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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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Indicated for central monitoring of multiple adult, pediatric, and neonatal patients; and where the clinician decides to monitor cardiac arrhythmia of adult, pediatric, and neonatal patients and/or ST segment of adult patients to gain information for treatment, to monitor adequacy of treatment, or to exclude causes of symptoms.

M3290A IntelliVue Information Center Software, Release L.0

This Philips Medical Systems 510(k) submission (K081983) for the M3290A IntelliVue Information Center Software, Release L.0, primarily focuses on demonstrating substantial equivalence to predicate devices based on modifications that add new features and integrations. The document does not contain specific acceptance criteria, reported device performance metrics against those criteria, or details of a study that statistically proves the device meets such criteria in terms of analytical or clinical performance (e.g., sensitivity, specificity, accuracy for arrhythmia detection or ST-segment monitoring).

Instead, the submission emphasizes that "Verification, validation, and testing activities establish the performance, functionality, and reliability characteristics of the new device with respect to the predicate. Testing involved system level tests, performance tests, and safety testing from hazard analysis. Pass/Fail criteria were based on the specifications cleared for the predicate device and test results showed substantial equivalence. The results demonstrate that M3290A IntelliVue Information Center Software, Release L.0 meets all defined reliability requirements and performance claims."

This statement indicates that the testing performed was primarily to ensure the new software release maintained equivalence to the previously cleared versions and met established internal specifications and safety requirements, rather than presenting a de novo clinical performance study against specific, quantifiable acceptance criteria for diagnostic accuracy.

Therefore, the requested information elements related to specific performance metrics, sample sizes for test/training sets, expert qualifications, and comparison studies are not available in the provided document.

Here's a breakdown of what is and is not present, based on the input:

Acceptance Criteria and Device Performance

• 1. A table of acceptance criteria and the reported device performance:

  • Not provided. The document states "Pass/Fail criteria were based on the specifications cleared for the predicate device," but these specific criteria and the corresponding performance results (e.g., accuracy, sensitivity, specificity for arrhythmia detection) are not detailed.

• 2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

  • Not provided. The document mentions "system level tests, performance tests, and safety testing," but does not specify sample sizes for any test sets or the origin/nature of the data used in these tests.

• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):

  • Not provided. Given that specific performance metrics and test sets are not detailed, information about expert ground truth establishment is absent.

• 4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

  • Not provided.

• 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:

  • Not provided and not applicable for this type of device. The device is a patient monitoring information center software, not an AI-assisted diagnostic tool that would typically involve human readers. The mention of "Integration of the ST/AR J.0 algorithm (K080461)" suggests algorithmic processing within the device itself, not necessarily for human interpretation improvement in an MRMC study context.

• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

  • Likely yes, implicitly, but no specific study details are provided. The testing mentioned (system level, performance, safety) for the "ST/AR J.0 algorithm" integration would imply standalone performance evaluation against specifications, but no data is presented.

• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):

  • Not provided. The document states "Pass/Fail criteria were based on the specifications cleared for the predicate device." For physiological monitoring, ground truth often involves expert review of raw physiological waveforms or correlation with other established diagnostic methods, but specifics are missing here.

• 8. The sample size for the training set:

  • Not provided. This information is typically relevant for machine learning algorithms, and while an "ST/AR J.0 algorithm" is mentioned, no details about its development or training are included in this 510(k) summary.

• 9. How the ground truth for the training set was established:

  • Not provided.

Summary Conclusion:

The provided 510(k) summary focuses on demonstrating substantial equivalence to predicate devices by detailing new features and stating that "Verification, validation, and testing activities establish the performance, functionality, and reliability characteristics of the new device with respect to the predicate." It indicates that "Pass/Fail criteria were based on the specifications cleared for the predicate device and test results showed substantial equivalence." However, it does not provide the specific acceptance criteria, reported performance metrics, or the detailed study design elements requested in the prompt, such as sample sizes, data provenance, expert qualifications, or ground truth establishment methods for a clinical performance study. The submission appears to rely on the established performance of its predicate devices and internal verification activities for the new software release.

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