(297 days)
Not Found
Yes
The device description explicitly states, "It uses models derived from machine learning".
No.
The product predictions are "for reference only and no therapeutic decisions should be made based solely on the CLEWICU predictions."
Yes
The device provides "physiological insight into a patient's likelihood of future hemodynamic instability" and information regarding "predicted future risk for clinical deterioration, as well as identifying patients at low risk for deterioration." This is indicative of a diagnostic function, even though "no therapeutic decisions should be made based solely on the CLEWICU predictions."
Yes
The device description explicitly states that "ClewICUServer and ClewICUnitor are software-only devices that are installed on user-provided hardware." It also describes the system as a "stand-alone analytical software product."
Based on the provided information, this device is not an IVD (In Vitro Diagnostic).
Here's why:
- IVD Definition: In vitro diagnostics are tests performed on samples taken from the human body, such as blood, urine, or tissue, to detect diseases, conditions, or infections.
- CLEWICU's Function: CLEWICU analyzes physiological data (likely from EHR and medical devices) to predict the likelihood of future hemodynamic instability. It does not perform tests on biological samples taken from the patient.
- Intended Use: The intended use describes providing "physiological insight" and predicting "future risk for clinical deterioration." This is based on analyzing existing patient data, not on analyzing samples in vitro.
Therefore, CLEWICU falls outside the scope of an In Vitro Diagnostic device. It is a software-based medical device that uses machine learning to analyze patient data for predictive purposes.
No
The input clearly states "Control Plan Authorized (PCCP) and relevant text: Not Found", meaning there is no mention of PCCP authorization.
Intended Use / Indications for Use
CLEWICU provides the clinician with physiological insight into a patient's likelihood of future hemodynamic instability. CLEWICU is intended for use with intensive care unit (ICU) patients 18 years and over.
CLEWICU 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 CLEWICU predictions.
Product codes
QNL
Device Description
The CLEWICU System is a stand-alone analytical software product that includes the ClewICUServer and the ClewICUnitor. It uses models derived from machine learning to calculate the likelihood of occurrence of certain clinically significant events for patients in the intensive care unit (ICU). ClewICUServer and ClewICUnitor are software-only devices that are installed on user-provided hardware.
The ClewICUServer is a backend software platform that imports patient data from various sources including Electronic Health Record (EHR) data and medical device data. The data are then used by models operating within the ClewICUServer to compute and store the CLEWHI index (likelihood of hemodynamic instability requiring vasopressor / inotrope support), and CLEWLR (indication that the patient is at "low risk" for deterioration).
The ClewICUnitor is the web-based user interface displaying CLEWHI, and CLEWLR associated notifications and related measures, as well as presenting the overall unit status.
Mentions image processing
Not Found
Mentions AI, DNN, or ML
It uses models derived from machine learning to calculate the likelihood of occurrence of certain clinically significant events for patients in the intensive care unit (ICU).
Input Imaging Modality
Not Found
Anatomical Site
Not Found
Indicated Patient Age Range
18 years and over
Intended User / Care Setting
clinician; intensive care unit (ICU)
Description of the training set, sample size, data source, and annotation protocol
A tagging system was developed and validated (against human physician readers as ground truth). Once validated, the tagging system was used to generate the clinical truth labels needed, both for training of the CLEWICU predictive models.
Description of the test set, sample size, data source, and annotation protocol
CLEWICU was assessed in a retrospective clinical validation with a dataset from the WakeMed Health System. As an initial matter, a tagging system was developed and validated (against human physician readers as ground truth). Once validated, the tagging system was used to generate the clinical truth labels needed, both for training of the CLEWICU predictive models and for validation of the clinical performance of those models. The WakeMed dataset included patient stays in 7 intensive care units across 2 hospitals between 5 November 2019 and 30 June 2020. Patients were male or female and at least 18 years of age.
Summary of Performance Studies (study type, sample size, AUC, MRMC, standalone performance, key results)
The CLEWICU was subjected to extensive software verification testing, as well as risk analysis and cybersecurity review.
Human factors testing was conducted to confirm that the user manual provides adequate instructions and that the CLEWICU can be used by the intended users without use errors. Included in the study were 15 intended users, comprised of five nurses and ten physicians. Testing was successfully completed, demonstrating that the product has no expected user errors associated with potential for serious injury to users.
Further, CLEWICU was assessed in a retrospective clinical validation with a dataset from the WakeMed Health System. As an initial matter, a tagging system was developed and validated (against human physician readers as ground truth). Once validated, the tagging system was used to generate the clinical truth labels needed, both for training of the CLEWICU predictive models and for validation of the clinical performance of those models. The WakeMed dataset included patient stays in 7 intensive care units across 2 hospitals between 5 November 2019 and 30 June 2020. Patients were male or female and at least 18 years of age.
Following validation of the tagging system (comparing performance against human physician readers), the predictive models were evaluated by assessing each model's ability to predict the events of interest.
The performance was determined acceptable considering the software is intended to provide visual-only indicators with a typical time horizon of several hours for an already closely monitored population.
Key Metrics (Sensitivity, Specificity, PPV, NPV, etc.)
For the Hemodynamic Instability (HI) model:
Sensitivity: 60.6% (95% Lower CI: 56.9%, 95% Upper CI: 63.9%)
Positive Predictive Value: 22.3% (95% Lower CI: 20.7%, 95% Upper CI: 24.3%)
Lead-time (true positive alerts): Median 3.0 hours, 25th Percentile 1.6 hours, 75th Percentile 4.8 hours
For the Low Risk (LR) model:
Specificity: 95.7% (95% Lower Cl: 94.8%, 95% Upper Cl: 96.6%)
Sensitivity: 21.4% (95% Lower Cl: 21.2%, 95% Upper Cl: 21.6%)
Predicate Device(s)
Reference Device(s)
Not Found
Predetermined Change Control Plan (PCCP) - All Relevant Information
Not Found
§ 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.
0
January 9, 2021
CLEW Medical Ltd. % Yarmela Pavlovic Partner Manatt, Phelps & Phillips, LLP 1 Embarcadero Center, 30th Floor San Francisco, California 94111
Re: K200717
Trade/Device Name: CLEWICU System (ClewICUServer and ClewICUnitor) Regulation Number: 21 CFR 870.2210 Regulation Name: Adjunctive Predictive Cardiovascular Indicator Regulatory Class: Class II Product Code: QNL Dated: January 6, 2021 Received: January 8, 2021
Dear Yarmela Pavlovic:
We have reviewed your Section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database located at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
1
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting of medical device-related adverse events) (21 CFR 803) for devices or postmarketing safety reporting (21 CFR 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reportingcombination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR 1000-1050.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR Part 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-device-safety/medical-device-reportingmdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medicaldevices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-device-advice-comprehensive-regulatoryassistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely.
for LT Stephen Browning Assistant Director Division of Cardiac Electrophysiology, Diagnostics and Monitoring Devices Office of Cardiovascular Devices Office of Product Evaluation and Quality Center for Devices and Radiological Health
Enclosure
2
Indications for Use
510(k) Number (if known) K200717
Device Name CLEWICU System
Indications for Use (Describe)
CLEWICU provides the clinician with physiological insight into a patient's likelihood of future hemodynamic instability. CLEWICU is intended for use with intensive care unit (ICU) patients 18 years and over.
CLEWICU 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 CLEWICU predictions.
| Type of Use (Select one or both, as applicable) | |
|---|---|
| X Prescription Use (Part 21 CFR 801 Subpart D) | Over-The-Counter Use (21 CFR 801 Subpart C) |
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3
K200717
510(k) SUMMARY CLEW Medical's CLEWICU
Submitter
CLEW Medical, Ltd. 5 Hamelacha St. Poleg, Netanya lsrael 4206002
Phone: +972-9-779-5995 Contact Person: Avigdor Faians
Date Prepared:January 7, 2021
Name of Device: CLEWICU System
Common or Usual Name: CLEWICU
Classification Name: Adjunctive Predictive Cardiovascular Indicator
Requlation Number: 21 CFR 870.2210
Regulatory Class: Class II
Product Code: QNL
Predicate Device
Acumen Hypotension Prediction Index feature by Edwards Lifesciences (DEN160044, K183646, K190205)
Device Description
The CLEWICU System is a stand-alone analytical software product that includes the ClewICUServer and the ClewICUnitor. It uses models derived from machine learning to calculate the likelihood of occurrence of certain clinically significant events for patients in the intensive care unit (ICU). ClewICUServer and ClewICUnitor are software-only devices that are installed on user-provided hardware.
The ClewICUServer is a backend software platform that imports patient data from various sources including Electronic Health Record (EHR) data and medical device data. The data are then used by models operating within the ClewICUServer to compute and store the CLEWHI index (likelihood of hemodynamic instability requiring vasopressor / inotrope support), and CLEWLR (indication that the patient is at "low risk" for deterioration).
The ClewICUnitor is the web-based user interface displaying CLEWHI, and CLEWLR associated notifications and related measures, as well as presenting the overall unit status.
4
Intended Use / Indications for Use
The CLEWICU's Indications for Use are as follows:
CLEWICU provides the clinician with physiological insight into a patient's likelihood of future hemodynamic instability. CLEWICU is intended for use with intensive care unit (ICU) patients 18 years and over. CLEWICU 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 CLEWICU predictions.
While there are differences in the indications for use statements between CLEWICU and Acumen HPI, these differences are not critical to the intended use. Both devices are generally intended to calculate patient health status indices and predict the likelihood of future cardiovascular clinical events. The differences in the indications statements do not impact the safety or effectiveness, as neither device is intended to be the sole factor upon which a patient's clinical care is based. Instead, the analyses are intended to notify healthcare providers of potential patient deterioration and should be considered in conjunction with other physiological parameters before any treatment is provided.
Summary of Technological Characteristics
Both the subject and predicate devices are software decision support systems that receive patient data, process it, and display calculated insights. At a high level, the subject and predicate devices are based on the following same technological elements:
- . Both devices receive near-real-time patient health data;
- . Both devices analyze the available data to generate predictions of a patient's risk of experiencing a future clinically significant cardiac event;
- . Both devices alert the healthcare provider when a patient's risk has reached a predefined threshold.
Although the two products produce slightly different outputs (e.g., risk of hypotensive event versus risk of hemodynamic instability), those differences do not raise different questions of safety or effectiveness, because in both instances, the key point is whether the predictions are accurate and in a timeframe that permits appropriate clinical review and action.
Additionally, though the subject device incorporates additional data inputs relative to the predicate device (i.e., vital signs, nursing assessments, flowsheet data, medications, lab data), this does not raise different questions of safety or effectiveness. The key question in both cases is whether the indices accurately predict the associated future cardiac events.
Performance Data
The CLEWICU was subjected to extensive software verification testing, as well as risk analysis and cybersecurity review.
5
K200717
Human factors testing was conducted to confirm that the user manual provides adequate instructions and that the CLEWICU can be used by the intended users without use errors. Included in the study were 15 intended users, comprised of five nurses and ten physicians. Testing was successfully completed, demonstrating that the product has no expected user errors associated with potential for serious injury to users.
Further, CLEWICU was assessed in a retrospective clinical validation with a dataset from the WakeMed Health System. As an initial matter, a tagging system was developed and validated (against human physician readers as ground truth). Once validated, the tagging system was used to generate the clinical truth labels needed, both for training of the CLEWICU predictive models and for validation of the clinical performance of those models. The WakeMed dataset included patient stays in 7 intensive care units across 2 hospitals between 5 November 2019 and 30 June 2020. Patients were male or female and at least 18 years of age.
Following validation of the tagging system (comparing performance against human physician readers), the predictive models were evaluated by assessing each model's ability to predict the events of interest. The summary of the data from this study is presented below:
| Point Estimate | 95% Lower CI | 95% Upper CI | |
|---|---|---|---|
| Sensitivity | 60.6% | 56.9% | 63.9% |
| Positive Predictive Value | 22.3% | 20.7% | 24.3% |
| For the Hemodynamic Instability (HI) model | Point Estimate |
|---|---|
| -------------------------------------------- | ---------------- |
| Median | 25th Percentile | 75th Percentile | |
|---|---|---|---|
| Lead-time | |||
| (true positive alerts) | 3.0 hours | 1.6 hours | 4.8 hours |
For the Low Risk (LR) model
| Point Estimate | 95% Lower Cl | 95% Upper Cl | |
|---|---|---|---|
| Specificity | 95.7% | 94.8% | 96.6% |
| Sensitivity | 21.4% | 21.2% | 21.6% |
The performance was determined acceptable considering the software is intended to provide visualonly indicators with a typical time horizon of several hours for an already closely monitored population.
Conclusions
The CLEWICU is as safe and effective as the Acumen HPI. The CLEWICU has the same intended uses and similar indications, technological characteristics, and principles of operation as its predicate device. The minor differences in indications do not alter the intended use of the device and do not raise different questions of safety and effectiveness when used as labeled. In addition, the minor technological differences between the CLEWICU and its predicate device raise no new issues of safety or effectiveness. Performance data demonstrate that the CLEWICU performs as expected and in a manner that is substantially equivalent to the Acumen HPI. Thus, the CLEWICU is substantially equivalent.