(143 days)
The Revogene® instrument is intended for in vitro diagnostic (IVD) use in performing nucleic acid testing of specific IVD assays in clinical laboratories. Revogene is capable of automated lysis and dilution of samples originating from various clinical specimen types. Revogene performs automated amplification and detection of target nucleic acid sequences by fluorescence-based real-time PCR.
The Revogene was previously cleared under K170558. Meridian Biosciences, Inc. is submitting this 510(k) to implement a software modification to the Revogene that updates the current software with a PMT surveillance algorithm. The software monitors raw data fluorescence signal during assay testing and identifies issues due to a malfunction of the photomultiplier tube (the "PMT"), a key component in the Revogene instrument's optics system used in the management of fluorescence signals. Upon detection of a PMT malfunction, the PMT surveillance algorithm software produces a specific error code to the user labeled "Detection Error" and will lock the instrument thereby preventing further use.
The provided text describes a 510(k) submission for a software modification to the Revogene instrument, specifically the addition of a PMT (photomultiplier tube) surveillance algorithm. This modification is intended to monitor raw data fluorescence signals and identify issues due to a malfunction of the PMT, a key component in the instrument's optics system. Upon detection of a PMT malfunction, the algorithm produces an error code and locks the instrument, preventing further use.
The document states that this change does not affect the device's intended use nor alter the device's fundamental scientific technology. Therefore, the acceptance criteria and performance study details are focused on validating the new PMT surveillance algorithm's functionality and ensuring it does not negatively impact the previously cleared performance of the Revogene instrument.
Based on the provided text, a formal table of acceptance criteria and reported device performance, akin to what would be provided for a diagnostic or AI algorithm's clinical performance, is not explicitly present for the PMT surveillance algorithm itself. The document emphasizes that the modification is minor and focuses on the software's ability to detect and report PMT malfunctions.
However, we can infer the acceptance criteria and study proving the device meets them from the description of the software modification and the context of a 510(k) submission for a software update.
Here's a breakdown based on the provided information, addressing each point as much as possible:
Acceptance Criteria and Reported Device Performance
The core acceptance criterion for this software modification is that the PMT surveillance algorithm successfully detects and reports PMT malfunctions. The reported performance would be the successful implementation of this functionality.
Inferred Acceptance Criteria Table:
| Acceptance Criterion (Inferred) | Reported Device Performance (Inferred) |
|---|---|
| Functional Requirement 1: Accurate detection of PMT malfunctions | The PMT surveillance algorithm successfully monitors raw data fluorescence signals. |
| Functional Requirement 2: Generation of specific error code | Upon detection of a PMT malfunction, the software produces a specific error code labeled "Detection Error". |
| Functional Requirement 3: Instrument lock-out | Upon detection of a PMT malfunction, the instrument is locked, preventing further use. |
| Non-Functional Requirement 1: No impact on intended use | The software update does not affect the Revogene's intended use (in vitro diagnostic nucleic acid testing) as previously cleared. |
| Non-Functional Requirement 2: No alteration of fundamental scientific technology | The software update does not alter the fundamental scientific technology (fluorescence-based real-time PCR) of the Revogene instrument. |
Study Proving Acceptance Criteria:
The document implicitly indicates that a validation study was performed to demonstrate the functionality of the PMT surveillance algorithm. While details are scarce, the submission implies that the testing confirmed the algorithm's ability to detect PMT issues and trigger the appropriate error and lock-out mechanisms.
Detailed Study Information (Based on Inferences and General 510(k) Practices for Software Updates)
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A table of acceptance criteria and the reported device performance:
(See above table for inferred criteria and performance, as direct explicit table is not provided in the document for the new software feature). The document stresses that the overall performance characteristics of the Revogene instrument remain as previously cleared (K170558, K170557, etc.), and this software update doesn't change those. -
Sample sizes used for the test set and the data provenance:
- Test Set Sample Size: Not explicitly stated for the PMT surveillance algorithm. For a software update of this nature (detecting a hardware malfunction), the "test set" would likely involve inducing PMT malfunctions (or simulating conditions that would lead to them) on multiple instruments to verify the algorithm's response. The general statement "The submitted information demonstrates that the modified Revogene instrument is safe, effective" implies a sufficient level of testing.
- Data Provenance: Not specified. Given it's a software update for a commercialized instrument, the testing would typically be performed internally by the manufacturer (Meridian Bioscience, Inc.). It's likely retrospective in that it's testing a new feature on existing hardware, but the testing itself would be prospective for evaluating the new software. Country of origin not specified, but the manufacturer is based in Ohio, USA.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- This is unlikely to involve a panel of "experts" in the same way an AI diagnostic algorithm's ground truth is established. The ground truth for a PMT malfunction would be a measurable hardware degradation or induced failure that clearly indicates the PMT is not functioning correctly. This would be established by engineers or instrument specialists, not clinical experts like radiologists.
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Adjudication method for the test set:
- Not applicable in the context of this software update. Adjudication methods like 2+1 or 3+1 are typically for establishing ground truth for subjective human interpretations (e.g., medical image reads). Here, the judgment is objective: either the PMT is malfunctioning or it's not, and the software either detects it or it doesn't.
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If a multi-reader multi-case (MRMC) comparative effectiveness study was done:
- No. An MRMC study is not relevant for this type of software modification. MRMC studies are used to evaluate the impact of an AI algorithm on human reader performance for tasks involving perception and interpretation (e.g., diagnosing disease from medical images). This software is performing an automated internal diagnostic check on the instrument itself, not assisting human clinical interpretation.
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If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- Yes, implicitly. The PMT surveillance algorithm operates automatically as an internal check. Its performance is evaluated purely on its ability to detect PMT malfunctions and trigger the pre-defined error and lock-out, without human intervention in its real-time operation.
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The type of ground truth used:
- Instrumental/Hardware Ground Truth: The ground truth would be based on objective measurements and engineered conditions that reliably indicate a PMT malfunction within the Revogene instrument's optics system. This could involve simulating PMT degradation, intentionally causing component failures, or verifying against known hardware states.
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The sample size for the training set:
- Not specified. For a diagnostic algorithm like this, the "training set" would involve data collected from instrument operations, potentially including data from instruments with known good or failing PMT conditions, to develop and refine the detection algorithms. The complexity of the algorithm (e.g., rule-based vs. machine learning) would influence the need for and size of a specific "training set." Given the description, it sounds more like a rule-based or threshold-based detection system rather than a complex machine learning model that requires a large, annotated training set in the typical sense.
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How the ground truth for the training set was established:
- If a "training set" was used (e.g., for setting detection thresholds or developing rules), the ground truth would have been established by engineering teams through controlled experiments, measurements of PMT performance over time, and potentially by inducing known PMT issues on instruments. This would involve characterization of the instrument's optical signals under various conditions, including states indicative of PMT malfunction.
§ 862.2570 Instrumentation for clinical multiplex test systems.
(a)
Identification. Instrumentation for clinical multiplex test systems is a device intended to measure and sort multiple signals generated by an assay from a clinical sample. This instrumentation is used with a specific assay to measure multiple similar analytes that establish a single indicator to aid in diagnosis. Such instrumentation may be compatible with more than one specific assay. The device includes a signal reader unit, and may also integrate reagent handling, hybridization, washing, dedicated instrument control, and other hardware components, as well as raw data storage mechanisms, data acquisition software, and software to process detected signals.(b)
Classification. Class II (special controls). The device is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 862.9. The special control is FDA's guidance document entitled “Class II Special Controls Guidance Document: Instrumentation for Clinical Multiplex Test Systems.” See § 862.1(d) for the availability of this guidance document.