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The intended use of Afib detection program (QP-039P software) is used for processing adult patient if atrial fibrillation (AF) is present continuously for more than 2.5 minutes. QP-039P software is intended to be used by qualified health care professionals in hospital or clinical environment.

QP-039P is the atrial fibrillation (AF) processing software is intended to detect AF using patient's ECG. The Software is "Modular" and is to be used as an accessory to Patient Monitoring Devices (Host Devices). The software has a specific function to detect AF using RR interval and P wave and provides the result of atrial fibrillation detection to other software modules (See Figure1-1).

AF detection is performed using both the RR intervals and the P waves of input ECG. More precisely, the algorithm uses three features of input ECG to output AF detection result; RR irregularity, PR interval variability, and P wave variability.

The software detects if atrial fibrillation (AF) is present or not by 2 minutes of analysis at the earliest, and notifies AF presence every time it is detected.

Functionality shall include:

• 1. QP-039P receives RR intervals and ECG wave from other software modules.
• 2. QP-039P detects AF using three features for AF detection which are derived from input data, including RR irregularity, PR interval variability, and P wave variability.
• 3. QP-039P provides that AF is present or not to other software modules.

The Nihon Kohden Afib Detection Program QP-039P is software intended to detect atrial fibrillation (AF) continuously for more than 2.5 minutes in adult patients using their ECG.

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

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state numerical acceptance criteria (e.g., minimum sensitivity or specificity targets) for AF detection. However, it indicates comprehensive testing was performed to demonstrate "proper functional operation, correct implementation of risk control measures, and support substantial equivalence." The overall conclusion is that the device "performs as well or better than the predicate devices."

The document focuses on the type of tests conducted rather than specific quantitative performance metrics against predefined thresholds.

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

The document mentions "ECG waveform database testing" but does not specify the sample size (number of patients or ECG recordings) used for this test set nor the data provenance (e.g., country of origin, retrospective or prospective).

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

The document does not specify the number of experts used to establish the ground truth for the test set or their qualifications.

4. Adjudication Method for the Test Set

The document does not specify any adjudication method (e.g., 2+1, 3+1, none) for establishing ground truth regarding AF presence in the test set.

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

A multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned or detailed in the provided information. Therefore, no effect size of human readers improving with AI vs. without AI assistance can be determined from this document.

6. Standalone Performance Study

Yes, a standalone (algorithm only without human-in-the-loop performance) study was performed. The "ECG waveform database testing" would fall under standalone performance, as it tests the algorithm's detection capabilities against existing ECG waveforms with known conditions. The indications for use and description of the software as "detecting AF using patient's ECG" and "notifies AF presence every time it is detected" also confirm its standalone functionality.

7. Type of Ground Truth Used

The document does not explicitly state the specific type of ground truth used (e.g., expert consensus, pathology, outcomes data) for the "ECG waveform database testing." Given the context of arrhythmia detection, it is highly likely that the ground truth within the ECG waveform database would have been established by expert review of the ECGs, but this is not explicitly confirmed.

8. Sample Size for the Training Set

The document does not specify the sample size for the training set. It primarily focuses on the verification and validation of the device, implying that the algorithm development (training) phase occurred prior to these tests.

9. How the Ground Truth for the Training Set Was Established

The document does not specify how the ground truth for the training set was established.

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