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SmartGuard technology is intended for use with compatible integrated continuous glucose monitors (iCGM), compatible Medtronic continuous glucose monitors (CGMs), and alternate controller enabled (ACE) pumps to automatically adjust the delivery of basal insulin and to automatically deliver correction boluses based on sensor glucose values.
SmartGuard technology is intended for the management of Type 1 diabetes mellitus in persons 7 years of age and older requiring insulin.
SmartGuard technology is intended for single patient use and requires a prescription.

Predictive Low Glucose technology is intended for use with compatible integrated continuous glucose monitors (iCGM), compatible Medtronic continuous glucose monitors (CGMs), and alternate controller enabled (ACE) pumps to automatically suspend delivery of insulin when the sensor glucose value falls below or is predicted to fall below predefined threshold values.
Predictive Low Glucose technology is intended for the management of Type 1 diabetes mellitus in persons 7 years of age and older requiring insulin.
Predictive Low Glucose technology is intended for single patient use and requires a prescription.

SmartGuard Technology, also referred to as Advanced Hybrid Closed Loop (AHCL) algorithm, is a software-only device intended for use by people with Type 1 diabetes for ages 7 years or older. It is an interoperable automated glycemic controller (iAGC) that is intended for use with compatible integrated continuous glucose monitors (iCGM), compatible interoperable Medtronic continuous glucose monitors (CGM) and compatible alternate controller enabled (ACE) pumps to automatically adjust the delivery of basal insulin and to automatically deliver correction boluses based on sensor glucose (SG) values.
The AHCL algorithm resides on the compatible ACE pump, which serves as the host device. It is meant to be integrated in a compatible ACE pump and is an embedded part of the ACE pump firmware.
Inputs to the AHCL algorithm (e.g., SG values, user inputs) are received from the ACE pump (host device), and outputs from the AHCL algorithm (e.g., insulin delivery commands) are sent by the algorithm to the ACE pump. As an embedded part of the firmware, the AHCL algorithm does not connect to or receive data from compatible CGMs; instead, sensor glucose (SG) values or other inputs received by the ACE pump from compatible CGMs via Bluetooth Low Energy (BLE) technology are transmitted to the embedded AHCL algorithm.
The AHCL algorithm works in conjunction with the ACE pump and is responsible for controlling insulin delivery when the ACE pump is in Auto Mode. It includes adaptive control algorithms that autonomously and continually adapt to the ever-changing insulin requirements of each individual.
The AHCL algorithm requires specific therapy settings (target setpoint, insulin-to-carb ratios and active insulin time) that need to be established with the help of a health care provider (HCP) before activation. It also requires five (5) consecutive hours of insulin delivery history, a minimum of two (2) days of total daily dose (TDD) of insulin, a valid sensor glucose (SG) and blood glucose (BG) values to start automated insulin delivery.
When activated, the AHCL algorithm adjusts the insulin dose at five-minute intervals based on CGM data. A basal insulin dose (auto basal) is commanded by the AHCL algorithm to manage glucose levels to the user's target setpoint of 100 mg/dL, 110 mg/dL or 120 mg/dL. The user can also set a temporary target of 150 mg/dL for up to 24 hours. In addition, under certain conditions the algorithm can also automatically command correction boluses (auto correction bolus) without user input.
Meal boluses are the responsibility of the user. The AHCL algorithm includes an integrated bolus calculation feature for user-initiated boluses for meals. When the user inputs their carbohydrate intake, the AHCL algorithm automatically calculates a bolus amount based off available glucose information, entered carbohydrate amount and other patient parameters.
The AHCL algorithm contains several layers of "safeguards" (mitigations) to provide protection against over-delivery or under-delivery of insulin to reduce risk of hypoglycemia and hyperglycemia, respectively.
The AHCL algorithm is a software-only device and does not have a user interface (UI). The compatible ACE pump provides a UI to the user to configure the therapy settings and interact with the algorithm. The AHCL-related alerts/alarms are displayed and managed by the pump.

Predictive Low Glucose Technology, also referred to as the Predictive Low Glucose Management (PLGM) algorithm is a software-only device intended for use by people with Type 1 diabetes for ages 7 years or older. It is an interoperable automated glycemic controller (iAGC) that is intended for use with compatible integrated continuous glucose monitors (iCGM), compatible interoperable Medtronic continuous glucose monitors (CGM) and compatible alternate controller enabled (ACE) pumps to automatically suspend delivery of insulin when the sensor glucose value falls below or is predicted to fall below predefined threshold values.
The PLGM algorithm resides on the compatible ACE Pump, which serves as the host device. It is meant to be integrated in a compatible ACE pump and is an embedded part of the ACE pump firmware.
Inputs to PLGM algorithm (e.g., sensor glucose values, user inputs) are received from the ACE pump (host device), and outputs from PLGM algorithm (e.g., suspend/resume commands) are sent by the algorithm to the ACE pump. As an embedded part of the ACE pump firmware, the PLGM algorithm does not connect to or receive data from compatible CGMs; instead, sensor glucose (SG) values or other inputs are received by the ACE pump from compatible CGMs via Bluetooth Low Energy (BLE) technology are transmitted to the embedded PLGM algorithm.
The PLGM algorithm works in conjunction with the ACE pump. When enabled, the PLGM algorithm is able to suspend insulin delivery for a minimum of 30 minutes and for a maximum of 2 hours based on current or predicted sensor glucose values. It will automatically resume insulin delivery when maximum suspend time of 2 hours has elapsed or when underlying conditions resolve. The user is also able to manually resume insulin at any time.
The PLGM algorithm is a software-only device and does not have a user interface (UI). The compatible ACE pump provides the UI to configure therapy settings and interact with the algorithm. The PLGM-related alerts/alarms are displayed and managed by the pump.

The provided FDA 510(k) clearance letter and supporting documentation detail the acceptance criteria and the studies conducted to prove that Medtronic's SmartGuard Technology and Predictive Low Glucose Technology meet these criteria.

It's important to note that the provided text focuses on demonstrating substantial equivalence to a predicate device, as is typical for 510(k) submissions, rather than establishing de novo acceptance criteria for an entirely novel device. The "acceptance criteria" here refer to the performance benchmarks that demonstrate safety and effectiveness comparable to the predicate and compliance with regulatory special controls.

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

Acceptance Criteria and Device Performance

The acceptance criteria are generally implied by the comparative data presented against the predicate device (Control-IQ Technology) and the compliance with "iAGC Special Controls requirements defined in 21 CFR 862.1356." The clinical study primarily aims to demonstrate non-inferiority or beneficial outcomes in key glycemic metrics compared to baseline (run-in period).

Table of Acceptance Criteria and Reported Device Performance

Given that this is a 510(k) submission showing substantial equivalence, the "acceptance criteria" are largely derived from the performance of the predicate device and clinical guidelines (e.g., ADA guidelines for Time Below Range). While specific numerical thresholds for acceptance are not explicitly listed as "acceptance criteria" in a table format within the provided text, the results presented serve as the evidence that these implicit criteria are met.

For the purpose of this response, I will synthesize the implied performance targets from the "Pivotal Study Observed Results" and "Supplemental Clinical Data" sections and present the device's reported performance against them.

Table 1: Implied Acceptance Criteria (via Predicate Performance/Clinical Guidelines) and Reported Device Performance for SmartGuard Technology (AHCL Algorithm)

Table 2: Implied Acceptance Criteria and Reported Device Performance for Predictive Low Glucose Technology (PLGM Algorithm)

Study Details

1. Sample Sizes and Data Provenance

Test Set (Clinical Studies):

• SmartGuard Technology (AHCL Algorithm) - Pivotal Study:

  • Adults (18-80 years): 110 subjects enrolled (105 completed).
  • Pediatrics (7-17 years): 112 subjects enrolled (107 completed).
  • Total: 222 subjects enrolled (212 completed).
  • Provenance: Multi-center, single-arm study conducted across 25 sites in the U.S. This implies prospective data collection, specifically designed for this regulatory submission. Home-setting study.

• Predictive Low Glucose Technology (PLGM Algorithm) - Clinical Testing:

  • Adults (14-75 years): 71 subjects. In-clinic study.
  • Pediatrics (7-13 years): 105 subjects. In-clinic evaluation.
  • Total: 176 subjects.
  • Provenance: Multi-center, single-arm, in-clinic studies. Location not explicitly stated but part of a US FDA submission, implying US or international sites adhering to FDA standards. Prospective data.

Training Set:

• SmartGuard Technology & Predictive Low Glucose Technology (Virtual Patient Model):
  • Sample Size: Not explicitly stated as a number of "patients" but referred to as "extensive validation of the simulation environment" and "virtual patient (VP) model."
  • Data Provenance: In-silico simulation studies using Medtronic Diabetes' simulation environment. This is synthetic data generated by computational models, validated against real patient data.

2. Number of Experts and Qualifications for Ground Truth (Test Set)

The clinical studies for both SmartGuard and PLGM technologies involved direct measurement of glucose values via CGM and blood samples (YSI for PLGM study, and HbA1c for SmartGuard study). These are objective physiological measures, not subjective interpretations requiring external expert consensus for "ground truth."

• For SmartGuard Technology: Glucose values were measured by the Simplera Sync CGM and HbA1c by laboratory tests. These are considered objective measures of glycemic control.
• For Predictive Low Glucose Technology: Hypoglycemia induction was monitored with frequent sample testing (FST) and frequent blood sampling for glucose measurements (likely laboratory-grade methods like YSI [Yellow Springs Instrument]).
• Expert involvement: While healthcare professionals (investigators, study coordinators, endocrinologists, nurses) were undoubtedly involved in conducting the clinical studies, managing patient care, and interpreting results, their role was not to establish "ground truth" through consensus or adjudication in the sense of image review. The ground truth was physiological measurements.

3. Adjudication Method for the Test Set

Not applicable in the typical sense of subjective clinical assessments (e.g., radiology image interpretation). Ground truth was established by direct physiological measurements (CGM data, HbA1c, YSI/FST blood glucose). The clinical studies were single-arm studies where subject outcomes were measured, not comparative assessments where multiple readers adjudicate on decisions.

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

No, an MRMC comparative effectiveness study was not described. The clinical studies were single-arm (evaluating the device's performance in a standalone setting against a baseline or predefined safety/efficacy metrics) and did not involve human readers interpreting data from the device to make clinical decisions and then comparing human performance with and without AI assistance. Instead, the AI (algorithm) directly controlled insulin delivery and was evaluated based on patient physiological outcomes.

5. Standalone (Algorithm Only) Performance

Yes, the core of the evaluation is the standalone performance of the algorithms (SmartGuard AHCL and PLGM) in managing glucose, as they are "software-only devices" that reside on the ACE pump firmware. The clinical studies directly measured the physiological impact of the algorithm's actions on glucose levels, HbA1c, and safety parameters in a human population.

6. Type of Ground Truth Used

• Clinical Studies (SmartGuard & PLGM): Objective Physiological Measurements

  • Sensor Glucose (SG) values: From compatible CGMs (Simplera Sync CGM, Guardian 4 CGM)
  • HbA1c: Laboratory measurements of glycosylated hemoglobin.
  • Frequent Sample Testing (FST) / Blood Glucose (BG) values: Clinical laboratory measurements (e.g., YSI) to confirm hypoglycemia during PLGM induction.
  • Adverse Events (AEs): Clinically reported and documented events.
  • These are considered the definitive "ground truth" for evaluating glycemic control and safety.

• In-Silico Simulation Studies: Virtual Patient Model Outputs

  • The "ground truth" for these simulations is the metabolic response of the validated virtual patient models. This computational modeling is used to extend the clinical evidence to various parameter settings and demonstrate equivalence to real-world scenarios.

7. Sample Size for the Training Set

The document does not explicitly state a numerical "sample size" for a distinct "training set" of patients in the traditional machine learning sense for the algorithms themselves. The algorithms are likely developed and refined using a combination of:

• Physiological modeling: Based on established understanding of glucose-insulin dynamics.
• Historical clinical data: From previous similar devices or general diabetes patient populations (though not specified in this document for algorithm training).
• Clinical expertise: Incorporated into the algorithm design.
• The "Virtual Patient Model" itself is a form of simulated data that aids in development and testing. The validation of this model is mentioned as "extensive validation" and establishment of "credibility," implying a robust dataset used to verify its accuracy against real patient responses.

It's typical for complex control algorithms like these to be developed iteratively with physiological models and potentially large historical datasets, but a specific "training set" size for a machine learning model isn't detailed.

8. How the Ground Truth for the Training Set was Established

As noted above, a distinct "training set" with ground truth in the conventional sense of labeling data for a machine learning model isn't described. The development of control algorithms often involves:

• Physiological Simulation: The ground truth for this is the accurate metabolic response as modeled mathematically.
• Clinical Expertise & Design Principles: The ground truth is embedded in the scientific and medical principles guiding the algorithm's control logic.
• Validation of Virtual Patient Model: The "equivalency was demonstrated between Real Patients (RPs) and Virtual Patients (VPs) in terms of predetermined characteristics and clinical outcomes." This suggests that real patient data was used to validate and establish the "ground truth" for the virtual patient model itself, ensuring it accurately mirrors human physiology. This validated virtual patient model then serves as a crucial tool for in-silico testing.

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Halyard Purple Nitrile-XTRA* Powder-Free Exam Gloves, Low Dermatitis Potential, Tested for Use with Chemotherapy Drugs, Fentanyl Citrate and Fentanyl Citrate in Simulated Gastric Acid are disposable devices intended for medical purposes that is worn on the examiner's hand to prevent contamination between patient and examiner.

The following chemotherapy drugs and concentration had NO breakthrough detected up to 240 minutes:

• Arsenic Trioxide (1 mg/ml)
• Bendamustine, (5 mg/ml)
• Blenoxane (15 mg/ml)
• Bleomycin (15 mg/ml)
• Bortezomib (1 mg/ml)
• Busulfan (6 mg/ml)
• Carboplatln (10 mg/ml)
• Carfilzomib (2 mg/ml)
• Cetuximab (2 mg/ml)
• Cisplatin (1 mg/ml)
• Cyclophosphamide (Cytoxan) (20 mg/ml)
• Cytarabine (100 mg/ml)
• Dacarbazine (DTIC) {10 mg/ml)
• Daunorubicin {5 mg/ml)
• Decitabine (5 mg/ml)
• Docetaxel (10 mg/ml)
• Doxorubicin HCL (2 mg/ml)
• Ellence (2 mg/ml)
• Erbitux (2 mg/ml)
• Eribilin Mesylate (0.5 mg/ml)
• Etoposide (Toposar) (20 mg/ml)
• Fludarabine (25 mg/ml)
• Fulvestrant (50 mg/ml)
• Gemcitabine (Gemzar) (38 mg/ml)
• Idarubicin (1 mg/ml)
• Ifosfamide (IFEX) (50 mg/ml)
• Irinotecan (20 mg/ml)
• Mechlorethamine HCL (1 mg/ml)
• Melphalan (5 mg/ml)
• Methotrexate (25 mg/ml)
• Mitomycin C (0.5 mg/ml)
• Mitoxantrone (2 mg/ml)
• Oxaliplatin (2 mg/ml)
• Paclitaxel (Taxol) (6 mg/ml)
• Paraplatin (10 mg/ml)
• Pemetrexed Disodium (25 mg/ml)
• Pertuzumab (30 mg/ml)
• Raltitrexed (0.5 mg/ml)
• Rituximab (Rituxan) (10 mg/ml)
• Temsirolimus (25 mg/ml)
• Thiotepa (10 mg/ml)
• Topotecan HCL (1 mg/ml)
• Trastuzumab (21 mg/ml)
• Trisenox (1 mg/ml)
• Velcade (1 mg/ml)
• Vinblastine (1 mg/ml)
• Vinorelbine (10 mg/ml)

Carmustine (3.3 mg/ml) permeation occurred at 60.0 minutes.

The following hazardous drugs (opioids) and concentration had NO breakthrough detected up to 240 minutes:

• Fentanyl Citrate Injection (100 mcg/2 ml)
• Gastric Acid Fluid/Fentanyl Citrate Injection Mix (50/50 Solution)

Caution: Testing showed a minimum breakthrough time of 60.0 minutes with Carmustine.

The following hazardous drugs and concentration had NO breakthrough detected up to 240 minutes:

• Cytovene (10 mg/ml)
• Retrovir (10 mg/ml)
• Triclosan (2 mg/ml)
• Zoledronic Acid (0.8 mg/ml)

Halyard Purple Nitrile-XTRA* Powder-Free Exam Gloves, Low Dermatitis Potential, Tested for Use with Chemotherapy Drugs, Fentanyl Citrate and Fentanyl Citrate in Simulated Gastric Acid are disposable, 12"purple-colored, chlorinated, nitrile, powder-free, textured fingertip, ambidextrous, nonsterile patient examination gloves.

The provided text is an FDA 510(k) clearance letter and summary for a medical glove, not an AI-powered medical device. Therefore, many of the requested fields related to AI study design (like "multi-reader multi-case (MRMC) comparative effectiveness study," "standalone performance," "number of experts," etc.) are not applicable and cannot be found in the document.

However, I can extract the acceptance criteria and performance data for the glove based on the provided information, focusing on the non-clinical and clinical tests described.

Here's a breakdown of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

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The ECG-AI LEF 12-Lead algorithm is software intended to aid in earlier detection of Left Ventricular Ejection Fraction (LVEF) less than or equal to 40% in adults at risk for heart failure. This population includes, but is not limited to:

• patients with cardiomyopathies
• patients who are post-myocardial infarction
• patients with aortic stenosis
• patients with chronic atrial fibrillation
• patients receiving pharmaceutical therapies that are cardiotoxic, and
• postpartum women.

The ECG-AI LEF 12-Lead algorithm is not intended to be a stand-alone diagnostic device for cardiac conditions, should not be used for monitoring of patients, and should not be used on ECGs with a paced rhythm.

A positive result may suggest the need for further clinical evaluation in order to establish a diagnosis of Left Ventricular Ejection Fraction (LVEF) less than or equal to 40%. Additionally, if the patient is at high risk for the cardiac condition, a negative result should not rule out further non-invasive evaluation.

The ECG-AI LEF 12-Lead Algorithm should be applied jointly with clinician judgment.

The ECG-AI LEF 12-Lead algorithm interprets 12-lead ECG voltage times series data using an artificial intelligence-based algorithm. The device analyzes 10 seconds of a single 12-lead ECG acquisition, and within seconds provides likelihood of LVEF (ejection fraction less than or equal to 40%) to third party software. The results are displayed by the third party software on a device such as a smartphone, tablet, or PC. The ECG-AI LEF 12-Lead algorithm was trained to detect Low LVEF using positive and control cohorts, and the detection of Low LVEF in patients is generated using defined conditions and covariates.

The ECG-AI LEF 12-Lead algorithm device is intended to address the unmet need for a point-of-care screen for LVEF less than or equal to 40% and is expected to be used by cardiologists, frontline clinicians at primary care, urgent care, and emergency care settings, where cardiac imaging may not be available or may be difficult or unreliable for clinicians to operate. Clinicians will use the ECG-AI LEF 12-Lead algorithm to aid in earlier detection of LVEF less than or equal to 40% and making a decision for further cardiac evaluation.

The software module can be integrated into a client application to be accessed by clinicians and results viewed through an Electronic Medical Record (EMR) system or an ECG Management System (EMS) accessed via a PC, mobile device, or another medical device. In each case, the physician imports 12-lead ECG data in digital format. The tool analyzes the 10 seconds or longer duration of voltage data collected during a standard 12-lead ECG and outputs a binary result of the likelihood of low ejection fraction as an API result.

The provided text is a 510(k) clearance letter and summary for the Anumana, Inc. ECG-AI Low Ejection Fraction (LEF) 12-Lead Algorithm ([K250652](https://510k.innolitics.com/search/K250652)). While it describes the device, its intended use, and substantial equivalence to a predicate device, it does not contain the detailed performance study results, acceptance criteria tables, sample sizes, or ground truth establishment methods that would typically be found in the clinical study section of a full 510(k) submission.

The document discusses a "Predetermined Change Control Plan (PCCP)" which mentions future performance enhancement validation studies, but it doesn't present the specific results of the validation study that led to this clearance ([K250652](https://510k.innolitics.com/search/K250652)). It only states that "The performance characteristics for the ECG-AI LEF 12-Lead algorithm were evaluated through software verification and labeling verification," which refers to non-clinical data.

Therefore, many of the requested details cannot be extracted from the provided text. I will populate the table and answer the questions based only on the information available in the given document.

Acceptance Criteria and Device Performance Study (Extracted from provided 510(k) Summary)

The provided 510(k) summary (K250652) serves as an update to a previously cleared device (K232699). It focuses on expanding compatibility and minor changes, asserting substantial equivalence based on the predicate's performance rather than detailing a new, comprehensive clinical study for this specific submission. The document emphasizes "software verification and labeling verification" as the evaluation methods for performance characteristics for this particular submission, rather than a clinical performance study with specific metrics for acceptance criteria.

The Predetermined Change Control Plan (PCCP) section alludes to future performance enhancements and their validation, stating: "To be implemented, a modified version must demonstrate improved performance by meeting pre-specified acceptance criteria. These criteria require the new version's sensitivity and specificity point estimates to be greater than or equal to the previous version, with an improvement shown by either an increased point estimate or a tighter confidence interval lower bound for at least one of these metrics." However, these are future criteria for updates, not the current acceptance criteria for the clearance of K250652 based on a new clinical study.

Therefore, the specific quantitative acceptance criteria and reported device performance for the clinical study supporting the K250652 clearance are not explicitly stated in the provided text. The clearance is largely based on demonstrating substantial equivalence to the predicate (K232699) and software/labeling verification.

Based on the provided text, the specific details regarding the clinical performance study (including acceptance criteria, reported performance values, sample sizes, expert details, adjudication methods, MRMC studies, standalone performance, and ground truth establishment for the test set) are NOT available.

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

As noted above, the provided text does not contain a table of explicit quantitative acceptance criteria or reported device performance metrics (e.g., sensitivity, specificity, AUC) from a clinical study for K250652. The document claims "The performance characteristics for the ECG-AI LEF 12-Lead algorithm were evaluated through software verification and labeling verification" for this submission, indicating that a new, detailed clinical performance study with such metrics was not the basis for this specific clearance. The PCCP section specifies criteria for future updates, but not for this clearance.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

• Sample Size for Test Set: Not specified in the provided document.
• Data Provenance: Not specified in the provided document. The PCCP mentions "multi-center retrospective clinical study" for future validations, but this isn't linked to the original clearance's test set.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

• Not specified in the provided document.

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

• Not specified in the provided document.

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

• Not specified in the provided document. The current indication is "to aid in earlier detection" and "applied jointly with clinician judgment," which implies human-in-the-loop, but an MRMC study comparing performance with and without AI assistance is not detailed.

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

• The document states: "The ECG-AI LEF 12-Lead algorithm is not intended to be a stand-alone diagnostic device for cardiac conditions," and "should be applied jointly with clinician judgment." This implies the device is not intended for standalone use in practice. However, whether a standalone performance study was conducted to assess its raw diagnostic capability (e.g., area under the curve) is not explicitly stated. The statement "outputs a binary result of the likelihood of low ejection fraction as an API result" suggests a standalone algorithm output, but the FDA's clearance is for an "aid," not a primary diagnostic tool.

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

• The document mentions the device "was trained to detect Low LVEF using positive and control cohorts." For LVEF, the common ground truth is often echocardiography (measuring ejection fraction), but the specific method used for ground truth (e.g., echocardiography, MRI, or a combination/adjudication) is not specified.

8. The sample size for the training set

• Not specified in the provided document.

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

• The document states the device "was trained to detect Low LVEF using positive and control cohorts," but it does not describe how the ground truth was established for these training cohorts (e.g., type of diagnostic test, clinical adjudication process).

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Tempus ECG-Low EF is software intended to analyze resting, non-ambulatory 12-lead ECG recordings and detect signs associated with having a low left ventricular ejection fraction (LVEF less than or equal to 40%). It is for use on clinical diagnostic ECG recordings collected at a healthcare facility from patients 40 years of age or older at risk of heart failure. This population includes but is not limited to patients with atrial fibrillation, aortic stenosis, cardiomyopathy, myocardial infarction, diabetes, hypertension, mitral regurgitation, and ischemic heart disease.

Tempus ECG-Low EF only analyzes ECG data and provides a binary output for interpretation. Tempus ECG-Low EF is not intended to be a stand-alone diagnostic tool for cardiac conditions, should not be used for patient monitoring, and should not be used on ECGs with paced rhythms. Results should be interpreted in conjunction with other diagnostic information, including the patient's original ECG recordings and other tests, as well as the patient's symptoms and clinical history.

A positive result may suggest the need for further clinical evaluation in order to establish a diagnosis of low LVEF. Patients receiving a negative result should continue to be evaluated in accordance with current medical practice standards using all available clinical information.

Tempus ECG-Low EF is a cardiovascular machine learning software intended for analysis of 12-lead resting ECG recordings using machine-learning techniques to detect signs of cardiovascular conditions for further referral or diagnostic follow-up. The software employs machine learning techniques to analyze ECG recordings and detect signs associated with a patient experiencing low left ventricular ejection fraction (LVEF), less than or equal to 40%. The device is designed to extract otherwise unavailable information from ECGs conducted under the standard of care, to help health care providers better identify patients who may be at risk for undiagnosed LVEF in order to evaluate them for further referral or diagnostic follow up.

As input, the software takes data from a patient's 12-lead resting ECG (including age and sex). It is only compatible with ECG recordings collected using 'wet' Ag/AgCl electrodes with conductive gel/paste, and using FDA authorized 12-lead resting ECG machines manufactured by GE Medical Systems or Philips Medical Systems with a 500 Hz sampling rate. It checks the format and quality of the input data, analyzes the data via a trained and 'locked' machine-learning model to generate an uncalibrated risk score, converts the model results to a binary output (or reports that the input data are unclassifiable), and evaluates the uncalibrated risk score against pre-set operating points (thresholds) to produce a final result. Uncalibrated risk scores at or above the threshold are returned as 'Low LVEF Detected,' and uncalibrated risk scores below the threshold are returned as 'Low LVEF Not Detected.' This information is used to support clinical decision making regarding the need for further referral or diagnostic follow-up. Typical diagnostic follow-up could include transthoracic echocardiogram (TTE) to detect previously undiagnosed LVEF, as described in device labeling. Results should not be used to direct any therapy against LVEF itself. Tempus ECG-Low EF is not intended to replace other diagnostic tests.

Tempus ECG-Low EF does not have a dedicated user interface (UI). Input data comprising ECG tracings, tracing metadata (e.g., sample count, sample rate, patient age/sex), is provided to Tempus ECG-Low EF through standard communication protocols (e.g., file exchange) with other medical systems (e.g., electronic health record systems, hospital information systems, or other data display, transfer, storage, or format-conversion software). Results from Tempus ECG-Low EF are returned to users in an equivalent manner.

Here's a detailed breakdown of the acceptance criteria and the study that proves the Tempus ECG-Low EF device meets them, based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

Study Details

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

• Test Set Sample Size: Greater than 15,000 ECGs (specifically, 14,924 patient records are detailed in Table 1, with each patient having one ECG).
• Data Provenance: Retrospective observational cohort study. The data was derived from 4 geographically distinct US clinical sites.

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

The document does not explicitly state the number of experts used or their qualifications for establishing the ground truth. It mentions that a clinical diagnosis of Low EF (LVEF ≤ 40%) was determined by a Transthoracic Echocardiogram (TTE), which is considered the gold standard for LVEF measurement. The interpretation of these TTE results to establish the ground truth would typically be done by cardiologists or trained echocardiography specialists, but the specific number and qualifications are not provided in this document.

4. Adjudication Method for the Test Set

The document does not explicitly state an adjudication method (such as 2+1 or 3+1) for the ground truth of the test set. The ground truth was established by correlating ECGs with TTEs to determine the presence or absence of a clinical diagnosis of low EF. It is implied that the TTE results themselves, as the gold standard, served as the definitive ground truth without a further adjudication process by multiple human readers for the TTE results in the context of this AI device validation.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

The document does not indicate that an MRMC comparative effectiveness study was performed, nor does it provide an effect size for human reader improvement with AI assistance. The study focuses on the standalone performance of the AI device.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Yes, a standalone study was done. The described clinical performance validation evaluated the device's ability to "detect signs associated with a clinical diagnosis of low LVEF" and provided sensitivity and specificity metrics for the algorithm's output. The device "only analyzes ECG data and provides a binary output for interpretation," indicating a standalone performance assessment.

7. The Type of Ground Truth Used

The ground truth used was established by Transthoracic Echocardiogram (TTE), specifically used to determine the presence or absence of a clinical diagnosis of Low EF (LVEF ≤ 40%). This is a form of outcomes data / reference standard as TTE is the established clinical diagnostic method for LVEF.

8. The Sample Size for the Training Set

• Training Set Sample Size: More than 930,000 ECGs (specifically, 930,689 ECGs are detailed in Table 1).

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

The document does not explicitly state how the ground truth for the training set was established. However, given that the model was trained to "detect signs associated with having a low left ventricular ejection fraction (LVEF less than or equal to 40%)" and the validation set used TTE for ground truth, it is highly probable that the training set also used LVEF measurements (likely from echocardiograms) as the ground truth. The description states the model was trained "on data from more than 930,000 ECGs," but does not detail the specific methodology for establishing the LVEF ground truth for each of these training examples.

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A patient examination glove is a disposable device intended for medical purposes that is worn on the examiner's hand to prevent contamination between patient and examiner.

These gloves were tested for use with chemotherapy drugs, fentanyl citrate and gastric acid as per ASTM D6978-05 (2023) Standard Practice for Assessment of Resistance of Medical Gloves to Permeation by Chemotherapy Drugs:

The following drugs and concentration had NO breakthrough detected up to 240 minutes:

• Bendamustine HCl (5 mg/mL)
• Bleomycin Sulfate (15 mg/mL)
• Busulfan (6 mg/mL)
• Carboplatin (10 mg/mL)
• Carfilzomib (2 mg/mL)
• Cetuximab (Erbitux) (2 mg/mL)
• Chloroquine (50 mg/mL)
• Cisplatin (1 mg/mL)
• Cladribine (1 mg/mL)
• Cyclophosphamide (20 mg/mL)
• Cyclosporin A (100 mg/mL)
• Cytarabine HCl (100 mg/mL)
• Cytovene (10 mg/mL)
• Dacarbazine (10 mg/mL)
• Daunorubicin HCl (5 mg/mL)
• Decitabine (5 mg/mL)
• Docetaxel (10 mg/mL)
• Doxorubicin HCl (2 mg/mL)
• Epirubicin HCl (2 mg/mL)
• Etoposide (20 mg/mL)
• Fludarabine Phosphate (25 mg/mL)
• Fluorouracil (50 mg/mL)
• Fulvestrant (50 mg/mL)
• Gemcitabine HCl (38 mg/mL)
• Idarubicin HCl (1 mg/mL)
• Ifosfamide (50 mg/mL)
• Irinotecan HCl (20 mg/mL)
• Mechlorethamine HCl (1 mg/mL)
• Melphalan HCl (5 mg/mL)
• MESNA (100 mg/mL)
• Methotrexate (25 mg/mL)
• Mitomycin C (0.5 mg/mL)
• Mitoxantrone HCl (2 mg/mL)
• Oxaliplatin (2 mg/mL)
• Paclitaxel (6 mg/mL)
• Pemetrexed (25 mg/mL)
• Propofol (10 mg/mL)
• Raltitrexed (0.5 mg/mL)
• Retrovir (10 mg/mL)
• Rituximab (10 mg/mL)
• Temsirolimus (25 mg/mL)
• Topotecan HCl (1 mg/mL)
• Triclosan (1 mg/mL)
• Trisenox (Arsenic Trioxide) (1 mg/mL)
• Velcade (Bortezomib) (1 mg/mL)
• Vidaza (Azacitidine) (25 mg/mL)
• Vinblastine Sulfate (1 mg/mL)
• Vincristine Sulfate (1 mg/mL)
• Vinorelbine Tartrate (10 mg/mL)
• Zoledronic Acid (0.8 mg/mL)

The following chemotherapy drugs and concentration showed breakthrough detected in less than 60 minutes:

• Carmustine (3.3 mg/mL), breakthrough detected at 55.5 minutes
• Thiotepa (10 mg/mL), breakthrough detected at 50.8 minutes

Warning: Not recommended for use with Carmustine and Thiotepa

The following hazardous drugs (opioids) and concentration had NO breakthrough detected up to 240 minutes:

• Fentanyl Citrate Injection (100mcg/2mL)
• Simulated Gastric Acid Fluid/Fentanyl Citrate Injection Mix 50/50 Solution

The subject device, Nitrile Powder Free Examination Gloves with Low Dermatitis Potential, Tested for Use with Chemotherapy Drugs, Fentanyl Citrate and Gastric Acid is a single use, disposable device intended for medical purposes that is worn on the examiner's hand to prevent contamination between patient and examiner. This product demonstrated reduced potential for sensitizing users to chemical additives, supported by a negative skin sensitization test (Modified Draize-95 Test) and tested for use with chemotherapy drugs, fentanyl citrate and gastric acid.

The gloves are made of nitrile rubber, powder free, ambidextrous with beaded cuff. Inner surface of gloves undergoes surface treatment process to produce a smooth surface that facilitates the user in donning the gloves without using lubricant and donning powder on the glove surface. These gloves are offered in six sizes (XS, S, M, L, XL, XXL), and supplied in non-sterile state.

The provided FDA 510(k) clearance letter and summary concern a medical device, "Nitrile Powder Free Examination Gloves with Low Dermatitis Potential, Tested for Use with Chemotherapy Drugs, Fentanyl Citrate and Gastric Acid." This document details the product's attributes, testing, and comparison to a predicate device for regulatory clearance.

However, the questions you've asked (acceptance criteria, study details, sample sizes, expert qualifications, adjudication, MRMC studies, standalone performance, ground truth, and training set details) are highly specific to the performance of an AI/ML medical device rather than a physical product like examination gloves. The provided document describes the physical and chemical performance of the gloves (e.g., resistance to permeation by chemotherapy drugs, physical properties, biocompatibility, and skin sensitization), not the performance of an artificial intelligence or machine learning algorithm.

Therefore, many of your questions are not applicable to the information contained within this 510(k) clearance for medical gloves. I will answer the applicable questions based on the provided text, and explicitly state when a question is not relevant to this type of device.

Acceptance Criteria and Device Performance for Nitrile Examination Gloves

The "acceptance criteria" for these gloves are primarily physical, chemical, and biological performance standards, as demonstrated through various ASTM and ISO tests. The "reported device performance" indicates whether the gloves met these pre-defined standards.

1. Table of Acceptance Criteria and Reported Device Performance

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The SpotLight / SpotLight Duo is intended to produce cross-sectional images of the body by computer reconstruction of X-ray transmission projection data taken at different angles. The system has the capability to image cardiovascular and thoracic anatomies, including the heart, in a single rotation. The system may acquire data using Axial, Cine and Cardiac scan techniques from patients of all ages (DLIR is limited for patient use above the age of 2 years). These images may be obtained either with or without contrast. This device may include signal analysis and display equipment, patient and equipment supports, components and accessories.

This device may include data and image processing to produce images in a variety of trans-axial and reformatted planes. The system is indicated for x-ray Computed Tomography imaging of cardiovascular and thoracic anatomies that fit in the scan field-of-view.

The Low Dose CT Lung Cancer Screening Option for SpotLight / SpotLight Duo is indicated for using low dose CT for lung cancer screening. The screening must be conducted with the established program criteria and protocols that have been approved and published by a governmental body or a professional medical society. Information from professional societies related to lung cancer screening can be found but is not limited to: American College of Radiology® (ACR) – resources and technical specification; accreditation American Association of Physicists in Medicine (AAPM) – Lung Cancer Screening Protocols; radiation management. Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information. The DLIR and ASIR-CV algorithms are not compatible with the Low Dose Lung Cancer Screening option.

The device output is useful for diagnosis of disease or abnormality and for planning of therapy procedures.

The Low Dose Lung Cancer Screening (LD LCS) option indication for Arineta's SpotLight and SpotLight Duo scanners is being expanded to include small patients, as defined by AAPM (American Association of Physicists in Medicine). This expansion ensures comprehensive coverage of the intended lung cancer screening population, following the previous clearance of the LD LCS option for medium and large patients under K241200.

The proposed LD LCS option for the SpotLight and SpotLight Duo includes scan protocols with CTDI that comply with AAPM's requirements for the whole LD LCS population patient size groups, as detailed in the following table:

There are not any functional, performance, feature, or design changes required for the CT systems to which the option is applied.

The proposed full LD LCS protocols option, as the cleared K241200, will be activated by service or production personnel, with no additional installation required (option activation only).

This FDA 510(k) clearance letter describes the acceptance criteria and study proving the SpotLight / SpotLight Duo with Low Dose Lung Cancer Screening Option (specifically for small patients) meets these criteria.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the device are largely based on image quality and nodule detectability being maintained at low dose levels, specifically for small patients (50-70 kg) within the AAPM guidelines for CTDI. The reported performance confirms these criteria are met.

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The glove is a disposable device intended for medical purposes that is worn on the examiner's hand to prevent contamination between patient and examiner.

Gloves have been tested for use with chemotherapy drugs and Fentanyl Citrate using ASTM D6978-05.

Powder Free Nitrile Patient Examination Gloves, Blue Colored, Non Sterile, Low Dermatitis Potential. Tested for Use with Chemotherapy Drugs and Fentanyl Citrate Are Class I Patient Examination Gloves and Specialty Chemotherapy Gloves. They are ambidextrous and come in different sizes - Extra Small, Small, Medium, Large, Extra Large and XXL.

Gloves meet the specification of ASTM D6319-19 and have been tested for resistance to permeation by chemotherapy drugs and Fentanyl Citrate as per ASTM D6978-05. The gloves are single use, disposable, and provided non-sterile.

Here's a breakdown of the acceptance criteria and the studies that prove the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

The device in question is "Powder Free Nitrile Patient Examination Gloves, Blue Colored, Non Sterile, Low Dermatitis Potential. Tested for Use with Chemotherapy Drugs and Fentanyl Citrate" (K242533).

Non-Clinical Performance Data

Clinical Performance Data

Study Information

Due to the nature of the device (patient examination gloves) and the provided documentation, several sections of your request are not directly applicable or explicitly detailed. This document is a 510(k) summary for a Class I medical device, which typically relies more on performance testing against established standards and equivalence to predicates rather than complex clinical trials like those for novel therapeutic devices.

Here's what can be extracted from the document:

1. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective):

   • Permeation Tests (ASTM D6978-05): The document does not specify the exact sample size used for the permeation tests for each chemical. The standard ASTM D6978-05 typically outlines the number of replicates required (e.g., three specimens).
   • Biocompatibility Tests (ISO 10993 series): Similarly, the sample sizes for these tests are not explicitly stated in the summary but would be specified by the respective ISO standards.
   • Clinical Test (Modified DRAIZE-95 Test): A 305-subject study was completed. The country of origin and whether it was retrospective or prospective is not specified, but such a test is typically prospective to evaluate a new or modified device.
   • Data Provenance: The document generally refers to testing "under the conditions of this study," without specifying the country of origin for non-clinical tests. The manufacturer is based in China.

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

   • For the non-clinical tests (physical properties, chemical permeation, biocompatibility), the "ground truth" is established by the standardized methods themselves (ASTM and ISO standards) and objective measurements by qualified laboratory personnel. The number of "experts" and their specific qualifications beyond standard lab certifications are not typically detailed in these summaries.
   • For the clinical test on dermatitis potential, the "ground truth" is derived from the subjects' reactions as evaluated by the study investigators. The qualifications of these investigators are not provided.

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

   • For the non-clinical tests, adjudication methods like 2+1 or 3+1 are not applicable. Results are based on objective measurements against defined criteria.
   • For the clinical test (Modified DRAIZE-95), the document does not specify an adjudication method. Clinical studies of this nature usually involve clinical investigators observing and documenting reactions, and a statistical analysis of the aggregate results.

4. 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:

   • Not applicable. This device is a physical product (gloves), not an AI-powered diagnostic or assistive tool. Therefore, MRMC studies and AI assistance metrics are irrelevant.

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

   • Not applicable. This is a physical device, not an algorithm.

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

   • Physical and Chemical Tests: The ground truth is based on the objective measurements of the glove's properties (dimensions, strength, elongation, watertightness, powder residue) and breakthrough detection times for chemicals, all conducted according to recognized ASTM and ISO standards.
   • Biocompatibility Tests: Ground truth is determined by the biological response observed in in vitro (cytotoxicity) or in vivo (irritation, sensitization, acute systemic toxicity) models as interpreted against the acceptance criteria of the ISO 10993 standards.
   • Dermatitis Potential Clinical Test: The ground truth is the observed clinical reactions of human subjects to the device, evaluated against criteria for allergic contact sensitization.

7. The sample size for the training set:

   • Not applicable. This pertains to an algorithm or machine learning model. This device is a physical product.

8. How the ground truth for the training set was established:

   • Not applicable. As above, this is for an algorithm or machine learning model.

Ask a specific question about this device

The SpotLight / SpotLight Duo is intended to produce cross-sectional images of the body by computer reconstruction of X-ray transmission projection data taken at different angles. The system has the capability to image cardiovascular and thoracic anatomies, including the heart, in a single rotation. The system may acquire data using Axial, Cine and Cardiac scan techniques from patients of all ages (DLR is limited for patient use of 2 years). These images may be obtained either with or without contrast. This device may include signal analysis and display equipment, patient and equipment supports, components and accessories.

This device may include data and image processing to produce images in a variety of trans-axial and reformatted planes. The system is indicated for x-ray Computed Tomography imaging of cardiovascular and thoracic anatomies that fit in the scan field-of-view.

The Low Dose CT Lung Cancer Screening Option for SpotLight / SpotLight Duo is indicated for using low dose CT for lung cancer screening. The screening must be conducted with the established program criteria and protocols (for medium and large patients) that have been approved and published by a governmental body or a professional medical society. Information from professional societies related to lung cancer screening can be found but is not limited to: American College of Radiology® (ACR) - resources and technical specification American Association of Physicists in Medicine (AAPM) - Lung Cancer Screening Protocols; radiation management. Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information. The DLIR and ASIR-CV algorithms are not compatible with the Low Dose Lung Cancer Screening option.

The device output is useful for diagnosis of disease or abnormality and for planning of therapy procedures.

The Low Dose Lung Cancer Screening (LD LCS) option is an indication being added to the existing Arineta scanners for SpotLight and SpotLight Duo, previously cleared by the FDA (K230370, K213465).

There are not any functional, performance, feature, or design changes required for the CT systems to which the option is applied.

This option includes scan protocols with CTDI that comply with AAPM's requirements for Low Dose Lung Cancer Screening.

No Hardware modifications and minor Software modifications (for compatibility with the Low-Dose Lung Cancer Screening protocols) are required for the subject device, which includes the following LD LCS protocol characteristics:

• Lung Cancer Screening protocols for medium and large patients according to AAPM's definitions.

· Lung Cancer Screening protocols option will be activated by service or production personnel (no need for additional installation, option activation only).

Here's a breakdown of the acceptance criteria and the study proving the device's performance, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size:
  • Bench Testing: Not explicitly stated as a number of phantom scans, but described as "extensive bench testing" using "standard phantoms" and a "semi-anthropomorphic clinical simulation lung phantom."
  • Clinical Image Quality Assessment: Fourteen (14) cases.
• Data Provenance:
  • Bench Testing: Internal laboratory testing ("extensive bench testing").
  • Clinical Image Quality Assessment: Collected from two (2) U.S. sites. The text doesn't specify if it was retrospective or prospective, but the phrasing "were collected" often implies retrospective.

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

• Number of Experts: Two (2) U.S. board-certified radiologists.
• Qualifications: U.S. board-certified radiologists. No specific years of experience are mentioned.

4. Adjudication Method for the Test Set

The provided text only states "a clinical image quality assessment was performed by two U.S. board-certified radiologists." It does not specify an adjudication method (e.g., 2+1, 3+1, none). It implies both radiologists performed the assessment, but not how disagreements (if any) were resolved or if their readings were merged.

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 MRMC comparative effectiveness study was mentioned. The study described focuses on whether the device's low-dose protocols produce diagnostic-quality images and maintain image quality compared to the predicate device, not on human reader performance with or without AI assistance.

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

The "device" in this context is the CT scanner itself with an added low-dose lung cancer screening option, which includes specific scan protocols and minor software modifications for compatibility. The "acceptance criteria" and "study" described are for the performance of the CT system under these low-dose conditions, as an image acquisition and reconstruction device. It is not an AI algorithm that provides diagnostic readings independently. Therefore, the concept of a "standalone" AI performance study is not directly applicable here. The performance evaluated is the image quality produced by the system.

7. The Type of Ground Truth Used

• Bench Testing: Phantom specifications or known values within the phantoms (e.g., specific nodule sizes, CT number values of materials). Comparison was also made against a "reference device" (GE Revolution CT).
• Clinical Image Quality Assessment: The "ground truth" for the clinical evaluation was the qualitative assessment by the two board-certified radiologists that the images were "diagnostic for the indications for use" and "enable the detection of findings relevant to LD LCS," including 2mm nodules. This is essentially expert consensus on clinical diagnostic utility. It does not refer to histopathological ground truth for nodules, for example.

8. The Sample Size for the Training Set

The document does not mention any training set size. This is because the submission describes an option for an existing CT system (SpotLight/SpotLight Duo) to perform Low Dose Lung Cancer Screening. It does not describe a new AI algorithm that requires a separate training set. The changes are primarily in scan protocols and minor software adjustments for compatibility. The core image reconstruction algorithms (Modified FDK, MBAF, MBAF2) are pre-existing.

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

As no training set is mentioned (since it's not a new AI algorithm being trained), this information is not applicable.

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