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Hypotension Decision Assist is indicated to acquire, process and display arterial pressure and other key cardiovascular characteristics of adult patients who are at least eighteen years of age that are undergoing surgery where their arterial pressure is being continuously monitored by a vital-signs monitor. It is indicated for use to assist anesthesia healthcare professionals manage the blood pressure, hemodynamic stability and the cardiovascular system during such surgery.

Hypotension Decision Assist (HDA) is a clinical decision support Software as a Medical Device (SaMD) that is installed upon a medically-rated touch-screen computer. HDA connects to a multi-parameter patient monitor supplied by other manufacturers, from which it acquires vital signs data continuously including the arterial blood pressure waveform and cardiovascular-related numeric parameters.

HDA continually processes this data to display, in graphical charts and numeric format, vital signs data and derived variables including mean arterial pressure (MAP), heart rate, systolic and diastolic blood pressure, cardiac output and systemic vascular resistance. HDA compares MAP to user set targets to indicate when MAP is above or below the target range. It allows the user to mark the administration of vasopressors and volume challenges to the MAP trend.

The Hypotension Decision Assist (HDA-OR2) device, as described in the provided FDA 510(k) summary, is a clinical decision support software intended to assist healthcare professionals in managing blood pressure and cardiovascular stability during surgery.

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

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present acceptance criteria in a quantitative table format with corresponding performance metrics for the HDA-OR2 device's intended use (i.e., assisting anesthesia healthcare professionals manage blood pressure, hemodynamic stability, and the cardiovascular system). Instead, the performance data focuses on system verification, measurement accuracy, artifact detection, and software validation.

The "Performance Data" section primarily details verification tests rather than clinical performance acceptance criteria directly related to the device's indications for use. The overall conclusion states that "the accuracy of its measurements is substantially equivalent to its predicate device and that HDA-OR2 performs as well as its predicate device."

Here's an interpretation of the performance data as it relates to implicit acceptance criteria:

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

The document describes bench testing for verification of the device's technical performance (measurement accuracy, artefact detection, power interruption, remote updates, software V&V, electrical safety/EMC).

• Test Set Sample Size: No specific "sample size" of patients or cases is mentioned for the performance data section, as the testing described is primarily technical and bench-level, not clinical. For example, for measurement accuracy, it mentions verification "across the intended use measuring range" and "over the serial and network connections available," implying a range of test conditions and inputs rather than a patient count.
• Data Provenance: The data provenance is not specified as clinical patient data (e.g., country of origin, retrospective/prospective). The described tests are laboratory/bench tests, not studies on patient data. The device acquires data from "multi-parameter patient monitor supplied by other manufacturers," but the testing described here relates to the device's capability to process that signal and interact with its environment, not its performance in specific patient scenarios or outcomes.

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

• Not Applicable: The studies described are technical verification tests (measurement accuracy, artefact detection, electrical safety, software validation), not clinical studies requiring expert ground truth establishment from patient data for diagnostic or prognostic performance. The "ground truth" for these technical tests would be derived from calibrated instruments, known signal inputs, and established engineering standards.

4. Adjudication Method for the Test Set

• Not Applicable: As the described tests are technical verification rather than clinical performance studies using human experts evaluating patient cases, an adjudication method for a test set of clinical data is not mentioned or required.

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

• No MRMC Study was done: The document does not mention any MRMC comparative effectiveness study, or any study involving human readers/users comparing performance with and without AI assistance. The device is a "clinical decision support" tool, implying assistance, but no study is presented to quantify this assistance's effect on human performance. The 510(k) pathway for this device did not require such a study, as it demonstrated substantial equivalence primarily through technical performance and predicate comparison.

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

• Standalone Performance Studied (Technically): The performance data presented (measurement accuracy, artifact detection, power interruption, remote updates) represents the standalone technical performance of the algorithm and hardware. The device "continually processes this data to display... vital signs data and derived variables." The verification tests confirm the accuracy and robustness of these processing and display functions.
• However, no clinical outcome study or diagnostic accuracy study (e.g., predicting hypotension with a specific accuracy) was performed in a standalone context. The device's role is to "assist" healthcare professionals, not autonomously diagnose or treat.

7. Type of Ground Truth Used

• Technical/Engineering Standards and Calibrated Inputs: The ground truth for the verification tests was established based on:
  • IEC 60601-2-34 Edition 3.0 2011-05: For measurement accuracy verification using bench testing. This implies using known, precisely controlled electrical or physiological signals as inputs and comparing the device's output to these known inputs.
  • Known Artefacts: For artifact detection, specific types of known signal aberrations were introduced to test the device's ability to detect them.
  • Controlled Power Situations: For power interruption testing, the power supply was intentionally cut.
  • Controlled Electromagnetic Environments: For remote update testing in noisy environments.
  • Software Design Specifications and Requirements: For software verification and validation.

8. Sample Size for the Training Set

• Not applicable / Not disclosed: The document describes a "Software as a Medical Device (SaMD)" but does not specify if it employs machine learning or AI models that require a training set in the conventional sense. The device "aquires, processes and displays" data and "derives and displays" variables. This sounds more like rule-based or signal-processing software rather than a learned AI model, at least not one that would typically require a large training dataset for its core function of calculating and displaying vital signs. If there are any internal predictive or pattern-recognition algorithms, their training data and sample size are not mentioned.

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

• Not applicable / Not disclosed: Since the existence and nature of a training set (in the context of machine learning) are not discussed, the method for establishing its ground truth is also not mentioned.

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Hypotension Decision Assist is indicated to acquire, process and display arterial pressure and other key cardiovascular characteristics of adult patients who are at least eighteen years of age that are undergoing surgery where their arterial pressure is being continuously monitored by a vital-signs monitor. It is indicated for use to assist anesthesia healtheare professionals manage the blood pressure, hemodynamic stability and the cardiovascular system during such surgery.

Hypotension Decision Assist (HDA) is a clinical decision support Software as a Medical Device (SaMD) that is installed upon a medically-rated touch-screen computer. HDA connects to a multi-parameter patient monitor supplied by other manufacturers, from which it acquires vital signs data continuously including the arterial blood pressure waveform and cardiovascular-related numeric parameters.

HDA continually processes this data to display, in graphical charts and numeric format, vital signs data and derived variables including mean arterial pressure (MAP), heart rate, systolic and diastolic blood pressure, cardiac output and systemic vascular resistance. HDA compares MAP to user set targets to indicate when MAP is above or below the target range. It allows the user to mark the administration of vasopressors and volume challenges to the MAP trend.

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

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

The document primarily focuses on demonstrating substantial equivalence to a predicate device and meeting various technical and safety standards, rather than defining specific numerical performance acceptance criteria for clinical outcomes. However, it does highlight areas of verification.

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

• Sample Size for Test Set: Not explicitly stated as a number of patients or cases for clinical performance assessment. The "test set" primarily refers to hardware and software testing.
• Data Provenance: "Patient data sets obtained from internationally recognized databases" were used for the original system verification and "bench testing performed to compare the performance of HDA to the predicate device." The data was "representative of the range of data input and signal quality that will be encountered in the intended use population and environment of use of the device." No specific countries of origin or whether the data was retrospective or prospective are mentioned beyond being from "internationally recognized databases."

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

Not applicable. The document states that "Clinical studies were not performed." Therefore, there was no expert consensus or ground truth established by human experts for a clinical test set in the traditional sense. The "ground truth" for the bench testing was derived from established standards (e.g., IEC 60601-2-34) and comparison to predicate/reference device measurements.

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

Not applicable. Since no clinical studies were performed, there was no adjudication of clinical outcomes by multiple experts.

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

No. A MRMC comparative effectiveness study was not performed. The device is a clinical decision support software, not an AI for image interpretation that would typically involve human readers. Clinical studies involving human users were not performed.

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

Yes, in a sense. The "Performance Data" section describes "Measurement accuracy verification," "Artefact Detection Verification," and "Predicate comparison testing" which evaluate the algorithm's direct output and processing capabilities against established standards or predicate device outputs. This represents a standalone performance evaluation of the algorithms and software functionality, rather than human-in-the-loop performance.

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

The ground truth for the device's technical performance verification (e.g., measurement accuracy) was established through:

• Bench testing methodologies derived from recognized standards (e.g., IEC 60601-2-34).
• Comparison to predicate or reference devices' established performance for specific parameters (e.g., cardiac output, SVR events, physiological parameter derivation).
• Pre-defined specifications for artifact detection and power interruption tolerance.

For claims of "clinical decision support" or "assisting healthcare professionals," the ground truth implicitly relies on the widely accepted understanding that accurate display and processing of vital signs aid clinical decision-making, rather than a specific clinical outcome study being performed with this device.

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 model development. The device is described as "clinical decision support software" that "continually processes this data." If machine learning was used implicitly, no details are provided about its training data. The "patient data sets obtained from internationally recognized databases" were used for "original system verification" and "bench testing," which might imply they were used for validation or testing, but not necessarily for training a model.

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

Not applicable, as a clear "training set" and its ground truth establishment are not described in the provided text. The device's functionality appears to be primarily based on processing established physiological parameters and rules, rather than learning from a labeled training dataset in the AI sense.

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