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Bunkerhill ECG-EF is software intended to aid in screening for Left Ventricular Ejection Fraction (LVEF) less than or equal to 40% in adults at risk for, but not already diagnosed with low LVEF.

Bunkerhill ECG-EF is not intended to be a stand-alone diagnostic device for cardiac conditions, should not be used for monitoring of patients, and should not be used on ECGs with a paced rhythm. A positive result may suggest the need for further clinical evaluation in order to establish a diagnosis of Left Ventricular Ejection Fraction (LVEF) less than or equal to 40%.

Additionally, if the patient is at high risk for the cardiac condition, a negative result should not rule out further non-invasive evaluation.

Bunkerhill ECG-EF is adjunctive and must be interpreted in conjunction with the clinician's judgment, the patient's medical history, symptoms, and additional diagnostic tests. For a final clinical diagnosis, further confirmatory testing, such as echocardiography, is required.

ECG-EF is a software-only medical device that employs deep learning algorithms to analyze 12-lead ECG data for the detection of low left ventricular ejection fraction (LVEF < 40%). The algorithm processes 10-second ECG waveform snippets, providing predictions to assist healthcare professionals in the early identification of patients at risk for heart failure.

ECG-EF algorithm receives digital 12-lead ECG data and processes it through its machine learning model. The output of the analysis is transmitted to integrated third-party software systems, such as Electronic Medical Records (EMR) or ECG Management Systems (EMS). The results are displayed by the third-party software on a device such as a smartphone, tablet, or PC.

ECG-EF algorithm produces a result indicating "Low EF Screen Positive - High probability of low ejection fraction based on the ECG", " Low EF Screen Negative - Low probability of low ejection fraction based on the ECG" or "Error – device input criteria not met " for cases that do not meet data input requirements. These results are not intended to be definitive diagnostic outputs but rather serve as adjunctive information to support clinical decision-making. A disclaimer accompanies the output, stating: "Not for diagnostic use. The results are not final and must be reviewed alongside clinical judgment and other diagnostic methods."

The Low Ejection Fraction AI-ECG Algorithm device is intended to address the unmet need for a point-of-care screen for LVEF less than or equal to 40% and is expected to be used by cardiologists, front-line clinicians at primary care, urgent care, and emergency care settings, where cardiac imaging may not be available or may be difficult or unreliable for clinicians to operate. Clinicians will use the Low Ejection Fraction AI-ECG Algorithm to aid in screening for LVEF less than or equal to 40% and making a decision for further cardiac evaluation.

Here's a breakdown of the acceptance criteria and the study details for the Bunkerhill ECG-EF device, based on the provided FDA 510(k) Clearance Letter:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size for the Test Set and Data Provenance

• Sample Size for Test Set: 15,994 patient records.
• Data Provenance:
  • Country of Origin: United States.
  • Source: Two health systems.
  • Type: Retrospective study.
  • Diversity: Representative of the U.S. population (65.5% White, 18.8% Hispanic, 5.7% American Indian or Alaska Native, 3.9% Asian, 3.0% Black/African American, 2.8% Other; 53% Male, 47% Female).
  • Geographical Distribution: Curated from 5 geographically distributed sites throughout the United States.

3. Number of Experts and Qualifications for Ground Truth

The document does not explicitly state the number of experts used or their specific qualifications for establishing the ground truth. It only mentions that the ground truth was established from echocardiograms.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method (e.g., 2+1, 3+1, none) for the test set. The ground truth was derived directly from echocardiogram measurements.

5. MRMC Comparative Effectiveness Study

The document does not mention a Multi-Reader Multi-Case (MRMC) comparative effectiveness study or any effect size of human readers improving with AI vs. without AI assistance. The study focuses solely on the standalone performance of the AI algorithm.

6. Standalone Performance Study (Algorithm Only)

Yes, a standalone study evaluating the algorithm's performance without human-in-the-loop was conducted. The performance metrics (Sensitivity, Specificity, PPV, NPV) and the confusion matrix presented are for the algorithm's direct output.

7. Type of Ground Truth Used

The ground truth used was Transthoracic Echocardiogram (TTE) with disease, specifically using the Simpson's Biplane measurement method to determine Left Ventricular Ejection Fraction (LVEF) less than or equal to 40%. The echocardiogram was taken less than 15 days apart from the ECG scan.

8. Sample Size for the Training Set

The document does not explicitly state the sample size used for the training set. It only mentions the retrospective study for validation involved 15,994 patient records.

9. How Ground Truth for the Training Set Was Established

The document states that the "Ground Truth for Model Training" was Transthoracic echocardiogram (TTE) with disease. It can be inferred that this same method (TTE, likely Simpson's Biplane) was used to establish ground truth for the training data, similar to the test set, but specific details on the process for the training set are not provided.

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