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Eko Low Ejection Fraction Tool (ELEFT) is a software intended to aid clinicians in identifying individuals with Left Ventricular Ejection Fraction (LVEF) less than or equal to 40%. ELEFT takes as input ECG and heart sounds and is intended for use on patients at risk for heart failure. This population includes, but is not limited to, patients with: coronary artery disease; diabetes mellitus; cardiomyopathy; hypertension; and obesity.

The interpretations of heart sounds and ECG offered by the software are meant only to assist healthcare providers in assessing Left Ventricular Ejection Fraction ≤ 40% , who may use the result in conjunction with their own evaluation and clinical judgment. It is not a diagnosis or for monitoring of patients diagnosed with heart failure. This software is for use on adults (18 years and older).

Eko Low Ejection Fraction Tool (ELEFT) is an algorithm that is intended to aid clinicians to identify individuals with Left Ventricular Ejection Fraction (LVEF) less than or equal to 40%. ELEFT takes as input ECG and heart sounds from patients at risk for heart failure. The software uses signal processing as well as machine learning algorithms, to analyze the electrocardiogram (ECG) and heart sound/phonocardiogram (PCG) recording signals generated by FDA-cleared Eko Stethoscopes and saved as .WAV file recordings in the Eko Cloud. ELEFT is a machine learning based notification software which employs machine learning techniques to suggest the likelihood of LVEF

The Eko Low Ejection Fraction Tool (ELEFT) is a software intended to aid clinicians in identifying individuals with Left Ventricular Ejection Fraction (LVEF) less than or equal to 40%. The device takes ECG and heart sound inputs and processes them using signal processing and machine learning algorithms.

Here's an analysis of its acceptance criteria and the study proving its performance:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document doesn't explicitly state "acceptance criteria" in a numerical target format (e.g., "Sensitivity must be >= X%"). However, the clinical performance results presented demonstrate the device's capability to detect Low EF. The acceptance effectively hinges on the presented sensitivity and specificity values.

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: 3,456 unique subjects. After excluding 307 recordings due to poor ECG quality, the performance analysis was based on the remaining suitable recordings.
• Data Provenance: Retrospective data collected from:
  • US, 5 sites: 2,960 patients.
  • India, 1 site: 496 patients.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Number of Experts: Not explicitly stated as a number, but the ground truth for ejection fraction was "overread by a board-certified cardiologist." This implies at least one, and potentially multiple, board-certified cardiologists were involved in reviewing the echocardiogram results.
• Qualifications of Experts: Board-certified cardiologists.

4. Adjudication Method for the Test Set

The document does not explicitly describe an adjudication method like 2+1 or 3+1 for resolving discrepancies in ground truth establishment. It states that the "subject's true ejection fraction was measured by the echocardiogram machine's integrated cardiac quantification software at the echocardiogram and then overread by a board-certified cardiologist." This suggests a single expert review after automated measurement, with no mention of multiple reviewers or a formal reconciliation process if initial measurements or interpretations differed.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted. The study focuses solely on the standalone performance of the ELEFT algorithm without a human-in-the-loop component or evaluating the improvement of human readers with AI assistance.

6. Standalone (Algorithm Only) Performance

Yes, a standalone (algorithm only) performance study was conducted. The results for sensitivity, specificity, PPV, and NPV presented in Table 2 and the subsequent text (page 9) are for the ELEFT algorithm's performance in differentiating between Low EF (≤40%) and Normal EF (>40%).

7. Type of Ground Truth Used

The type of ground truth used was expert consensus / pathology based on instrumental measurements and expert review:

• Echocardiogram (Instrumental Measurement): The true ejection fraction was measured by the echocardiogram machine's integrated cardiac quantification software.
• Expert Overread: This measurement was "overread by a board-certified cardiologist."
• Categorization: Ejection status (Low EF or Normal EF) was then assigned based on these measured and reviewed values.

8. Sample Size for the Training Set

The sample size for the training set was 1,852 patients. This data was contributed from:

• US, 7 sites: 1,515 patients.
• India, 1 site: 337 patients.

9. How Ground Truth for the Training Set Was Established

The document does not explicitly detail the exact process for establishing ground truth for the training set. However, given the consistency in the data description and the validation methodology, it is highly probable that the ground truth for the training set was established using the same methodology as the test set: gold standard echocardiogram measurements, subsequently overread by board-certified cardiologists, and then categorized into Low EF (≤40%) or Normal EF (>40%).

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