(332 days)
The Analytic for Hemodynamic Instability (AHI) software is intended for use by healthcare professionals managing in-hospital patients 18 years or older who are receiving continuous physiological monitoring with electrocardiography (ECG).
AHI provides a frequently undated binary output over time based on pattern analysis of a lead-II ECG waveform intended to describe a patient's hemodynamic status and indicate if a patient is showing signs of hemodynamic stability or instability. Signs of hemodynamic instability are defined as hypotension (systolic blood pressure
Analytic for Hemodynamic Instability (AHI) is a software as a medical device (SaMD) that analyzes Lead-II ECG signals to identify patients who are showing signs of hemodynamic instability. Signs of hemodynamic instability are defined as low blood pressure (BP) and high heart rate (HR). The device processes 5 minutes of continuously recorded Lead II ECG data to determine the presence of a combination of HR ≥ 100 bpm and SBP
Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
| Windows-Based Performance Measure | Acceptable Threshold | Observed Estimate | 95% one-sided confidence interval |
|---|---|---|---|
| Sensitivity | >85% | 95.6% | 2.5% LCB: 88.9% |
| 1-Specificity | 75% | 84.9% | n/a (calculated from 1-Specificity) |
2. Sample Size Used for the Test Set and Data Provenance
- Test Set Sample Size: 222 patients contributed data for the primary analysis. These patients generated a large, unspecified number of "windows" of 5-minute ECG data, with (b) (4) windows containing valid paired data for comparison. Each patient contributed at most data from 150 windows.
- Data Provenance: Retrospective, single-center, observational study. The data was collected from November 26, 2019, through January 29, 2020, at the University of Michigan Medical Center. The data was "privately collected" and "IRB approved."
3. Number of Experts Used to Establish Ground Truth and Qualifications
The document does not explicitly state the number of experts used to establish the ground truth for the test set or their qualifications. Instead, the ground truth was established by:
- Continuous ECG monitoring.
- Continuous arterial line blood pressure monitoring.
The definition of hemodynamic instability used for ground truth (SBP
§ 870.2220 Adjunctive hemodynamic indicator with decision point.
(a)
Identification. An adjunctive hemodynamic indicator with decision point is a device that identifies and monitors hemodynamic condition(s) of interest and provides notifications at a clinically meaningful decision point. This device is intended to be used adjunctively along with other monitoring and patient information.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Software description, verification, and validation based on comprehensive hazard analysis and risk assessment must be provided, including:
(i) Full characterization of technical parameters of the software, including algorithm(s);
(ii) Description of the expected impact of all applicable sensor acquisition hardware characteristics on performance and any associated hardware specifications;
(iii) Specification of acceptable incoming sensor data quality control measures;
(iv) Mitigation of impact of user error or failure of any subsystem components (signal detection and analysis, data display, and storage) on output accuracy; and
(v) The sensitivity, specificity, positive predictive value, and negative predictive value in both percentage and number form for clinically meaningful pre-specified time windows consistent with the device output.
(2) Scientific justification for the validity of the hemodynamic indicator algorithm(s) must be provided. Verification of algorithm calculations and validation testing of the algorithm must use an independent data set.
(3) Usability assessment must be provided to demonstrate that risk of misinterpretation of the status indicator is appropriately mitigated.
(4) Clinical data must support the intended use and include 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) Output measure(s) must be compared to an acceptable reference method to demonstrate that the output represents the measure(s) that the device provides in an accurate and reproducible manner;
(iii) The data set must be representative of the intended use population for the device. Any selection criteria or limitations of the samples must be fully described and justified;
(iv) Where continuous measurement variables are displayed, agreement of the output with the reference measure(s) must be assessed across the full measurement range; and
(v) Data must be provided within the clinical validation study or using equivalent datasets to demonstrate the consistency of the output and be representative of the range of data sources and data quality likely to be encountered in the intended use population and relevant use conditions in the intended use environment.
(5) Labeling must include the following:
(i) The type of sensor data used, including specification of compatible sensors for data acquisition, and a clear description of what the device measures and outputs to the user;
(ii) Warnings identifying factors that may impact output results;
(iii) Guidance for interpretation of the outputs, including warning(s) specifying adjunctive use of the measurements;
(iv) Key assumptions made in the calculation and determination of measurements; and
(v) A summary of the clinical validation data, including details of the patient population studied (
e.g., age, gender, race/ethnicity), clinical study protocols, and device performance with confidence intervals for all intended use populations.