(105 days)
The Global Hypoperfusion Index (GHI) algorithm provides the clinician with physiological insight into a patient's likelihood of future hemodynamic instability. The GHI algorithm provides the risk of a global hypoperfusion event (defined as SvO2 ≤ 60% for at least 1 minute) occurring in the next 10-15 minutes.
The GHI algorithm is intended for use in surgical patients receiving advanced hemodynamic monitoring with the Swan-Ganz catheter.
The GHI algorithm is considered to provide additional information regarding the patient's predicted future risk for clinical deterioration, as well as identifying patients at low risk for deterioration. The product predictions are for reference only and no therapeutic decisions should be made based solely on the GHI algorithm predictions.
The Global Hypoperfusion Index (GHI) parameter provides the clinician with physiological insight into a patient's likelihood of a global hypoperfusion event on average 10-15 minutes before mixed venous oxygen saturation (SvO2) reaches 60%. The GHI feature is intended for use in surgical or nonsurgical patients. The product predictions are adjunctive for reference only and no therapeutic decisions should be made based solely on the GHI parameter.
The provided text does not contain detailed acceptance criteria or a comprehensive study report with all the requested information. It primarily presents the FDA's 510(k) clearance letter and a summary of the device, its indications for use, and a comparison to predicate devices, stating that performance testing was executed and that no clinical trial was performed for the 510(k) submission.
However, based on the available information, here's what can be extracted and inferred:
1. A table of acceptance criteria and the reported device performance:
The document mentions that the GHI algorithm provides the risk of a global hypoperfusion event (defined as SvO2 ≤ 60% for at least 1 minute) occurring in the next 10-15 minutes and alerts the clinician on average 10-15 minutes before SvO2 reaches 60%. It also states that the GHI algorithm provides an index from 0 to 100 where the higher the value, the increased likelihood that a global hypoperfusion event will occur.
While specific numerical acceptance criteria (e.g., minimum sensitivity, specificity, or AUC) and their corresponding achieved performance values are not explicitly stated in the provided text, the overall conclusion is that the algorithm "has successfully passed functional and performance testing" and "meets the predetermined design and performance specifications." This implies that internal acceptance criteria were met, even if they are not detailed here.
Example (Hypothetical, as not provided in text):
| Metric | Acceptance Criteria (Hypothetical) | Reported Device Performance (Implied as "met") |
|---|---|---|
| Time to Alert | Average 10-15 minutes before event | Achieved average 10-15 minutes before event |
| Ability to Identify Risk | GHI 0-100, higher = increased risk | GHI provides increased likelihood with higher values |
| Overall Performance | Meets predetermined specifications | Met predetermined specifications |
2. Sample size used for the test set and the data provenance:
- Sample Size: The text states, "Prospective analyses of retrospective clinical data from multiple independent datasets, comprised of data from a diverse set of patients over the age of 18 years undergoing surgical procedures with invasive monitoring, were analyzed to verify the safety and performance of the subject device." However, the exact sample size (number of patients or data points) for the test set is not specified.
- Data Provenance:
- Country of Origin: Not specified in the provided text.
- Retrospective or Prospective: "Prospective analyses of retrospective clinical data" implies that existing (retrospective) data was collected and then analyzed in a forward-looking (prospective) manner for the purpose of the study.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
The text does not provide any information regarding the number of experts, their qualifications, or their involvement in establishing ground truth for the test set.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
The text does not provide any information regarding an adjudication method for the test set.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and if so, what was the effect size of how much human readers improve with AI vs without AI assistance:
The text explicitly states: "No clinical trial was performed in support of the subject 510(k)." This indicates that an MRMC comparative effectiveness study involving human readers and AI assistance was not conducted for this submission.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
Yes, a standalone performance evaluation was conducted. The text states:
- "Algorithm performance was tested using clinical data."
- "The algorithm was tested at the algorithm level to ensure the safety of the device. All tests passed."
- "Prospective analyses of retrospective clinical data... were analyzed to verify the safety and performance of the subject device."
This confirms that the algorithm's performance was assessed independently.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
The ground truth for a "global hypoperfusion event" is explicitly defined in the Indications for Use as: "SvO2 ≤ 60% for at least 1 minute." This is an objective physiological measurement (outcomes data) rather than expert consensus or pathology.
8. The sample size for the training set:
While the text mentions that "patient waveforms were collected in support of the development and validation of the GHI algorithm," the sample size for the training set is not specified.
9. How the ground truth for the training set was established:
Given that the ground truth for the device's output is based on SvO2 measurements, it is highly probable that the ground truth for the training set was established using the same objective physiological measurement: SvO2 ≤ 60% for at least 1 minute. The text implies that clinical data (patient waveforms) were used for both development and validation.
§ 870.2210 Adjunctive predictive cardiovascular indicator.
(a)
Identification. The adjunctive predictive cardiovascular indicator is a prescription device that uses software algorithms to analyze cardiovascular vital signs and predict future cardiovascular status or events. This device is intended for adjunctive use with other physical vital sign parameters and patient information and is not intended to independently direct therapy.(b)
Classification. Class II (special controls). The special controls for this device are:(1) A software description and the results of verification and validation testing based on a comprehensive hazard analysis and risk assessment must be provided, including:
(i) A full characterization of the software technical parameters, including algorithms;
(ii) A description of the expected impact of all applicable sensor acquisition hardware characteristics and associated hardware specifications;
(iii) A description of sensor data quality control measures;
(iv) A description of all mitigations for user error or failure of any subsystem components (including signal detection, signal analysis, data display, and storage) on output accuracy;
(v) A description of the expected time to patient status or clinical event for all expected outputs, accounting for differences in patient condition and environment; and
(vi) The sensitivity, specificity, positive predictive value, and negative predictive value in both percentage and number form.
(2) A scientific justification for the validity of the predictive cardiovascular indicator algorithm(s) must be provided. This justification must include verification of the algorithm calculations and validation using an independent data set.
(3) A human factors and usability engineering assessment must be provided that evaluates the risk of misinterpretation of device output.
(4) A clinical data assessment must be provided. This assessment must fulfill the following:
(i) The assessment must include a summary of the clinical data used, including source, patient demographics, and any techniques used for annotating and separating the data.
(ii) The clinical data must be representative of the intended use population for the device. Any selection criteria or sample limitations must be fully described and justified.
(iii) The assessment must demonstrate output consistency using the expected range of data sources and data quality encountered in the intended use population and environment.
(iv) The assessment must evaluate how the device output correlates with the predicted event or status.
(5) Labeling must include:
(i) A description of what the device measures and outputs to the user;
(ii) Warnings identifying sensor acquisition factors that may impact measurement results;
(iii) Guidance for interpretation of the measurements, including a statement that the output is adjunctive to other physical vital sign parameters and patient information;
(iv) A specific time or a range of times before the predicted patient status or clinical event occurs, accounting for differences in patient condition and environment;
(v) Key assumptions made during calculation of the output;
(vi) The type(s) of sensor data used, including specification of compatible sensors for data acquisition;
(vii) The expected performance of the device for all intended use populations and environments; and
(viii) Relevant characteristics of the patients studied in the clinical validation (including age, gender, race or ethnicity, and patient condition) and a summary of validation results.