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The RENU Medical Reprocessed Tri Pulse Compression Garment are to be used as a non-invasive therapeutic method to help prevent Deep Vein Thrombosis (DVT) and resulting pulmonary embolism.

The Reprocessed Tri Pulse Compression Garment is a compression garment that is attached to a patient's limb. It is designed to work with the Arjo Flowtron ACS800 Pump and Arjo Flowtron ACS900 Pump only. Each garment is compressed alternately, applying pressure to the patient's limb, to help prevent deep vein thrombosis.

Tri Pulse garments are constructed with a three-chamber bladder enclosed in a polyester garment, which is wrapped around the limb and secured with hook and eye tabs. When connected to the pump, the garment inflates through a single connecting tube to generate a sequential compression effect on the limb.

Reprocessing the Tri-Pulse Compression Garment is conducted by achieving high-level disinfection via thermal disinfection methods. This process is conducted by holding the devices at or above a predetermined specified temperature for a predetermined specified duration.

Although the document describes the regulatory clearance for a reprocessed medical device (Tri Pulse Compression Garment), it does not contain the information requested regarding acceptance criteria and a study proving a device meets those criteria for an AI/ML medical device.

The provided text focuses on:

• Device Type: Reprocessed medical device (compression garment)
• Regulatory Pathway: 510(k) premarket notification
• Substantial Equivalence: Demonstrated through bench testing (cycle verification testing) to ensure reprocessing does not adversely affect the device's performance compared to the OEM predicate device.
• Testing Method: Cycle Verification Testing, which involves reprocessing garments multiple times (10 cycles) and performing functional pressure and inflation time readings after each cycle.
• Ground Truth: For this type of device, the "ground truth" is adherence to OEM specifications (pressure, inflation time) after reprocessing, not clinical outcomes or expert consensus on image interpretation.
• Study Design: Bench testing, not a clinical study involving human subjects or AI algorithms.

Therefore, I cannot extract the following information from the provided text as it pertains to AI/ML device studies:

1. A table of acceptance criteria and the reported device performance: The document mentions "specifications outlined by the OEM predicate device" and "functional requirements" but doesn't present these in a table format with specific acceptance criteria and reported values for an AI/ML model.
2. Sample size used for the test set and the data provenance: The document mentions a "sample of devices" being reprocessed "10 times each" for the bench test, but this isn't an AI test set.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable as the ground truth for this device is based on physical performance metrics (pressure, inflation time) measured by equipment, not human experts interpreting data.
4. Adjudication method for the test set: Not applicable.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done: Not applicable.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): The ground truth is the physical performance specifications of the OEM device (pressure and inflation time) after reprocessing.
8. The sample size for the training set: Not applicable, as this is not an AI/ML device.
9. How the ground truth for the training set was established: Not applicable, as this is not an AI/ML device.

In summary, the provided document does not contain the information needed to answer the prompt regarding AI/ML device acceptance criteria and study details.

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The ReNu Medical Reprocessed Aircast VenaFlow calf, thigh and foot DVT garments are to be used by patients in both the home and institutional settings as a non-invasive therapeutic method to prevent deep vien thrombosis and resulting pulmonary embolism.

The ReNu Medical Reprocessed Aircast VenaFlow calf, thigh and foot DVT garments. (All sizes)

Acceptance Criteria and Device Performance Study for ReNu Medical Reprocessed Aircast VenaFlow DVT garments

This document describes the acceptance criteria and the study conducted to demonstrate that the ReNu Medical Reprocessed Aircast VenaFlow Calf, Thigh, and Foot DVT garments meet these criteria, as presented in K121145.

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state the specific numerical sample size for the bench testing. However, it mentions "100% verification of bladder integrity," which implies that all reprocessed Aircast VenaFlow DVT garments underwent leak testing.

• Sample Size for Test Set: Not explicitly stated as a number, but "100% verification" implies all reprocessed devices were tested for bladder integrity. The number of devices tested for Velcro adhesion is not specified but is part of the general "bench testing."
• Data Provenance: The data is generated from retrospective reprocessing of Aircast VenaFlow DVT garments by ReNu Medical, Inc. The country of origin of the data is implied to be the United States (specifically, Everett, WA, where ReNu Medical, Inc. is located).

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

This study does not involve human experts establishing ground truth for a test set in the conventional sense of clinical trials or image interpretation. The testing performed is physical bench testing. Therefore, this question is not applicable to the provided information.

4. Adjudication Method for the Test Set

The concept of "adjudication method" (like 2+1, 3+1) typically applies to expert review of medical cases, often for establishing ground truth in clinical studies involving interpretation. Since this study is focused on physical bench testing of reprocessed medical devices, no adjudication method as described is applicable or mentioned. The testing involves objective measurements (pressure, leak detection, adhesion) rather than subjective expert consensus.

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

No MRMC comparative effectiveness study was done. This study focuses on the physical performance and safety of a reprocessed medical device through bench testing, not on the interpretative performance of human readers, with or without AI assistance.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

No standalone algorithm-only performance study was done. This device is a physical medical garment, not an algorithm or AI system. The "performance" being evaluated is the physical functionality of the reprocessed device.

7. Type of Ground Truth Used

The "ground truth" for this study is based on objective physical measurements and standards for medical device functionality after reprocessing. Specifically:

• Bladder integrity: Verified by the absence of leaks when pressurized to 52mmHg.
• Velcro adhesion: Verified by the Velcro remaining intact at 52mmHg pressure.
• Biocompatibility: Stated as unaffected, implying adherence to a prior established biocompatibility profile of the original device. This is a characteristic that reprocessing aims not to alter, rather than a dynamic ground truth established for each reprocessed unit.

8. Sample Size for the Training Set

This information is not applicable. The device is a physical medical garment, not a machine learning model or algorithm. Therefore, there is no "training set" in the context of AI or statistical model development. The manufacturing and reprocessing process can be considered analogous to a "training" phase where procedures are refined, but specific data for a "training set" as understood in AI is not relevant here.

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

This information is not applicable. As there is no "training set" for an algorithm, there is no ground truth established for it in this context. The "ground truth" for the manufacturing and reprocessing process would be the design specifications and performance requirements for the original and reprocessed devices, established through engineering design, regulatory standards, and predicate device performance.

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OxiMax Adult: continuous non-invasive arterial oxygen saturation and pulse rate monitoring of patients > 30 kg.

OxiMax Pediatric: continuous non-invasive arterial oxygen saturation and pulse rate monitoring of patients 10 - 50 kg.

OxiMax Infant: continuous non-invasive arterial oxygen saturation and pulse rate monitoring of patients between 3 kg and 20 kg.

OxiMax Neonate: continuous non-invasive arterial oxygen saturation and pulse rate monitoring of patients foot if 40 kg.

The ReNy Medical Reprocessed OxiMax Sensors are accessory devices to an oximeter monitoring system. The oxisensor is designed as a transducer for the transmission of electrical signals from the oximeter to the patient and the return of patient modified signals back to the oximeter for analysis and display of patient information. The sensor contains three optical components; two light emitting diodes (LEDs) serve as light sources and one photodiode acting as a light detector LED and sensor are contained in a laminated envelope provided with an adhesive bandage for attachment a patient. A sensor package is attached to a cable terminated in a multi-pin connector that plugs into the oximeter.

The provided text describes a 510(k) summary for ReNu Medical's Reprocessed OxiMax Sensors, which are oximetry sensors. The submission focuses on demonstrating substantial equivalence to predicate devices, particularly regarding safety and effectiveness.

Here's an analysis of the provided text in relation to your questions, focusing on what is explicitly stated:

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

The document does not explicitly provide a table of acceptance criteria with corresponding device performance metrics in numerical form. It broadly states that:

• "Reprocessed OxiMax Sensors were tested to demonstrate functional characteristics by bench testing and in vivo clinical studies."
• "Bench testing was performed to determine pulse rate accuracy using an OxiTest 7 simulator. Varying environmental conditions and physical tests were performed for temperature and humidity."
• "Clinical studies in vivo were performed on both Adult and Neonate subjects to demonstrate accuracy of SpO2 in the reprocessed sensors."

While it mentions "pulse rate accuracy" and "accuracy of SpO2," it does not specify what the acceptance thresholds for these accuracies were, nor does it provide the measured performance values for the reprocessed sensors.

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

• Sample size: The document states "Clinical studies in vivo were performed on both Adult and Neonate subjects," but it does not provide specific sample sizes for these subject groups.
• Data provenance: The document does not specify the country of origin of the data or whether the studies were retrospective or prospective. Given they are "in vivo clinical studies," they were likely prospective.

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)

This information is not provided in the document. The studies mentioned are clinical, implying direct measurement rather than expert interpretation of data. For oximetry sensors, "ground truth" for SpO2 accuracy would typically come from a co-oximetry blood gas analyzer, not expert consensus on images or other subjective data.

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

This is not applicable to the type of device and study described. Oximetry sensor accuracy is typically determined by comparing the device's readings to a reference standard (e.g., co-oximetry), not through human adjudication of ambiguous cases.

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

• MRMC study: No, an MRMC comparative effectiveness study was not done. This type of study is relevant for AI-powered diagnostic imaging devices where human interpretation is a key component. The ReNu Medical device is an oximetry sensor, a physiological measurement device, not an AI diagnostic tool that assists human readers.
• Effect size of human reader improvement: Therefore, this information is not applicable and not provided.

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

• Standalone performance: Yes, the described "bench testing" and "in vivo clinical studies" focus on the performance of the reprocessed sensors themselves, indicating a standalone assessment of the device's accuracy. This is not an "algorithm only" performance as the device is hardware, but it evaluates the device's output independently.
• Without human-in-the-loop: The accuracy measurements (pulse rate and SpO2) are direct device outputs, not dependent on human interpretation in the sense of a human-in-the-loop system for AI.

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

The document does not explicitly state the specific ground truth method, but for oximetry sensors, the established scientific method for "accuracy of SpO2" in clinical studies involves:

• Inducing a range of oxygen saturations in subjects.
• Simultaneously measuring arterial oxygen saturation using a co-oximeter (a laboratory device that directly measures different hemoglobin species from arterial blood samples).
• Comparing the sensor's readings to the co-oximeter's readings. This is the accepted "gold standard" for blood oxygen saturation measurement.

For "pulse rate accuracy," the ground truth would typically be derived from an ECG monitor.

8. The sample size for the training set

This information is not applicable as the device is a reprocessed medical sensor, not an AI/ML algorithm that requires a training set. The "reprocessing" involves cleaning, testing, and sometimes replacing components, but it does not involve machine learning model training.

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

This information is not applicable for the same reason as point 8.

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The ReNu Medical Reprocessed Vaso Press CTC calf, thigh and foot DVT garments are to be used by patients in both the home and institutional settings as a non-invasive therapeutic method to prevent deep vein thrombosis and resulting pulmonary embolism.

The ReNu Medical Reprocessed Vaso Press CTC calf, thigh and foot DVT garments. (All sizes)

This document describes the 510(k) submission for the ReNu Medical Reprocessed Vaso Press CTC DVT garments. It focuses on demonstrating substantial equivalence to predicate devices rather than providing a detailed study proving performance against specific acceptance criteria for a new device.

Therefore, much of the requested information about a study proving the device meets acceptance criteria cannot be extracted because such a study, with defined acceptance criteria and detailed performance metrics, is not present in the provided text. The submission relies on "bench testing" to ensure reprocessing doesn't compromise performance compared to predicate devices.

Here's what can be extracted based on the provided text, and where information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

Missing Information: Specific quantitative acceptance criteria (e.g., pressure output ranges, cycle life, material strength after reprocessing) are not provided. The performance is described qualitatively as "not compromised" and "substantially equivalent."

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

• Sample Size for Test Set: Not specified. The document mentions "bench testing" but does not detail the number of units tested.
• Data Provenance: Not specified. The testing was conducted by ReNu Medical, Inc., but the location or whether the data is retrospective or prospective is not mentioned.

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

• This information is not applicable as the submission describes bench testing for substantial equivalence, not a clinical study requiring expert-established ground truth for a diagnostic or AI device.

4. Adjudication Method for the Test Set

• This information is not applicable for the type of bench testing described.

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

• An MRMC comparative effectiveness study was not done. This submission is for reprocessed medical devices, not an AI-powered diagnostic or assistive device.

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

• This information is not applicable. This is not an algorithmic or AI device.

7. The Type of Ground Truth Used

• The "ground truth" for this submission is the performance of the original, new, legally marketed predicate devices. The reprocessed device is compared against the performance characteristics of these predicate devices to demonstrate substantial equivalence, particularly that reprocessing did not degrade these characteristics.

8. The Sample Size for the Training Set

• This information is not applicable. This is not a machine learning or AI device that requires a training set.

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

• This information is not applicable as there is no training set for this type of device submission.

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The ReNu Medical Reprocessed Oximetry Sensors are intended as single patient use O2 transducer/accessory sensors for use in conjunction with the Masimo™ Oximeter system.

The LNCS Adult is used for patients >30 kg. The LNCS Pediatric is used for patients 10-50 kg. The LNCS Infant is used for patients 3-20 kg. The LNCS Neo is used for neonates 40 kg.

All sensors are used for non-invasive monitoring of pulse oxygen hemoglobin saturation (SpO2) and pulse rate.

The ReNu Medical Reprocessed Masimo™ LNCS probes are accessory devices to an oximeter monitoring system. The oxisensor is designed as a transducer for the transmission of electrical signals from the oximeter to the patient and the return of patient modified signals back to the oximeter for analysis and display of patient information. The sensor contains three optical components; two light emitting diodes (LEDs) serve as light sources and one photodiode acting as a light detector LED and sensor are contained in a laminated envelope provided with an adhesive bandage for attachment a patient. A sensor package is attached to a cable terminated in a multi-pin connector that plugs into the oximeter.

The provided text does not contain detailed acceptance criteria or a comprehensive study report with the specific performance metrics typically found in such documents for a medical device. The submission is a 510(k) summary for reprocessed oximetry sensors, focused on demonstrating substantial equivalence to a predicate device through comparison tests.

However, based on the information provided, here's what can be extracted and inferred:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state numerical acceptance criteria. Instead, it indicates that "ReNu Medical Reprocessed Oximetry Sensors function in a manner that is Substantially Equivalent to that of the predicate devices." This implies that the performance of the reprocessed sensors, across all measured parameters, met a benchmark of being comparable to the predicate Masimo™ LNCS sensors.

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

The document states "clinical performance data," but does not specify the sample size for the test set or the data provenance (e.g., country of origin, retrospective or prospective).

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

This information is not provided in the document. The filing is a 510(k) summary for reprocessed medical devices, which typically focuses on technical equivalence and performance, rather than requiring expert-adjudicated ground truth as would be common for diagnostic AI algorithms.

4. Adjudication Method for the Test Set

This information is not provided in the document. Given the nature of oximetry sensor testing, "adjudication" in the context of expert consensus for ground truth is unlikely to be relevant; instead, measurement accuracy against a standard or established reliable method would be the primary focus.

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

This is not applicable. The device is an oximetry sensor, not an AI-assisted diagnostic tool for human readers. Therefore, an MRMC study related to human reader improvement with AI would not be relevant.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

This is not applicable. The device is a physical sensor, not a standalone AI algorithm. The performance being assessed is the sensor's ability to accurately measure SpO2 and pulse rate when connected to an oximeter system.

7. The Type of Ground Truth Used

The document refers to "bench testing and clinical performance data." For oximetry sensors, the "ground truth" for clinical performance is typically established by comparison against a gold standard method for measuring blood oxygen saturation (e.g., arterial blood gas analysis) and heart rate, under controlled conditions. For bench testing, it would involve measurement against calibrated equipment simulating physiological signals. The specific method used is not detailed in the provided text.

8. The Sample Size for the Training Set

This information is not provided in the document. The concept of a "training set" is usually relevant for machine learning algorithms. While there might be internal development and refinement leading to the reprocessed device, it's not described in terms of a formal training set for an algorithm as understood in AI/ML submissions.

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

This information is not provided as the concept of an AI training set and its associated ground truth is not applicable to this device type.

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D-25 continuous non-invasive arterial oxygen saturation and pulse rate monitoring of patients > 30 kg.

N-25: continuous non-invasive arterial oxygen saturation and pulse rate monitoring of patients foot if 40 kg.

The ReNu Medical Reprocessed Nellcor™ D-25 Oximetry Sensor and Reprocessed Nellcor™ N-25 Oximetry Sensor are accessory devices to an oximeter monitoring system. The oxisensor is designed as a transducer for the transmission of electrical signals from the oximeter to the patient and the return of patient modified signals back to the oximeter for analysis and display of patient information. The sensor contains three optical components; two light emitting diodes (LEDs) serve as light sources and one photodiode acting as a light detector LED and sensor are contained in a laminated envelope provided with an adhesive bandage for attachment a patient. A sensor package is attached to a cable terminated in a multi-pin connector that plugs into the oximeter.

This looks like a 510(k) premarket notification for reprocessed oximetry sensors. Unfortunately, the provided text does not contain detailed information about acceptance criteria or a specific study proving the device meets those criteria, as one might find in a full clinical trial report.

The document states that the reprocessed sensors are "substantially equivalent" to predicate devices based on "bench testing, clinical performance data, and non-clinical performance data." However, it does not provide the specifics of these tests, including:

• Acceptance Criteria: What specific metrics (e.g., accuracy against a gold standard) were targeted, and what were the thresholds for success?
• Reported Device Performance: What were the numerical results of these tests for the reprocessed devices?
• Study Design Details: How many subjects/samples were used, where the data came from, details about ground truth, expert qualifications, etc.

Therefore, I cannot populate the table or answer most of the specific questions based only on the provided text.

Here's what I can extract and state regarding the questions, with the understanding that much information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size (Test Set): Not specified.
• Data Provenance: The document mentions "clinical performance data" but does not specify the country of origin or whether it was retrospective or prospective.

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

• Not specified. The document does not describe the establishment of a "ground truth" using experts for performance testing.

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

• Not specified. This level of detail about study design is not present.

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 device is an oximetry sensor, not an AI-assisted diagnostic tool that would typically involve human readers or MRMC studies for improved diagnostic performance.

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

• Not applicable in the context of an algorithm. However, the device itself (sensor) performs its measurement "stand-alone" in producing SpO2 and pulse rate data for the oximeter. The testing mentioned (bench, clinical, non-clinical) would assess its direct sensing performance.

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

• Not specified. For oximetry sensors, ground truth for SpO2 would typically involve co-oximetry of arterial blood samples. The document does not detail this.

8. The sample size for the training set

• Not applicable. This is not a machine learning or AI algorithm in the context of typical training sets. The "reprocessed" aspect implies a manufacturing process, and performance is evaluated against predicate device functionality.

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

• Not applicable (as above).

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D-20 continuous non-invasive arterial oxygen saturation and pulse rate monitoring of for patients between 10 and 50 kg

I-20: continuous non-invasive arterial oxygen saturation and pulse rate monitoring of for patients between 3 and 20 kg

The ReNu Medical Reprocessed Nellcor™ D-20 Oxysensor® II and Reprocessed Nellcor™ I-20 Oxysensor II are accessory devices to an oximeter monitoring system. The oxisensor is designed as a transducer for the transmission of electrical signals from the oximeter to the patient and the return of patient modified signals back to the oximeter for analysis and display of patient information. The sensor contains three optical components; two light emitting diodes (LEDs) serve as light sources and one photodiode acting as a light detector LED and sensor are contained in a laminated envelope provided with an adhesive bandage for attachment a patient. A sensor package is attached to a cable terminated in a multi-pin connector that plugs into the oximeter.

The provided text is a 510(k) summary for ReNu Medical Reprocessed Nellcor™ D-20 and I-20 Oxysensors. It describes the device, its intended use, and claims substantial equivalence to predicate devices based on bench testing, clinical performance data, and non-clinical performance data. However, this document does not contain the specific acceptance criteria or the detailed study results that prove the device meets those criteria.

Here's an analysis of what information is and isn't present, according to your request:

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

• Not provided. The document states that "ReNu Medical Reprocessed Nellcor™ D-20 Oxysensor II and Reprocessed Nellcor™ I-20 Oxysensor® II function in a manner that is Substantially Equivalent to that of the predicate devices" based on various tests, but it does not specify what performance metrics were measured, what the acceptance criteria for those metrics were, or the actual results obtained in a table format.

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 "clinical performance data" but does not specify the sample size used for any test set or the provenance of the data (e.g., country of origin, retrospective/prospective nature).

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 applicable/Not provided. This document describes a reprocessed medical device (oximeter sensor). For such devices, "ground truth" typically refers to engineering specifications met through bench testing or direct comparison to existing validated devices, rather than expert interpretation of medical images or diagnostic outputs. There is no mention of experts or their qualifications for establishing ground truth.

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

• Not applicable/Not provided. Similar to point 3, adjudication methods are typically relevant for studies involving human interpretation (e.g., radiologists reviewing images). This document does not describe such a study design.

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 device is an oximeter sensor, not an AI-powered diagnostic tool for human readers. Therefore, an MRMC study comparing human reader performance with or without AI assistance is irrelevant and not mentioned.

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

• Not applicable. This device is a sensor, not an algorithm. The concept of "standalone algorithm performance" doesn't apply. The performance would be that of the sensor itself, typically measured through engineering and clinical validation studies as described under "bench testing, clinical performance data, and non-clinical performance data." However, the details of these studies are not provided.

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

• Implied ground truth through substantial equivalence. The document implies that the ground truth for the reprocessed sensors' performance is established by demonstrating that they function "Substantially Equivalent" to the original, legally marketed predicate devices (Nellcor™ D-20 Oxysensor® II and Nellcor™ I-20 Oxysensor® II). This would involve comparing the reprocessed devices' measurements (SpO2, pulse rate accuracy) against the known, validated performance of the predicate devices under various conditions. The specific "type" of ground truth would be the validated performance specifications of the predicate devices achieved through their original clinical and bench testing.

8. The sample size for the training set

• Not applicable/Not provided. This document describes a reprocessed hardware device, not a machine learning algorithm that requires a "training set."

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

• Not applicable. As above, there is no "training set" for this type of device.

In summary: The provided document is a regulatory submission focused on demonstrating substantial equivalence of a reprocessed medical sensor to its predicate device. It lacks the detailed study results and specific acceptance criteria that would typically be found in a comprehensive clinical or performance study report. The information required in your prompt primarily pertains to studies involving diagnostic algorithms or human interpretation of medical data, which is not the subject of this 510(k) summary.

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The ReNu Medical Reprocessed ALP®1 is to be used by patients in both the home and institutional settings as a non-invasive therapeutic method to:

• Prevent deep vein thrombosis and resulting pulmonary embolism.
• Intra-operative compression therapy.

The ReNu Medical Reprocessed (up to 18" calf circumference)

The provided text describes a 510(k) submission for a reprocessed medical device, the ReNu Medical Reprocessed ALP®1 Calf Garment. It focuses on demonstrating substantial equivalence to predicate devices and the impact of reprocessing.

Based on the provided text, the following information can be extracted and inferred:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not explicitly stated. The text mentions "Bench testing was conducted" but does not give a specific number of units tested.
• Data Provenance: Not explicitly stated. Given the context of a 510(k) submission from a US company (Everett, WA), it is highly likely the testing was conducted in the US. The testing appears to be internal (bench testing) rather than clinical.

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

This section is not applicable as the study described is a bench study comparing a reprocessed device to its original and predicate devices, not a study involving human interpretation or subjective assessment of medical conditions by experts. The "ground truth" here refers to the physical and functional properties of the device.

4. Adjudication method for the test set

This section is not applicable for the same reasons as point 3. There is no human interpretation requiring adjudication.

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

This section is not applicable. The device is a physical medical device (compression sleeve), not an AI diagnostic or assistive tool. Therefore, an MRMC study related to AI assistance is irrelevant.

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

This section is not applicable. The device is a physical medical device, not an algorithm.

7. The type of ground truth used

The ground truth used for the acceptance criteria was based on the physical and performance characteristics of the predicate devices and the original (unreprocessed) ALP™1 Calf Garment. The goal was to demonstrate that the reprocessed device remained "identical" in key aspects and that reprocessing did not compromise its safety or performance.

8. The sample size for the training set

This section is not applicable. There is no "training set" as this is a physical device re-processing validation, not an AI or machine learning model development.

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

This section is not applicable for the same reason as point 8.

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The Hygia West Reprocessed Compression Garments operate in the identical manner as the predicate devices. They are designed for the prevention of deep vein thrombosis (DVT) as well as the treatment of edema secondary to venous insulficiency. The devices are used in both the home and institutional settings.

The Hygia West Reprocessed Huntleigh DVT Sleeve is to be used by patients in both the home and institutional settings used as a non-invasive therapeutic method to:

• Prevent deep vein thrombosis and resulting pulmonary embolism .
• Intra-operative compression therapy

The Llygia West Reprocessed Compression Garments are intermittent compressible limb devices that are placed around cither the patient's foot (with the compression chamber placed under the plantar arch), calf (gastrocnemius muscle), thigh, or a combination there of. The garments are constructed from materials that are common to the medical device industry. Depending on the type of device, the method of fastening to the patient may vary from a hook and loop closure system to a uni-body style that is slipped over the limb. In foot compression devices, as the garment compresses the plantar plexus, the veins collapse longitudinally; this action causes an increase in the venous pressure thus eiceting the blood upward. After compression, the devices deflate allowing the veins to refill bringing oxygenared blood to the lower limbs. In devices used on the calf or thigh the compression to the area causes the blood to be ejected upwards to the heart, as the compression relaxes the valves in the veins close allowing them to refill. This cyclical method removes deoxygenated or stale blood from the region allowing fresh blood to be received. On all devices the controller predetermines the inflation and deflation sequence. The operational characteristics such as the pressure of compression, hold time, and inflation time are determined by the controller. It is the responsibility of the end user to ensure that the device is connected to an approved controller and to ensure that the controller settings are accurate. Hygia West does not repair, refurhish, or reprocess the controlling unit. Hygia West guarantees 100% of our devices ONLY if it has heen connected to an approved, functional, and correctly adjusted controller.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Hygia West Reprocessed Compression Garments:

1. Table of Acceptance Criteria and Reported Device Performance:

The document refers to the acceptance criteria implicitly through the concept of "substantial equivalence" to predicate devices. The study aims to demonstrate that the reprocessed devices perform identically to the original devices.

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

• Sample Size for Test Set: The document mentions "Comparative bench testing was utilized" and "a NTH level combination irritation/sensitization human skin patch test was conducted." However, no specific numerical sample sizes are provided for either the bench testing or the human skin patch test. The "NTH level" for the skin patch test is unclear without further context.
• Data Provenance: The data is generated by Hygia West, Inc. as part of their 510(k) submission. It represents prospective testing conducted by the manufacturer to demonstrate substantial equivalence for their reprocessed devices. The country of origin for the data is implicitly the USA, where Hygia West, Inc. is based and where the regulatory submission is made to the FDA.

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

• Number of Experts: The document does not specify the number of experts used for establishing ground truth, nor does it detail any expert involvement in the testing process beyond the general statement of "testing was utilized."
• Qualifications of Experts: Not provided.

4. Adjudication Method for the Test Set:

• The document does not describe any adjudication method for the test set. The tests mentioned appear to be objective performance and biocompatibility tests rather than assessments requiring human adjudication of subjective interpretations.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size:

• No, an MRMC comparative effectiveness study was not done. The device in question is a reprocessed compression garment, and the studies performed are bench tests and a human skin patch test, not studies involving human readers interpreting medical cases. Therefore, there is no mention of "human readers" or "AI assistance."

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

• This question is not applicable to the device described. The Hygia West Reprocessed Compression Garments are physical medical devices (compressible limb sleeves), not an AI algorithm or software. The "performance" refers to the physical function of the garment, not an algorithm's output.

7. The Type of Ground Truth Used:

• For functional performance, the ground truth appears to be the performance characteristics of the original, new predicate devices. The reprocessed devices are compared against these known, established performance metrics.
• For biocompatibility, the ground truth is established based on accepted standards for human skin irritation and sensitization, as evaluated by the "NTH level combination irritation/sensitization human skin patch test."
• For intended use and technological characteristics, the ground truth is the stated and established properties of the predicate devices.

8. The Sample Size for the Training Set:

• This question is not applicable, as the device is a reprocessed physical medical device, not an AI algorithm or machine learning model that requires a training set.

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

• This question is not applicable for the same reason as point 8.

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