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This device is used for treatment of acute non-toxic megacolon, (Ogilvie's syndrome) and colonic strictures.

The CDSG device consists of a colon decompression tube, guiding catheter and wire guide. The colon decompression tube is 14Fr in diameter with a length of 175cm. The device design includes side ports which allow for colon decompression. The device has connections to allow attachment to drainage collection bag or for the device to be flushed. The colon decompression tube, guiding catheter and wire guide can be observed fluoroscopically. The device is supplied sterile, intended for single use only. Use of this device is restricted to a trained healthcare professional.

The CDSM device consists of a colon decompression tube and wire guide. The colon decompression tube is supplied in a range of diameter options, 7Fr. 8.5Fr and 10Fr. The colon decompression tube has a length of 350cm. The device design includes side ports which allow for colon decompression. The device has connections to allow attachment to drainage collection bag or for the device to be flushed. The colon decompression tube and wireguide can be observed fluoroscopically. The colon decompression tube has a design feature (pigtail) at the distal end of the device which helps reduce migration. The device is supplied sterile, intended for single use only. Use of this device is restricted to a trained healthcare professional.

The same wire guide is supplied with both the CDSG and CDSM devices; the wire guide has a flexible distal ball tip.

This document describes a 510(k) premarket notification for a medical device called the "14Fr Colon Decompression Set (CDSG) / Marcon Colon Decompression Set (CDSM)". This is a Class II device (21 CFR § 876.5980, Gastrointestinal Tube and Accessories). The application aims to demonstrate substantial equivalence to a predicate device (Cook Colon Decompression Set, K900035, cleared March 7, 1990).

The provided text does not contain information about a study proving that an AI-driven device meets acceptance criteria. This document describes a traditional medical device (colon decompression sets) and its equivalence with a predicate device based on manufacturing, material, and design comparisons, along with performance testing relevant to its physical function and safety.

Therefore, I cannot provide the requested information regarding AI acceptance criteria, study details, sample sizes for AI training/test sets, expert adjudication, MRMC studies, standalone AI performance, or ground truth establishment for AI.

The document discusses non-clinical performance testing of the device itself (not an AI component). Here’s what is mentioned regarding acceptance criteria and performance data for the described device:

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

The document broadly states: "Performance testing included simulated use, dimensional testing, resistance to collapse, flow rate, tensile strength testing, dimensional and leakage tests, MR safety testing, radiopacity and shelf life testing." And "The results of the non-clinical testing demonstrates that the Colon decompression sets met the design input requirements based on the intended use, and do not raise new questions of safety or effectiveness."

However, specific quantitative acceptance criteria and their corresponding reported performance values for each test are not provided in this regulatory letter. The letter confirms that the tests were performed and the device met the requirements.

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

• Sample Size: Not specified for any of the performance tests (simulated use, dimensional, resistance to collapse, flow rate, tensile strength, leakage, MR safety, radiopacity, shelf life).
• Data Provenance: Not explicitly stated, but typically, these tests are conducted by the manufacturer (Cook Ireland Ltd.) in controlled laboratory settings. There is no mention of country of origin for the "data" as it pertains to patient data; it's product performance data.
• Retrospective or Prospective: These are laboratory performance tests on the manufactured device, not clinical studies on patients. Therefore, the terms "retrospective" or "prospective" do not directly apply in the context of clinical data collection.

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

• This information is not applicable. The "ground truth" for this device's performance is established by direct physical measurements, engineering specifications, and established biocompatibility standards, not by expert interpretation of medical images or clinical outcomes in the same way an AI device's ground truth would be.

4. Adjudication method for the test set:

• Not applicable for the type of performance testing described. Adjudication methods (like 2+1, 3+1) are typically used for clinical or image-based ground truth establishment, not for direct physical property measurements of a device.

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 not an AI product, and no MRMC study involving human readers or AI assistance is mentioned or relevant to its 510(k) submission.

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

• Not applicable. This is not an algorithm or AI device.

7. The type of ground truth used:

• For the performance testing: The "ground truth" is defined by engineering specifications, material science standards, and biocompatibility standards (e.g., ISO 10993-1, FDA's biocompatibility guidance), which are measured directly.

8. The sample size for the training set:

• Not applicable. This device does not involve a "training set" in the context of AI or machine learning.

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

• Not applicable.

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The Medline Anti-Reflux Valve allows the vent lumen of the gastric sump tube to neutralize the vacuum pressure in the stomach when the contents are fully evacuated, but prevents stomach contents from exiting the vent lumen. This reduces the risk that healthcare workers will be exposed to potentially infectious gastric fluids and also prevents unnecessary patient bedding and gown changes.

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I am sorry, but the provided text is a 510(k) premarket notification of intent to market a medical device (Medline Anti-Reflux Valve) and does not contain any information about acceptance criteria or a study proving the device meets acceptance criteria.

The document primarily focuses on the FDA's "substantial equivalence" determination, indicating that the device is similar enough to previously marketed devices that it doesn't require a full Premarket Approval (PMA) application. It discusses:

• Device Name: Medline Anti-Reflux Valve
• Regulation Number and Name: 21 CFR § 876.5980, Gastrointestinal tube and accessories
• Regulatory Class: II
• Product Code: FEG
• Indications for Use: Allows the vent lumen of the gastric sump tube to neutralize vacuum pressure in the stomach while preventing stomach contents from exiting, reducing exposure to gastric fluids for healthcare workers and preventing bedding/gown changes.

To provide the information you requested (acceptance criteria, study details, sample sizes, ground truth, etc.), I would need a different type of document, such as a clinical study report, a validation report, or a summary of safety and effectiveness data, which are not present in this 510(k) letter.

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The device is intended for gastric decompression and delivery of fluids, including irrigation, nutritional supplements, and medication.

The GiEntriport with Universal Single Lumen Adaptor is a multiport Device Description. The GiEnthport with Offirstal Surges or to a single lumen feeding tube.

The provided text contains information about a 510(k) Premarket Notification for the GiEntriport Single Lumen Adaptor. However, it does not describe a study involving acceptance criteria, reported device performance, sample sizes for test/training sets, expert involvement, or comparative effectiveness studies of AI. The document focuses on regulatory approval based on substantial equivalence to predicate devices, rather than a performance study with detailed metrics.

Therefore, many of the requested sections cannot be filled from the provided text.

Here's a breakdown of what can and cannot be extracted:

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

• Cannot be provided. The document states that "Engineering evaluation, and testing were conducted to demonstrate that the design of the proposed device was equivalent to the predicate devices, and/or met the requirements for gastric decompression and delivery of fluids as such devices, as defined in EN1615:2000." This indicates that some testing was done to meet standards (EN1615:2000) and demonstrate equivalence, but the specific acceptance criteria and detailed reported performance metrics are not provided in this 510(k) summary.

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

• Cannot be provided. The document mentions "Engineering evaluation, and testing" but does not detail sample sizes, types of tests performed, or data provenance.

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. This device is a medical tube/adaptor, not a diagnostic imaging AI system. The concept of "ground truth" established by experts for a test set, as typically applied to AI/Machine Learning in diagnostics, does not apply here. The "ground truth" for this device would relate to its physical performance and safety characteristics.

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

• Not applicable. See point 3.

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 a physical medical device, not an AI system. No MRMC studies or AI assistance are mentioned or relevant to this type of device.

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

• Not applicable. This is a physical medical device.

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

• Cannot be directly answered in the AI/ML context. For this device, the "truth" would be its adherence to established engineering standards (EN1615:2000) for safety and performance, and functional equivalence to predicate devices for its intended use (gastric decompression, fluid/nutritional delivery).

8. The sample size for the training set

• Not applicable. There is no mention of a "training set" as this is not an AI/ML device.

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

• Not applicable. See point 8.

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The Next Generation Salem Sump is intended for gastric decompression and delivery of fluids, including irrigation, nutritional supplements, and medication, during the time period that gastric decompression is required.

The Next Generation Salem Sump is a dual lumen Naso Gastric tube with a multiport connector/valve to facilitate switching among the various function of the device.

The provided text is a 510(k) summary for the "Next Generation Salem Sump," which is a medical device. This type of submission focuses on demonstrating substantial equivalence to a predicate device, rather than proving performance against specific acceptance criteria through extensive clinical studies as one might find for a novel, higher-risk device.

Therefore, the information typically requested in your prompt (e.g., sample size for test set, number of experts for ground truth, MRMC studies, standalone performance, training set details) is not applicable or not available in this type of document because the device is a re-engineered version of an existing device and the "studies" are primarily non-clinical comparisons to predicate devices and industry standards.

Here's a breakdown based on the provided text, addressing your points where possible, and indicating where information is not present:

Acceptance Criteria and Device Performance Study for Next Generation Salem Sump (K040388)

This submission is a 510(k) Premarket Notification for a Class II medical device (Gastrointestinal tube and accessories). The device, the "Next Generation Salem Sump," is intended for gastric decompression and fluid delivery. The primary method of demonstrating its suitability for market clearance is by proving substantial equivalence to legally marketed predicate devices, rather than through extensive clinical efficacy trials against pre-defined acceptance criteria for performance. The "study" here refers to non-clinical testing to demonstrate equivalence or adherence to industry standards.

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. The document refers to "Nonclinical Testing" which likely involves specific device prototypes or components, but the number of units tested is not detailed in this summary.
• Data Provenance: Not specified, but given it's non-clinical testing performed by the manufacturer (The Kendall Company / Tyco Healthcare Group), it would be proprietary testing. Contextually, such testing is generally performed in a controlled laboratory setting, not with patient data.

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

• Not Applicable. For a device like a nasogastric tube undergoing non-clinical testing for substantial equivalence, "ground truth" and "experts" in the context of clinical interpretation (e.g., radiologists interpreting images) are not relevant. The "ground truth" here would be adherence to engineering specifications and performance standards as determined by test methods.

4. Adjudication method for the test set

• Not Applicable. No human interpretation or adjudication in the clinical sense is described. Non-clinical testing follows predefined protocols and acceptance criteria based on engineering and material science principles.

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 a physical medical device (nasogastric tube), not an AI/software-as-a-medical-device (SaMD) or imaging device that would involve human readers or AI assistance.

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

• Not Applicable. See point 5.

7. The type of ground truth used

• Not Applicable / Engineering Specifications and Standards. For this type of submission, "ground truth" refers to the established engineering specifications for the device components and performance metrics derived from industry standards (EN1615:2000) or direct comparison to the predicate devices. It is not clinical data like pathology reports, expert consensus of images, or patient outcomes.

8. The sample size for the training set

• Not Applicable. There is no "training set" as this is not a machine learning or AI-driven device.

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

• Not Applicable. See point 8.

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To permit withdrawal, introduction of fluids into the stomach or removal of stomach gas via a tube inserted through the nostril.

The Knott NG tube is designed to permit withdrawal or introduction of fluids through a tube which is inserted through the nostril and into the stomach.

The provided document is a 510(k) premarket notification for the "Knott Nasogastric Tube," seeking clearance based on substantial equivalence to a predicate device. It is not a study reporting device performance against acceptance criteria in the manner typically associated with clinical trials or diagnostic algorithm evaluations.

Therefore, most of the requested information regarding acceptance criteria, study design, sample sizes, expert ground truth, adjudication, and comparative effectiveness (MRMC) or standalone studies cannot be extracted from this document because such studies were not conducted or reported for this type of device submission.

This document focuses on demonstrating substantial equivalence in design, materials, and intended use as per FDA 510(k) requirements, not on presenting performance data from a clinical study with predefined acceptance criteria.

However, I can extract the following relevant information based on the provided text:

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

The document does not specify "acceptance criteria" for performance in a quantitative sense as would be found in a clinical trial evaluating a novel diagnostic or therapeutic device. Instead, it compares the attributes of the proposed device to a predicate device to establish substantial equivalence.

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

Not applicable. This is a 510(k) premarket notification for a medical device (nasogastric tube), not a study involving a test set of data or human subjects for performance evaluation in the described manner. The submission relies on a comparison to a legally marketed predicate device.

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. No "test set" or "ground truth" in this context as it's not a diagnostic or AI-based device performance study.

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

Not applicable.

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 not an AI-assisted diagnostic tool, and no MRMC study was performed or reported.

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

Not applicable. This device does not involve an algorithm.

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

Not applicable. The "proof" of meeting criteria for this device is based on demonstrating substantial equivalence to a legally marketed predicate device through comparison of attributes (design, materials, intended use), rather than clinical performance against a ground truth.

8. The sample size for the training set

Not applicable. There is no training set mentioned or implied for this device's submission.

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

Not applicable.

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The Davol Nasogastric Sump Tube with PreVent Anti-Reflux Filter is intended for gastric decompression, gastric lavage, and administration of nutritional supplements and medication.

The Davol Nasogastric Sump Tube with PreVent Anti-Reflux Filter is a dual lumen device used for gastric decompression (removal of gastric secretions and gases), gastric lavage, and administration of nutritional supplements and medication. Nasogastric sump tubes are generally used with a suction source to facilitate gastric drainage. The primary suction lumen of the tube is used for drainage, the second lumen, the vent lumen, provides an air inlet as a mechanism to break suction minimizing the potential for tube blockage secondary to mucosal tissue invagination into the side holes of the primary suction lumen. With the dual lumen nasogastric sump tube, if the primary lumen becomes obstric contents can back up into the vent lumen causing spillage onto the patient and possible clinical staff exposure to the gastric contents. The PreVent Anti-Reflux Filter provided with the Davol Nasogastric Sump Tube prevents reflux from exiting the vent lumen and spilling from the vent tube. The anti-reflux filter was incorporated for convenience, so that the clinical staff would not have to contend with and be exposed to spilled gastric contents and subsequent patient gown and linen changes. Although spillage from the vent tube is prevented, the user is still able to visualize reflux in the vent tube and therefore, the ability to diagnose a blocked primary lumen is maintained.

The provided text is a 510(k) summary for a medical device (Nasogastric Sump Tube) and does not describe an AI/ML powered device or a study involving acceptance criteria for an AI/ML model's performance. Therefore, most of the requested information (sample sizes, ground truth, experts, MRMC, standalone performance, training set) is not applicable to this document.

However, I can extract the acceptance criteria and the performed studies related to the physical medical device Davol8 Nasogastric Sump Tube with PreVent™ Anti-Reflux Filter.

Here's the information based on the provided text, recognizing its limitations regarding AI/ML:

1. Table of Acceptance Criteria and Reported Device Performance

Regarding AI/ML-specific questions (not applicable to this document):

2. Sample size used for the test set and the data provenance: Not applicable. This document describes a physical medical device, not an AI/ML model.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
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.
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.): Not applicable. The "ground truth" here refers to the physical properties and performance of the device itself (e.g., flow rates, material composition, biocompatibility).
8. The sample size for the training set: Not applicable.
9. How the ground truth for the training set was established: Not applicable.

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