. It is particularly effective in low-output states (CO < 6 L/min), which are of high clinical concern.
What it Does Not Support
The study does not support high-accuracy monitoring in subjects with high cardiac output (>= 6 L/min). It is not yet validated for independent clinical diagnostic use without invasive catheterization as a baseline in diverse clinical settings.
🛠 Technical Context
- DOI: 10.1016/j.amjcard.2025.09.037
- Authors: T. Wang et al.
- Confidence Tier: Supporting
