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The Vantera® Clinical Analyzer is an automated laboratory test analyzer which measures the 400 MHz proton nuclear magnetic resonance (NMR) spectrum of clinical samples to produce signal amplitudes, converting these signal amplitudes to analyte concentration. The device includes a 400 MHz NMR spectrometer and software to analyze digitized spectral data. This instrumentation is intended to be used with NMR based assays to detect multiple analytes from clinical samples.

The NMR LipoProfile® test, when used with the Vantera® Clinical Analyzer, an automated NMR spectrometer, measures lipoprotein particles to quantify LDL particle number (LDL-P), HDL cholesterol (HDL-C), and triglycerides in human serum and plasma using nuclear magnetic resonance (NMR) spectroscopy. LDL-P and these NMR-derived concentrations of HDL-C and triglycerides are used in conjunction with other lipid measurements and clinical evaluation to aid in the management of lipoprotein disorders associated with cardiovascular disease.

The Vantera Clinical Analyzer is a clinical laboratory analyzer that employs nuclear magnetic resonance spectroscopic detection to quantify multiple analytes in biological fluid specimens, specifically blood plasma and serum.

The Vantera Clinical Analyzer system design is divided into 3 major subassemblies: a sample handling assembly, an NMR subassembly, and an enclosure. The Vantera Clinical Analyzer control system is distributed across three separate computers:

• The Host (1 U) controls user interface, data handling, results calculation, system startup and shutdown.
• The Process Control (4U) schedules and manages all activities required to process a sample, controls all hardware in the sample handling subsystem.
• The NMR Control Computer controls all magnet operations. Two of these computers are contained within the Sample Handling Subassembly (1 U and 4U) and one in the NMR Subassembly (NMR Console).

The NMR LipoProfile test involves measurement of the 400 MHz proton NMR spectrum of a plasma/serum sample, deconvolution of the composite signal at approximately 0.8 ppm to produce signal amplitudes of the lipoprotein subclasses that contribute to the composite plasma/serum signal, and conversion of these subclass signal amplitudes to Lipoprotein subclass concentrations. The -0.8 ppm plasma NMR signal arises from the methyl group protons of the lipids carried in the LDL, HDL and VLDL subclasses of varying diameters. The NMR signals from the various lipoprotein subclasses have unique and distinctive frequencies and lineshapes, each of which is accounted for in the deconvolution analysis model. Each subclass signal amplitude is proportional to the number of subclass particles emitting the signal, which enables subclass particle concentrations to be calculated from the subclass signal amplitudes derived from the spectral deconvolution analysis. LDL subclass particle concentrations, in units of nanomoles of particles per liter (nmol/L), are summed to give the reported total LDL particle concentration (LDL-P). By employing conversion factors assuming that the various lipoprotein subclass particles have cholesterol and triglyceride contents characteristic of normolipidemic individuals, HDL cholesterol and triglyceride concentrations are also derived.

Here's a breakdown of the acceptance criteria and study information for the Vantera Clinical Analyzer and NMR LipoProfile® test, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document primarily focuses on demonstrating substantial equivalence to a predicate device (K113830) rather than explicitly stating pre-defined "acceptance criteria" for novel performance features. However, the comparisons in the tables serve as the de-facto acceptance criteria: the candidate device's performance must be comparable to or better than the predicate device across various analytical metrics.

2. Sample Size and Data Provenance for the Test Set

The document does not explicitly state the specific sample sizes for each analytical validation study for the "test set" in terms of number of patient samples. It mentions "Level 1, Level 2, Level 3" for precision studies, implying control samples at different concentrations. For linearity, there are ranges of values. For the method comparison, it gives regression parameters, which typically require a reasonable number of samples, but the exact count isn't specified.

The data provenance (country of origin, retrospective/prospective) is not provided in the document.

3. Number of Experts and Qualifications for Ground Truth of Test Set

This information is not applicable and not provided. The device (Vantera Clinical Analyzer with NMR LipoProfile® test) is an IVD for quantitative measurement of lipoprotein particles, HDL cholesterol, and triglycerides. The ground truth for these measurements in analytical validation studies would typically be established by:

• Reference methods (e.g., ultracentrifugation for lipoproteins, enzymatic assays for cholesterol/triglycerides).
• Certified reference materials.
• Highly qualified laboratory personnel following established protocols for the reference methods.

Experts in the sense of clinical reviewers (e.g., radiologists for imaging devices) are not typically involved in establishing ground truth for this type of analytical device.

4. Adjudication Method for the Test Set

Not applicable. This is not a human interpretation-based diagnostic device requiring adjudication of expert opinions.

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

Not applicable. This is an automated analytical laboratory device, not an imaging or interpretation aid for human readers. Therefore, an MRMC study and effects of AI assistance on human readers are not relevant.

6. Standalone Performance Study

Yes, the studies presented are standalone (algorithm only) performance studies. The results in the tables (Measuring Range, LoB, LoD, LoQ, Linearity, Precision, Method Comparison, Carryover, Interference) directly reflect the performance of the Vantera Clinical Analyzer and NMR LipoProfile® test itself, without any human-in-the-loop interaction for interpretation, beyond the standard operation of an automated laboratory instrument.

7. Type of Ground Truth Used

The ground truth for the analytical validation studies would be established using reference methods or reference materials. For example:

• Method Comparison: Comparison against a recognized reference method for lipoprotein quantification, HDL-C, and triglycerides. While not explicitly stated, standard practice would involve a comparison to a well-characterized, clinically accepted method. The "Deming fit" and "Linear regression" indicate comparison to another quantitative measurement.
• Linearity, LoB, LoD, LoQ: These are typically established using characterized control materials (known concentrations) or serial dilutions of patient samples.
• Precision: Established using control materials at different concentrations.

8. Sample Size for the Training Set

The document does not specify a separate "training set" or its size. This device is an analytical instrument based on Nuclear Magnetic Resonance (NMR) spectroscopy and a deconvolution analysis model. While the deconvolution model itself would have been developed and "trained" or optimized using a dataset of known NMR spectra and corresponding reference measurements, the document focuses on the validation of the integrated device. The details of the dataset used for the initial development/training of the deconvolution algorithm are not provided here.

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

As above, the document does not provide details on the training set for the deconvolution model. However, for a device based on a physical principle like NMR spectroscopy, the ground truth for establishing the deconvolution model and conversion factors would likely involve:

• Carefully characterized samples with known concentrations of lipoproteins, HDL-C, and triglycerides, determined by highly accurate reference methods (e.g., ultracentrifugation for lipoprotein subfractions, established enzymatic methods for cholesterol and triglycerides).
• Spectral data from these characterized samples would be used to build and optimize the deconvolution algorithm (i.e., establish unique frequencies and lineshapes) and the conversion factors from signal amplitudes to concentrations.

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The Vantera® Clinical Analyzer is an automated laboratory test analyzer which measures the 400 MHz proton nuclear magnetic resonance (NMR) spectrum of clinical samples to produce signal amplitudes, converting these signal amplitudes to analyte concentration. The device includes a 400 MHz NMR spectrometer and software to analyze digitized spectral data. This instrumentation is intended to be used with NMR based assays to detect multiple analytes from clinical samples.

The NMR LipoProfile® test, when used with the Vantera® Clinical Analyzer, an automated NMR spectrometer, measures lipoprotein particles to quantify LDL particle number (LDL-P), HDL cholesterol (HDL-C), and triglycerides in human serum and plasma using nuclear magnetic resonance (NMR) spectroscopy. LDL-P and these NMR-derived concentrations of HDL-C and triglycerides are used in conjunction with other lipid measurements and clinical evaluation to aid in the management of lipoprotein disorders associated with cardiovascular disease.

The Vantera Clinical Analyzer is a clinical laboratory analyzer that employs nuclear magnetic resonance spectroscopic detection to quantify multiple analytes in biological fluid specimens, specifically blood plasma and serum. The Vantera Clinical Analyzer system design is divided into 3 major subassemblies: a sample handling assembly, an NMR subassembly, and an enclosure. The Vantera Clinical Analyzer control system is distributed across three separate computers: The Host (1U) controls user interface, data handling, results calculation, system startup and shutdown. The Process Control (4U) schedules and manages all activities required to process a sample, controls all hardware in the sample handling subsystem, and manages remote access to the system. The NMR Control Computer controls all magnet operations. Two of these computers are contained within the Sample Handling Subassembly (1U and 4U) and one in the NMR Subassembly (NMR Console).

The NMR LipoProfile test involves measurement of the 400 MHz proton NMR spectrum of a plasma/serum sample, deconvolution of the composite signal at approximately 0.8 ppm to produce signal amplitudes of the lipoprotein subclasses that contribute to the composite plasma/serum signal, and conversion of these subclass signal amplitudes to lipoprotein subclass concentrations.

The provided 510(k) summary focuses on the analytical performance of the Vantera® Clinical Analyzer and the NMR LipoProfile® test compared to predicate devices, establishing substantial equivalence rather than providing explicit acceptance criteria as would be typical for a novel device. The study described primarily demonstrates that the proposed device performs comparably to its predicate devices in terms of analytical accuracy and precision.

Here's an analysis of the acceptance criteria and study information provided:

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

The document does not explicitly state acceptance criteria in a pass/fail format. Instead, it demonstrates performance by comparing the analytical results of the Vantera® Clinical Analyzer with the NMR LipoProfile® test to its predicate device (NMR Profiler for the assay) across various metrics. The unstated acceptance criteria for each analytical performance metric would be that the proposed device's performance must be comparable to or better than the predicate device's performance.

Study Proving Acceptance Criteria:

The study conducted was an analytical validation comparing the performance of the Vantera® Clinical Analyzer with the NMR LipoProfile® test to its predicate device (NMR Profiler for the assay) across various analytical parameters. The overall conclusion is that the new device is "as safe and effective as its predicate device."

2. Sample size used for the test set and the data provenance

• Test Sets (Analytical Studies):
  • Analytical Sensitivity (LoB, LoD, LoQ): Five serum pools for low concentrations tested in replicates of 4 for 3 days. Non-lipoprotein specimens analyzed 60 consecutive times for 3 days for LoB.
  • Assay Precision (Within-run & Within-Laboratory): 20 replicates of three patient serum pools in the same run and in 20 different runs over 20 days. Reproducibility study used 5 levels of serum panels tested for 5 days, 6 runs per day, 2 replicates per run at 3 sites (n=60 per panel per site, total N=177-180 across all sites for each panel).
  • Linearity: Three serum pools prepared from patient specimens mixed and diluted to produce eleven (for LDL-P) or twelve (for TG and HDL-C) different samples, with four replicates of each pool analyzed.
  • Method Comparison:
    • LDL-P: n=1483 serum samples.
    • HDL-C: n=1518 serum samples.
    • Triglycerides: n=1520 serum samples.
  • Interfering Substances: 7 endogenous agents and 23 drugs were screened.
  • Reference Range: Serum samples (n=452) from apparently healthy men (n=158) and women (n=294).
• Data Provenance: The document does not specify the country of origin. The studies were described as "analytical validations" and included testing using "patient specimens" and "serum pools." There is no explicit mention of the data being either retrospective or prospective, but the nature of the analytical studies suggests controlled laboratory environments rather than a large-scale clinical trial with patient follow-up. For the reference range, it states "serum samples... were analyzed from apparently healthy men and women," which implies a prospective collection for this specific purpose or a well-characterized existing cohort. The MESA (Multi-Ethnic Study of Atherosclerosis) is mentioned for the predicate device's reference range, suggesting a US context for that, but it's not explicitly stated for the proposed device's reference population.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

The document does not describe the use of human experts to establish "ground truth" for the test set in the context of interpretation or diagnosis. This device is an automated laboratory analyzer for quantifying analytes. The "ground truth" for its analytical performance studies (e.g., precision, linearity, method comparison) is established by comparing its measurements to a reference method or known concentrations, or by assessing consistency internally.

4. Adjudication method for the test set

Not applicable. There was no clinical study involving human interpretation or diagnosis that would require an adjudication method. The testing involved direct analytical measurements.

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 applicable. This is an in-vitro diagnostic (IVD) device for quantifying analytes (LDL-P, HDL-C, Triglycerides), not an AI-assisted diagnostic imaging or interpretation device that would involve human readers.

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

Yes, the primary performance studies presented are "standalone" in the sense that they assess the device's analytical performance (algorithm + instrument) in quantifying the specified analytes without human-in-the-loop performance for diagnosis or interpretation. The device's output is explicit numerical analyte concentrations.

7. The type of ground truth used

The ground truth used for verifying the analytical performance of the device was:

• Reference Methods/Known Concentrations: For analytical sensitivity (LoB, LoD, LoQ), linearity, and precision, the "ground truth" was established through precisely prepared serum pools and non-lipoprotein specimens with known or target concentrations, or through statistical determination methods (e.g., EP17-A).
• Comparison to Predicate Device: For method comparison studies, the "ground truth" or reference was the measurements obtained from the legally marketed predicate device (NMR Profiler) using patient samples. The goal was to show high correlation and similar results between the two devices.
• CLSI Guidelines: Standardized guidelines (e.g., EP5-A2, EP6-A, EP7-A2, EP9-A2, EP17-A) from the Clinical and Laboratory Standards Institute (CLSI) were referenced for establishing protocols for these analytical validations.

8. The sample size for the training set

The document does not explicitly mention a "training set" in the context of machine learning or AI models. This device is an automated NMR spectrometer that measures signals and converts them to concentrations based on specified deconvolution analysis models. The development of these deconvolution models would have involved a form of "training" or optimization, but the document does not detail the dataset size or methodology used for this prior model development. The document focuses on the analytical validation of the manufactured device.

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

As there is no explicit mention of a "training set" in the conventional AI/ML sense, this question cannot be directly answered from the provided text. However, the assay description mentions:
"The NMR signals from the various lipoprotein subclasses have unique and distinctive frequencies and lineshapes, each of which is accounted for in the deconvolution analysis model. Each subclass signal amplitude is proportional to the number of subclass particles emitting the signal, which enables subclass particle concentrations to be calculated from the subclass signal amplitudes derived from the spectral deconvolution analysis."
This suggests that the deconvolution analysis model was developed using a "ground truth" based on the established biophysical properties of lipoprotein subclasses and their NMR spectral characteristics. This likely involved:

• Carefully characterized lipoprotein samples with known subclass concentrations.
• Expert knowledge of NMR spectroscopy and signal processing.
• Calibration against established reference methods for lipoprotein analysis.

The document does not provide details on the specific data sets or expert consensus used for the initial development and establishment of this deconvolution model.

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The NMR LipoProfile® test, when used with the NMR Profiler, an automated NMR spectrometer, measures lipoprotein particles to quantify LDL particle number (LDL-P), HDL cholesterol (HDL-C), and triglycerides in human serum and plasma using nuclear magnetic resonance (NMR) spectroscopy. LDL-P and these NMR-derived concentrations of HDL-C and triglycerides are used in conjunction with other lipid measurements and clinical evaluation to aid in the management of lipoprotein disorders associated with cardiovascular disease. This test is performed and provided as a service by LipoScience Laboratory.

The NMR LipoProfile® test and NMR Profiler involves measurement of the 400 MHz proton NMR spectrum of a plasma/serum sample, deconvolution of the composite signal at approximately 0.8 ppm to produce signal amplitudes of the lipoprotein subclasses that contribute to the composite plasma/serum signal, and conversion of these subclass signal amplitudes to lipoprotein subclass concentrations. The ~0.8 ppm plasma NMR signal arises from the methyl group protons of the lipids carried in the LDL, HDL and VLDL subclasses of varying diameters. The NMR signals from the various lipoprotein subclasses have unique and distinctive frequencies and lineshapes, each of which is accounted for in the deconvolution analysis model. Each subclass signal amplitude is proportional to the number of subclass particles emitting the signal, which enables subclass particle concentrations to be calculated from the subclass signal amplitudes derived from the spectral deconvolution analysis. LDL subclass particle concentrations, in units of nanomoles of particles per liter (nmol/L), are summed to give the reported total LDL particle concentration (LDL-P). By employing conversion factors assuming that the various lipoprotein subclass particles have cholesterol and triglyceride contents characteristic of normolipidemic individuals, HDL cholesterol and triglyceride concentrations are also derived.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

The provided document focuses on analytical performance rather than clinical acceptance criteria. The acceptance criteria for the analytical evaluation (sensitivity, precision, linearity, and method comparison) are largely implied through the reported performance metrics and the use of CLSI guidelines. The document does not explicitly state pre-defined acceptance criteria values for bias or CVs in a standalone table, but rather presents the reported performance data.

Table 1: Acceptance Criteria and Reported Device Performance (Analytical)

Study Details

The provided text describes analytical performance studies designed to demonstrate substantial equivalence to a predicate device.

2. Sample size used for the test set and the data provenance:

• Analytical Sensitivity (LOQ): "20 replicates of each were analyzed" for serially diluted serum specimens with low concentrations. The number of unique patient samples used for initial dilution is not specified.
• Assay Precision:
  • Within-run: 20 replicates of three patient serum pools.
  • Within-laboratory: 20 different runs over 20 days using three patient serum pools (n=80 for each pool across runs).
• Linearity: Four replicates of each of eleven (for LDL-P) or twelve (for TG and HDL-C) different samples derived from three serum pools.
• Method Comparison:
  • LDL-P: n=1555 serum samples
  • HDL-C: n=1599 serum samples
  • Triglycerides: n=1597 serum samples
• Interfering Substances: "Five endogenous agents and twenty two drugs were screened". No sample size for patient samples is given, but likely spiked samples.

Data Provenance: The document does not explicitly state the country of origin. The samples are described as "patient serum pools" and "serum samples," implying they are from human subjects, likely in a clinical laboratory setting. The terms "retrospective" or "prospective" are not used; these appear to be analytical validation studies on collected samples.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. The ground truth for these analytical studies is based on quantitative measurements (e.g., predicate device measurements for method comparison, or target values established through dilution for linearity and sensitivity). This is not an image-based or clinical diagnostic study requiring expert interpretation.

4. Adjudication method for the test set: Not applicable. These are quantitative analytical studies, not studies requiring expert adjudication of results.

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 applicable. This is an analytical device for quantifying lipoprotein particles, not an AI-assisted diagnostic imaging device with human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Yes, the entire performance data section (Analytical Sensitivity, Assay Precision, Linearity, Interfering Substances, Method Comparison) represents standalone algorithm performance, as the device is an automated NMR spectrometer with deconvolution software. There is no human interpretation or intervention in the measurement process itself.

7. The type of ground truth used:

• Analytical Sensitivity (LOQ): Target values from serial dilution.
• Assay Precision: Mean values from repeated measurements of serum pools.
• Linearity: Expected target values derived from mixing and diluting serum pools.
• Method Comparison: Measurements from the legally marketed predicate device (NMR Profiler and NMR Lipoprofile Assay, K063841).
• Interfering Substances: Pre-established target concentrations of potential interferents.

8. The sample size for the training set: Not applicable. This is an analytical device validation, not a machine learning model that undergoes a distinct training phase in the context of this 510(k) submission. The underlying deconvolution software likely had development and calibration phases, but those are not described as a "training set" in this document.

9. How the ground truth for the training set was established: Not applicable, as no training set is explicitly described for this 510(k) submission.

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The NMR LipoProfile® -2 test, used with the NMR Profiler, an automated NMR spectrometer, measures lipoprotein particles to quantify LDL particle number (LDL-P), HDL cholesterol (HDL-C), and triglycerides in serum and plasma using nuclear magnetic resonance (NMR) spectroscopy. LDL-P and these NMR-derived concentrations of triglycerides and HDL-C are used in conjunction with other lipid measurements and clinical evaluation to aid in the management of lipoprotein disorders associated with cardiovascular disease. This test is performed and provided as a service by LipoScience Laboratory.

The NMR LipoProfile Test involves measurement of the 400 MHz proton NMR spectrum of a plasma or serum sample, deconvolution of the composite signal at ~0.8 ppm to produce the signal amplitudes of the lipoprotein subclasses that contribute to the composite plasma signal, and conversion of these subclass signal amplitudes to lipoprotein subclass concentrations. The 0.8 ppm plasma NMR signal arises from the methyl group protons of the lipids carried in the VLDL, and HDL subclasses of varying diameter. The NMR signals from the various lipoprotein subclasses have unique and distinctive frequencies and lineshapes, each of which are accounted for in the deconvolution analysis model. Each subclass signal amplitude is proportional to the number of subclass particles emitting the signal, which enables subclass particle concentrations to be calculated from the subclass signal amplitudes derived from the spectral deconvolution analysis. LDL subclass particle concentrations, in units of nanomoles of particles per liter (nmol/L), are summed to give the reported total LDL particle concentration (LDL-P). By employing conversion factors that assume that the various lipoprotein subclass particles have cholesterol and triglyceride contents characteristic of normolipidemic individuals, HDL cholesterol and triglyceride concentrations are also derived.

AI Device Acceptance Criteria and Study Summary

Here's an analysis of the provided text regarding the NMR LipoProfile-2 Assay and NMR Profiler Instrument Test System:

1. Acceptance Criteria and Reported Device Performance

The document describes several analytical and clinical performance aspects. While explicit "acceptance criteria" for all metrics are not always stated with numerical thresholds, they are implied through the performance data and comparisons to predicate devices. For analytical sensitivity and linearity, specific ranges are provided.

Table 1: Acceptance Criteria (Implied/Stated) and Reported Device Performance

2. Sample Sizes and Data Provenance

Test Set (for method comparison):

• Sample Size: 5,362 plasma samples (for HDL-C and Triglycerides method comparison).
• Data Provenance: From individuals who were part of the Multi-Ethnic Study of Atherosclerosis (MESA). The specific country of origin is not explicitly stated, but MESA is a US-based study. This data is retrospective.

Test Set (for clinical performance of LDL-P measurement):

• VA-HIT Study: 1,061 plasma samples (364 cases, 697 controls). Data is retrospective from a previous randomized placebo-controlled clinical trial. The trial was conducted in the US (Veterans Affairs).
• EPIC-Norfolk Study: 2,888 serum samples (1,003 cases, 1,885 controls). Data is retrospective from the prospective EPIC-Norfolk study conducted in Norfolk, United Kingdom.
• Women's Health Study: 27,673 plasma samples (1,015 CVD events). Data is retrospective from a randomized, double-blind, placebo-controlled trial of US-based female healthcare professionals.

Test Set (for analytical performance - sensitivity, precision, linearity, interfering substances):

• Analytical Sensitivity: Serum specimens with low initial concentrations were serially diluted. 20 replicates of each dilution were analyzed. Exact number of initial specimens not stated.
• Assay Precision: 2 patient serum pools tested with 20 replicates each (intra-assay) and over 20 different runs (inter-assay).
• Linearity: 2 serum pools prepared and diluted to produce 12 different samples. 6 replicates analyzed for each.
• Interfering Substances: 6 plasma pools spiked with potential interferents.

3. Number of Experts and Qualifications for Ground Truth

The document does not mention the use of experts to establish ground truth for the test sets in the context of radiologists or similar clinical diagnosticians. The ground truth for the method comparison studies was established by commercially available chemistry analyzer systems which are considered standard clinical methods.

For the clinical outcome studies (VA-HIT, EPIC-Norfolk, Women's Health Study), the "ground truth" for disease events (e.g., CHD event, CAD, CVD event) would have been established by clinical adjudication committees or established diagnostic criteria within those long-term epidemiological studies. The qualifications of these individuals are not specified in this 510(k) summary, but they would typically involve cardiologists and other medical specialists.

4. Adjudication Method for the Test Set

Since the ground truth for clinical outcomes was based on events recorded in large epidemiological studies, there was no "adjudication method" in the sense of comparing human reads against an AI output. The lipid measurements from the NMR LipoProfile device were used as a predictor for these retrospectively determined clinical events. For the method comparison studies, the comparison was directly against existing commercial chemistry analyzer systems.

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

No Multi-Reader Multi-Case (MRMC) comparative effectiveness study was mentioned. The device is an in vitro diagnostic device, not an imaging AI designed to assist human readers. Its performance is measured directly against established lab methods and its predictive power for clinical outcomes.

6. Standalone Performance Study

Yes, the studies described are primarily standalone (algorithm only without human-in-the-loop performance).

• The "Method Comparison" studies (HDL-C, Triglycerides) directly compare the device's output to that of predicate devices.
• The "Clinical Performance of NMR LipoProfile test for LDL-P Measurement" studies assess the association of the device's LDL-P output with future cardiovascular events directly, not in conjunction with human interpretation.

7. Type of Ground Truth Used

• Analytical Performance (Sensitivity, Precision, Linearity, Interfering Substances): Ground truth was established internally using laboratory standards, dilutions, and spiked samples, often by comparing observed values to target values or calculated expected values.
• Method Comparison (HDL-C, Triglycerides): Ground truth was established by predicate commercial chemistry analyzer systems.
• Clinical Performance (LDL-P): Ground truth for the outcomes (CHD events, CAD, CVD events) was established by outcomes data (clinical events recorded during follow-up in the respective large-scale epidemiological studies: VA-HIT, EPIC-Norfolk, Women's Health Study).

8. Sample Size for the Training Set

The document does not specify a distinct "training set" size. The NMR LipoProfile Test involves "deconvolution of the composite signal at ~0.8 ppm" and relies on an "algorithm resident on the LipoProfile Analysis Server [which] is the foundation of the LipoProfile assay." This suggests that the algorithm was developed and likely trained using a set of data, but details about this training dataset (size, ground truth, etc.) are not provided in this 510(k) summary, as is common for in vitro diagnostic devices where the focus is often on analytical and clinical validation of the final product.

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

As no specific training set is detailed, the method for establishing its ground truth is not explicitly mentioned. However, based on the technology description (deconvolution of NMR spectra), the "ground truth" for developing the algorithm would likely involve characterized lipoprotein standards and/or meticulously analyzed human samples where lipoprotein subclass concentrations were determined by established gold-standard methods or extensive biochemical profiling. These are typically developed through iterative research and development processes by the manufacturer.

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