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510(k) Data Aggregation
(297 days)
The eWave monitor is an ambulatory ECG monitor intended to provide continuous extended duration cardiac monitoring. It is indicated for use on patients who may be asymptomatic or who may suffer from transient symptoms such as palpitations, shortness of breath, dizziness, lightheadedness, pre-syncope, fatigue, or anxiety.
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This document is an FDA 510(k) clearance letter for the eWave Monitor. While it describes the intended use and regulatory classification, it does not contain the detailed information required to fill out the table and answer all the questions about acceptance criteria and the study that proves the device meets them.
The information typically found in a 510(k) summary or a more detailed submission document, which is not provided here, would be needed to answer your specific questions.
Therefore, I cannot extract the requested information from the provided text.
However, based on the type of device (ambulatory ECG monitor), I can provide examples of what might be expected for such a device in a regulatory submission:
1. A table of acceptance criteria and the reported device performance
| Performance Metric | Acceptance Criteria (Example) | Reported Device Performance (Example) |
|---|---|---|
| Arrhythmia Detection Accuracy | ||
| - Sensitivity for AFib | ≥ 90% | 92.5% |
| - Specificity for AFib | ≥ 80% | 85.1% |
| - Sensitivity for Tachycardia | ≥ 90% | 91.0% |
| - Specificity for Tachycardia | ≥ 80% | 83.2% |
| - Sensitivity for Bradycardia | ≥ 90% | 90.5% |
| - Specificity for Bradycardia | ≥ 80% | 82.9% |
| Signal Quality | Percentage of analyzable data > 95% | 97.2% analyzable data |
| Wear Time | Mean wear time > 10 days | 12.3 days (average continuous wear) |
| False positive rate for critical arrhythmias | < 0.1 per 24 hours | 0.05 per 24 hours |
2. Sample size used for the test set and the data provenance
- Test Set Sample Size (Example): 500 patients / 10,000 hours of ECG data
- Data Provenance (Example): Retrospective, multi-center study from hospitals in the United States and Europe.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Number of Experts (Example): 3
- Qualifications (Example): Board-certified cardiologists or clinical electrophysiologists with 5+ years of experience in ECG interpretation and arrhythmia diagnosis.
4. Adjudication method for the test set
- Adjudication Method (Example): 2+1 (Two experts independently reviewed each ECG trace, and any discrepancies were resolved by a third senior expert).
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance
- This document does not indicate if an MRMC study was done, nor would it contain such detailed results. An ambulatory ECG monitor primarily focuses on automated detection and recording, not necessarily on improving human reader performance directly like an AI-assisted diagnostic imaging tool might. If an MRMC study were performed, it would likely focus on human over-read accuracy with and without the device's automated analysis.
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
- Yes (Likely): For an ECG monitor with automated arrhythmia detection, a standalone algorithm performance study is crucial. The acceptance criteria in point 1 (Sensitivity, Specificity) are typically derived from such a standalone study.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- Type of Ground Truth (Example): Expert consensus by a panel of cardiologists/electrophysiologists upon review of the full ECG recordings. In some cases, correlation with patient symptoms recorded in a diary might also be part of the ground truth establishment.
8. The sample size for the training set
- Training Set Sample Size (Example): 5,000 patients / 100,000 hours of ECG data (significantly larger than the test set).
9. How the ground truth for the training set was established
- Ground Truth for Training Set (Example): Similar to the test set, expert consensus or annotations by qualified ECG technicians under cardiologist supervision. This data would often be sourced from large, previously collected clinical datasets.
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