(266 days)
The T3 Platform™ software features the T3 Data Aggregation & Visualization software module version 5.0 and the T3 Risk Analytics Engine software module version 8.0.
The T3 Data Aggregation & Visualization software module is intended for the recording and display of multiple physiological parameters of the adult, pediatric, and neonatal patients from supported bedside devices. The software module is not intended for alarm notification or waveform display, 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 T3 Data Aggregation & Visualization software module can display numeric physiologic data 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)
The T3 Data Aggregation & Visualization software module can display laboratory measurements including arterial and venous blood gases, complete blood count, and lactic acid. T3 Data Aggregation & Visualization software module can display information captured by the T3 Risk Analytics Engine software module,
The T3 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. The IDO2 Index™ is derived by mathematical manipulations of the physiologic data and laboratory measurements received by the T3 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 health care professionals with invasively ventilated patients 0 to 12 years of age and weighing 2 kg or more under intensive care. The IVCO2 Index™ is derived by mathematical manipulations of the physiologic data and laboratory measurements received by the T3 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 IVCO2 Index™ presents partial quantitative information about the patient's respiratory condition, and no therapy or drugs can be administered based solely on the internets.
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 T3 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 T3 Data Aggregation & Visualization software module. When the HLA Index™ is increasing, it means that there is an increasing risk of hyperlactaternia 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 Tracking, Trajectory, Trigger (73) intensive care unit software solution allows clinicians and quality improvement teams in the ICU to aggregate data from multiple sources, store it in a database for analysis, and view the streaming data. System features include:
- Adjunctive status indicators
- Customizable display of physiologic parameters over 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 proving the device meets those criteria, based on the provided text:
Device: T3 Platform™ software (specifically the T3 Risk Analytics Engine software module version 8.0, which calculates the IDO2 Index™, IVCO2 Index™, ACD Index™, and HLA Index™).
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria are broadly defined as "discriminatory power, range utilization, resolution/limitation, and robustness." While specific numerical thresholds for these criteria are not provided in the document, the conclusion states that "All results met acceptance criteria for discriminatory power, range utilization, resolution/limitation, and robustness."
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Discriminatory Power | Met acceptance criteria (all indices) |
| Range Utilization | Met acceptance criteria (all indices) |
| Resolution/Limitation | Met acceptance criteria (all indices) |
| Robustness | Met acceptance criteria (all indices) |
2. Sample Size Used for the Test Set and Data Provenance
The document provides sample sizes for the validation of the new indices (ACD and HLA Indexes):
- HLA Index Validation: 58,168 whole blood lactate measurements from 3,496 patients.
- ACD Index Validation: 24,431 arterial blood pH measurements from 1,858 patients.
Data Provenance:
- Country of Origin: United States (data from eleven different clinical sites in the US).
- Retrospective or Prospective: Retrospectively computed on all de-identified patients.
3. Number of Experts Used to Establish Ground Truth and Qualifications
The document does not specify the number of experts or their qualifications used to establish the ground truth for the test set. It states that the indices are "derived by mathematical manipulations of the physiologic data and laboratory measurements" and that the "underlying physiology model" for the indices has been updated. The ground truth for the ACD Index is defined as "arterial pH less than 7.25" and for the HLA Index as "whole blood lactate level concentration above 4 mmol / L." These appear to be objective thresholds based on laboratory measurements rather than expert consensus on interpretations of images or complex clinical states.
4. Adjudication Method for the Test Set
No adjudication method (e.g., 2+1, 3+1) is mentioned or appears to be relevant given the nature of the ground truth (objective laboratory measurements).
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No MRMC study was done, as the device is a risk analytics engine providing calculated indices, not an imaging AI designed to assist human readers in interpreting medical images. The primary use is for "remotely consult regarding a patient's status" and "remotely review other standard or critical near real-time patient data in order to aid in clinical decisions and deliver patient care." The indices provide "partial quantitative information" and "no therapy or drugs can be administered based solely on the interpretation statements." Furthermore, the warnings state: "Do not rely on the T3 Platform software as the sole source of patient status information." This indicates an adjunctive role rather than one where human interpretation is directly assisted in a comparative effectiveness study.
6. Standalone (Algorithm Only) Performance Study
Yes, the validation of the ACD Index and HLA Index appears to be a standalone (algorithm only) performance study against objective ground truth derived from laboratory measurements. The indices were "retrospectively computed on all de-identified patients" and evaluated against the defined acceptance criteria.
7. Type of Ground Truth Used
The ground truth used is primarily outcomes data / objective laboratory measurements.
- For the ACD Index: Arterial blood pH measurements, with acidemia defined as arterial pH less than 7.25.
- For the HLA Index: Whole blood lactate measurements, with hyperlactatemia defined as whole blood lactate level concentration above 4 mmol/L.
8. Sample Size for the Training Set
The document does not explicitly state the sample size for a dedicated "training set." It mentions that the "approach is designed based on principles of physiology, and parameters are chosen to reflect those specified in the medical literature and employed development testing data sets and validation sets." It distinguishes between "Development testing sets" (used to evaluate impact of changes during development) and "Validation sets" (used after development is complete). The sample sizes provided (3,496 patients for HLA, 1,858 patients for ACD) are for the validation sets. The size of the "development testing sets" is not specified.
9. How Ground Truth for the Training Set Was Established
The document implies that the model's development and parameter choices were guided by "principles of physiology" and "medical literature," which would have formed the basis for the ground truth during the development ("training") phase. For the actual performance testing, the ground truth was established by objective laboratory measurements (arterial pH and whole blood lactate levels) with pre-defined thresholds for acidemia and hyperlactatemia. The details of how ground truth was precisely established for the "development testing data sets" are not explicitly detailed, but it would logically follow the same principles of using objective physiological and laboratory data.
§ 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.