(264 days)
The Etiometry Platform™ software features the Data Aggregation software module version 5.4 and the Risk Analytics Engine software module version 9.2.
The Data Aggregation & Visualization software module is intended to record and display multiple physiological parameters of adult, pediatric, and neonatal patients from supported bedside devices. The software module is not intended for alarm notification, nor is it intended to control any of the independent bedside devices to which it is connected. The software module is intended to be used by healthcare professionals for the following purposes:
To remotely consult regarding a patient's status and
To remotely review other standard or critical near real-time patient data in order to aid in clinical decisions and deliver patient care in a timely manner.
The Data Aggregation & Visualization software module can display numeric physiologic data and waveforms captured by other medical devices:
Airway flow, volume, and pressure Arterial blood pressure (invasive and non-invasive, systolic, diastolic, and mean) Bispectral index (BIS, signal quality index, suppression ratio) Cardiac Index Cardiac output Central venous pressure Cerebral perfusion pressure End-tidal CO2 Heart rate Heart rate variability Intracranial pressure Left atrium pressure Oxygen saturation (intravascular, regional, SpO2) Premature ventricular counted beats Pulmonary artery pressure (systolic, diastolic, and mean) Pulse pressure variation Pulse Rate Respiratory rate Right atrium pressure Temperature (rectal, esophageal, tympanic, blood, core, nasopharyngeal, skin) Umbilical arterial pressure (systolic, diastolic, and mean) Electrocardiogram Plethysmograph
The Data Aggregation & Visualization software module can display laboratory measurements including arterial and venous blood gases, complete blood count, and lactic acid.
The Data Aggregation & Visualization software module can display information captured by the Risk Analytics Engine software module.
The Risk Analytics Engine software module calculates four indices: the IDO2 Index™ for inadequate delivery of oxygen, the IVCO2 Index™ for inadequate ventilation of carbon dioxide, the ACD Index™ for acidemia, and the HLA Index™ for hyperlactatemia.
The IDO2 Index is indicated for use by health care professionals with post-surgical patients 0 to 12 years of age and weighing 2 kg or more under intensive care and patients 18 years of age or older under intensive care and not on Mechanical Circulatory Support. The IDO2 Index is derived by mathematical manipulations of the physiologic data and laboratory measurements received by the Data Aggregation & Visualization software module. When the IDO2 Index is increasing, it means that there is an increasing risk of inadequate oxygen delivery, and attention should be brought to the patient. The IDO2 Index presents partial quantitative information about the patient's cardiovascular condition, and no therapy or drugs can be administered based solely on the interpretation statements.
The IVCO2 Index is indicated for use by healthcare professionals with invasively ventilated patients 0 to 12 years of age under intensive care. The IVCO2 Index is derived by mathematical manipulations of the physiologic data and laboratory measurements received by the Data Aggregation and Visualization software module. When the IVCO2 Index is increasing, it means that there is an increasing risk of inadequate carbon dioxide ventilation, and attention should be brought to the patient. The IVCO2 Index presents partial quantitative information about the patient's respiratory condition, and no therapy or drugs can be administered based solely on the interpretation statements.
The ACD Index is indicated for use by health care professionals with invasively ventilated patients 0 to 12 years of age and weighing 2 kg or more under intensive care. The ACD Index is derived by mathematical manipulations of the physiologic data and laboratory measurements received by the Data Aggregation and Visualization software module. When the ACD Index is increasing, it means that there is an increasing risk of acidemia, and attention should be brought to the patient. The ACD Index presents partial quantitative information about the patient's respiratory condition, and no therapy or drugs can be administered based solely on the interpretation statements.
The HLA Index is indicated for use by health care professionals with post-surgical patients 0 to 12 years of age and weighing 2 kg or more under intensive care. The HLA Index is derived by mathematical manipulations of the physiologic data and laboratory measurements received by the Data Aggregation software module. When the HLA Index is increasing, it means that there is an increasing risk of hyperlactatemia, and attention should be brought to the patient. The HLA Index presents partial quantitative information about the patient's cardiovascular condition, and no therapy or drugs can be administered based solely on the interpretation statements.
The Etiometry Platform allows ICU clinicians and quality improvement teams to aggregate data from multiple sources, store it in a database for analysis, and view the streaming data. The platform features include:
- Adjunctive status indicators
- Customizable display of physiologic parameters over the entire patient stay
- Configurable annotation
- . Web-based visualization that may be used on any standard browser
- Minimal IT footprint
- . Software-only solution – no new bedside hardware required
- . Highly reliable and robust operation
- . Auditable data storage
Here's an analysis of the acceptance criteria and study information for the Etiometry Platform (DAV 5.4 RAE 9.2), based on the provided text:
Based on the provided text, the Etiometry Platform (DAV 5.4 RAE 9.2) was evaluated against "the same acceptance criteria as the predicate device," which are explicitly listed as: discriminatory power, range utilization, resolution/limitation, and robustness.
Unfortunately, the document does not provide specific quantitative acceptance criteria values (e.g., "discriminatory power > X, range utilization between Y and Z") nor does it provide the reported device performance values for each of these criteria. It only states that "All results met the same acceptance criteria as the predicate device."
Therefore, the table below will list the acceptance criteria as described, but the "Reported Device Performance" column cannot be filled with specific numbers from this document.
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Discriminatory Power | Met acceptance criteria |
| Range Utilization | Met acceptance criteria |
| Resolution/Limitation | Met acceptance criteria |
| Robustness | Met acceptance criteria |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: 4721 data points (referred to as "points") from 779 patients.
- Data Provenance: "data from different clinical sites in the US." The study was retrospective, as stated by "The adjunctive status indicators were retrospectively computed on all de-idents."
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
The document does not specify the number of experts used or their qualifications for establishing ground truth for the test set. It mentions that the "adjunctive status indicators" were "designed based on principles of physiology, with parameters chosen to reflect those specified in the medical literature," implying a foundation in expert knowledge, but not a direct expert review of the test set for ground truth.
4. Adjudication Method for the Test Set
The document does not describe an adjudication method for the test set.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study.
6. Standalone (Algorithm Only) Performance
The study primarily describes the performance of the integrated "Risk Analytics Engine software module" which calculates the indices based on manipulated physiological data and laboratory measurements. This indicates a standalone (algorithm only) performance evaluation as it assessed the indices themselves against predetermined criteria. The "Data Aggregation & Visualization software module" also has standalone functions of displaying data.
7. Type of Ground Truth Used
The ground truth for evaluating the adjunctive status indicators (IDO2, IVCO2, ACD, HLA Indices) was based on their ability to predict "risk of inadequate oxygen delivery," "risk of inadequate carbon dioxide ventilation," "increasing risk of acidemia," and "increasing risk of hyperlactatemia." The indices were "derived by mathematical manipulations of the physiologic data and laboratory measurements" and validated against acceptance criteria related to discriminatory power, range utilization, resolution/limitation, and robustness.
While not explicitly stating a direct "ground truth" label like "expert consensus" or "pathology," the nature of the indices suggests they are evaluated against an expected physiological response or correlation based on established medical understanding and literature, rather than a definitive "true positive/negative" diagnosis from a gold standard. The validation against "discriminatory power" implies an ability to differentiate between states, which would require some form of reference for those states.
8. Sample Size for the Training Set
The document does not specify the sample size for the training set. It only mentions "Test datasets were used to evaluate the impact of the changes during the development process. Validation datasets were used after development was complete to validate performance using independent data." The 4721 data points / 779 patients explicitly refer to the validation dataset.
9. How the Ground Truth for the Training Set Was Established
The document does not explicitly describe how the ground truth for the training set was established. It states that the models (indices) were "designed based on principles of physiology, with parameters chosen to reflect those specified in the medical literature." This implies an expert-driven design process informed by medical knowledge rather than a label-based "ground truth" derived for a training set in a traditional supervised machine learning context. Without details on specific training data, further information on its ground truth establishment cannot be provided.
§ 870.2200 Adjunctive cardiovascular status indicator.
(a)
Identification. The adjunctive cardiovascular status indicator is a prescription device based on sensor technology for the measurement of a physical parameter(s). 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) Software description, verification, and validation based on comprehensive hazard analysis must be provided, including:
(i) Full characterization of technical parameters of the software, including any proprietary 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; and
(iv) Mitigation of impact of user error or failure of any subsystem components (signal detection and analysis, data display, and storage) on accuracy of patient reports.
(2) Scientific justification for the validity of the status indicator algorithm(s) must be provided. Verification of algorithm calculations and validation testing of the algorithm using a data set separate from the training data must demonstrate the validity of modeling.
(3) Usability assessment must be provided to demonstrate that risk of misinterpretation of the status indicator is appropriately mitigated.
(4) Clinical data must be provided in support of the intended use and include the following:
(i) Output measure(s) must be compared to an acceptable reference method to demonstrate that the output measure(s) represent(s) the predictive measure(s) that the device provides in an accurate and reproducible manner;
(ii) 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;
(iii) Agreement of the measure(s) with the reference measure(s) must be assessed across the full measurement range; and
(iv) 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;
(ii) A description of what the device measures and outputs to the user;
(iii) Warnings identifying sensor reading acquisition factors that may impact measurement results;
(iv) Guidance for interpretation of the measurements, including warning(s) specifying adjunctive use of the measurements;
(v) Key assumptions made in the calculation and determination of measurements;
(vi) The measurement performance of the device for all presented parameters, with appropriate confidence intervals, and the supporting evidence for this performance; and
(vii) A detailed description of the patients studied in the clinical validation (
e.g., age, gender, race/ethnicity, clinical stability) as well as procedural details of the clinical study.