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510(k) Data Aggregation

    K Number
    K240032
    Manufacturer
    Date Cleared
    2024-04-17

    (104 days)

    Product Code
    Regulation Number
    880.2800
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    Celerity Chemical Indicator for enspire 3000 CLCSPS

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The enspire 3000 Chemical Indicator is a peracetic acid concentration indicator for routine monitoring of the liquid chemical sterilization cycle of the enspire 3000 employing S40 Sterilant.

    The unprocessed enspire 3000 Chemical Indicator is blue. When exposed in the enspire 3000 processor to a concentration of >1820 ppm (mg/L) peracetic acid found in the S40 use dilution during a controlled 6-minute exposure at 45.5 - 60°C, the indicator color changes from the start to the pass color. See reference colors on the bottle.

    Device Description

    The enspire 3000 Chemical Indicator is a chemical indicator strip consisting of indicator ink containing the reactive chemicals printed on one end of a polypropylene strip. The product is manufactured by application of the indicator ink by screen printing to a substrate with the indicator ink printed thereon. A clear, sterilant-permeable polyether block amide laminate is adhesively bonded to the polypropylene strip following printing of the ink, completely covering the ink.

    AI/ML Overview

    Here's a breakdown of the acceptance criteria and study information for the Celerity Chemical Indicator for enspire 3000 CLCSPS, based on the provided document:

    1. Table of Acceptance Criteria and Reported Device Performance

    Testing CategoryAcceptance CriteriaReported Device Performance
    Non-Reactive Ink Suitability Study100% of indicator text ink shows no smearing, no discoloration and no fadingPASS
    Comparative SensitivityA minimum of 75% of indicator from each lot to show a PASS result when exposed to Pass cyclePASS (100% PASS)
    Comparative Specificity100% of indicators to show a FAIL result when exposed to the Fail cyclePASS (100% FAIL)
    Analytic SensitivityA minimum of 75% of indicator from each lot to show a PASS result when exposed to Pass cyclePASS (100% PASS)
    Analytic Specificity100% of indicators to show a FAIL result when exposed to the Fail cyclePASS (100% FAIL)
    Post-Processing Stability (Outside Processor) StudyA minimum of 75% of indicator from each lot to show a PASS result when exposed to Pass cycle at each time point; 100% of indicators to show a FAIL result when exposed to the Fail cycle at each time pointPASS
    Post-Processing Stability (Inside Processor) StudyA minimum of 75% of indicator from each lot to show a PASS result when exposed to Pass cycle at each time point; 100% of indicators to show a FAIL result when exposed to the Fail cycle at each time point; 100% CI from each lot correctly interpreted by inexperienced readerPASS
    Blind StudyA minimum of 75% of indicator from each lot to show a PASS result when exposed to Pass cycle; 100% of indicators to show a FAIL result when exposed to the Fail cycle; 100% CI from each lot correctly interpreted by inexperienced readerPASS
    Contaminants Study100% of indicators to show a FAIL result when exposed to the Fail cyclePASS
    Exposure to Temperature Extremes StudyCI start color to remain unchanged after exposure to three freeze/thaw cycles before processing; ≥75% CI from each lot to show a PASS result; 100% of indicators to show a FAIL result when exposed to the Fail cyclePASS
    Light Stability StudyCI start color to remain unchanged after exposure to fluorescent light before processing; ≥75% of indicator from each lot to show a PASS result when exposed to Pass cycle; 100% of indicators to show a FAIL result when exposed to the Fail cyclePASS
    Open Bottle Stability Study≥75% of indicator from each lot to show a PASS result when exposed to Pass cycle; 100% of indicators to show a FAIL result when exposed to the Fail cyclePASS
    Shelf Life Study≥75% of indicator from each lot to show a PASS result when exposed to Pass cycle; 100% of indicators to show a FAIL result when exposed to the Fail cyclePASS
    Human Factors StudyUsers complete all critical tasksPASS

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

    • Test Set Sample Size:
      • For the Comparative Sensitivity, Comparative Specificity, Analytic Sensitivity, and Analytic Specificity studies (and likely many other studies based on the acceptance criteria phrase "each lot"), 3 lots of the proposed device were tested.
      • The exact number of individual indicators within each lot used for testing is not explicitly stated. However, the acceptance criteria are given as percentages of indicators ("minimum of 75% of indicator from each lot" or "100% of indicators").
    • Data Provenance: The document does not specify the country of origin of the data. The studies are non-clinical performance testing, meaning they are laboratory or engineering-based tests, not human subject studies. They are presumably prospective for this submission.

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

    • Number of Experts: For the "Post-Processing Stability (Inside Processor) Study" and the "Blind Study," it mentions "inexperienced reader." This implies at least one, and likely more, inexperienced readers were used. It does not state how many experts, if any, were used alongside or to establish ground truth for these readings beyond the "inexperienced reader."
    • Qualifications of Experts: The document specifies "inexperienced reader" for certain studies. It does not provide further qualifications for these readers or for any experts involved in establishing ground truth for other tests. For most of the chemical indicator tests, the "ground truth" is intrinsically defined by the chemical conditions (concentration of peracetic acid, temperature, time) to which the indicator is exposed.

    4. Adjudication Method for the Test Set

    • The document does not describe an adjudication method for reconciling interpretations from multiple readers. For tests involving "inexperienced readers," it simply states whether "100% CI from each lot correctly interpreted." This suggests a binary outcome (correct/incorrect) without detail on reconciliation if there were multiple readers.

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

    • No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. The performance testing is for a chemical indicator, not a diagnostic imaging device that typically uses MRMC studies. The "Human Factors Study" is mentioned, indicating usability assessment, but not an MRMC study related to diagnostic accuracy improvement.

    6. Standalone (i.e., algorithm only without human-in-the-loop performance) Study

    • This device is a chemical indicator, which is a physical material that changes color based on chemical exposure. It is not an algorithm or software. Therefore, the concept of a "standalone algorithm" study does not apply. Its performance is inherent to the material and its chemical reaction, which is then visually interpreted.

    7. Type of Ground Truth Used

    • The ground truth for this device is based on defined chemical conditions (e.g., concentration of peracetic acid, exposure time, temperature). For "Pass Conditions" and "Fail Conditions," specific mg/L of peracetic acid (PAA) are used as the reference standard (e.g., ≥2200 mg/L PAA for Pass, 1750-1820 mg/L PAA and 0 mg/L PAA for Fail). This is a highly controlled and objective measure of the chemical environment.

    8. Sample Size for the Training Set

    • This device is a passive chemical indicator, not an AI/ML algorithm. Therefore, there is no training set in the typical sense used for machine learning models. The device's color change mechanism is based on established chemical principles, not on learned patterns from a dataset.

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

    • As there is no training set for an AI/ML algorithm, this question is not applicable. The "ground truth" for the device's design and verification is based on fundamental chemistry and engineering principles for chemical indicators.
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