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The Nano-Check ™AMI 3 IN 1 Test is a rapid immunoassay for the qualitative determination of Cardiac Troponin I (cTnl), Creatine Kinase MB (CK-MB), and Myoglobin in human serum and plasma specimens at cutoff concentrations of 0.5 ng/ml, 5.0 ng/ml, and 80 ng/ml, respectively, as an aid in the diagnosis of Acute Myocardial Infarction (AMI).

The Nano-Check™ AMI 3 IN 1 Test is a qualitative assay, which can not monitor the rise and fall of cTnl, CK-MB, and Myoglobin in single testing. Single testing is not recommended for AMI monitoring. Test results should be interpreted by the physician in conjunction with other laboratory test results and patient clinical findings.

The Nano-Check ™ AMI 3 IN 1 Test is a one-step lateral flow immunochromatography assay for the qualitative determination of three cardiac markers simultaneously in serum and heparin plasma.

The test is a single-use, visually read, cassette device in a plastic housing. Membrane strip inside the plastic housing contains immobilized molecules at three test lines and one control line; CK-MB antibody, Myoglobin antibody, streptavidine and goat anti-mouse antibody. Dye pad at the end of the membrane strip contains biotinylated cTnl antibody and gold colloidal particles coupled with CK-MM, cTnl and Myoqlobin antibodies. Cutoff level of each marker is 0.5 ng/ml, 5 ng/ml and 80 ng/ml for cTnl. CK-MB and Myoglobin respectively.

Device is sealed in pouch with desiccant and provided with instructions for use and disposable sample dropper.

The provided document is a 510(k) summary for the Nano-Check™ AMI 3 IN 1 Test, which is a qualitative immunochromatography assay for cardiac markers. This type of regulatory submission focuses on demonstrating substantial equivalence to a predicate device rather than providing detailed clinical study results with specific acceptance criteria and performance metrics typically found in a clinical trial report for AI/CADe devices.

Therefore, much of the requested information (like specific acceptance criteria values, sample sizes for test sets, number/qualifications of experts, adjudication methods, MRMC studies, standalone performance with metrics like sensitivity/specificity/AUC, detailed ground truth establishment for training, and training set size) is not present in this document. This submission primarily relies on "analytical performance" and "method comparison" studies, not complex clinical effectiveness studies with human readers or large-scale AI validation.

Here's a summary of what can be extracted or inferred:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state "acceptance criteria" as clear numerical thresholds for performance metrics. Instead, it refers to "overall performance and characteristics" and "analytical performance" being addressed to support substantial equivalence. The "performance" is demonstrated by addressing analytical characteristics and comparing them to a predicate device.

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

• Sample Size: Not specified in the provided summary. The document mentions "Method Comparison with Predicate Device" and "Analytical Performance" studies, but does not provide the number of samples used in these non-clinical tests.
• Data Provenance: Not specified. Given it's a 510(k) for an IVD, these stability and analytical studies are typically conducted at the manufacturer's site or contracted labs. Country of origin for data is not mentioned.
• Retrospective/Prospective: Not specified, but analytical and method comparison studies are often conducted using banked samples (retrospective) or spiked samples.

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

• Not Applicable. This device is a diagnostic assay (an IVD) run on a sample (serum/plasma), not an imaging AI device that relies on expert interpretation for ground truth. The "ground truth" for such assays is established through reference methods (e.g., highly accurate laboratory analyzers) and spiked samples with known concentrations. The summary does not involve human readers interpreting images.

4. Adjudication Method for the Test Set:

• Not Applicable. See point 3. This is an in-vitro diagnostic device, not an AI/CADe system requiring human adjudication of interpretations.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was Done:

• No. An MRMC study is not mentioned or implied because this is an in-vitro diagnostic test. These studies are relevant for imaging devices where human readers interpret medical images with and without AI assistance.

6. If a Standalone (algorithm only without human-in-the-loop performance) was Done:

• Yes, implicitly. The entire device is the "algorithm only" in the context of its function as an IVD. It's a qualitative test that produces a visual result (red bands) indicating the presence or absence of cardiac markers above a cutoff. Its performance is evaluated through analytical studies (precision, accuracy relative to reference, detection limit, specificity, etc.) which collectively represent its standalone performance characteristics. However, detailed results of these studies (e.g., sensitivity, specificity derived from analytical studies) are not provided in this summary.

7. The Type of Ground Truth Used:

• For analytical performance studies (Detection Limit, Analytical Specificity, Assay Cut-off), the ground truth would typically be established using:
  • Known concentrations: Samples (serum/plasma) spiked with known, precise concentrations of cTnl, CK-MB, and Myoglobin.
  • Reference methods: Comparison against established, high-accuracy laboratory reference assays for these cardiac markers.
  • Clinical correlation: While not a direct "ground truth" for the device's output, the overall utility is "as an aid in the diagnosis of Acute Myocardial Infarction (AMI)," implying that the presence of these markers above the cutoff correlates with AMI, which would be pathology-confirmed or clinically adjudicated. However, the study supporting this 510(k) focused on analytical performance and comparison to a predicate, not clinical outcomes directly.

8. The Sample Size for the Training Set:

• Not Applicable/Not Specified. This device is a lateral-flow immunochromatographic assay, not a machine learning or AI model that requires a distinct "training set" in the conventional sense. Its "training" is inherent in the chemical and manufacturing process design to ensure appropriate binding and visual signaling at the defined cutoffs.

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

• Not Applicable/No training set in the conventional sense. As explained in point 8, this device doesn't have a "training set" like an AI algorithm. The assay's "truth" (i.e., its ability to correctly identify positive/negative samples) is built into its biochemical design and validated through analytical studies, as outlined in section 7a.

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The Nano-Check™ DAT 5 Multi Drug Screening Test for Marijuana, Opiates, Cocaine, Methamphetamine and Phencyclidine is a rapid, self-controlled immunoassay for the qualitative detection of Cannabinoids (THC), Opiates (OPI), Benzoylecgonine (COC), Methamphetamine (mAMP) and Phencyclidine (PCP) compounds and their metabolites in human urine. The detection limits (cut-off concentrations) of this test are as follows: Cannabinoids at 50 ng/ml, Opiates at 2000 ng/ml, Cocaine at 300 ng/ml, Methamphetamine at 1000 ng/ml and Phencyclidine at 25 ng/ml. This assay is intended for Professional and Laboratory In-Vitro Use Only.

The Nano-Check™ DAT 5M test is a one step, type II, competitive immuochromatographic assay for the qualitative detection of Cannabinoid, Opiate, Benzoylecgonine, Methamphetamine and Phencyclidine compounds and their metabolites in human urine. The Nano-Check™ DAT 5M test device contains a membrane strip on which either antibodies against drug or drug conjugate to protein are immobilized at each specific test line. The colored indicator antibody or antigen coupled with Gold colloidal particles is place at the end of membrane.

The test is a single-use visually read cassette device in a plastic housing. It contains the test strip containing 5 test lines and 1 control line. Urine sample can be dropped onto sample well using plastic disposable dropper, which is provided. Drug positive urines will not show a colored band, while drug negative urine sample or urine sample containing drugs below cutoff level will generate red colored band.

The device is sealed in a pouch desiccant and provided with instructions for use and a disposable sample dropper.

The provided document describes the Nano-Check™ DAT 5 Multi Drug Screening Test for Cannabinoids (THC), Opiates (OPI), Cocaine (COC), Methamphetamine (mAMP), and Phencyclidine (PCP).

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state acceptance criteria in terms of performance metrics (e.g., sensitivity, specificity, accuracy) with specific thresholds. Instead, it focuses on demonstrating substantial equivalence to predicate devices. The "acceptance criteria" can be inferred from the comparison table highlighting similarities and the successful outcome of the non-clinical tests. The primary performance characteristic mentioned is the detection limits (cut-off concentrations).

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

The document explicitly states "Discussion of Clinical Tests Performed: Not Applicable." This indicates that no clinical test set with human subject samples was used for this submission. The evaluation was based on non-clinical tests performed to demonstrate analytical performance and comparison to predicate devices.

Therefore:

• Sample size for the test set: Not applicable (no clinical test set).
• Data provenance: Not applicable (no clinical data).

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

Since no clinical tests were performed and thus no clinical test set was used, there were no experts involved in establishing ground truth for a clinical test set.

4. Adjudication Method for the Test Set

As there was no clinical test set involved, there was no adjudication method applied.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, an MRMC comparative effectiveness study was not done. The submission explicitly states "Discussion of Clinical Tests Performed: Not Applicable," and the performance evaluation was based on analytical characteristics and comparison to existing predicate devices.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, in a sense, a "standalone" performance was evaluated, though it's important to clarify the context. The device is a visually read cassette, meaning a human reads the results. However, the tests performed (analytical performance, detection limit, specificity, etc.) evaluate the device's intrinsic ability to detect the drugs at specified cut-off levels prior to human interpretation. The analytical performance data represents this standalone capability.

7. The Type of Ground Truth Used

The ground truth for the non-clinical tests would have been established by:

• Known concentrations of drug analytes: For tests like precision/reproducibility, detection limit, and assay cut-off, spiked urine samples or controlled synthetic urine samples with precisely known concentrations of the target drugs and their metabolites would be used.
• Known interfering substances: For analytical specificity, samples with known concentrations of potential interfering substances would be used.
• Predicate device results: For method comparison studies, the results obtained from the predicate devices (ACON test strips) on the same samples would serve as a comparative "ground truth."

8. The Sample Size for the Training Set

The document does not mention a "training set" in the context of machine learning or AI. This device is an immunoassay, not an AI/ML device, so typical AI training sets do not apply. The development process would have involved internal validation and optimization, but not typically referred to as a "training set" in this manner.

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

Since this is not an AI/ML device, the concept of a training set and its ground truth in that context is not applicable. The assay's parameters (e.g., antibody concentrations, membrane properties) would have been optimized during its development using controlled experiments with known concentrations of analytes, similar to the ground truth described in point 7.

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